index int64 0 0 | repo_id stringclasses 596 values | file_path stringlengths 31 168 | content stringlengths 1 6.2M |
|---|---|---|---|
0 | lc_public_repos/langchain/libs/partners/anthropic/tests | lc_public_repos/langchain/libs/partners/anthropic/tests/unit_tests/test_llms.py | import os
from langchain_anthropic import AnthropicLLM
os.environ["ANTHROPIC_API_KEY"] = "foo"
def test_anthropic_model_params() -> None:
# Test standard tracing params
llm = AnthropicLLM(model="foo") # type: ignore[call-arg]
ls_params = llm._get_ls_params()
assert ls_params == {
"ls_provider": "anthropic",
"ls_model_type": "llm",
"ls_model_name": "foo",
"ls_max_tokens": 1024,
}
llm = AnthropicLLM(model="foo", temperature=0.1) # type: ignore[call-arg]
ls_params = llm._get_ls_params()
assert ls_params == {
"ls_provider": "anthropic",
"ls_model_type": "llm",
"ls_model_name": "foo",
"ls_max_tokens": 1024,
"ls_temperature": 0.1,
}
|
0 | lc_public_repos/langchain/libs/partners/anthropic/tests/unit_tests | lc_public_repos/langchain/libs/partners/anthropic/tests/unit_tests/__snapshots__/test_standard.ambr | # serializer version: 1
# name: TestAnthropicStandard.test_serdes[serialized]
dict({
'id': list([
'langchain',
'chat_models',
'anthropic',
'ChatAnthropic',
]),
'kwargs': dict({
'anthropic_api_key': dict({
'id': list([
'ANTHROPIC_API_KEY',
]),
'lc': 1,
'type': 'secret',
}),
'anthropic_api_url': 'https://api.anthropic.com',
'default_request_timeout': 60.0,
'max_retries': 2,
'max_tokens': 100,
'model': 'claude-3-haiku-20240307',
'stop_sequences': list([
]),
'stream_usage': True,
'temperature': 0.0,
}),
'lc': 1,
'name': 'ChatAnthropic',
'type': 'constructor',
})
# ---
|
0 | lc_public_repos/langchain/libs/partners/anthropic | lc_public_repos/langchain/libs/partners/anthropic/scripts/lint_imports.sh | #!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi
|
0 | lc_public_repos/langchain/libs/partners/anthropic | lc_public_repos/langchain/libs/partners/anthropic/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file) # noqa: T201
traceback.print_exc()
print() # noqa: T201
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/groq/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
test tests integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/groq --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_groq
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_groq -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/groq/LICENSE | MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/groq/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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lock-version = "2.0"
python-versions = ">=3.9,<4.0"
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0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/groq/README.md | # langchain-groq
## Welcome to Groq! 🚀
At Groq, we've developed the world's first Language Processing Unit™, or LPU. The Groq LPU has a deterministic, single core streaming architecture that sets the standard for GenAI inference speed with predictable and repeatable performance for any given workload.
Beyond the architecture, our software is designed to empower developers like you with the tools you need to create innovative, powerful AI applications. With Groq as your engine, you can:
* Achieve uncompromised low latency and performance for real-time AI and HPC inferences 🔥
* Know the exact performance and compute time for any given workload 🔮
* Take advantage of our cutting-edge technology to stay ahead of the competition 💪
Want more Groq? Check out our [website](https://groq.com) for more resources and join our [Discord community](https://discord.gg/JvNsBDKeCG) to connect with our developers!
## Installation and Setup
Install the integration package:
```bash
pip install langchain-groq
```
Request an [API key](https://wow.groq.com) and set it as an environment variable
```bash
export GROQ_API_KEY=gsk_...
```
## Chat Model
See a [usage example](https://python.langchain.com/docs/integrations/chat/groq).
## Development
To develop the `langchain-groq` package, you'll need to follow these instructions:
### Install dev dependencies
```bash
poetry install --with test,test_integration,lint,codespell
```
### Build the package
```bash
poetry build
```
### Run unit tests
Unit tests live in `tests/unit_tests` and SHOULD NOT require an internet connection or a valid API KEY. Run unit tests with
```bash
make tests
```
### Run integration tests
Integration tests live in `tests/integration_tests` and require a connection to the Groq API and a valid API KEY.
```bash
make integration_tests
```
### Lint & Format
Run additional tests and linters to ensure your code is up to standard.
```bash
make lint spell_check check_imports
``` |
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/groq/pyproject.toml | [build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-groq"
version = "0.2.1"
description = "An integration package connecting Groq and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.mypy]
disallow_untyped_defs = "True"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/groq"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-groq%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
groq = ">=0.4.1,<1"
[tool.ruff.lint]
select = ["E", "F", "I", "W"]
[tool.coverage.run]
omit = ["tests/*"]
[tool.pytest.ini_options]
addopts = "--strict-markers --strict-config --durations=5"
markers = [
"compile: mark placeholder test used to compile integration tests without running them",
"scheduled: mark tests to run in scheduled testing",
]
asyncio_mode = "auto"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
pytest-mock = "^3.10.0"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
[tool.poetry.group.test.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.test.dependencies.langchain-tests]
path = "../../standard-tests"
develop = true
[tool.poetry.group.dev.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.test_integration.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.typing.dependencies.langchain-core]
path = "../../core"
develop = true
|
0 | lc_public_repos/langchain/libs/partners/groq/tests | lc_public_repos/langchain/libs/partners/groq/tests/integration_tests/test_standard.py | """Standard LangChain interface tests"""
from typing import Optional, Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_tests.integration_tests import (
ChatModelIntegrationTests,
)
from langchain_groq import ChatGroq
rate_limiter = InMemoryRateLimiter(requests_per_second=0.2)
class BaseTestGroq(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatGroq
@pytest.mark.xfail(reason="Not yet implemented.")
def test_tool_message_histories_list_content(self, model: BaseChatModel) -> None:
super().test_tool_message_histories_list_content(model)
class TestGroqLlama(BaseTestGroq):
@property
def chat_model_params(self) -> dict:
return {
"model": "llama-3.1-8b-instant",
"temperature": 0,
"rate_limiter": rate_limiter,
}
@property
def tool_choice_value(self) -> Optional[str]:
"""Value to use for tool choice when used in tests."""
return "any"
@pytest.mark.xfail(
reason=("Fails with 'Failed to call a function. Please adjust your prompt.'")
)
def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
super().test_tool_calling_with_no_arguments(model)
@pytest.mark.xfail(
reason=("Fails with 'Failed to call a function. Please adjust your prompt.'")
)
def test_tool_message_histories_string_content(self, model: BaseChatModel) -> None:
super().test_tool_message_histories_string_content(model)
@pytest.mark.xfail(
reason=(
"Sometimes fails with 'Failed to call a function. "
"Please adjust your prompt.'"
)
)
def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
super().test_bind_runnables_as_tools(model)
|
0 | lc_public_repos/langchain/libs/partners/groq/tests | lc_public_repos/langchain/libs/partners/groq/tests/integration_tests/test_chat_models.py | """Test ChatGroq chat model."""
import json
from typing import Any, Optional
import pytest
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_core.tools import tool
from pydantic import BaseModel, Field
from langchain_groq import ChatGroq
from tests.unit_tests.fake.callbacks import (
FakeCallbackHandler,
FakeCallbackHandlerWithChatStart,
)
#
# Smoke test Runnable interface
#
@pytest.mark.scheduled
def test_invoke() -> None:
"""Test Chat wrapper."""
chat = ChatGroq( # type: ignore[call-arg]
temperature=0.7,
base_url=None,
groq_proxy=None,
timeout=10.0,
max_retries=3,
http_client=None,
n=1,
max_tokens=10,
default_headers=None,
default_query=None,
)
message = HumanMessage(content="Welcome to the Groqetship")
response = chat.invoke([message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
@pytest.mark.scheduled
async def test_ainvoke() -> None:
"""Test ainvoke tokens from ChatGroq."""
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
result = await chat.ainvoke("Welcome to the Groqetship!", config={"tags": ["foo"]})
assert isinstance(result, BaseMessage)
assert isinstance(result.content, str)
@pytest.mark.scheduled
def test_batch() -> None:
"""Test batch tokens from ChatGroq."""
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
result = chat.batch(["Hello!", "Welcome to the Groqetship!"])
for token in result:
assert isinstance(token, BaseMessage)
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_abatch() -> None:
"""Test abatch tokens from ChatGroq."""
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
result = await chat.abatch(["Hello!", "Welcome to the Groqetship!"])
for token in result:
assert isinstance(token, BaseMessage)
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_stream() -> None:
"""Test streaming tokens from Groq."""
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
for token in chat.stream("Welcome to the Groqetship!"):
assert isinstance(token, BaseMessageChunk)
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_astream() -> None:
"""Test streaming tokens from Groq."""
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
full: Optional[BaseMessageChunk] = None
chunks_with_token_counts = 0
async for token in chat.astream("Welcome to the Groqetship!"):
assert isinstance(token, AIMessageChunk)
assert isinstance(token.content, str)
full = token if full is None else full + token
if token.usage_metadata is not None:
chunks_with_token_counts += 1
if chunks_with_token_counts != 1:
raise AssertionError(
"Expected exactly one chunk with token counts. "
"AIMessageChunk aggregation adds counts. Check that "
"this is behaving properly."
)
assert isinstance(full, AIMessageChunk)
assert full.usage_metadata is not None
assert full.usage_metadata["input_tokens"] > 0
assert full.usage_metadata["output_tokens"] > 0
assert (
full.usage_metadata["input_tokens"] + full.usage_metadata["output_tokens"]
== full.usage_metadata["total_tokens"]
)
#
# Test Legacy generate methods
#
@pytest.mark.scheduled
def test_generate() -> None:
"""Test sync generate."""
n = 1
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
message = HumanMessage(content="Hello", n=1)
response = chat.generate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
assert response.llm_output
assert response.llm_output["model_name"] == chat.model_name
for generations in response.generations:
assert len(generations) == n
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
async def test_agenerate() -> None:
"""Test async generation."""
n = 1
chat = ChatGroq(max_tokens=10, n=1) # type: ignore[call-arg]
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
assert response.llm_output
assert response.llm_output["model_name"] == chat.model_name
for generations in response.generations:
assert len(generations) == n
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
#
# Test streaming flags in invoke and generate
#
@pytest.mark.scheduled
def test_invoke_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
chat = ChatGroq( # type: ignore[call-arg]
max_tokens=2,
streaming=True,
temperature=0,
callbacks=[callback_handler],
)
message = HumanMessage(content="Welcome to the Groqetship")
response = chat.invoke([message])
assert callback_handler.llm_streams > 0
assert isinstance(response, BaseMessage)
@pytest.mark.scheduled
async def test_agenerate_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandlerWithChatStart()
chat = ChatGroq( # type: ignore[call-arg]
max_tokens=10,
streaming=True,
temperature=0,
callbacks=[callback_handler],
)
message = HumanMessage(content="Welcome to the Groqetship")
response = await chat.agenerate([[message], [message]])
assert callback_handler.llm_streams > 0
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
assert response.llm_output is not None
assert response.llm_output["model_name"] == chat.model_name
for generations in response.generations:
assert len(generations) == 1
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
#
# Misc tests
#
def test_streaming_generation_info() -> None:
"""Test that generation info is preserved when streaming."""
class _FakeCallback(FakeCallbackHandler):
saved_things: dict = {}
def on_llm_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
# Save the generation
self.saved_things["generation"] = args[0]
callback = _FakeCallback()
chat = ChatGroq( # type: ignore[call-arg]
max_tokens=2,
temperature=0,
callbacks=[callback],
)
list(chat.stream("Respond with the single word Hello", stop=["o"]))
generation = callback.saved_things["generation"]
# `Hello!` is two tokens, assert that that is what is returned
assert isinstance(generation, LLMResult)
assert generation.generations[0][0].text == "Hell"
def test_system_message() -> None:
"""Test ChatGroq wrapper with system message."""
chat = ChatGroq(max_tokens=10) # type: ignore[call-arg]
system_message = SystemMessage(content="You are to chat with the user.")
human_message = HumanMessage(content="Hello")
response = chat.invoke([system_message, human_message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
@pytest.mark.xfail(reason="Groq tool_choice doesn't currently force a tool call")
def test_tool_choice() -> None:
"""Test that tool choice is respected."""
llm = ChatGroq() # type: ignore[call-arg]
class MyTool(BaseModel):
name: str
age: int
with_tool = llm.bind_tools([MyTool], tool_choice="MyTool")
resp = with_tool.invoke("Who was the 27 year old named Erick?")
assert isinstance(resp, AIMessage)
assert resp.content == "" # should just be tool call
tool_calls = resp.additional_kwargs["tool_calls"]
assert len(tool_calls) == 1
tool_call = tool_calls[0]
assert tool_call["function"]["name"] == "MyTool"
assert json.loads(tool_call["function"]["arguments"]) == {
"age": 27,
"name": "Erick",
}
assert tool_call["type"] == "function"
assert isinstance(resp.tool_calls, list)
assert len(resp.tool_calls) == 1
tool_call = resp.tool_calls[0]
assert tool_call["name"] == "MyTool"
assert tool_call["args"] == {"name": "Erick", "age": 27}
@pytest.mark.xfail(reason="Groq tool_choice doesn't currently force a tool call")
def test_tool_choice_bool() -> None:
"""Test that tool choice is respected just passing in True."""
llm = ChatGroq() # type: ignore[call-arg]
class MyTool(BaseModel):
name: str
age: int
with_tool = llm.bind_tools([MyTool], tool_choice=True)
resp = with_tool.invoke("Who was the 27 year old named Erick?")
assert isinstance(resp, AIMessage)
assert resp.content == "" # should just be tool call
tool_calls = resp.additional_kwargs["tool_calls"]
assert len(tool_calls) == 1
tool_call = tool_calls[0]
assert tool_call["function"]["name"] == "MyTool"
assert json.loads(tool_call["function"]["arguments"]) == {
"age": 27,
"name": "Erick",
}
assert tool_call["type"] == "function"
@pytest.mark.xfail(reason="Groq tool_choice doesn't currently force a tool call")
def test_streaming_tool_call() -> None:
"""Test that tool choice is respected."""
llm = ChatGroq() # type: ignore[call-arg]
class MyTool(BaseModel):
name: str
age: int
with_tool = llm.bind_tools([MyTool], tool_choice="MyTool")
resp = with_tool.stream("Who was the 27 year old named Erick?")
additional_kwargs = None
for chunk in resp:
assert isinstance(chunk, AIMessageChunk)
assert chunk.content == "" # should just be tool call
additional_kwargs = chunk.additional_kwargs
assert additional_kwargs is not None
tool_calls = additional_kwargs["tool_calls"]
assert len(tool_calls) == 1
tool_call = tool_calls[0]
assert tool_call["function"]["name"] == "MyTool"
assert json.loads(tool_call["function"]["arguments"]) == {
"age": 27,
"name": "Erick",
}
assert tool_call["type"] == "function"
assert isinstance(chunk, AIMessageChunk)
assert isinstance(chunk.tool_call_chunks, list)
assert len(chunk.tool_call_chunks) == 1
tool_call_chunk = chunk.tool_call_chunks[0]
assert tool_call_chunk["name"] == "MyTool"
assert isinstance(tool_call_chunk["args"], str)
assert json.loads(tool_call_chunk["args"]) == {"name": "Erick", "age": 27}
@pytest.mark.xfail(reason="Groq tool_choice doesn't currently force a tool call")
async def test_astreaming_tool_call() -> None:
"""Test that tool choice is respected."""
llm = ChatGroq() # type: ignore[call-arg]
class MyTool(BaseModel):
name: str
age: int
with_tool = llm.bind_tools([MyTool], tool_choice="MyTool")
resp = with_tool.astream("Who was the 27 year old named Erick?")
additional_kwargs = None
async for chunk in resp:
assert isinstance(chunk, AIMessageChunk)
assert chunk.content == "" # should just be tool call
additional_kwargs = chunk.additional_kwargs
assert additional_kwargs is not None
tool_calls = additional_kwargs["tool_calls"]
assert len(tool_calls) == 1
tool_call = tool_calls[0]
assert tool_call["function"]["name"] == "MyTool"
assert json.loads(tool_call["function"]["arguments"]) == {
"age": 27,
"name": "Erick",
}
assert tool_call["type"] == "function"
assert isinstance(chunk, AIMessageChunk)
assert isinstance(chunk.tool_call_chunks, list)
assert len(chunk.tool_call_chunks) == 1
tool_call_chunk = chunk.tool_call_chunks[0]
assert tool_call_chunk["name"] == "MyTool"
assert isinstance(tool_call_chunk["args"], str)
assert json.loads(tool_call_chunk["args"]) == {"name": "Erick", "age": 27}
@pytest.mark.scheduled
def test_json_mode_structured_output() -> None:
"""Test with_structured_output with json"""
class Joke(BaseModel):
"""Joke to tell user."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
chat = ChatGroq().with_structured_output(Joke, method="json_mode") # type: ignore[call-arg]
result = chat.invoke(
"Tell me a joke about cats, respond in JSON with `setup` and `punchline` keys"
)
assert type(result) is Joke
assert len(result.setup) != 0
assert len(result.punchline) != 0
def test_tool_calling_no_arguments() -> None:
# Note: this is a variant of a test in langchain_tests
# that as of 2024-08-19 fails with "Failed to call a function. Please
# adjust your prompt." when `tool_choice="any"` is specified, but
# passes when `tool_choice` is not specified.
model = ChatGroq(model="llama-3.1-70b-versatile", temperature=0) # type: ignore[call-arg]
@tool
def magic_function_no_args() -> int:
"""Calculates a magic function."""
return 5
model_with_tools = model.bind_tools([magic_function_no_args])
query = "What is the value of magic_function()? Use the tool."
result = model_with_tools.invoke(query)
assert isinstance(result, AIMessage)
assert len(result.tool_calls) == 1
tool_call = result.tool_calls[0]
assert tool_call["name"] == "magic_function_no_args"
assert tool_call["args"] == {}
assert tool_call["id"] is not None
assert tool_call["type"] == "tool_call"
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in model_with_tools.stream(query):
full = chunk if full is None else full + chunk # type: ignore
assert isinstance(full, AIMessage)
assert len(full.tool_calls) == 1
tool_call = full.tool_calls[0]
assert tool_call["name"] == "magic_function_no_args"
assert tool_call["args"] == {}
assert tool_call["id"] is not None
assert tool_call["type"] == "tool_call"
# Groq does not currently support N > 1
# @pytest.mark.scheduled
# def test_chat_multiple_completions() -> None:
# """Test ChatGroq wrapper with multiple completions."""
# chat = ChatGroq(max_tokens=10, n=5)
# message = HumanMessage(content="Hello")
# response = chat._generate([message])
# assert isinstance(response, ChatResult)
# assert len(response.generations) == 5
# for generation in response.generations:
# assert isinstance(generation.message, BaseMessage)
# assert isinstance(generation.message.content, str)
|
0 | lc_public_repos/langchain/libs/partners/groq/tests | lc_public_repos/langchain/libs/partners/groq/tests/integration_tests/test_compile.py | import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/partners/groq/tests | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests/test_standard.py | """Standard LangChain interface tests"""
from typing import Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests.chat_models import (
ChatModelUnitTests,
)
from langchain_groq import ChatGroq
class TestGroqStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatGroq
|
0 | lc_public_repos/langchain/libs/partners/groq/tests | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests/test_chat_models.py | """Test Groq Chat API wrapper."""
import json
import os
from typing import Any
from unittest.mock import AsyncMock, MagicMock, patch
import langchain_core.load as lc_load
import pytest
from langchain_core.messages import (
AIMessage,
FunctionMessage,
HumanMessage,
InvalidToolCall,
SystemMessage,
ToolCall,
)
from langchain_groq.chat_models import ChatGroq, _convert_dict_to_message
if "GROQ_API_KEY" not in os.environ:
os.environ["GROQ_API_KEY"] = "fake-key"
def test_groq_model_param() -> None:
llm = ChatGroq(model="foo") # type: ignore[call-arg]
assert llm.model_name == "foo"
llm = ChatGroq(model_name="foo") # type: ignore[call-arg]
assert llm.model_name == "foo"
def test_function_message_dict_to_function_message() -> None:
content = json.dumps({"result": "Example #1"})
name = "test_function"
result = _convert_dict_to_message(
{
"role": "function",
"name": name,
"content": content,
}
)
assert isinstance(result, FunctionMessage)
assert result.name == name
assert result.content == content
def test__convert_dict_to_message_human() -> None:
message = {"role": "user", "content": "foo"}
result = _convert_dict_to_message(message)
expected_output = HumanMessage(content="foo")
assert result == expected_output
def test__convert_dict_to_message_ai() -> None:
message = {"role": "assistant", "content": "foo"}
result = _convert_dict_to_message(message)
expected_output = AIMessage(content="foo")
assert result == expected_output
def test__convert_dict_to_message_tool_call() -> None:
raw_tool_call = {
"id": "call_wm0JY6CdwOMZ4eTxHWUThDNz",
"function": {
"arguments": '{"name":"Sally","hair_color":"green"}',
"name": "GenerateUsername",
},
"type": "function",
}
message = {"role": "assistant", "content": None, "tool_calls": [raw_tool_call]}
result = _convert_dict_to_message(message)
expected_output = AIMessage(
content="",
additional_kwargs={"tool_calls": [raw_tool_call]},
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="call_wm0JY6CdwOMZ4eTxHWUThDNz",
type="tool_call",
)
],
)
assert result == expected_output
# Test malformed tool call
raw_tool_calls = [
{
"id": "call_wm0JY6CdwOMZ4eTxHWUThDNz",
"function": {
"arguments": "oops",
"name": "GenerateUsername",
},
"type": "function",
},
{
"id": "call_abc123",
"function": {
"arguments": '{"name":"Sally","hair_color":"green"}',
"name": "GenerateUsername",
},
"type": "function",
},
]
message = {"role": "assistant", "content": None, "tool_calls": raw_tool_calls}
result = _convert_dict_to_message(message)
expected_output = AIMessage(
content="",
additional_kwargs={"tool_calls": raw_tool_calls},
invalid_tool_calls=[
InvalidToolCall(
name="GenerateUsername",
args="oops",
id="call_wm0JY6CdwOMZ4eTxHWUThDNz",
error="Function GenerateUsername arguments:\n\noops\n\nare not valid JSON. Received JSONDecodeError Expecting value: line 1 column 1 (char 0)\nFor troubleshooting, visit: https://python.langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE ", # noqa: E501
type="invalid_tool_call",
),
],
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="call_abc123",
type="tool_call",
),
],
)
assert result == expected_output
def test__convert_dict_to_message_system() -> None:
message = {"role": "system", "content": "foo"}
result = _convert_dict_to_message(message)
expected_output = SystemMessage(content="foo")
assert result == expected_output
@pytest.fixture
def mock_completion() -> dict:
return {
"id": "chatcmpl-7fcZavknQda3SQ",
"object": "chat.completion",
"created": 1689989000,
"model": "test-model",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Bar Baz",
},
"finish_reason": "stop",
}
],
}
def test_groq_invoke(mock_completion: dict) -> None:
llm = ChatGroq() # type: ignore[call-arg]
mock_client = MagicMock()
completed = False
def mock_create(*args: Any, **kwargs: Any) -> Any:
nonlocal completed
completed = True
return mock_completion
mock_client.create = mock_create
with patch.object(
llm,
"client",
mock_client,
):
res = llm.invoke("bar")
assert res.content == "Bar Baz"
assert type(res) is AIMessage
assert completed
async def test_groq_ainvoke(mock_completion: dict) -> None:
llm = ChatGroq() # type: ignore[call-arg]
mock_client = AsyncMock()
completed = False
async def mock_create(*args: Any, **kwargs: Any) -> Any:
nonlocal completed
completed = True
return mock_completion
mock_client.create = mock_create
with patch.object(
llm,
"async_client",
mock_client,
):
res = await llm.ainvoke("bar")
assert res.content == "Bar Baz"
assert type(res) is AIMessage
assert completed
def test_chat_groq_extra_kwargs() -> None:
"""Test extra kwargs to chat groq."""
# Check that foo is saved in extra_kwargs.
with pytest.warns(UserWarning) as record:
llm = ChatGroq(foo=3, max_tokens=10) # type: ignore[call-arg]
assert llm.max_tokens == 10
assert llm.model_kwargs == {"foo": 3}
assert len(record) == 1
assert type(record[0].message) is UserWarning
assert "foo is not default parameter" in record[0].message.args[0]
# Test that if extra_kwargs are provided, they are added to it.
with pytest.warns(UserWarning) as record:
llm = ChatGroq(foo=3, model_kwargs={"bar": 2}) # type: ignore[call-arg]
assert llm.model_kwargs == {"foo": 3, "bar": 2}
assert len(record) == 1
assert type(record[0].message) is UserWarning
assert "foo is not default parameter" in record[0].message.args[0]
# Test that if provided twice it errors
with pytest.raises(ValueError):
ChatGroq(foo=3, model_kwargs={"foo": 2}) # type: ignore[call-arg]
# Test that if explicit param is specified in kwargs it errors
with pytest.raises(ValueError):
ChatGroq(model_kwargs={"temperature": 0.2}) # type: ignore[call-arg]
# Test that "model" cannot be specified in kwargs
with pytest.raises(ValueError):
ChatGroq(model_kwargs={"model": "test-model"}) # type: ignore[call-arg]
def test_chat_groq_invalid_streaming_params() -> None:
"""Test that an error is raised if streaming is invoked with n>1."""
with pytest.raises(ValueError):
ChatGroq( # type: ignore[call-arg]
max_tokens=10,
streaming=True,
temperature=0,
n=5,
)
def test_chat_groq_secret() -> None:
"""Test that secret is not printed"""
secret = "secretKey"
not_secret = "safe"
llm = ChatGroq(api_key=secret, model_kwargs={"not_secret": not_secret}) # type: ignore[call-arg, arg-type]
stringified = str(llm)
assert not_secret in stringified
assert secret not in stringified
@pytest.mark.filterwarnings("ignore:The function `loads` is in beta")
def test_groq_serialization() -> None:
"""Test that ChatGroq can be successfully serialized and deserialized"""
api_key1 = "top secret"
api_key2 = "topest secret"
llm = ChatGroq(api_key=api_key1, temperature=0.5) # type: ignore[call-arg, arg-type]
dump = lc_load.dumps(llm)
llm2 = lc_load.loads(
dump,
valid_namespaces=["langchain_groq"],
secrets_map={"GROQ_API_KEY": api_key2},
)
assert type(llm2) is ChatGroq
# Ensure api key wasn't dumped and instead was read from secret map.
assert llm.groq_api_key is not None
assert llm.groq_api_key.get_secret_value() not in dump
assert llm2.groq_api_key is not None
assert llm2.groq_api_key.get_secret_value() == api_key2
# Ensure a non-secret field was preserved
assert llm.temperature == llm2.temperature
# Ensure a None was preserved
assert llm.groq_api_base == llm2.groq_api_base
|
0 | lc_public_repos/langchain/libs/partners/groq/tests | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests/test_imports.py | from langchain_groq import __all__
EXPECTED_ALL = ["ChatGroq"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests/__snapshots__/test_standard.ambr | # serializer version: 1
# name: TestGroqStandard.test_serdes[serialized]
dict({
'id': list([
'langchain_groq',
'chat_models',
'ChatGroq',
]),
'kwargs': dict({
'groq_api_key': dict({
'id': list([
'GROQ_API_KEY',
]),
'lc': 1,
'type': 'secret',
}),
'max_retries': 2,
'max_tokens': 100,
'model_name': 'mixtral-8x7b-32768',
'n': 1,
'request_timeout': 60.0,
'stop': list([
]),
'temperature': 1e-08,
}),
'lc': 1,
'name': 'ChatGroq',
'type': 'constructor',
})
# ---
|
0 | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests | lc_public_repos/langchain/libs/partners/groq/tests/unit_tests/fake/callbacks.py | """A fake callback handler for testing purposes."""
from itertools import chain
from typing import Any, Dict, List, Optional, Union
from uuid import UUID
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
from langchain_core.messages import BaseMessage
from pydantic import BaseModel
class BaseFakeCallbackHandler(BaseModel):
"""Base fake callback handler for testing."""
starts: int = 0
ends: int = 0
errors: int = 0
errors_args: List[Any] = []
text: int = 0
ignore_llm_: bool = False
ignore_chain_: bool = False
ignore_agent_: bool = False
ignore_retriever_: bool = False
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technically equal
fake_id: Union[str, None] = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
chain_ends: int = 0
llm_starts: int = 0
llm_ends: int = 0
llm_streams: int = 0
tool_starts: int = 0
tool_ends: int = 0
agent_actions: int = 0
agent_ends: int = 0
chat_model_starts: int = 0
retriever_starts: int = 0
retriever_ends: int = 0
retriever_errors: int = 0
retries: int = 0
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
"""Base fake callback handler mixin for testing."""
def on_llm_start_common(self) -> None:
self.llm_starts += 1
self.starts += 1
def on_llm_end_common(self) -> None:
self.llm_ends += 1
self.ends += 1
def on_llm_error_common(self, *args: Any, **kwargs: Any) -> None:
self.errors += 1
self.errors_args.append({"args": args, "kwargs": kwargs})
def on_llm_new_token_common(self) -> None:
self.llm_streams += 1
def on_retry_common(self) -> None:
self.retries += 1
def on_chain_start_common(self) -> None:
self.chain_starts += 1
self.starts += 1
def on_chain_end_common(self) -> None:
self.chain_ends += 1
self.ends += 1
def on_chain_error_common(self) -> None:
self.errors += 1
def on_tool_start_common(self) -> None:
self.tool_starts += 1
self.starts += 1
def on_tool_end_common(self) -> None:
self.tool_ends += 1
self.ends += 1
def on_tool_error_common(self) -> None:
self.errors += 1
def on_agent_action_common(self) -> None:
self.agent_actions += 1
self.starts += 1
def on_agent_finish_common(self) -> None:
self.agent_ends += 1
self.ends += 1
def on_chat_model_start_common(self) -> None:
self.chat_model_starts += 1
self.starts += 1
def on_text_common(self) -> None:
self.text += 1
def on_retriever_start_common(self) -> None:
self.starts += 1
self.retriever_starts += 1
def on_retriever_end_common(self) -> None:
self.ends += 1
self.retriever_ends += 1
def on_retriever_error_common(self) -> None:
self.errors += 1
self.retriever_errors += 1
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
"""Fake callback handler for testing."""
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
@property
def ignore_retriever(self) -> bool:
"""Whether to ignore retriever callbacks."""
return self.ignore_retriever_
def on_llm_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_start_common()
def on_llm_new_token(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_new_token_common()
def on_llm_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_end_common()
def on_llm_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_llm_error_common(*args, **kwargs)
def on_retry(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retry_common()
def on_chain_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_chain_start_common()
def on_chain_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_chain_end_common()
def on_chain_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_chain_error_common()
def on_tool_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_tool_start_common()
def on_tool_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_tool_end_common()
def on_tool_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_tool_error_common()
def on_agent_action(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_agent_action_common()
def on_agent_finish(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_agent_finish_common()
def on_text(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_text_common()
def on_retriever_start(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retriever_start_common()
def on_retriever_end(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retriever_end_common()
def on_retriever_error(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retriever_error_common()
# Overriding since BaseModel has __deepcopy__ method as well
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore
return self
class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
self.on_chat_model_start_common()
class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixin):
"""Fake async callback handler for testing."""
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
async def on_retry(
self,
*args: Any,
**kwargs: Any,
) -> Any:
self.on_retry_common()
async def on_llm_start(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_start_common()
async def on_llm_new_token(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_new_token_common()
async def on_llm_end(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_end_common()
async def on_llm_error(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_llm_error_common(*args, **kwargs)
async def on_chain_start(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_chain_start_common()
async def on_chain_end(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_chain_end_common()
async def on_chain_error(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_chain_error_common()
async def on_tool_start(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_tool_start_common()
async def on_tool_end(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_tool_end_common()
async def on_tool_error(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_tool_error_common()
async def on_agent_action(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_agent_action_common()
async def on_agent_finish(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_agent_finish_common()
async def on_text(
self,
*args: Any,
**kwargs: Any,
) -> None:
self.on_text_common()
# Overriding since BaseModel has __deepcopy__ method as well
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore
return self
|
0 | lc_public_repos/langchain/libs/partners/groq | lc_public_repos/langchain/libs/partners/groq/langchain_groq/chat_models.py | """Groq Chat wrapper."""
from __future__ import annotations
import json
import warnings
from operator import itemgetter
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypedDict,
Union,
cast,
)
from langchain_core._api import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
LangSmithParams,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
InvalidToolCall,
SystemMessage,
SystemMessageChunk,
ToolCall,
ToolMessage,
ToolMessageChunk,
)
from langchain_core.output_parsers import (
JsonOutputParser,
PydanticOutputParser,
)
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
PydanticToolsParser,
make_invalid_tool_call,
parse_tool_call,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import (
from_env,
get_pydantic_field_names,
secret_from_env,
)
from langchain_core.utils.function_calling import (
convert_to_openai_function,
convert_to_openai_tool,
)
from langchain_core.utils.pydantic import is_basemodel_subclass
from pydantic import (
BaseModel,
ConfigDict,
Field,
SecretStr,
model_validator,
)
from typing_extensions import Self
class ChatGroq(BaseChatModel):
"""`Groq` Chat large language models API.
To use, you should have the
environment variable ``GROQ_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the groq.create call
can be passed in, even if not explicitly saved on this class.
Setup:
Install ``langchain-groq`` and set environment variable
``GROQ_API_KEY``.
.. code-block:: bash
pip install -U langchain-groq
export GROQ_API_KEY="your-api-key"
Key init args — completion params:
model: str
Name of Groq model to use. E.g. "mixtral-8x7b-32768".
temperature: float
Sampling temperature. Ranges from 0.0 to 1.0.
max_tokens: Optional[int]
Max number of tokens to generate.
model_kwargs: Dict[str, Any]
Holds any model parameters valid for create call not
explicitly specified.
Key init args — client params:
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
Groq API key. If not passed in will be read from env var GROQ_API_KEY.
base_url: Optional[str]
Base URL path for API requests, leave blank if not using a proxy
or service emulator.
custom_get_token_ids: Optional[Callable[[str], List[int]]]
Optional encoder to use for counting tokens.
See full list of supported init args and their descriptions in the params
section.
Instantiate:
.. code-block:: python
from langchain_groq import ChatGroq
llm = ChatGroq(
model="mixtral-8x7b-32768",
temperature=0.0,
max_retries=2,
# other params...
)
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user
sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(content='The English sentence "I love programming" can
be translated to French as "J\'aime programmer". The word
"programming" is translated as "programmer" in French.',
response_metadata={'token_usage': {'completion_tokens': 38,
'prompt_tokens': 28, 'total_tokens': 66, 'completion_time':
0.057975474, 'prompt_time': 0.005366091, 'queue_time': None,
'total_time': 0.063341565}, 'model_name': 'mixtral-8x7b-32768',
'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop',
'logprobs': None}, id='run-ecc71d70-e10c-4b69-8b8c-b8027d95d4b8-0')
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
content='' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content='The' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content=' English' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content=' sentence' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
...
content=' program' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content='".' id='run-4e9f926b-73f5-483b-8ef5-09533d925853'
content='' response_metadata={'finish_reason': 'stop'}
id='run-4e9f926b-73f5-483b-8ef5-09533d925853
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(content='The English sentence "I love programming"
can be translated to French as "J\'aime programmer".
Here\'s the breakdown of the sentence:\n\n* "J\'aime" is the
French equivalent of "I love"\n* "programmer" is the French
infinitive for "to program"\n\nSo, the literal translation
is "I love to program". However, in English we often omit the
"to" when talking about activities we love, and the same applies
to French. Therefore, "J\'aime programmer" is the correct and
natural way to express "I love programming" in French.',
response_metadata={'finish_reason': 'stop'},
id='run-a3c35ac4-0750-4d08-ac55-bfc63805de76')
Async:
.. code-block:: python
await llm.ainvoke(messages)
.. code-block:: python
AIMessage(content='The English sentence "I love programming" can
be translated to French as "J\'aime programmer". The word
"programming" is translated as "programmer" in French. I hope
this helps! Let me know if you have any other questions.',
response_metadata={'token_usage': {'completion_tokens': 53,
'prompt_tokens': 28, 'total_tokens': 81, 'completion_time':
0.083623752, 'prompt_time': 0.007365126, 'queue_time': None,
'total_time': 0.090988878}, 'model_name': 'mixtral-8x7b-32768',
'system_fingerprint': 'fp_c5f20b5bb1', 'finish_reason': 'stop',
'logprobs': None}, id='run-897f3391-1bea-42e2-82e0-686e2367bcf8-0')
Tool calling:
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state,
e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state,
e.g. San Francisco, CA")
model_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = model_with_tools.invoke("What is the population of NY?")
ai_msg.tool_calls
.. code-block:: python
[{'name': 'GetPopulation',
'args': {'location': 'NY'},
'id': 'call_bb8d'}]
See ``ChatGroq.bind_tools()`` method for more.
Structured output:
.. code-block:: python
from typing import Optional
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke
is, from 1 to 10")
structured_model = llm.with_structured_output(Joke)
structured_model.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(setup="Why don't cats play poker in the jungle?",
punchline='Too many cheetahs!', rating=None)
See ``ChatGroq.with_structured_output()`` for more.
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{'token_usage': {'completion_tokens': 70,
'prompt_tokens': 28,
'total_tokens': 98,
'completion_time': 0.111956391,
'prompt_time': 0.007518279,
'queue_time': None,
'total_time': 0.11947467},
'model_name': 'mixtral-8x7b-32768',
'system_fingerprint': 'fp_c5f20b5bb1',
'finish_reason': 'stop',
'logprobs': None}
"""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(default="mixtral-8x7b-32768", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
stop: Optional[Union[List[str], str]] = Field(None, alias="stop_sequences")
"""Default stop sequences."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
groq_api_key: Optional[SecretStr] = Field(
alias="api_key", default_factory=secret_from_env("GROQ_API_KEY", default=None)
)
"""Automatically inferred from env var `GROQ_API_KEY` if not provided."""
groq_api_base: Optional[str] = Field(
alias="base_url", default_factory=from_env("GROQ_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
# to support explicit proxy for Groq
groq_proxy: Optional[str] = Field(
default_factory=from_env("GROQ_PROXY", default=None)
)
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to Groq completion API. Can be float, httpx.Timeout or
None."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
streaming: bool = False
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
max_tokens: Optional[int] = None
"""Maximum number of tokens to generate."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = None
"""Optional httpx.Client."""
http_async_client: Union[Any, None] = None
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
model_config = ConfigDict(
populate_by_name=True,
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.n > 1 and self.streaming:
raise ValueError("n must be 1 when streaming.")
if self.temperature == 0:
self.temperature = 1e-8
client_params: Dict[str, Any] = {
"api_key": (
self.groq_api_key.get_secret_value() if self.groq_api_key else None
),
"base_url": self.groq_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
try:
import groq
sync_specific: Dict[str, Any] = {"http_client": self.http_client}
if not self.client:
self.client = groq.Groq(
**client_params, **sync_specific
).chat.completions
if not self.async_client:
async_specific: Dict[str, Any] = {"http_client": self.http_async_client}
self.async_client = groq.AsyncGroq(
**client_params, **async_specific
).chat.completions
except ImportError:
raise ImportError(
"Could not import groq python package. "
"Please install it with `pip install groq`."
)
return self
#
# Serializable class method overrides
#
@property
def lc_secrets(self) -> Dict[str, str]:
return {"groq_api_key": "GROQ_API_KEY"}
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
#
# BaseChatModel method overrides
#
@property
def _llm_type(self) -> str:
"""Return type of model."""
return "groq-chat"
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="groq",
ls_model_name=self.model_name,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None) or self.stop:
ls_params["ls_stop"] = ls_stop if isinstance(ls_stop, list) else [ls_stop]
return ls_params
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {
**params,
**kwargs,
}
response = self.client.create(messages=message_dicts, **params)
return self._create_chat_result(response)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
message_dicts, params = self._create_message_dicts(messages, stop)
params = {
**params,
**kwargs,
}
response = await self.async_client.create(messages=message_dicts, **params)
return self._create_chat_result(response)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
for chunk in self.client.create(messages=message_dicts, **params):
if not isinstance(chunk, dict):
chunk = chunk.model_dump()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
message_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class)
generation_info = {}
if finish_reason := choice.get("finish_reason"):
generation_info["finish_reason"] = finish_reason
logprobs = choice.get("logprobs")
if logprobs:
generation_info["logprobs"] = logprobs
default_chunk_class = message_chunk.__class__
generation_chunk = ChatGenerationChunk(
message=message_chunk, generation_info=generation_info or None
)
if run_manager:
run_manager.on_llm_new_token(
generation_chunk.text, chunk=generation_chunk, logprobs=logprobs
)
yield generation_chunk
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
message_dicts, params = self._create_message_dicts(messages, stop)
params = {**params, **kwargs, "stream": True}
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
async for chunk in await self.async_client.create(
messages=message_dicts, **params
):
if not isinstance(chunk, dict):
chunk = chunk.model_dump()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
message_chunk = _convert_chunk_to_message_chunk(chunk, default_chunk_class)
generation_info = {}
if finish_reason := choice.get("finish_reason"):
generation_info["finish_reason"] = finish_reason
logprobs = choice.get("logprobs")
if logprobs:
generation_info["logprobs"] = logprobs
default_chunk_class = message_chunk.__class__
generation_chunk = ChatGenerationChunk(
message=message_chunk, generation_info=generation_info or None
)
if run_manager:
await run_manager.on_llm_new_token(
token=generation_chunk.text,
chunk=generation_chunk,
logprobs=logprobs,
)
yield generation_chunk
#
# Internal methods
#
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling Groq API."""
params = {
"model": self.model_name,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
"stop": self.stop,
**self.model_kwargs,
}
if self.max_tokens is not None:
params["max_tokens"] = self.max_tokens
return params
def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
generations = []
if not isinstance(response, dict):
response = response.model_dump()
token_usage = response.get("usage", {})
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
if token_usage and isinstance(message, AIMessage):
input_tokens = token_usage.get("prompt_tokens", 0)
output_tokens = token_usage.get("completion_tokens", 0)
message.usage_metadata = {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": token_usage.get(
"total_tokens", input_tokens + output_tokens
),
}
generation_info = dict(finish_reason=res.get("finish_reason"))
if "logprobs" in res:
generation_info["logprobs"] = res["logprobs"]
gen = ChatGeneration(
message=message,
generation_info=generation_info,
)
generations.append(gen)
llm_output = {
"token_usage": token_usage,
"model_name": self.model_name,
"system_fingerprint": response.get("system_fingerprint", ""),
}
return ChatResult(generations=generations, llm_output=llm_output)
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
params = self._default_params
if stop is not None:
params["stop"] = stop
message_dicts = [_convert_message_to_dict(m) for m in messages]
return message_dicts, params
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
system_fingerprint = None
for output in llm_outputs:
if output is None:
# Happens in streaming
continue
token_usage = output["token_usage"]
if token_usage is not None:
for k, v in token_usage.items():
if k in overall_token_usage and v is not None:
overall_token_usage[k] += v
else:
overall_token_usage[k] = v
if system_fingerprint is None:
system_fingerprint = output.get("system_fingerprint")
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
if system_fingerprint:
combined["system_fingerprint"] = system_fingerprint
return combined
@deprecated(
since="0.2.1",
alternative="langchain_groq.chat_models.ChatGroq.bind_tools",
removal="1.0.0",
)
def bind_functions(
self,
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
function_call: Optional[
Union[_FunctionCall, str, Literal["auto", "none"]]
] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind functions (and other objects) to this chat model.
Model is compatible with OpenAI function-calling API.
NOTE: Using bind_tools is recommended instead, as the `functions` and
`function_call` request parameters are officially deprecated.
Args:
functions: A list of function definitions to bind to this chat model.
Can be a dictionary, pydantic model, or callable. Pydantic
models and callables will be automatically converted to
their schema dictionary representation.
function_call: Which function to require the model to call.
Must be the name of the single provided function or
"auto" to automatically determine which function to call
(if any).
**kwargs: Any additional parameters to pass to
:meth:`~langchain_groq.chat_models.ChatGroq.bind`.
"""
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
if function_call is not None:
function_call = (
{"name": function_call}
if isinstance(function_call, str)
and function_call not in ("auto", "none")
else function_call
)
if isinstance(function_call, dict) and len(formatted_functions) != 1:
raise ValueError(
"When specifying `function_call`, you must provide exactly one "
"function."
)
if (
isinstance(function_call, dict)
and formatted_functions[0]["name"] != function_call["name"]
):
raise ValueError(
f"Function call {function_call} was specified, but the only "
f"provided function was {formatted_functions[0]['name']}."
)
kwargs = {**kwargs, "function_call": function_call}
return super().bind(
functions=formatted_functions,
**kwargs,
)
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "any", "none"], bool]
] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model.
Args:
tools: A list of tool definitions to bind to this chat model.
Supports any tool definition handled by
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function,
"auto" to automatically determine which function to call
with the option to not call any function, "any" to enforce that some
function is called, or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
"""
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
if tool_choice is not None and tool_choice:
if tool_choice == "any":
tool_choice = "required"
if isinstance(tool_choice, str) and (
tool_choice not in ("auto", "none", "required")
):
tool_choice = {"type": "function", "function": {"name": tool_choice}}
if isinstance(tool_choice, bool):
if len(tools) > 1:
raise ValueError(
"tool_choice can only be True when there is one tool. Received "
f"{len(tools)} tools."
)
tool_name = formatted_tools[0]["function"]["name"]
tool_choice = {
"type": "function",
"function": {"name": tool_name},
}
kwargs["tool_choice"] = tool_choice
return super().bind(tools=formatted_tools, **kwargs)
def with_structured_output(
self,
schema: Optional[Union[Dict, Type[BaseModel]]] = None,
*,
method: Literal["function_calling", "json_mode"] = "function_calling",
include_raw: bool = False,
**kwargs: Any,
) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema:
The output schema. Can be passed in as:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class (supported added in 0.1.9),
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
.. versionchanged:: 0.1.9
Added support for TypedDict class.
method:
The method for steering model generation, either "function_calling"
or "json_mode". If "function_calling" then the schema will be converted
to an OpenAI function and the returned model will make use of the
function-calling API. If "json_mode" then OpenAI's JSON mode will be
used. Note that if using "json_mode" then you must include instructions
for formatting the output into the desired schema into the model call.
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
an instance of ``schema`` (i.e., a Pydantic object).
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
If ``include_raw`` is True, then Runnable outputs a dict with keys:
- ``"raw"``: BaseMessage
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- ``"parsing_error"``: Optional[BaseException]
Example: schema=Pydantic class, method="function_calling", include_raw=False:
.. code-block:: python
from typing import Optional
from langchain_groq import ChatGroq
from pydantic import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
# If we provide default values and/or descriptions for fields, these will be passed
# to the model. This is an important part of improving a model's ability to
# correctly return structured outputs.
justification: Optional[str] = Field(
default=None, description="A justification for the answer."
)
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
Example: schema=Pydantic class, method="function_calling", include_raw=True:
.. code-block:: python
from langchain_groq import ChatGroq
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, include_raw=True
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
Example: schema=TypedDict class, method="function_calling", include_raw=False:
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict
from langchain_groq import ChatGroq
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
Example: schema=OpenAI function schema, method="function_calling", include_raw=False:
.. code-block:: python
from langchain_groq import ChatGroq
oai_schema = {
'name': 'AnswerWithJustification',
'description': 'An answer to the user question along with justification for the answer.',
'parameters': {
'type': 'object',
'properties': {
'answer': {'type': 'string'},
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
},
'required': ['answer']
}
}
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
Example: schema=Pydantic class, method="json_mode", include_raw=True:
.. code-block::
from langchain_groq import ChatGroq
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
llm = ChatGroq(model="llama-3.1-405b-reasoning", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification,
method="json_mode",
include_raw=True
)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
# 'parsing_error': None
# }
Example: schema=None, method="json_mode", include_raw=True:
.. code-block::
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
# 'parsed': {
# 'answer': 'They are both the same weight.',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
# },
# 'parsing_error': None
# }
""" # noqa: E501
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
is_pydantic_schema = _is_pydantic_class(schema)
if method == "function_calling":
if schema is None:
raise ValueError(
"schema must be specified when method is 'function_calling'. "
"Received None."
)
tool_name = convert_to_openai_tool(schema)["function"]["name"]
llm = self.bind_tools([schema], tool_choice=tool_name)
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
)
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
if is_pydantic_schema
else JsonOutputParser()
)
else:
raise ValueError(
f"Unrecognized method argument. Expected one of 'function_calling' or "
f"'json_mode'. Received: '{method}'"
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
def _is_pydantic_class(obj: Any) -> bool:
return isinstance(obj, type) and is_basemodel_subclass(obj)
class _FunctionCall(TypedDict):
name: str
#
# Type conversion helpers
#
def _convert_message_to_dict(message: BaseMessage) -> dict:
"""Convert a LangChain message to a dictionary.
Args:
message: The LangChain message.
Returns:
The dictionary.
"""
message_dict: Dict[str, Any]
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
# If function call only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
if message.tool_calls or message.invalid_tool_calls:
message_dict["tool_calls"] = [
_lc_tool_call_to_groq_tool_call(tc) for tc in message.tool_calls
] + [
_lc_invalid_tool_call_to_groq_tool_call(tc)
for tc in message.invalid_tool_calls
]
elif "tool_calls" in message.additional_kwargs:
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
# If tool calls only, content is None not empty string
if message_dict["content"] == "":
message_dict["content"] = None
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": message.content}
elif isinstance(message, FunctionMessage):
message_dict = {
"role": "function",
"content": message.content,
"name": message.name,
}
elif isinstance(message, ToolMessage):
message_dict = {
"role": "tool",
"content": message.content,
"tool_call_id": message.tool_call_id,
}
else:
raise TypeError(f"Got unknown type {message}")
if "name" in message.additional_kwargs:
message_dict["name"] = message.additional_kwargs["name"]
return message_dict
def _convert_chunk_to_message_chunk(
chunk: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
choice = chunk["choices"][0]
_dict = choice["delta"]
role = cast(str, _dict.get("role"))
content = cast(str, _dict.get("content") or "")
additional_kwargs: Dict = {}
if _dict.get("function_call"):
function_call = dict(_dict["function_call"])
if "name" in function_call and function_call["name"] is None:
function_call["name"] = ""
additional_kwargs["function_call"] = function_call
if _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = _dict["tool_calls"]
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
if usage := (chunk.get("x_groq") or {}).get("usage"):
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
usage_metadata = {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": usage.get("total_tokens", input_tokens + output_tokens),
}
else:
usage_metadata = None
return AIMessageChunk(
content=content,
additional_kwargs=additional_kwargs,
usage_metadata=usage_metadata, # type: ignore[arg-type]
)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content)
elif role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict["name"])
elif role == "tool" or default_class == ToolMessageChunk:
return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role)
else:
return default_class(content=content) # type: ignore
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
"""Convert a dictionary to a LangChain message.
Args:
_dict: The dictionary.
Returns:
The LangChain message.
"""
id_ = _dict.get("id")
role = _dict.get("role")
if role == "user":
return HumanMessage(content=_dict.get("content", ""))
elif role == "assistant":
content = _dict.get("content", "") or ""
additional_kwargs: Dict = {}
if function_call := _dict.get("function_call"):
additional_kwargs["function_call"] = dict(function_call)
tool_calls = []
invalid_tool_calls = []
if raw_tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
for raw_tool_call in raw_tool_calls:
try:
tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
except Exception as e:
invalid_tool_calls.append(
make_invalid_tool_call(raw_tool_call, str(e))
)
return AIMessage(
content=content,
id=id_,
additional_kwargs=additional_kwargs,
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
elif role == "system":
return SystemMessage(content=_dict.get("content", ""))
elif role == "function":
return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name")) # type: ignore[arg-type]
elif role == "tool":
additional_kwargs = {}
if "name" in _dict:
additional_kwargs["name"] = _dict["name"]
return ToolMessage(
content=_dict.get("content", ""),
tool_call_id=_dict.get("tool_call_id"),
additional_kwargs=additional_kwargs,
)
else:
return ChatMessage(content=_dict.get("content", ""), role=role) # type: ignore[arg-type]
def _lc_tool_call_to_groq_tool_call(tool_call: ToolCall) -> dict:
return {
"type": "function",
"id": tool_call["id"],
"function": {
"name": tool_call["name"],
"arguments": json.dumps(tool_call["args"]),
},
}
def _lc_invalid_tool_call_to_groq_tool_call(
invalid_tool_call: InvalidToolCall,
) -> dict:
return {
"type": "function",
"id": invalid_tool_call["id"],
"function": {
"name": invalid_tool_call["name"],
"arguments": invalid_tool_call["args"],
},
}
|
0 | lc_public_repos/langchain/libs/partners/groq | lc_public_repos/langchain/libs/partners/groq/langchain_groq/__init__.py | from langchain_groq.chat_models import ChatGroq
__all__ = ["ChatGroq"]
|
0 | lc_public_repos/langchain/libs/partners/groq | lc_public_repos/langchain/libs/partners/groq/scripts/lint_imports.sh | #!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi
|
0 | lc_public_repos/langchain/libs/partners/groq | lc_public_repos/langchain/libs/partners/groq/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file)
traceback.print_exc()
print()
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/openai/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
# unit tests are run with the --disable-socket flag to prevent network calls
# use tiktoken cache to enable token counting without socket (internet) access
test tests:
mkdir -p tiktoken_cache
@if [ ! -f tiktoken_cache/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 ]; then \
curl -o tiktoken_cache/9b5ad71b2ce5302211f9c61530b329a4922fc6a4 https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken; \
fi
@if [ ! -f tiktoken_cache/fb374d419588a4632f3f557e76b4b70aebbca790 ]; then \
curl -o tiktoken_cache/fb374d419588a4632f3f557e76b4b70aebbca790 https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken; \
fi
TIKTOKEN_CACHE_DIR=tiktoken_cache poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/openai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_openai
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_openai -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/openai/LICENSE | MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/openai/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
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]
[package.extras]
watchmedo = ["PyYAML (>=3.10)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "ded25b72c77fad9a869f3308c1bba084b58f54eb13df2785f061bc340d6ec748"
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/openai/README.md | # langchain-openai
This package contains the LangChain integrations for OpenAI through their `openai` SDK.
## Installation and Setup
- Install the LangChain partner package
```bash
pip install langchain-openai
```
- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
## LLM
See a [usage example](http://python.langchain.com/docs/integrations/llms/openai).
```python
from langchain_openai import OpenAI
```
If you are using a model hosted on `Azure`, you should use different wrapper for that:
```python
from langchain_openai import AzureOpenAI
```
For a more detailed walkthrough of the `Azure` wrapper, see [here](http://python.langchain.com/docs/integrations/llms/azure_openai)
## Chat model
See a [usage example](http://python.langchain.com/docs/integrations/chat/openai).
```python
from langchain_openai import ChatOpenAI
```
If you are using a model hosted on `Azure`, you should use different wrapper for that:
```python
from langchain_openai import AzureChatOpenAI
```
For a more detailed walkthrough of the `Azure` wrapper, see [here](http://python.langchain.com/docs/integrations/chat/azure_chat_openai)
## Text Embedding Model
See a [usage example](http://python.langchain.com/docs/integrations/text_embedding/openai)
```python
from langchain_openai import OpenAIEmbeddings
```
If you are using a model hosted on `Azure`, you should use different wrapper for that:
```python
from langchain_openai import AzureOpenAIEmbeddings
```
For a more detailed walkthrough of the `Azure` wrapper, see [here](https://python.langchain.com/docs/integrations/text_embedding/azureopenai) |
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/openai/pyproject.toml | [build-system]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-openai"
version = "0.2.11"
description = "An integration package connecting OpenAI and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.mypy]
disallow_untyped_defs = "True"
[[tool.mypy.overrides]]
module = "transformers"
ignore_missing_imports = true
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/openai"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-openai%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.21"
openai = "^1.54.0"
tiktoken = ">=0.7,<1"
[tool.ruff.lint]
select = [ "E", "F", "I", "T201",]
[tool.ruff.format]
docstring-code-format = true
skip-magic-trailing-comma = true
[tool.coverage.run]
omit = [ "tests/*",]
[tool.pytest.ini_options]
addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5 --cov=langchain_openai"
markers = [ "requires: mark tests as requiring a specific library", "compile: mark placeholder test used to compile integration tests without running them", "scheduled: mark tests to run in scheduled testing",]
asyncio_mode = "auto"
filterwarnings = [ "ignore::langchain_core._api.beta_decorator.LangChainBetaWarning",]
[tool.poetry.group.test]
optional = true
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
freezegun = "^1.2.2"
pytest-mock = "^3.10.0"
syrupy = "^4.0.2"
pytest-watcher = "^0.3.4"
pytest-asyncio = "^0.21.1"
pytest-cov = "^4.1.0"
pytest-socket = "^0.6.0"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1"
python = "<3.12"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.26.0"
python = ">=3.12"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
[tool.poetry.group.test_integration.dependencies]
httpx = "^0.27.0"
pillow = "^10.3.0"
[[tool.poetry.group.test_integration.dependencies.numpy]]
version = "^1"
python = "<3.12"
[[tool.poetry.group.test_integration.dependencies.numpy]]
version = "^1.26.0"
python = ">=3.12"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
types-tqdm = "^4.66.0.5"
[tool.poetry.group.test.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.test.dependencies.langchain-tests]
path = "../../standard-tests"
develop = true
[tool.poetry.group.dev.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.typing.dependencies.langchain-core]
path = "../../core"
develop = true
|
0 | lc_public_repos/langchain/libs/partners/openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/__init__.py | from langchain_openai.chat_models import AzureChatOpenAI, ChatOpenAI
from langchain_openai.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
from langchain_openai.llms import AzureOpenAI, OpenAI
__all__ = [
"OpenAI",
"ChatOpenAI",
"OpenAIEmbeddings",
"AzureOpenAI",
"AzureChatOpenAI",
"AzureOpenAIEmbeddings",
]
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/llms/base.py | from __future__ import annotations
import logging
import sys
from typing import (
AbstractSet,
Any,
AsyncIterator,
Collection,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Set,
Tuple,
Union,
)
import openai
import tiktoken
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.utils import get_pydantic_field_names
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
logger = logging.getLogger(__name__)
def _update_token_usage(
keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
"""Update token usage."""
_keys_to_use = keys.intersection(response["usage"])
for _key in _keys_to_use:
if _key not in token_usage:
token_usage[_key] = response["usage"][_key]
else:
token_usage[_key] += response["usage"][_key]
def _stream_response_to_generation_chunk(
stream_response: Dict[str, Any],
) -> GenerationChunk:
"""Convert a stream response to a generation chunk."""
if not stream_response["choices"]:
return GenerationChunk(text="")
return GenerationChunk(
text=stream_response["choices"][0]["text"],
generation_info=dict(
finish_reason=stream_response["choices"][0].get("finish_reason", None),
logprobs=stream_response["choices"][0].get("logprobs", None),
),
)
class BaseOpenAI(BaseLLM):
"""Base OpenAI large language model class."""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(default="gpt-3.5-turbo-instruct", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
max_tokens: int = 256
"""The maximum number of tokens to generate in the completion.
-1 returns as many tokens as possible given the prompt and
the models maximal context size."""
top_p: float = 1
"""Total probability mass of tokens to consider at each step."""
frequency_penalty: float = 0
"""Penalizes repeated tokens according to frequency."""
presence_penalty: float = 0
"""Penalizes repeated tokens."""
n: int = 1
"""How many completions to generate for each prompt."""
best_of: int = 1
"""Generates best_of completions server-side and returns the "best"."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[SecretStr] = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
openai_api_base: Optional[str] = Field(
alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
openai_organization: Optional[str] = Field(
alias="organization",
default_factory=from_env(
["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None
),
)
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
batch_size: int = 20
"""Batch size to use when passing multiple documents to generate."""
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
logit_bias: Optional[Dict[str, float]] = None
"""Adjust the probability of specific tokens being generated."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
seed: Optional[int] = None
"""Seed for generation"""
logprobs: Optional[int] = None
"""Include the log probabilities on the logprobs most likely output tokens,
as well the chosen tokens."""
streaming: bool = False
"""Whether to stream the results or not."""
allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
"""Set of special tokens that are allowed。"""
disallowed_special: Union[Literal["all"], Collection[str]] = "all"
"""Set of special tokens that are not allowed。"""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = None
"""Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you'd like a custom client for async invocations.
"""
http_async_client: Union[Any, None] = None
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
extra_body: Optional[Mapping[str, Any]] = None
"""Optional additional JSON properties to include in the request parameters when
making requests to OpenAI compatible APIs, such as vLLM."""
model_config = ConfigDict(populate_by_name=True)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.streaming and self.n > 1:
raise ValueError("Cannot stream results when n > 1.")
if self.streaming and self.best_of > 1:
raise ValueError("Cannot stream results when best_of > 1.")
client_params: dict = {
"api_key": (
self.openai_api_key.get_secret_value() if self.openai_api_key else None
),
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if not self.client:
sync_specific = {"http_client": self.http_client}
self.client = openai.OpenAI(**client_params, **sync_specific).completions # type: ignore[arg-type]
if not self.async_client:
async_specific = {"http_client": self.http_async_client}
self.async_client = openai.AsyncOpenAI(
**client_params,
**async_specific, # type: ignore[arg-type]
).completions
return self
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
normal_params: Dict[str, Any] = {
"temperature": self.temperature,
"top_p": self.top_p,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"n": self.n,
"seed": self.seed,
"logprobs": self.logprobs,
}
if self.logit_bias is not None:
normal_params["logit_bias"] = self.logit_bias
if self.max_tokens is not None:
normal_params["max_tokens"] = self.max_tokens
if self.extra_body is not None:
normal_params["extra_body"] = self.extra_body
# Azure gpt-35-turbo doesn't support best_of
# don't specify best_of if it is 1
if self.best_of > 1:
normal_params["best_of"] = self.best_of
return {**normal_params, **self.model_kwargs}
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
params = {**self._invocation_params, **kwargs, "stream": True}
self.get_sub_prompts(params, [prompt], stop) # this mutates params
for stream_resp in self.client.create(prompt=prompt, **params):
if not isinstance(stream_resp, dict):
stream_resp = stream_resp.model_dump()
chunk = _stream_response_to_generation_chunk(stream_resp)
if run_manager:
run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
logprobs=(
chunk.generation_info["logprobs"]
if chunk.generation_info
else None
),
)
yield chunk
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
params = {**self._invocation_params, **kwargs, "stream": True}
self.get_sub_prompts(params, [prompt], stop) # this mutates params
async for stream_resp in await self.async_client.create(
prompt=prompt, **params
):
if not isinstance(stream_resp, dict):
stream_resp = stream_resp.model_dump()
chunk = _stream_response_to_generation_chunk(stream_resp)
if run_manager:
await run_manager.on_llm_new_token(
chunk.text,
chunk=chunk,
verbose=self.verbose,
logprobs=(
chunk.generation_info["logprobs"]
if chunk.generation_info
else None
),
)
yield chunk
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint with k unique prompts.
Args:
prompts: The prompts to pass into the model.
stop: Optional list of stop words to use when generating.
Returns:
The full LLM output.
Example:
.. code-block:: python
response = openai.generate(["Tell me a joke."])
"""
# TODO: write a unit test for this
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
choices.append(
{
"text": generation.text,
"finish_reason": (
generation.generation_info.get("finish_reason")
if generation.generation_info
else None
),
"logprobs": (
generation.generation_info.get("logprobs")
if generation.generation_info
else None
),
}
)
else:
response = self.client.create(prompt=_prompts, **params)
if not isinstance(response, dict):
# V1 client returns the response in an PyDantic object instead of
# dict. For the transition period, we deep convert it to dict.
response = response.model_dump()
# Sometimes the AI Model calling will get error, we should raise it.
# Otherwise, the next code 'choices.extend(response["choices"])'
# will throw a "TypeError: 'NoneType' object is not iterable" error
# to mask the true error. Because 'response["choices"]' is None.
if response.get("error"):
raise ValueError(response.get("error"))
choices.extend(response["choices"])
_update_token_usage(_keys, response, token_usage)
if not system_fingerprint:
system_fingerprint = response.get("system_fingerprint")
return self.create_llm_result(
choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
)
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
"""Call out to OpenAI's endpoint async with k unique prompts."""
params = self._invocation_params
params = {**params, **kwargs}
sub_prompts = self.get_sub_prompts(params, prompts, stop)
choices = []
token_usage: Dict[str, int] = {}
# Get the token usage from the response.
# Includes prompt, completion, and total tokens used.
_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
system_fingerprint: Optional[str] = None
for _prompts in sub_prompts:
if self.streaming:
if len(_prompts) > 1:
raise ValueError("Cannot stream results with multiple prompts.")
generation: Optional[GenerationChunk] = None
async for chunk in self._astream(
_prompts[0], stop, run_manager, **kwargs
):
if generation is None:
generation = chunk
else:
generation += chunk
assert generation is not None
choices.append(
{
"text": generation.text,
"finish_reason": (
generation.generation_info.get("finish_reason")
if generation.generation_info
else None
),
"logprobs": (
generation.generation_info.get("logprobs")
if generation.generation_info
else None
),
}
)
else:
response = await self.async_client.create(prompt=_prompts, **params)
if not isinstance(response, dict):
response = response.model_dump()
choices.extend(response["choices"])
_update_token_usage(_keys, response, token_usage)
return self.create_llm_result(
choices, prompts, params, token_usage, system_fingerprint=system_fingerprint
)
def get_sub_prompts(
self,
params: Dict[str, Any],
prompts: List[str],
stop: Optional[List[str]] = None,
) -> List[List[str]]:
"""Get the sub prompts for llm call."""
if stop is not None:
params["stop"] = stop
if params["max_tokens"] == -1:
if len(prompts) != 1:
raise ValueError(
"max_tokens set to -1 not supported for multiple inputs."
)
params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
sub_prompts = [
prompts[i : i + self.batch_size]
for i in range(0, len(prompts), self.batch_size)
]
return sub_prompts
def create_llm_result(
self,
choices: Any,
prompts: List[str],
params: Dict[str, Any],
token_usage: Dict[str, int],
*,
system_fingerprint: Optional[str] = None,
) -> LLMResult:
"""Create the LLMResult from the choices and prompts."""
generations = []
n = params.get("n", self.n)
for i, _ in enumerate(prompts):
sub_choices = choices[i * n : (i + 1) * n]
generations.append(
[
Generation(
text=choice["text"],
generation_info=dict(
finish_reason=choice.get("finish_reason"),
logprobs=choice.get("logprobs"),
),
)
for choice in sub_choices
]
)
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
if system_fingerprint:
llm_output["system_fingerprint"] = system_fingerprint
return LLMResult(generations=generations, llm_output=llm_output)
@property
def _invocation_params(self) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return self._default_params
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {**{"model_name": self.model_name}, **self._default_params}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "openai"
def get_token_ids(self, text: str) -> List[int]:
"""Get the token IDs using the tiktoken package."""
if self.custom_get_token_ids is not None:
return self.custom_get_token_ids(text)
# tiktoken NOT supported for Python < 3.8
if sys.version_info[1] < 8:
return super().get_num_tokens(text)
model_name = self.tiktoken_model_name or self.model_name
try:
enc = tiktoken.encoding_for_model(model_name)
except KeyError:
enc = tiktoken.get_encoding("cl100k_base")
return enc.encode(
text,
allowed_special=self.allowed_special,
disallowed_special=self.disallowed_special,
)
@staticmethod
def modelname_to_contextsize(modelname: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a model.
Args:
modelname: The modelname we want to know the context size for.
Returns:
The maximum context size
Example:
.. code-block:: python
max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")
"""
model_token_mapping = {
"gpt-4o-mini": 128_000,
"gpt-4o": 128_000,
"gpt-4o-2024-05-13": 128_000,
"gpt-4": 8192,
"gpt-4-0314": 8192,
"gpt-4-0613": 8192,
"gpt-4-32k": 32768,
"gpt-4-32k-0314": 32768,
"gpt-4-32k-0613": 32768,
"gpt-3.5-turbo": 4096,
"gpt-3.5-turbo-0301": 4096,
"gpt-3.5-turbo-0613": 4096,
"gpt-3.5-turbo-16k": 16385,
"gpt-3.5-turbo-16k-0613": 16385,
"gpt-3.5-turbo-instruct": 4096,
"text-ada-001": 2049,
"ada": 2049,
"text-babbage-001": 2040,
"babbage": 2049,
"text-curie-001": 2049,
"curie": 2049,
"davinci": 2049,
"text-davinci-003": 4097,
"text-davinci-002": 4097,
"code-davinci-002": 8001,
"code-davinci-001": 8001,
"code-cushman-002": 2048,
"code-cushman-001": 2048,
}
# handling finetuned models
if "ft-" in modelname:
modelname = modelname.split(":")[0]
context_size = model_token_mapping.get(modelname, None)
if context_size is None:
raise ValueError(
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
"Known models are: " + ", ".join(model_token_mapping.keys())
)
return context_size
@property
def max_context_size(self) -> int:
"""Get max context size for this model."""
return self.modelname_to_contextsize(self.model_name)
def max_tokens_for_prompt(self, prompt: str) -> int:
"""Calculate the maximum number of tokens possible to generate for a prompt.
Args:
prompt: The prompt to pass into the model.
Returns:
The maximum number of tokens to generate for a prompt.
Example:
.. code-block:: python
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
"""
num_tokens = self.get_num_tokens(prompt)
return self.max_context_size - num_tokens
class OpenAI(BaseOpenAI):
"""OpenAI completion model integration.
Setup:
Install ``langchain-openai`` and set environment variable ``OPENAI_API_KEY``.
.. code-block:: bash
pip install -U langchain-openai
export OPENAI_API_KEY="your-api-key"
Key init args — completion params:
model: str
Name of OpenAI model to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
logprobs: Optional[bool]
Whether to return logprobs.
stream_options: Dict
Configure streaming outputs, like whether to return token usage when
streaming (``{"include_usage": True}``).
Key init args — client params:
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY.
base_url: Optional[str]
Base URL for API requests. Only specify if using a proxy or service
emulator.
organization: Optional[str]
OpenAI organization ID. If not passed in will be read from env
var OPENAI_ORG_ID.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_openai import OpenAI
llm = OpenAI(
model="gpt-3.5-turbo-instruct",
temperature=0,
max_retries=2,
# api_key="...",
# base_url="...",
# organization="...",
# other params...
)
Invoke:
.. code-block:: python
input_text = "The meaning of life is "
llm.invoke(input_text)
.. code-block:: none
"a philosophical question that has been debated by thinkers and scholars for centuries."
Stream:
.. code-block:: python
for chunk in llm.stream(input_text):
print(chunk, end="|")
.. code-block:: none
a| philosophical| question| that| has| been| debated| by| thinkers| and| scholars| for| centuries|.
.. code-block:: python
"".join(llm.stream(input_text))
.. code-block:: none
"a philosophical question that has been debated by thinkers and scholars for centuries."
Async:
.. code-block:: python
await llm.ainvoke(input_text)
# stream:
# async for chunk in (await llm.astream(input_text)):
# print(chunk)
# batch:
# await llm.abatch([input_text])
.. code-block:: none
"a philosophical question that has been debated by thinkers and scholars for centuries."
""" # noqa: E501
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "llms", "openai"]
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
@property
def _invocation_params(self) -> Dict[str, Any]:
return {**{"model": self.model_name}, **super()._invocation_params}
@property
def lc_secrets(self) -> Dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@property
def lc_attributes(self) -> Dict[str, Any]:
attributes: Dict[str, Any] = {}
if self.openai_api_base:
attributes["openai_api_base"] = self.openai_api_base
if self.openai_organization:
attributes["openai_organization"] = self.openai_organization
if self.openai_proxy:
attributes["openai_proxy"] = self.openai_proxy
return attributes
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/llms/azure.py | from __future__ import annotations
import logging
from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional, Union
import openai
from langchain_core.language_models import LangSmithParams
from langchain_core.utils import from_env, secret_from_env
from pydantic import Field, SecretStr, model_validator
from typing_extensions import Self, cast
from langchain_openai.llms.base import BaseOpenAI
logger = logging.getLogger(__name__)
class AzureOpenAI(BaseOpenAI):
"""Azure-specific OpenAI large language models.
To use, you should have the ``openai`` python package installed, and the
environment variable ``OPENAI_API_KEY`` set with your API key.
Any parameters that are valid to be passed to the openai.create call can be passed
in, even if not explicitly saved on this class.
Example:
.. code-block:: python
from langchain_openai import AzureOpenAI
openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
"""
azure_endpoint: Optional[str] = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
Example: `https://example-resource.azure.openai.com/`
"""
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
Note: this means you won't be able to use non-deployment endpoints.
"""
openai_api_version: Optional[str] = Field(
alias="api_version",
default_factory=from_env("OPENAI_API_VERSION", default=None),
)
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
azure_ad_token: Optional[SecretStr] = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every sync request. For async requests,
will be invoked if `azure_ad_async_token_provider` is not provided.
"""
azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every async request.
"""
openai_api_type: Optional[str] = Field(
default_factory=from_env("OPENAI_API_TYPE", default="azure")
)
"""Legacy, for openai<1.0.0 support."""
validate_base_url: bool = True
"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
infer if it is a base_url or azure_endpoint and update accordingly.
"""
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "llms", "openai"]
@property
def lc_secrets(self) -> Dict[str, str]:
return {
"openai_api_key": "AZURE_OPENAI_API_KEY",
"azure_ad_token": "AZURE_OPENAI_AD_TOKEN",
}
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.streaming and self.n > 1:
raise ValueError("Cannot stream results when n > 1.")
if self.streaming and self.best_of > 1:
raise ValueError("Cannot stream results when best_of > 1.")
# For backwards compatibility. Before openai v1, no distinction was made
# between azure_endpoint and base_url (openai_api_base).
openai_api_base = self.openai_api_base
if openai_api_base and self.validate_base_url:
if "/openai" not in openai_api_base:
self.openai_api_base = (
cast(str, self.openai_api_base).rstrip("/") + "/openai"
)
raise ValueError(
"As of openai>=1.0.0, Azure endpoints should be specified via "
"the `azure_endpoint` param not `openai_api_base` "
"(or alias `base_url`)."
)
if self.deployment_name:
raise ValueError(
"As of openai>=1.0.0, if `deployment_name` (or alias "
"`azure_deployment`) is specified then "
"`openai_api_base` (or alias `base_url`) should not be. "
"Instead use `deployment_name` (or alias `azure_deployment`) "
"and `azure_endpoint`."
)
self.deployment_name = None
client_params: dict = {
"api_version": self.openai_api_version,
"azure_endpoint": self.azure_endpoint,
"azure_deployment": self.deployment_name,
"api_key": self.openai_api_key.get_secret_value()
if self.openai_api_key
else None,
"azure_ad_token": self.azure_ad_token.get_secret_value()
if self.azure_ad_token
else None,
"azure_ad_token_provider": self.azure_ad_token_provider,
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if not self.client:
sync_specific = {"http_client": self.http_client}
self.client = openai.AzureOpenAI(
**client_params,
**sync_specific, # type: ignore[arg-type]
).completions
if not self.async_client:
async_specific = {"http_client": self.http_async_client}
if self.azure_ad_async_token_provider:
client_params["azure_ad_token_provider"] = (
self.azure_ad_async_token_provider
)
self.async_client = openai.AsyncAzureOpenAI(
**client_params,
**async_specific, # type: ignore[arg-type]
).completions
return self
@property
def _identifying_params(self) -> Mapping[str, Any]:
return {
**{"deployment_name": self.deployment_name},
**super()._identifying_params,
}
@property
def _invocation_params(self) -> Dict[str, Any]:
openai_params = {"model": self.deployment_name}
return {**openai_params, **super()._invocation_params}
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = super()._get_ls_params(stop=stop, **kwargs)
invocation_params = self._invocation_params
params["ls_provider"] = "azure"
if model_name := invocation_params.get("model"):
params["ls_model_name"] = model_name
return params
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "azure"
@property
def lc_attributes(self) -> Dict[str, Any]:
return {
"openai_api_type": self.openai_api_type,
"openai_api_version": self.openai_api_version,
}
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/llms/__init__.py | from langchain_openai.llms.azure import AzureOpenAI
from langchain_openai.llms.base import OpenAI
__all__ = ["OpenAI", "AzureOpenAI"]
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/output_parsers/tools.py | from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
JsonOutputToolsParser,
PydanticToolsParser,
)
__all__ = ["PydanticToolsParser", "JsonOutputToolsParser", "JsonOutputKeyToolsParser"]
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/output_parsers/__init__.py | from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
JsonOutputToolsParser,
PydanticToolsParser,
)
__all__ = ["JsonOutputKeyToolsParser", "JsonOutputToolsParser", "PydanticToolsParser"]
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/embeddings/base.py | from __future__ import annotations
import logging
import warnings
from typing import (
Any,
Dict,
Iterable,
List,
Literal,
Mapping,
Optional,
Sequence,
Set,
Tuple,
Union,
cast,
)
import openai
import tiktoken
from langchain_core.embeddings import Embeddings
from langchain_core.utils import from_env, get_pydantic_field_names, secret_from_env
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
logger = logging.getLogger(__name__)
def _process_batched_chunked_embeddings(
num_texts: int,
tokens: List[Union[List[int], str]],
batched_embeddings: List[List[float]],
indices: List[int],
skip_empty: bool,
) -> List[Optional[List[float]]]:
# for each text, this is the list of embeddings (list of list of floats)
# corresponding to the chunks of the text
results: List[List[List[float]]] = [[] for _ in range(num_texts)]
# for each text, this is the token length of each chunk
# for transformers tokenization, this is the string length
# for tiktoken, this is the number of tokens
num_tokens_in_batch: List[List[int]] = [[] for _ in range(num_texts)]
for i in range(len(indices)):
if skip_empty and len(batched_embeddings[i]) == 1:
continue
results[indices[i]].append(batched_embeddings[i])
num_tokens_in_batch[indices[i]].append(len(tokens[i]))
# for each text, this is the final embedding
embeddings: List[Optional[List[float]]] = []
for i in range(num_texts):
# an embedding for each chunk
_result: List[List[float]] = results[i]
if len(_result) == 0:
# this will be populated with the embedding of an empty string
# in the sync or async code calling this
embeddings.append(None)
continue
elif len(_result) == 1:
# if only one embedding was produced, use it
embeddings.append(_result[0])
continue
else:
# else we need to weighted average
# should be same as
# average = np.average(_result, axis=0, weights=num_tokens_in_batch[i])
total_weight = sum(num_tokens_in_batch[i])
average = [
sum(
val * weight
for val, weight in zip(embedding, num_tokens_in_batch[i])
)
/ total_weight
for embedding in zip(*_result)
]
# should be same as
# embeddings.append((average / np.linalg.norm(average)).tolist())
magnitude = sum(val**2 for val in average) ** 0.5
embeddings.append([val / magnitude for val in average])
return embeddings
class OpenAIEmbeddings(BaseModel, Embeddings):
"""OpenAI embedding model integration.
Setup:
Install ``langchain_openai`` and set environment variable ``OPENAI_API_KEY``.
.. code-block:: bash
pip install -U langchain_openai
export OPENAI_API_KEY="your-api-key"
Key init args — embedding params:
model: str
Name of OpenAI model to use.
dimensions: Optional[int] = None
The number of dimensions the resulting output embeddings should have.
Only supported in `text-embedding-3` and later models.
Key init args — client params:
api_key: Optional[SecretStr] = None
OpenAI API key.
organization: Optional[str] = None
OpenAI organization ID. If not passed in will be read
from env var OPENAI_ORG_ID.
max_retries: int = 2
Maximum number of retries to make when generating.
request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None
Timeout for requests to OpenAI completion API
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_openai import OpenAIEmbeddings
embed = OpenAIEmbeddings(
model="text-embedding-3-large"
# With the `text-embedding-3` class
# of models, you can specify the size
# of the embeddings you want returned.
# dimensions=1024
)
Embed single text:
.. code-block:: python
input_text = "The meaning of life is 42"
vector = embeddings.embed_query("hello")
print(vector[:3])
.. code-block:: python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Embed multiple texts:
.. code-block:: python
vectors = embeddings.embed_documents(["hello", "goodbye"])
# Showing only the first 3 coordinates
print(len(vectors))
print(vectors[0][:3])
.. code-block:: python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Async:
.. code-block:: python
await embed.aembed_query(input_text)
print(vector[:3])
# multiple:
# await embed.aembed_documents(input_texts)
.. code-block:: python
[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
"""
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
model: str = "text-embedding-ada-002"
dimensions: Optional[int] = None
"""The number of dimensions the resulting output embeddings should have.
Only supported in `text-embedding-3` and later models.
"""
# to support Azure OpenAI Service custom deployment names
deployment: Optional[str] = model
# TODO: Move to AzureOpenAIEmbeddings.
openai_api_version: Optional[str] = Field(
default_factory=from_env("OPENAI_API_VERSION", default=None),
alias="api_version",
)
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# to support Azure OpenAI Service custom endpoints
openai_api_base: Optional[str] = Field(
alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
# to support Azure OpenAI Service custom endpoints
openai_api_type: Optional[str] = Field(
default_factory=from_env("OPENAI_API_TYPE", default=None)
)
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
embedding_ctx_length: int = 8191
"""The maximum number of tokens to embed at once."""
openai_api_key: Optional[SecretStr] = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
openai_organization: Optional[str] = Field(
alias="organization",
default_factory=from_env(
["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None
),
)
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
allowed_special: Union[Literal["all"], Set[str], None] = None
disallowed_special: Union[Literal["all"], Set[str], Sequence[str], None] = None
chunk_size: int = 1000
"""Maximum number of texts to embed in each batch"""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
request_timeout: Optional[Union[float, Tuple[float, float], Any]] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
headers: Any = None
tiktoken_enabled: bool = True
"""Set this to False for non-OpenAI implementations of the embeddings API, e.g.
the `--extensions openai` extension for `text-generation-webui`"""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
show_progress_bar: bool = False
"""Whether to show a progress bar when embedding."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
skip_empty: bool = False
"""Whether to skip empty strings when embedding or raise an error.
Defaults to not skipping."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
retry_min_seconds: int = 4
"""Min number of seconds to wait between retries"""
retry_max_seconds: int = 20
"""Max number of seconds to wait between retries"""
http_client: Union[Any, None] = None
"""Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you'd like a custom client for async invocations.
"""
http_async_client: Union[Any, None] = None
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
check_embedding_ctx_length: bool = True
"""Whether to check the token length of inputs and automatically split inputs
longer than embedding_ctx_length."""
model_config = ConfigDict(
extra="forbid", populate_by_name=True, protected_namespaces=()
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
warnings.warn(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.openai_api_type in ("azure", "azure_ad", "azuread"):
raise ValueError(
"If you are using Azure, "
"please use the `AzureOpenAIEmbeddings` class."
)
client_params: dict = {
"api_key": (
self.openai_api_key.get_secret_value() if self.openai_api_key else None
),
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if self.openai_proxy and (self.http_client or self.http_async_client):
openai_proxy = self.openai_proxy
http_client = self.http_client
http_async_client = self.http_async_client
raise ValueError(
"Cannot specify 'openai_proxy' if one of "
"'http_client'/'http_async_client' is already specified. Received:\n"
f"{openai_proxy=}\n{http_client=}\n{http_async_client=}"
)
if not self.client:
if self.openai_proxy and not self.http_client:
try:
import httpx
except ImportError as e:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`."
) from e
self.http_client = httpx.Client(proxy=self.openai_proxy)
sync_specific = {"http_client": self.http_client}
self.client = openai.OpenAI(**client_params, **sync_specific).embeddings # type: ignore[arg-type]
if not self.async_client:
if self.openai_proxy and not self.http_async_client:
try:
import httpx
except ImportError as e:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`."
) from e
self.http_async_client = httpx.AsyncClient(proxy=self.openai_proxy)
async_specific = {"http_client": self.http_async_client}
self.async_client = openai.AsyncOpenAI(
**client_params,
**async_specific, # type: ignore[arg-type]
).embeddings
return self
@property
def _invocation_params(self) -> Dict[str, Any]:
params: Dict = {"model": self.model, **self.model_kwargs}
if self.dimensions is not None:
params["dimensions"] = self.dimensions
return params
def _tokenize(
self, texts: List[str], chunk_size: int
) -> Tuple[Iterable[int], List[Union[List[int], str]], List[int]]:
"""
Take the input `texts` and `chunk_size` and return 3 iterables as a tuple:
We have `batches`, where batches are sets of individual texts
we want responses from the openai api. The length of a single batch is
`chunk_size` texts.
Each individual text is also split into multiple texts based on the
`embedding_ctx_length` parameter (based on number of tokens).
This function returns a 3-tuple of the following:
_iter: An iterable of the starting index in `tokens` for each *batch*
tokens: A list of tokenized texts, where each text has already been split
into sub-texts based on the `embedding_ctx_length` parameter. In the
case of tiktoken, this is a list of token arrays. In the case of
HuggingFace transformers, this is a list of strings.
indices: An iterable of the same length as `tokens` that maps each token-array
to the index of the original text in `texts`.
"""
tokens: List[Union[List[int], str]] = []
indices: List[int] = []
model_name = self.tiktoken_model_name or self.model
# If tiktoken flag set to False
if not self.tiktoken_enabled:
try:
from transformers import AutoTokenizer
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"This is needed for OpenAIEmbeddings to work without "
"`tiktoken`. Please install it with `pip install transformers`. "
)
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path=model_name
)
for i, text in enumerate(texts):
# Tokenize the text using HuggingFace transformers
tokenized: List[int] = tokenizer.encode(text, add_special_tokens=False)
# Split tokens into chunks respecting the embedding_ctx_length
for j in range(0, len(tokenized), self.embedding_ctx_length):
token_chunk: List[int] = tokenized[
j : j + self.embedding_ctx_length
]
# Convert token IDs back to a string
chunk_text: str = tokenizer.decode(token_chunk)
tokens.append(chunk_text)
indices.append(i)
else:
try:
encoding = tiktoken.encoding_for_model(model_name)
except KeyError:
encoding = tiktoken.get_encoding("cl100k_base")
encoder_kwargs: Dict[str, Any] = {
k: v
for k, v in {
"allowed_special": self.allowed_special,
"disallowed_special": self.disallowed_special,
}.items()
if v is not None
}
for i, text in enumerate(texts):
if self.model.endswith("001"):
# See: https://github.com/openai/openai-python/
# issues/418#issuecomment-1525939500
# replace newlines, which can negatively affect performance.
text = text.replace("\n", " ")
if encoder_kwargs:
token = encoding.encode(text, **encoder_kwargs)
else:
token = encoding.encode_ordinary(text)
# Split tokens into chunks respecting the embedding_ctx_length
for j in range(0, len(token), self.embedding_ctx_length):
tokens.append(token[j : j + self.embedding_ctx_length])
indices.append(i)
if self.show_progress_bar:
try:
from tqdm.auto import tqdm
_iter: Iterable = tqdm(range(0, len(tokens), chunk_size))
except ImportError:
_iter = range(0, len(tokens), chunk_size)
else:
_iter = range(0, len(tokens), chunk_size)
return _iter, tokens, indices
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
def _get_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
"""
Generate length-safe embeddings for a list of texts.
This method handles tokenization and embedding generation, respecting the
set embedding context length and chunk size. It supports both tiktoken
and HuggingFace tokenizer based on the tiktoken_enabled flag.
Args:
texts (List[str]): A list of texts to embed.
engine (str): The engine or model to use for embeddings.
chunk_size (Optional[int]): The size of chunks for processing embeddings.
Returns:
List[List[float]]: A list of embeddings for each input text.
"""
_chunk_size = chunk_size or self.chunk_size
_iter, tokens, indices = self._tokenize(texts, _chunk_size)
batched_embeddings: List[List[float]] = []
for i in _iter:
response = self.client.create(
input=tokens[i : i + _chunk_size], **self._invocation_params
)
if not isinstance(response, dict):
response = response.model_dump()
batched_embeddings.extend(r["embedding"] for r in response["data"])
embeddings = _process_batched_chunked_embeddings(
len(texts), tokens, batched_embeddings, indices, self.skip_empty
)
_cached_empty_embedding: Optional[List[float]] = None
def empty_embedding() -> List[float]:
nonlocal _cached_empty_embedding
if _cached_empty_embedding is None:
average_embedded = self.client.create(
input="", **self._invocation_params
)
if not isinstance(average_embedded, dict):
average_embedded = average_embedded.model_dump()
_cached_empty_embedding = average_embedded["data"][0]["embedding"]
return _cached_empty_embedding
return [e if e is not None else empty_embedding() for e in embeddings]
# please refer to
# https://github.com/openai/openai-cookbook/blob/main/examples/Embedding_long_inputs.ipynb
async def _aget_len_safe_embeddings(
self, texts: List[str], *, engine: str, chunk_size: Optional[int] = None
) -> List[List[float]]:
"""
Asynchronously generate length-safe embeddings for a list of texts.
This method handles tokenization and asynchronous embedding generation,
respecting the set embedding context length and chunk size. It supports both
`tiktoken` and HuggingFace `tokenizer` based on the tiktoken_enabled flag.
Args:
texts (List[str]): A list of texts to embed.
engine (str): The engine or model to use for embeddings.
chunk_size (Optional[int]): The size of chunks for processing embeddings.
Returns:
List[List[float]]: A list of embeddings for each input text.
"""
_chunk_size = chunk_size or self.chunk_size
_iter, tokens, indices = self._tokenize(texts, _chunk_size)
batched_embeddings: List[List[float]] = []
_chunk_size = chunk_size or self.chunk_size
for i in range(0, len(tokens), _chunk_size):
response = await self.async_client.create(
input=tokens[i : i + _chunk_size], **self._invocation_params
)
if not isinstance(response, dict):
response = response.model_dump()
batched_embeddings.extend(r["embedding"] for r in response["data"])
embeddings = _process_batched_chunked_embeddings(
len(texts), tokens, batched_embeddings, indices, self.skip_empty
)
_cached_empty_embedding: Optional[List[float]] = None
async def empty_embedding() -> List[float]:
nonlocal _cached_empty_embedding
if _cached_empty_embedding is None:
average_embedded = await self.async_client.create(
input="", **self._invocation_params
)
if not isinstance(average_embedded, dict):
average_embedded = average_embedded.model_dump()
_cached_empty_embedding = average_embedded["data"][0]["embedding"]
return _cached_empty_embedding
return [e if e is not None else await empty_embedding() for e in embeddings]
def embed_documents(
self, texts: List[str], chunk_size: int | None = None
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
chunk_size_ = chunk_size or self.chunk_size
if not self.check_embedding_ctx_length:
embeddings: List[List[float]] = []
for i in range(0, len(texts), self.chunk_size):
response = self.client.create(
input=texts[i : i + chunk_size_], **self._invocation_params
)
if not isinstance(response, dict):
response = response.dict()
embeddings.extend(r["embedding"] for r in response["data"])
return embeddings
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
engine = cast(str, self.deployment)
return self._get_len_safe_embeddings(texts, engine=engine)
async def aembed_documents(
self, texts: List[str], chunk_size: int | None = None
) -> List[List[float]]:
"""Call out to OpenAI's embedding endpoint async for embedding search docs.
Args:
texts: The list of texts to embed.
chunk_size: The chunk size of embeddings. If None, will use the chunk size
specified by the class.
Returns:
List of embeddings, one for each text.
"""
chunk_size_ = chunk_size or self.chunk_size
if not self.check_embedding_ctx_length:
embeddings: List[List[float]] = []
for i in range(0, len(texts), chunk_size_):
response = await self.async_client.create(
input=texts[i : i + chunk_size_], **self._invocation_params
)
if not isinstance(response, dict):
response = response.dict()
embeddings.extend(r["embedding"] for r in response["data"])
return embeddings
# NOTE: to keep things simple, we assume the list may contain texts longer
# than the maximum context and use length-safe embedding function.
engine = cast(str, self.deployment)
return await self._aget_len_safe_embeddings(texts, engine=engine)
def embed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
return self.embed_documents([text])[0]
async def aembed_query(self, text: str) -> List[float]:
"""Call out to OpenAI's embedding endpoint async for embedding query text.
Args:
text: The text to embed.
Returns:
Embedding for the text.
"""
embeddings = await self.aembed_documents([text])
return embeddings[0]
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/embeddings/azure.py | """Azure OpenAI embeddings wrapper."""
from __future__ import annotations
from typing import Awaitable, Callable, Optional, Union
import openai
from langchain_core.utils import from_env, secret_from_env
from pydantic import Field, SecretStr, model_validator
from typing_extensions import Self, cast
from langchain_openai.embeddings.base import OpenAIEmbeddings
class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
"""AzureOpenAI embedding model integration.
Setup:
To access AzureOpenAI embedding models you'll need to create an Azure account,
get an API key, and install the `langchain-openai` integration package.
You’ll need to have an Azure OpenAI instance deployed.
You can deploy a version on Azure Portal following this
[guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).
Once you have your instance running, make sure you have the name of your
instance and key. You can find the key in the Azure Portal,
under the “Keys and Endpoint” section of your instance.
.. code-block:: bash
pip install -U langchain_openai
# Set up your environment variables (or pass them directly to the model)
export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="https://<your-endpoint>.openai.azure.com/"
export AZURE_OPENAI_API_VERSION="2024-02-01"
Key init args — completion params:
model: str
Name of AzureOpenAI model to use.
dimensions: Optional[int]
Number of dimensions for the embeddings. Can be specified only
if the underlying model supports it.
Key init args — client params:
api_key: Optional[SecretStr]
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_openai import AzureOpenAIEmbeddings
embeddings = AzureOpenAIEmbeddings(
model="text-embedding-3-large"
# dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models
# azure_endpoint="https://<your-endpoint>.openai.azure.com/", If not provided, will read env variable AZURE_OPENAI_ENDPOINT
# api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY
# openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION
)
Embed single text:
.. code-block:: python
input_text = "The meaning of life is 42"
vector = embed.embed_query(input_text)
print(vector[:3])
.. code-block:: python
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Embed multiple texts:
.. code-block:: python
input_texts = ["Document 1...", "Document 2..."]
vectors = embed.embed_documents(input_texts)
print(len(vectors))
# The first 3 coordinates for the first vector
print(vectors[0][:3])
.. code-block:: python
2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
Async:
.. code-block:: python
vector = await embed.aembed_query(input_text)
print(vector[:3])
# multiple:
# await embed.aembed_documents(input_texts)
.. code-block:: python
[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
""" # noqa: E501
azure_endpoint: Optional[str] = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
Example: `https://example-resource.azure.openai.com/`
"""
deployment: Optional[str] = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
Note: this means you won't be able to use non-deployment endpoints.
"""
# Check OPENAI_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
openai_api_version: Optional[str] = Field(
default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15"),
alias="api_version",
)
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided.
Set to "2023-05-15" by default if env variable `OPENAI_API_VERSION` is not set.
"""
azure_ad_token: Optional[SecretStr] = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every sync request. For async requests,
will be invoked if `azure_ad_async_token_provider` is not provided.
"""
azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every async request.
"""
openai_api_type: Optional[str] = Field(
default_factory=from_env("OPENAI_API_TYPE", default="azure")
)
validate_base_url: bool = True
chunk_size: int = 2048
"""Maximum number of texts to embed in each batch"""
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
# For backwards compatibility. Before openai v1, no distinction was made
# between azure_endpoint and base_url (openai_api_base).
openai_api_base = self.openai_api_base
if openai_api_base and self.validate_base_url:
if "/openai" not in openai_api_base:
self.openai_api_base = cast(str, self.openai_api_base) + "/openai"
raise ValueError(
"As of openai>=1.0.0, Azure endpoints should be specified via "
"the `azure_endpoint` param not `openai_api_base` "
"(or alias `base_url`). "
)
if self.deployment:
raise ValueError(
"As of openai>=1.0.0, if `deployment` (or alias "
"`azure_deployment`) is specified then "
"`openai_api_base` (or alias `base_url`) should not be. "
"Instead use `deployment` (or alias `azure_deployment`) "
"and `azure_endpoint`."
)
client_params: dict = {
"api_version": self.openai_api_version,
"azure_endpoint": self.azure_endpoint,
"azure_deployment": self.deployment,
"api_key": (
self.openai_api_key.get_secret_value() if self.openai_api_key else None
),
"azure_ad_token": (
self.azure_ad_token.get_secret_value() if self.azure_ad_token else None
),
"azure_ad_token_provider": self.azure_ad_token_provider,
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if not self.client:
sync_specific: dict = {"http_client": self.http_client}
self.client = openai.AzureOpenAI(
**client_params, # type: ignore[arg-type]
**sync_specific,
).embeddings
if not self.async_client:
async_specific: dict = {"http_client": self.http_async_client}
if self.azure_ad_async_token_provider:
client_params["azure_ad_token_provider"] = (
self.azure_ad_async_token_provider
)
self.async_client = openai.AsyncAzureOpenAI(
**client_params, # type: ignore[arg-type]
**async_specific,
).embeddings
return self
@property
def _llm_type(self) -> str:
return "azure-openai-chat"
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/embeddings/__init__.py | from langchain_openai.embeddings.azure import AzureOpenAIEmbeddings
from langchain_openai.embeddings.base import OpenAIEmbeddings
__all__ = ["OpenAIEmbeddings", "AzureOpenAIEmbeddings"]
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/chat_models/base.py | """OpenAI chat wrapper."""
from __future__ import annotations
import base64
import json
import logging
import os
import sys
import warnings
from io import BytesIO
from math import ceil
from operator import itemgetter
from typing import (
Any,
AsyncIterator,
Callable,
Dict,
Iterator,
List,
Literal,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypedDict,
TypeVar,
Union,
cast,
)
from urllib.parse import urlparse
import openai
import tiktoken
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
LangSmithParams,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
InvalidToolCall,
SystemMessage,
SystemMessageChunk,
ToolCall,
ToolMessage,
ToolMessageChunk,
)
from langchain_core.messages.ai import (
InputTokenDetails,
OutputTokenDetails,
UsageMetadata,
)
from langchain_core.messages.tool import tool_call_chunk
from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser
from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
PydanticToolsParser,
make_invalid_tool_call,
parse_tool_call,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough, chain
from langchain_core.runnables.config import run_in_executor
from langchain_core.tools import BaseTool
from langchain_core.utils import get_pydantic_field_names
from langchain_core.utils.function_calling import (
convert_to_openai_function,
convert_to_openai_tool,
)
from langchain_core.utils.pydantic import (
PydanticBaseModel,
TypeBaseModel,
is_basemodel_subclass,
)
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
logger = logging.getLogger(__name__)
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
"""Convert a dictionary to a LangChain message.
Args:
_dict: The dictionary.
Returns:
The LangChain message.
"""
role = _dict.get("role")
name = _dict.get("name")
id_ = _dict.get("id")
if role == "user":
return HumanMessage(content=_dict.get("content", ""), id=id_, name=name)
elif role == "assistant":
# Fix for azure
# Also OpenAI returns None for tool invocations
content = _dict.get("content", "") or ""
additional_kwargs: Dict = {}
if function_call := _dict.get("function_call"):
additional_kwargs["function_call"] = dict(function_call)
tool_calls = []
invalid_tool_calls = []
if raw_tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
for raw_tool_call in raw_tool_calls:
try:
tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
except Exception as e:
invalid_tool_calls.append(
make_invalid_tool_call(raw_tool_call, str(e))
)
if audio := _dict.get("audio"):
additional_kwargs["audio"] = audio
return AIMessage(
content=content,
additional_kwargs=additional_kwargs,
name=name,
id=id_,
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
elif role == "system":
return SystemMessage(content=_dict.get("content", ""), name=name, id=id_)
elif role == "function":
return FunctionMessage(
content=_dict.get("content", ""), name=cast(str, _dict.get("name")), id=id_
)
elif role == "tool":
additional_kwargs = {}
if "name" in _dict:
additional_kwargs["name"] = _dict["name"]
return ToolMessage(
content=_dict.get("content", ""),
tool_call_id=cast(str, _dict.get("tool_call_id")),
additional_kwargs=additional_kwargs,
name=name,
id=id_,
)
else:
return ChatMessage(content=_dict.get("content", ""), role=role, id=id_) # type: ignore[arg-type]
def _format_message_content(content: Any) -> Any:
"""Format message content."""
if content and isinstance(content, list):
# Remove unexpected block types
formatted_content = []
for block in content:
if (
isinstance(block, dict)
and "type" in block
and block["type"] == "tool_use"
):
continue
else:
formatted_content.append(block)
else:
formatted_content = content
return formatted_content
def _convert_message_to_dict(message: BaseMessage) -> dict:
"""Convert a LangChain message to a dictionary.
Args:
message: The LangChain message.
Returns:
The dictionary.
"""
message_dict: Dict[str, Any] = {"content": _format_message_content(message.content)}
if (name := message.name or message.additional_kwargs.get("name")) is not None:
message_dict["name"] = name
# populate role and additional message data
if isinstance(message, ChatMessage):
message_dict["role"] = message.role
elif isinstance(message, HumanMessage):
message_dict["role"] = "user"
elif isinstance(message, AIMessage):
message_dict["role"] = "assistant"
if "function_call" in message.additional_kwargs:
message_dict["function_call"] = message.additional_kwargs["function_call"]
if message.tool_calls or message.invalid_tool_calls:
message_dict["tool_calls"] = [
_lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
] + [
_lc_invalid_tool_call_to_openai_tool_call(tc)
for tc in message.invalid_tool_calls
]
elif "tool_calls" in message.additional_kwargs:
message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
tool_call_supported_props = {"id", "type", "function"}
message_dict["tool_calls"] = [
{k: v for k, v in tool_call.items() if k in tool_call_supported_props}
for tool_call in message_dict["tool_calls"]
]
else:
pass
# If tool calls present, content null value should be None not empty string.
if "function_call" in message_dict or "tool_calls" in message_dict:
message_dict["content"] = message_dict["content"] or None
if "audio" in message.additional_kwargs:
# openai doesn't support passing the data back - only the id
# https://platform.openai.com/docs/guides/audio/multi-turn-conversations
raw_audio = message.additional_kwargs["audio"]
audio = (
{"id": message.additional_kwargs["audio"]["id"]}
if "id" in raw_audio
else raw_audio
)
message_dict["audio"] = audio
elif isinstance(message, SystemMessage):
message_dict["role"] = "system"
elif isinstance(message, FunctionMessage):
message_dict["role"] = "function"
elif isinstance(message, ToolMessage):
message_dict["role"] = "tool"
message_dict["tool_call_id"] = message.tool_call_id
supported_props = {"content", "role", "tool_call_id"}
message_dict = {k: v for k, v in message_dict.items() if k in supported_props}
else:
raise TypeError(f"Got unknown type {message}")
return message_dict
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
id_ = _dict.get("id")
role = cast(str, _dict.get("role"))
content = cast(str, _dict.get("content") or "")
additional_kwargs: Dict = {}
if _dict.get("function_call"):
function_call = dict(_dict["function_call"])
if "name" in function_call and function_call["name"] is None:
function_call["name"] = ""
additional_kwargs["function_call"] = function_call
tool_call_chunks = []
if raw_tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
try:
tool_call_chunks = [
tool_call_chunk(
name=rtc["function"].get("name"),
args=rtc["function"].get("arguments"),
id=rtc.get("id"),
index=rtc["index"],
)
for rtc in raw_tool_calls
]
except KeyError:
pass
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content, id=id_)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(
content=content,
additional_kwargs=additional_kwargs,
id=id_,
tool_call_chunks=tool_call_chunks, # type: ignore[arg-type]
)
elif role == "system" or default_class == SystemMessageChunk:
return SystemMessageChunk(content=content, id=id_)
elif role == "function" or default_class == FunctionMessageChunk:
return FunctionMessageChunk(content=content, name=_dict["name"], id=id_)
elif role == "tool" or default_class == ToolMessageChunk:
return ToolMessageChunk(
content=content, tool_call_id=_dict["tool_call_id"], id=id_
)
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role, id=id_)
else:
return default_class(content=content, id=id_) # type: ignore
def _convert_chunk_to_generation_chunk(
chunk: dict, default_chunk_class: Type, base_generation_info: Optional[Dict]
) -> Optional[ChatGenerationChunk]:
token_usage = chunk.get("usage")
choices = chunk.get("choices", [])
usage_metadata: Optional[UsageMetadata] = (
_create_usage_metadata(token_usage) if token_usage else None
)
if len(choices) == 0:
# logprobs is implicitly None
generation_chunk = ChatGenerationChunk(
message=default_chunk_class(content="", usage_metadata=usage_metadata)
)
return generation_chunk
choice = choices[0]
if choice["delta"] is None:
return None
message_chunk = _convert_delta_to_message_chunk(
choice["delta"], default_chunk_class
)
generation_info = {**base_generation_info} if base_generation_info else {}
if finish_reason := choice.get("finish_reason"):
generation_info["finish_reason"] = finish_reason
if model_name := chunk.get("model"):
generation_info["model_name"] = model_name
if system_fingerprint := chunk.get("system_fingerprint"):
generation_info["system_fingerprint"] = system_fingerprint
logprobs = choice.get("logprobs")
if logprobs:
generation_info["logprobs"] = logprobs
if usage_metadata and isinstance(message_chunk, AIMessageChunk):
message_chunk.usage_metadata = usage_metadata
generation_chunk = ChatGenerationChunk(
message=message_chunk, generation_info=generation_info or None
)
return generation_chunk
def _update_token_usage(
overall_token_usage: Union[int, dict], new_usage: Union[int, dict]
) -> Union[int, dict]:
# Token usage is either ints or dictionaries
# `reasoning_tokens` is nested inside `completion_tokens_details`
if isinstance(new_usage, int):
if not isinstance(overall_token_usage, int):
raise ValueError(
f"Got different types for token usage: "
f"{type(new_usage)} and {type(overall_token_usage)}"
)
return new_usage + overall_token_usage
elif isinstance(new_usage, dict):
if not isinstance(overall_token_usage, dict):
raise ValueError(
f"Got different types for token usage: "
f"{type(new_usage)} and {type(overall_token_usage)}"
)
return {
k: _update_token_usage(overall_token_usage.get(k, 0), v)
for k, v in new_usage.items()
}
else:
warnings.warn(f"Unexpected type for token usage: {type(new_usage)}")
return new_usage
class _FunctionCall(TypedDict):
name: str
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM], Type]
_DictOrPydantic = Union[Dict, _BM]
class _AllReturnType(TypedDict):
raw: BaseMessage
parsed: Optional[_DictOrPydantic]
parsing_error: Optional[BaseException]
class BaseChatOpenAI(BaseChatModel):
client: Any = Field(default=None, exclude=True) #: :meta private:
async_client: Any = Field(default=None, exclude=True) #: :meta private:
root_client: Any = Field(default=None, exclude=True) #: :meta private:
root_async_client: Any = Field(default=None, exclude=True) #: :meta private:
model_name: str = Field(default="gpt-3.5-turbo", alias="model")
"""Model name to use."""
temperature: float = 0.7
"""What sampling temperature to use."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `create` call not explicitly specified."""
openai_api_key: Optional[SecretStr] = Field(
alias="api_key", default_factory=secret_from_env("OPENAI_API_KEY", default=None)
)
openai_api_base: Optional[str] = Field(default=None, alias="base_url")
"""Base URL path for API requests, leave blank if not using a proxy or service
emulator."""
openai_organization: Optional[str] = Field(default=None, alias="organization")
"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = Field(
default_factory=from_env("OPENAI_PROXY", default=None)
)
request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
default=None, alias="timeout"
)
"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
None."""
max_retries: int = 2
"""Maximum number of retries to make when generating."""
presence_penalty: Optional[float] = None
"""Penalizes repeated tokens."""
frequency_penalty: Optional[float] = None
"""Penalizes repeated tokens according to frequency."""
seed: Optional[int] = None
"""Seed for generation"""
logprobs: Optional[bool] = None
"""Whether to return logprobs."""
top_logprobs: Optional[int] = None
"""Number of most likely tokens to return at each token position, each with
an associated log probability. `logprobs` must be set to true
if this parameter is used."""
logit_bias: Optional[Dict[int, int]] = None
"""Modify the likelihood of specified tokens appearing in the completion."""
streaming: bool = False
"""Whether to stream the results or not."""
n: int = 1
"""Number of chat completions to generate for each prompt."""
top_p: Optional[float] = None
"""Total probability mass of tokens to consider at each step."""
max_tokens: Optional[int] = Field(default=None)
"""Maximum number of tokens to generate."""
tiktoken_model_name: Optional[str] = None
"""The model name to pass to tiktoken when using this class.
Tiktoken is used to count the number of tokens in documents to constrain
them to be under a certain limit. By default, when set to None, this will
be the same as the embedding model name. However, there are some cases
where you may want to use this Embedding class with a model name not
supported by tiktoken. This can include when using Azure embeddings or
when using one of the many model providers that expose an OpenAI-like
API but with different models. In those cases, in order to avoid erroring
when tiktoken is called, you can specify a model name to use here."""
default_headers: Union[Mapping[str, str], None] = None
default_query: Union[Mapping[str, object], None] = None
# Configure a custom httpx client. See the
# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
http_client: Union[Any, None] = Field(default=None, exclude=True)
"""Optional httpx.Client. Only used for sync invocations. Must specify
http_async_client as well if you'd like a custom client for async invocations.
"""
http_async_client: Union[Any, None] = Field(default=None, exclude=True)
"""Optional httpx.AsyncClient. Only used for async invocations. Must specify
http_client as well if you'd like a custom client for sync invocations."""
stop: Optional[Union[List[str], str]] = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
extra_body: Optional[Mapping[str, Any]] = None
"""Optional additional JSON properties to include in the request parameters when
making requests to OpenAI compatible APIs, such as vLLM."""
include_response_headers: bool = False
"""Whether to include response headers in the output message response_metadata."""
disabled_params: Optional[Dict[str, Any]] = Field(default=None)
"""Parameters of the OpenAI client or chat.completions endpoint that should be
disabled for the given model.
Should be specified as ``{"param": None | ['val1', 'val2']}`` where the key is the
parameter and the value is either None, meaning that parameter should never be
used, or it's a list of disabled values for the parameter.
For example, older models may not support the 'parallel_tool_calls' parameter at
all, in which case ``disabled_params={"parallel_tool_calls: None}`` can ben passed
in.
If a parameter is disabled then it will not be used by default in any methods, e.g.
in :meth:`~langchain_openai.chat_models.base.ChatOpenAI.with_structured_output`.
However this does not prevent a user from directly passed in the parameter during
invocation.
"""
model_config = ConfigDict(populate_by_name=True)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="before")
@classmethod
def validate_temperature(cls, values: Dict[str, Any]) -> Any:
"""Currently o1 models only allow temperature=1."""
model = values.get("model_name") or values.get("model") or ""
if model.startswith("o1") and "temperature" not in values:
values["temperature"] = 1
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.n > 1 and self.streaming:
raise ValueError("n must be 1 when streaming.")
# Check OPENAI_ORGANIZATION for backwards compatibility.
self.openai_organization = (
self.openai_organization
or os.getenv("OPENAI_ORG_ID")
or os.getenv("OPENAI_ORGANIZATION")
)
self.openai_api_base = self.openai_api_base or os.getenv("OPENAI_API_BASE")
client_params: dict = {
"api_key": (
self.openai_api_key.get_secret_value() if self.openai_api_key else None
),
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if self.openai_proxy and (self.http_client or self.http_async_client):
openai_proxy = self.openai_proxy
http_client = self.http_client
http_async_client = self.http_async_client
raise ValueError(
"Cannot specify 'openai_proxy' if one of "
"'http_client'/'http_async_client' is already specified. Received:\n"
f"{openai_proxy=}\n{http_client=}\n{http_async_client=}"
)
if not self.client:
if self.openai_proxy and not self.http_client:
try:
import httpx
except ImportError as e:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`."
) from e
self.http_client = httpx.Client(proxy=self.openai_proxy)
sync_specific = {"http_client": self.http_client}
self.root_client = openai.OpenAI(**client_params, **sync_specific) # type: ignore[arg-type]
self.client = self.root_client.chat.completions
if not self.async_client:
if self.openai_proxy and not self.http_async_client:
try:
import httpx
except ImportError as e:
raise ImportError(
"Could not import httpx python package. "
"Please install it with `pip install httpx`."
) from e
self.http_async_client = httpx.AsyncClient(proxy=self.openai_proxy)
async_specific = {"http_client": self.http_async_client}
self.root_async_client = openai.AsyncOpenAI(
**client_params,
**async_specific, # type: ignore[arg-type]
)
self.async_client = self.root_async_client.chat.completions
return self
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
exclude_if_none = {
"presence_penalty": self.presence_penalty,
"frequency_penalty": self.frequency_penalty,
"seed": self.seed,
"top_p": self.top_p,
"logprobs": self.logprobs,
"top_logprobs": self.top_logprobs,
"logit_bias": self.logit_bias,
"stop": self.stop or None, # also exclude empty list for this
"max_tokens": self.max_tokens,
"extra_body": self.extra_body,
}
params = {
"model": self.model_name,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
**{k: v for k, v in exclude_if_none.items() if v is not None},
**self.model_kwargs,
}
return params
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
overall_token_usage: dict = {}
system_fingerprint = None
for output in llm_outputs:
if output is None:
# Happens in streaming
continue
token_usage = output["token_usage"]
if token_usage is not None:
for k, v in token_usage.items():
if v is None:
continue
if k in overall_token_usage:
overall_token_usage[k] = _update_token_usage(
overall_token_usage[k], v
)
else:
overall_token_usage[k] = v
if system_fingerprint is None:
system_fingerprint = output.get("system_fingerprint")
combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
if system_fingerprint:
combined["system_fingerprint"] = system_fingerprint
return combined
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
kwargs["stream"] = True
payload = self._get_request_payload(messages, stop=stop, **kwargs)
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
base_generation_info = {}
if self.include_response_headers:
raw_response = self.client.with_raw_response.create(**payload)
response = raw_response.parse()
base_generation_info = {"headers": dict(raw_response.headers)}
else:
response = self.client.create(**payload)
with response:
is_first_chunk = True
for chunk in response:
if not isinstance(chunk, dict):
chunk = chunk.model_dump()
generation_chunk = _convert_chunk_to_generation_chunk(
chunk,
default_chunk_class,
base_generation_info if is_first_chunk else {},
)
if generation_chunk is None:
continue
default_chunk_class = generation_chunk.message.__class__
logprobs = (generation_chunk.generation_info or {}).get("logprobs")
if run_manager:
run_manager.on_llm_new_token(
generation_chunk.text, chunk=generation_chunk, logprobs=logprobs
)
is_first_chunk = False
yield generation_chunk
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
generation_info = None
if "response_format" in payload:
if self.include_response_headers:
warnings.warn(
"Cannot currently include response headers when response_format is "
"specified."
)
payload.pop("stream")
response = self.root_client.beta.chat.completions.parse(**payload)
elif self.include_response_headers:
raw_response = self.client.with_raw_response.create(**payload)
response = raw_response.parse()
generation_info = {"headers": dict(raw_response.headers)}
else:
response = self.client.create(**payload)
return self._create_chat_result(response, generation_info)
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> dict:
messages = self._convert_input(input_).to_messages()
if stop is not None:
kwargs["stop"] = stop
return {
"messages": [_convert_message_to_dict(m) for m in messages],
**self._default_params,
**kwargs,
}
def _create_chat_result(
self,
response: Union[dict, openai.BaseModel],
generation_info: Optional[Dict] = None,
) -> ChatResult:
generations = []
response_dict = (
response if isinstance(response, dict) else response.model_dump()
)
# Sometimes the AI Model calling will get error, we should raise it.
# Otherwise, the next code 'choices.extend(response["choices"])'
# will throw a "TypeError: 'NoneType' object is not iterable" error
# to mask the true error. Because 'response["choices"]' is None.
if response_dict.get("error"):
raise ValueError(response_dict.get("error"))
token_usage = response_dict.get("usage")
for res in response_dict["choices"]:
message = _convert_dict_to_message(res["message"])
if token_usage and isinstance(message, AIMessage):
message.usage_metadata = _create_usage_metadata(token_usage)
generation_info = generation_info or {}
generation_info["finish_reason"] = (
res.get("finish_reason")
if res.get("finish_reason") is not None
else generation_info.get("finish_reason")
)
if "logprobs" in res:
generation_info["logprobs"] = res["logprobs"]
gen = ChatGeneration(message=message, generation_info=generation_info)
generations.append(gen)
llm_output = {
"token_usage": token_usage,
"model_name": response_dict.get("model", self.model_name),
"system_fingerprint": response_dict.get("system_fingerprint", ""),
}
if isinstance(response, openai.BaseModel) and getattr(
response, "choices", None
):
message = response.choices[0].message # type: ignore[attr-defined]
if hasattr(message, "parsed"):
generations[0].message.additional_kwargs["parsed"] = message.parsed
if hasattr(message, "refusal"):
generations[0].message.additional_kwargs["refusal"] = message.refusal
return ChatResult(generations=generations, llm_output=llm_output)
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
kwargs["stream"] = True
payload = self._get_request_payload(messages, stop=stop, **kwargs)
default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
base_generation_info = {}
if self.include_response_headers:
raw_response = await self.async_client.with_raw_response.create(**payload)
response = raw_response.parse()
base_generation_info = {"headers": dict(raw_response.headers)}
else:
response = await self.async_client.create(**payload)
async with response:
is_first_chunk = True
async for chunk in response:
if not isinstance(chunk, dict):
chunk = chunk.model_dump()
generation_chunk = _convert_chunk_to_generation_chunk(
chunk,
default_chunk_class,
base_generation_info if is_first_chunk else {},
)
if generation_chunk is None:
continue
default_chunk_class = generation_chunk.message.__class__
logprobs = (generation_chunk.generation_info or {}).get("logprobs")
if run_manager:
await run_manager.on_llm_new_token(
generation_chunk.text, chunk=generation_chunk, logprobs=logprobs
)
is_first_chunk = False
yield generation_chunk
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._astream(
messages, stop=stop, run_manager=run_manager, **kwargs
)
return await agenerate_from_stream(stream_iter)
payload = self._get_request_payload(messages, stop=stop, **kwargs)
generation_info = None
if "response_format" in payload:
if self.include_response_headers:
warnings.warn(
"Cannot currently include response headers when response_format is "
"specified."
)
payload.pop("stream")
response = await self.root_async_client.beta.chat.completions.parse(
**payload
)
elif self.include_response_headers:
raw_response = await self.async_client.with_raw_response.create(**payload)
response = raw_response.parse()
generation_info = {"headers": dict(raw_response.headers)}
else:
response = await self.async_client.create(**payload)
return await run_in_executor(
None, self._create_chat_result, response, generation_info
)
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {"model_name": self.model_name, **self._default_params}
def _get_invocation_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> Dict[str, Any]:
"""Get the parameters used to invoke the model."""
return {
"model": self.model_name,
**super()._get_invocation_params(stop=stop),
**self._default_params,
**kwargs,
}
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="openai",
ls_model_name=self.model_name,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("max_tokens", self.max_tokens) or params.get(
"max_completion_tokens", self.max_tokens
):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None):
ls_params["ls_stop"] = ls_stop
return ls_params
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "openai-chat"
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
if self.tiktoken_model_name is not None:
model = self.tiktoken_model_name
else:
model = self.model_name
try:
encoding = tiktoken.encoding_for_model(model)
except KeyError:
model = "cl100k_base"
encoding = tiktoken.get_encoding(model)
return model, encoding
def get_token_ids(self, text: str) -> List[int]:
"""Get the tokens present in the text with tiktoken package."""
if self.custom_get_token_ids is not None:
return self.custom_get_token_ids(text)
# tiktoken NOT supported for Python 3.7 or below
if sys.version_info[1] <= 7:
return super().get_token_ids(text)
_, encoding_model = self._get_encoding_model()
return encoding_model.encode(text)
def get_num_tokens_from_messages(
self,
messages: List[BaseMessage],
tools: Optional[
Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]]
] = None,
) -> int:
"""Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
**Requirements**: You must have the ``pillow`` installed if you want to count
image tokens if you are specifying the image as a base64 string, and you must
have both ``pillow`` and ``httpx`` installed if you are specifying the image
as a URL. If these aren't installed image inputs will be ignored in token
counting.
OpenAI reference: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb
Args:
messages: The message inputs to tokenize.
tools: If provided, sequence of dict, BaseModel, function, or BaseTools
to be converted to tool schemas.
"""
# TODO: Count bound tools as part of input.
if tools is not None:
warnings.warn(
"Counting tokens in tool schemas is not yet supported. Ignoring tools."
)
if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model.startswith("gpt-3.5-turbo-0301"):
# every message follows <im_start>{role/name}\n{content}<im_end>\n
tokens_per_message = 4
# if there's a name, the role is omitted
tokens_per_name = -1
elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
tokens_per_message = 3
tokens_per_name = 1
else:
raise NotImplementedError(
f"get_num_tokens_from_messages() is not presently implemented "
f"for model {model}. See "
"https://platform.openai.com/docs/guides/text-generation/managing-tokens" # noqa: E501
" for information on how messages are converted to tokens."
)
num_tokens = 0
messages_dict = [_convert_message_to_dict(m) for m in messages]
for message in messages_dict:
num_tokens += tokens_per_message
for key, value in message.items():
# This is an inferred approximation. OpenAI does not document how to
# count tool message tokens.
if key == "tool_call_id":
num_tokens += 3
continue
if isinstance(value, list):
# content or tool calls
for val in value:
if isinstance(val, str) or val["type"] == "text":
text = val["text"] if isinstance(val, dict) else val
num_tokens += len(encoding.encode(text))
elif val["type"] == "image_url":
if val["image_url"].get("detail") == "low":
num_tokens += 85
else:
image_size = _url_to_size(val["image_url"]["url"])
if not image_size:
continue
num_tokens += _count_image_tokens(*image_size)
# Tool/function call token counting is not documented by OpenAI.
# This is an approximation.
elif val["type"] == "function":
num_tokens += len(
encoding.encode(val["function"]["arguments"])
)
num_tokens += len(encoding.encode(val["function"]["name"]))
else:
raise ValueError(
f"Unrecognized content block type\n\n{val}"
)
elif not value:
continue
else:
# Cast str(value) in case the message value is not a string
# This occurs with function messages
num_tokens += len(encoding.encode(str(value)))
if key == "name":
num_tokens += tokens_per_name
# every reply is primed with <im_start>assistant
num_tokens += 3
return num_tokens
def _should_stream(
self,
*,
async_api: bool,
run_manager: Optional[
Union[CallbackManagerForLLMRun, AsyncCallbackManagerForLLMRun]
] = None,
response_format: Optional[Union[dict, type]] = None,
**kwargs: Any,
) -> bool:
if isinstance(response_format, type) and is_basemodel_subclass(response_format):
# TODO: Add support for streaming with Pydantic response_format.
warnings.warn("Streaming with Pydantic response_format not yet supported.")
return False
return super()._should_stream(
async_api=async_api, run_manager=run_manager, **kwargs
)
@deprecated(
since="0.2.1",
alternative="langchain_openai.chat_models.base.ChatOpenAI.bind_tools",
removal="1.0.0",
)
def bind_functions(
self,
functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
function_call: Optional[
Union[_FunctionCall, str, Literal["auto", "none"]]
] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind functions (and other objects) to this chat model.
Assumes model is compatible with OpenAI function-calling API.
NOTE: Using bind_tools is recommended instead, as the `functions` and
`function_call` request parameters are officially marked as deprecated by
OpenAI.
Args:
functions: A list of function definitions to bind to this chat model.
Can be a dictionary, pydantic model, or callable. Pydantic
models and callables will be automatically converted to
their schema dictionary representation.
function_call: Which function to require the model to call.
Must be the name of the single provided function or
"auto" to automatically determine which function to call
(if any).
**kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
"""
formatted_functions = [convert_to_openai_function(fn) for fn in functions]
if function_call is not None:
function_call = (
{"name": function_call}
if isinstance(function_call, str)
and function_call not in ("auto", "none")
else function_call
)
if isinstance(function_call, dict) and len(formatted_functions) != 1:
raise ValueError(
"When specifying `function_call`, you must provide exactly one "
"function."
)
if (
isinstance(function_call, dict)
and formatted_functions[0]["name"] != function_call["name"]
):
raise ValueError(
f"Function call {function_call} was specified, but the only "
f"provided function was {formatted_functions[0]['name']}."
)
kwargs = {**kwargs, "function_call": function_call}
return super().bind(functions=formatted_functions, **kwargs)
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
*,
tool_choice: Optional[
Union[dict, str, Literal["auto", "none", "required", "any"], bool]
] = None,
strict: Optional[bool] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model.
Assumes model is compatible with OpenAI tool-calling API.
Args:
tools: A list of tool definitions to bind to this chat model.
Supports any tool definition handled by
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call. Options are:
- str of the form ``"<<tool_name>>"``: calls <<tool_name>> tool.
- ``"auto"``: automatically selects a tool (including no tool).
- ``"none"``: does not call a tool.
- ``"any"`` or ``"required"`` or ``True``: force at least one tool to be called.
- dict of the form ``{"type": "function", "function": {"name": <<tool_name>>}}``: calls <<tool_name>> tool.
- ``False`` or ``None``: no effect, default OpenAI behavior.
strict: If True, model output is guaranteed to exactly match the JSON Schema
provided in the tool definition. If True, the input schema will be
validated according to
https://platform.openai.com/docs/guides/structured-outputs/supported-schemas.
If False, input schema will not be validated and model output will not
be validated.
If None, ``strict`` argument will not be passed to the model.
kwargs: Any additional parameters are passed directly to
:meth:`~langchain_openai.chat_models.base.ChatOpenAI.bind`.
.. versionchanged:: 0.1.21
Support for ``strict`` argument added.
""" # noqa: E501
formatted_tools = [
convert_to_openai_tool(tool, strict=strict) for tool in tools
]
if tool_choice:
if isinstance(tool_choice, str):
# tool_choice is a tool/function name
if tool_choice not in ("auto", "none", "any", "required"):
tool_choice = {
"type": "function",
"function": {"name": tool_choice},
}
# 'any' is not natively supported by OpenAI API.
# We support 'any' since other models use this instead of 'required'.
if tool_choice == "any":
tool_choice = "required"
elif isinstance(tool_choice, bool):
tool_choice = "required"
elif isinstance(tool_choice, dict):
tool_names = [
formatted_tool["function"]["name"]
for formatted_tool in formatted_tools
]
if not any(
tool_name == tool_choice["function"]["name"]
for tool_name in tool_names
):
raise ValueError(
f"Tool choice {tool_choice} was specified, but the only "
f"provided tools were {tool_names}."
)
else:
raise ValueError(
f"Unrecognized tool_choice type. Expected str, bool or dict. "
f"Received: {tool_choice}"
)
kwargs["tool_choice"] = tool_choice
return super().bind(tools=formatted_tools, **kwargs)
def with_structured_output(
self,
schema: Optional[_DictOrPydanticClass] = None,
*,
method: Literal[
"function_calling", "json_mode", "json_schema"
] = "function_calling",
include_raw: bool = False,
strict: Optional[bool] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, _DictOrPydantic]:
"""Model wrapper that returns outputs formatted to match the given schema.
Args:
schema:
The output schema. Can be passed in as:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class (support added in 0.1.20),
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
method: The method for steering model generation, one of:
- "function_calling":
Uses OpenAI's tool-calling (formerly called function calling)
API: https://platform.openai.com/docs/guides/function-calling
- "json_schema":
Uses OpenAI's Structured Output API: https://platform.openai.com/docs/guides/structured-outputs
Supported for "gpt-4o-mini", "gpt-4o-2024-08-06", and later
models.
- "json_mode":
Uses OpenAI's JSON mode. Note that if using JSON mode then you
must include instructions for formatting the output into the
desired schema into the model call:
https://platform.openai.com/docs/guides/structured-outputs/json-mode
Learn more about the differences between the methods and which models
support which methods here:
- https://platform.openai.com/docs/guides/structured-outputs/structured-outputs-vs-json-mode
- https://platform.openai.com/docs/guides/structured-outputs/function-calling-vs-response-format
include_raw:
If False then only the parsed structured output is returned. If
an error occurs during model output parsing it will be raised. If True
then both the raw model response (a BaseMessage) and the parsed model
response will be returned. If an error occurs during output parsing it
will be caught and returned as well. The final output is always a dict
with keys "raw", "parsed", and "parsing_error".
strict:
- True:
Model output is guaranteed to exactly match the schema.
The input schema will also be validated according to
https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
- False:
Input schema will not be validated and model output will not be
validated.
- None:
``strict`` argument will not be passed to the model.
If ``method`` is "json_schema" defaults to True. If ``method`` is
"function_calling" or "json_mode" defaults to None. Can only be
non-null if ``method`` is "function_calling" or "json_schema".
kwargs: Additional keyword args aren't supported.
Returns:
A Runnable that takes same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
| If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs an instance of ``schema`` (i.e., a Pydantic object). Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
| If ``include_raw`` is True, then Runnable outputs a dict with keys:
- "raw": BaseMessage
- "parsed": None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- "parsing_error": Optional[BaseException]
.. versionchanged:: 0.1.20
Added support for TypedDict class ``schema``.
.. versionchanged:: 0.1.21
Support for ``strict`` argument added.
Support for ``method`` = "json_schema" added.
.. note:: Planned breaking changes in version `0.3.0`
- ``method`` default will be changed to "json_schema" from
"function_calling".
- ``strict`` will default to True when ``method`` is
"function_calling" as of version `0.3.0`.
.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=False, strict=True
Note, OpenAI has a number of restrictions on what types of schemas can be
provided if ``strict`` = True. When using Pydantic, our model cannot
specify any Field metadata (like min/max constraints) and fields cannot
have default values.
See all constraints here: https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
.. code-block:: python
from typing import Optional
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Optional[str] = Field(
default=..., description="A justification for the answer."
)
llm = ChatOpenAI(model="gpt-4o", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, strict=True
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> AnswerWithJustification(
# answer='They weigh the same',
# justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
# )
.. dropdown:: Example: schema=Pydantic class, method="function_calling", include_raw=True
.. code-block:: python
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: str
llm = ChatOpenAI(model="gpt-4o", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification, include_raw=True
)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
# 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
# 'parsing_error': None
# }
.. dropdown:: Example: schema=TypedDict class, method="function_calling", include_raw=False
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict
from langchain_openai import ChatOpenAI
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
llm = ChatOpenAI(model="gpt-4o", temperature=0)
structured_llm = llm.with_structured_output(AnswerWithJustification)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
.. dropdown:: Example: schema=OpenAI function schema, method="function_calling", include_raw=False
.. code-block:: python
from langchain_openai import ChatOpenAI
oai_schema = {
'name': 'AnswerWithJustification',
'description': 'An answer to the user question along with justification for the answer.',
'parameters': {
'type': 'object',
'properties': {
'answer': {'type': 'string'},
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
},
'required': ['answer']
}
}
llm = ChatOpenAI(model="gpt-4o", temperature=0)
structured_llm = llm.with_structured_output(oai_schema)
structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
# -> {
# 'answer': 'They weigh the same',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
# }
.. dropdown:: Example: schema=Pydantic class, method="json_mode", include_raw=True
.. code-block::
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
class AnswerWithJustification(BaseModel):
answer: str
justification: str
llm = ChatOpenAI(model="gpt-4o", temperature=0)
structured_llm = llm.with_structured_output(
AnswerWithJustification,
method="json_mode",
include_raw=True
)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\\n "answer": "They are both the same weight.",\\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
# 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
# 'parsing_error': None
# }
.. dropdown:: Example: schema=None, method="json_mode", include_raw=True
.. code-block::
structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)
structured_llm.invoke(
"Answer the following question. "
"Make sure to return a JSON blob with keys 'answer' and 'justification'.\\n\\n"
"What's heavier a pound of bricks or a pound of feathers?"
)
# -> {
# 'raw': AIMessage(content='{\\n "answer": "They are both the same weight.",\\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \\n}'),
# 'parsed': {
# 'answer': 'They are both the same weight.',
# 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
# },
# 'parsing_error': None
# }
""" # noqa: E501
if kwargs:
raise ValueError(f"Received unsupported arguments {kwargs}")
if strict is not None and method == "json_mode":
raise ValueError(
"Argument `strict` is not supported with `method`='json_mode'"
)
is_pydantic_schema = _is_pydantic_class(schema)
if method == "function_calling":
if schema is None:
raise ValueError(
"schema must be specified when method is not 'json_mode'. "
"Received None."
)
tool_name = convert_to_openai_tool(schema)["function"]["name"]
bind_kwargs = self._filter_disabled_params(
tool_choice=tool_name, parallel_tool_calls=False, strict=strict
)
llm = self.bind_tools([schema], **bind_kwargs)
if is_pydantic_schema:
output_parser: Runnable = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
)
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[arg-type]
if is_pydantic_schema
else JsonOutputParser()
)
elif method == "json_schema":
if schema is None:
raise ValueError(
"schema must be specified when method is not 'json_mode'. "
"Received None."
)
response_format = _convert_to_openai_response_format(schema, strict=strict)
llm = self.bind(response_format=response_format)
if is_pydantic_schema:
output_parser = _oai_structured_outputs_parser.with_types(
output_type=cast(type, schema)
)
else:
output_parser = JsonOutputParser()
else:
raise ValueError(
f"Unrecognized method argument. Expected one of 'function_calling' or "
f"'json_mode'. Received: '{method}'"
)
if include_raw:
parser_assign = RunnablePassthrough.assign(
parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
)
parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
parser_with_fallback = parser_assign.with_fallbacks(
[parser_none], exception_key="parsing_error"
)
return RunnableMap(raw=llm) | parser_with_fallback
else:
return llm | output_parser
def _filter_disabled_params(self, **kwargs: Any) -> Dict[str, Any]:
if not self.disabled_params:
return kwargs
filtered = {}
for k, v in kwargs.items():
# Skip param
if k in self.disabled_params and (
self.disabled_params[k] is None or v in self.disabled_params[k]
):
continue
# Keep param
else:
filtered[k] = v
return filtered
class ChatOpenAI(BaseChatOpenAI): # type: ignore[override]
"""OpenAI chat model integration.
.. dropdown:: Setup
:open:
Install ``langchain-openai`` and set environment variable ``OPENAI_API_KEY``.
.. code-block:: bash
pip install -U langchain-openai
export OPENAI_API_KEY="your-api-key"
.. dropdown:: Key init args — completion params
model: str
Name of OpenAI model to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
logprobs: Optional[bool]
Whether to return logprobs.
stream_options: Dict
Configure streaming outputs, like whether to return token usage when
streaming (``{"include_usage": True}``).
See full list of supported init args and their descriptions in the params section.
.. dropdown:: Key init args — client params
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY.
base_url: Optional[str]
Base URL for API requests. Only specify if using a proxy or service
emulator.
organization: Optional[str]
OpenAI organization ID. If not passed in will be read from env
var OPENAI_ORG_ID.
See full list of supported init args and their descriptions in the params section.
.. dropdown:: Instantiate
.. code-block:: python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
model="gpt-4o",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# api_key="...",
# base_url="...",
# organization="...",
# other params...
)
**NOTE**: Any param which is not explicitly supported will be passed directly to the
``openai.OpenAI.chat.completions.create(...)`` API every time to the model is
invoked. For example:
.. code-block:: python
from langchain_openai import ChatOpenAI
import openai
ChatOpenAI(..., frequency_penalty=0.2).invoke(...)
# results in underlying API call of:
openai.OpenAI(..).chat.completions.create(..., frequency_penalty=0.2)
# which is also equivalent to:
ChatOpenAI(...).invoke(..., frequency_penalty=0.2)
.. dropdown:: Invoke
.. code-block:: python
messages = [
(
"system",
"You are a helpful translator. Translate the user sentence to French.",
),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: pycon
AIMessage(
content="J'adore la programmation.",
response_metadata={
"token_usage": {
"completion_tokens": 5,
"prompt_tokens": 31,
"total_tokens": 36,
},
"model_name": "gpt-4o",
"system_fingerprint": "fp_43dfabdef1",
"finish_reason": "stop",
"logprobs": None,
},
id="run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0",
usage_metadata={"input_tokens": 31, "output_tokens": 5, "total_tokens": 36},
)
.. dropdown:: Stream
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
AIMessageChunk(content="", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
AIMessageChunk(content="J", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
AIMessageChunk(content="'adore", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
AIMessageChunk(content=" la", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
AIMessageChunk(
content=" programmation", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0"
)
AIMessageChunk(content=".", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0")
AIMessageChunk(
content="",
response_metadata={"finish_reason": "stop"},
id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0",
)
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(
content="J'adore la programmation.",
response_metadata={"finish_reason": "stop"},
id="run-bf917526-7f58-4683-84f7-36a6b671d140",
)
.. dropdown:: Async
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
.. code-block:: python
AIMessage(
content="J'adore la programmation.",
response_metadata={
"token_usage": {
"completion_tokens": 5,
"prompt_tokens": 31,
"total_tokens": 36,
},
"model_name": "gpt-4o",
"system_fingerprint": "fp_43dfabdef1",
"finish_reason": "stop",
"logprobs": None,
},
id="run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0",
usage_metadata={"input_tokens": 31, "output_tokens": 5, "total_tokens": 36},
)
.. dropdown:: Tool calling
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
llm_with_tools = llm.bind_tools(
[GetWeather, GetPopulation]
# strict = True # enforce tool args schema is respected
)
ai_msg = llm_with_tools.invoke(
"Which city is hotter today and which is bigger: LA or NY?"
)
ai_msg.tool_calls
.. code-block:: python
[
{
"name": "GetWeather",
"args": {"location": "Los Angeles, CA"},
"id": "call_6XswGD5Pqk8Tt5atYr7tfenU",
},
{
"name": "GetWeather",
"args": {"location": "New York, NY"},
"id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
},
{
"name": "GetPopulation",
"args": {"location": "Los Angeles, CA"},
"id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
},
{
"name": "GetPopulation",
"args": {"location": "New York, NY"},
"id": "call_6ghfKxV264jEfe1mRIkS3PE7",
},
]
Note that ``openai >= 1.32`` supports a ``parallel_tool_calls`` parameter
that defaults to ``True``. This parameter can be set to ``False`` to
disable parallel tool calls:
.. code-block:: python
ai_msg = llm_with_tools.invoke(
"What is the weather in LA and NY?", parallel_tool_calls=False
)
ai_msg.tool_calls
.. code-block:: python
[
{
"name": "GetWeather",
"args": {"location": "Los Angeles, CA"},
"id": "call_4OoY0ZR99iEvC7fevsH8Uhtz",
}
]
Like other runtime parameters, ``parallel_tool_calls`` can be bound to a model
using ``llm.bind(parallel_tool_calls=False)`` or during instantiation by
setting ``model_kwargs``.
See ``ChatOpenAI.bind_tools()`` method for more.
.. dropdown:: Structured output
.. code-block:: python
from typing import Optional
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(
setup="Why was the cat sitting on the computer?",
punchline="To keep an eye on the mouse!",
rating=None,
)
See ``ChatOpenAI.with_structured_output()`` for more.
.. dropdown:: JSON mode
.. code-block:: python
json_llm = llm.bind(response_format={"type": "json_object"})
ai_msg = json_llm.invoke(
"Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
)
ai_msg.content
.. code-block:: python
'\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'
.. dropdown:: Image input
.. code-block:: python
import base64
import httpx
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
"The weather in the image appears to be clear and pleasant. The sky is mostly blue with scattered, light clouds, suggesting a sunny day with minimal cloud cover. There is no indication of rain or strong winds, and the overall scene looks bright and calm. The lush green grass and clear visibility further indicate good weather conditions."
.. dropdown:: Token usage
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
When streaming, set the ``stream_usage`` kwarg:
.. code-block:: python
stream = llm.stream(messages, stream_usage=True)
full = next(stream)
for chunk in stream:
full += chunk
full.usage_metadata
.. code-block:: python
{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
Alternatively, setting ``stream_usage`` when instantiating the model can be
useful when incorporating ``ChatOpenAI`` into LCEL chains-- or when using
methods like ``.with_structured_output``, which generate chains under the
hood.
.. code-block:: python
llm = ChatOpenAI(model="gpt-4o", stream_usage=True)
structured_llm = llm.with_structured_output(...)
.. dropdown:: Logprobs
.. code-block:: python
logprobs_llm = llm.bind(logprobs=True)
ai_msg = logprobs_llm.invoke(messages)
ai_msg.response_metadata["logprobs"]
.. code-block:: python
{
"content": [
{
"token": "J",
"bytes": [74],
"logprob": -4.9617593e-06,
"top_logprobs": [],
},
{
"token": "'adore",
"bytes": [39, 97, 100, 111, 114, 101],
"logprob": -0.25202933,
"top_logprobs": [],
},
{
"token": " la",
"bytes": [32, 108, 97],
"logprob": -0.20141791,
"top_logprobs": [],
},
{
"token": " programmation",
"bytes": [
32,
112,
114,
111,
103,
114,
97,
109,
109,
97,
116,
105,
111,
110,
],
"logprob": -1.9361265e-07,
"top_logprobs": [],
},
{
"token": ".",
"bytes": [46],
"logprob": -1.2233183e-05,
"top_logprobs": [],
},
]
}
.. dropdown:: Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{
"token_usage": {
"completion_tokens": 5,
"prompt_tokens": 28,
"total_tokens": 33,
},
"model_name": "gpt-4o",
"system_fingerprint": "fp_319be4768e",
"finish_reason": "stop",
"logprobs": None,
}
""" # noqa: E501
stream_usage: bool = False
"""Whether to include usage metadata in streaming output. If True, additional
message chunks will be generated during the stream including usage metadata.
"""
max_tokens: Optional[int] = Field(default=None, alias="max_completion_tokens")
"""Maximum number of tokens to generate."""
@property
def lc_secrets(self) -> Dict[str, str]:
return {"openai_api_key": "OPENAI_API_KEY"}
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "openai"]
@property
def lc_attributes(self) -> Dict[str, Any]:
attributes: Dict[str, Any] = {}
if self.openai_organization:
attributes["openai_organization"] = self.openai_organization
if self.openai_api_base:
attributes["openai_api_base"] = self.openai_api_base
if self.openai_proxy:
attributes["openai_proxy"] = self.openai_proxy
return attributes
@classmethod
def is_lc_serializable(cls) -> bool:
"""Return whether this model can be serialized by Langchain."""
return True
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
params = super()._default_params
if "max_tokens" in params:
params["max_completion_tokens"] = params.pop("max_tokens")
return params
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: Optional[List[str]] = None,
**kwargs: Any,
) -> dict:
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
# max_tokens was deprecated in favor of max_completion_tokens
# in September 2024 release
if "max_tokens" in payload:
payload["max_completion_tokens"] = payload.pop("max_tokens")
return payload
def _should_stream_usage(
self, stream_usage: Optional[bool] = None, **kwargs: Any
) -> bool:
"""Determine whether to include usage metadata in streaming output.
For backwards compatibility, we check for `stream_options` passed
explicitly to kwargs or in the model_kwargs and override self.stream_usage.
"""
stream_usage_sources = [ # order of preference
stream_usage,
kwargs.get("stream_options", {}).get("include_usage"),
self.model_kwargs.get("stream_options", {}).get("include_usage"),
self.stream_usage,
]
for source in stream_usage_sources:
if isinstance(source, bool):
return source
return self.stream_usage
def _stream(
self, *args: Any, stream_usage: Optional[bool] = None, **kwargs: Any
) -> Iterator[ChatGenerationChunk]:
"""Set default stream_options."""
stream_usage = self._should_stream_usage(stream_usage, **kwargs)
# Note: stream_options is not a valid parameter for Azure OpenAI.
# To support users proxying Azure through ChatOpenAI, here we only specify
# stream_options if include_usage is set to True.
# See https://learn.microsoft.com/en-us/azure/ai-services/openai/whats-new
# for release notes.
if stream_usage:
kwargs["stream_options"] = {"include_usage": stream_usage}
return super()._stream(*args, **kwargs)
async def _astream(
self, *args: Any, stream_usage: Optional[bool] = None, **kwargs: Any
) -> AsyncIterator[ChatGenerationChunk]:
"""Set default stream_options."""
stream_usage = self._should_stream_usage(stream_usage, **kwargs)
if stream_usage:
kwargs["stream_options"] = {"include_usage": stream_usage}
async for chunk in super()._astream(*args, **kwargs):
yield chunk
def _is_pydantic_class(obj: Any) -> bool:
return isinstance(obj, type) and is_basemodel_subclass(obj)
def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict:
return {
"type": "function",
"id": tool_call["id"],
"function": {
"name": tool_call["name"],
"arguments": json.dumps(tool_call["args"]),
},
}
def _lc_invalid_tool_call_to_openai_tool_call(
invalid_tool_call: InvalidToolCall,
) -> dict:
return {
"type": "function",
"id": invalid_tool_call["id"],
"function": {
"name": invalid_tool_call["name"],
"arguments": invalid_tool_call["args"],
},
}
def _url_to_size(image_source: str) -> Optional[Tuple[int, int]]:
try:
from PIL import Image # type: ignore[import]
except ImportError:
logger.info(
"Unable to count image tokens. To count image tokens please install "
"`pip install -U pillow httpx`."
)
return None
if _is_url(image_source):
try:
import httpx
except ImportError:
logger.info(
"Unable to count image tokens. To count image tokens please install "
"`pip install -U httpx`."
)
return None
response = httpx.get(image_source)
response.raise_for_status()
width, height = Image.open(BytesIO(response.content)).size
return width, height
elif _is_b64(image_source):
_, encoded = image_source.split(",", 1)
data = base64.b64decode(encoded)
width, height = Image.open(BytesIO(data)).size
return width, height
else:
return None
def _count_image_tokens(width: int, height: int) -> int:
# Reference: https://platform.openai.com/docs/guides/vision/calculating-costs
width, height = _resize(width, height)
h = ceil(height / 512)
w = ceil(width / 512)
return (170 * h * w) + 85
def _is_url(s: str) -> bool:
try:
result = urlparse(s)
return all([result.scheme, result.netloc])
except Exception as e:
logger.debug(f"Unable to parse URL: {e}")
return False
def _is_b64(s: str) -> bool:
return s.startswith("data:image")
def _resize(width: int, height: int) -> Tuple[int, int]:
# larger side must be <= 2048
if width > 2048 or height > 2048:
if width > height:
height = (height * 2048) // width
width = 2048
else:
width = (width * 2048) // height
height = 2048
# smaller side must be <= 768
if width > 768 and height > 768:
if width > height:
width = (width * 768) // height
height = 768
else:
height = (width * 768) // height
width = 768
return width, height
def _convert_to_openai_response_format(
schema: Union[Dict[str, Any], Type], *, strict: Optional[bool] = None
) -> Union[Dict, TypeBaseModel]:
if isinstance(schema, type) and is_basemodel_subclass(schema):
return schema
if (
isinstance(schema, dict)
and "json_schema" in schema
and schema.get("type") == "json_schema"
):
response_format = schema
elif isinstance(schema, dict) and "name" in schema and "schema" in schema:
response_format = {"type": "json_schema", "json_schema": schema}
else:
strict = strict if strict is not None else True
function = convert_to_openai_function(schema, strict=strict)
function["schema"] = function.pop("parameters")
response_format = {"type": "json_schema", "json_schema": function}
if strict is not None and strict is not response_format["json_schema"].get(
"strict"
):
msg = (
f"Output schema already has 'strict' value set to "
f"{schema['json_schema']['strict']} but 'strict' also passed in to "
f"with_structured_output as {strict}. Please make sure that "
f"'strict' is only specified in one place."
)
raise ValueError(msg)
return response_format
@chain
def _oai_structured_outputs_parser(ai_msg: AIMessage) -> PydanticBaseModel:
if ai_msg.additional_kwargs.get("parsed"):
return ai_msg.additional_kwargs["parsed"]
elif ai_msg.additional_kwargs.get("refusal"):
raise OpenAIRefusalError(ai_msg.additional_kwargs["refusal"])
else:
raise ValueError(
"Structured Output response does not have a 'parsed' field nor a 'refusal' "
"field. Received message:\n\n{ai_msg}"
)
class OpenAIRefusalError(Exception):
"""Error raised when OpenAI Structured Outputs API returns a refusal.
When using OpenAI's Structured Outputs API with user-generated input, the model
may occasionally refuse to fulfill the request for safety reasons.
See here for more on refusals:
https://platform.openai.com/docs/guides/structured-outputs/refusals
.. versionadded:: 0.1.21
"""
def _create_usage_metadata(oai_token_usage: dict) -> UsageMetadata:
input_tokens = oai_token_usage.get("prompt_tokens", 0)
output_tokens = oai_token_usage.get("completion_tokens", 0)
total_tokens = oai_token_usage.get("total_tokens", input_tokens + output_tokens)
input_token_details: dict = {
"audio": (oai_token_usage.get("prompt_tokens_details") or {}).get(
"audio_tokens"
),
"cache_read": (oai_token_usage.get("prompt_tokens_details") or {}).get(
"cached_tokens"
),
}
output_token_details: dict = {
"audio": (oai_token_usage.get("completion_tokens_details") or {}).get(
"audio_tokens"
),
"reasoning": (oai_token_usage.get("completion_tokens_details") or {}).get(
"reasoning_tokens"
),
}
return UsageMetadata(
input_tokens=input_tokens,
output_tokens=output_tokens,
total_tokens=total_tokens,
input_token_details=InputTokenDetails(
**{k: v for k, v in input_token_details.items() if v is not None}
),
output_token_details=OutputTokenDetails(
**{k: v for k, v in output_token_details.items() if v is not None}
),
)
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/chat_models/azure.py | """Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
import os
from typing import (
Any,
Awaitable,
Callable,
Dict,
List,
Optional,
Type,
TypedDict,
TypeVar,
Union,
)
import openai
from langchain_core.language_models.chat_models import LangSmithParams
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatResult
from langchain_core.utils import from_env, secret_from_env
from langchain_core.utils.pydantic import is_basemodel_subclass
from pydantic import BaseModel, Field, SecretStr, model_validator
from typing_extensions import Self
from langchain_openai.chat_models.base import BaseChatOpenAI
logger = logging.getLogger(__name__)
_BM = TypeVar("_BM", bound=BaseModel)
_DictOrPydanticClass = Union[Dict[str, Any], Type[_BM]]
_DictOrPydantic = Union[Dict, _BM]
class _AllReturnType(TypedDict):
raw: BaseMessage
parsed: Optional[_DictOrPydantic]
parsing_error: Optional[BaseException]
def _is_pydantic_class(obj: Any) -> bool:
return isinstance(obj, type) and is_basemodel_subclass(obj)
class AzureChatOpenAI(BaseChatOpenAI):
"""Azure OpenAI chat model integration.
Setup:
Head to the https://learn.microsoft.com/en-us/azure/ai-services/openai/chatgpt-quickstart?tabs=command-line%2Cpython-new&pivots=programming-language-python
to create your Azure OpenAI deployment.
Then install ``langchain-openai`` and set environment variables
``AZURE_OPENAI_API_KEY`` and ``AZURE_OPENAI_ENDPOINT``:
.. code-block:: bash
pip install -U langchain-openai
export AZURE_OPENAI_API_KEY="your-api-key"
export AZURE_OPENAI_ENDPOINT="https://your-endpoint.openai.azure.com/"
Key init args — completion params:
azure_deployment: str
Name of Azure OpenAI deployment to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
logprobs: Optional[bool]
Whether to return logprobs.
Key init args — client params:
api_version: str
Azure OpenAI API version to use. See more on the different versions here:
https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#rest-api-versioning
timeout: Union[float, Tuple[float, float], Any, None]
Timeout for requests.
max_retries: int
Max number of retries.
organization: Optional[str]
OpenAI organization ID. If not passed in will be read from env
var OPENAI_ORG_ID.
model: Optional[str]
The name of the underlying OpenAI model. Used for tracing and token
counting. Does not affect completion. E.g. "gpt-4", "gpt-35-turbo", etc.
model_version: Optional[str]
The version of the underlying OpenAI model. Used for tracing and token
counting. Does not affect completion. E.g., "0125", "0125-preview", etc.
See full list of supported init args and their descriptions in the params section.
Instantiate:
.. code-block:: python
from langchain_openai import AzureChatOpenAI
llm = AzureChatOpenAI(
azure_deployment="your-deployment",
api_version="2024-05-01-preview",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# organization="...",
# model="gpt-35-turbo",
# model_version="0125",
# other params...
)
**NOTE**: Any param which is not explicitly supported will be passed directly to the
``openai.AzureOpenAI.chat.completions.create(...)`` API every time to the model is
invoked. For example:
.. code-block:: python
from langchain_openai import AzureChatOpenAI
import openai
AzureChatOpenAI(..., logprobs=True).invoke(...)
# results in underlying API call of:
openai.AzureOpenAI(..).chat.completions.create(..., logprobs=True)
# which is also equivalent to:
AzureChatOpenAI(...).invoke(..., logprobs=True)
Invoke:
.. code-block:: python
messages = [
(
"system",
"You are a helpful translator. Translate the user sentence to French.",
),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
AIMessage(
content="J'adore programmer.",
usage_metadata={"input_tokens": 28, "output_tokens": 6, "total_tokens": 34},
response_metadata={
"token_usage": {
"completion_tokens": 6,
"prompt_tokens": 28,
"total_tokens": 34,
},
"model_name": "gpt-4",
"system_fingerprint": "fp_7ec89fabc6",
"prompt_filter_results": [
{
"prompt_index": 0,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
}
],
"finish_reason": "stop",
"logprobs": None,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
},
id="run-6d7a5282-0de0-4f27-9cc0-82a9db9a3ce9-0",
)
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
AIMessageChunk(content="", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="J", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="'", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ad", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ore", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=" la", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=" programm", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content="ation", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(content=".", id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f")
AIMessageChunk(
content="",
response_metadata={
"finish_reason": "stop",
"model_name": "gpt-4",
"system_fingerprint": "fp_811936bd4f",
},
id="run-a6f294d3-0700-4f6a-abc2-c6ef1178c37f",
)
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
AIMessageChunk(
content="J'adore la programmation.",
response_metadata={
"finish_reason": "stop",
"model_name": "gpt-4",
"system_fingerprint": "fp_811936bd4f",
},
id="run-ba60e41c-9258-44b8-8f3a-2f10599643b3",
)
Async:
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
Tool calling:
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(
..., description="The city and state, e.g. San Francisco, CA"
)
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke(
"Which city is hotter today and which is bigger: LA or NY?"
)
ai_msg.tool_calls
.. code-block:: python
[
{
"name": "GetWeather",
"args": {"location": "Los Angeles, CA"},
"id": "call_6XswGD5Pqk8Tt5atYr7tfenU",
},
{
"name": "GetWeather",
"args": {"location": "New York, NY"},
"id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi",
},
{
"name": "GetPopulation",
"args": {"location": "Los Angeles, CA"},
"id": "call_49CFW8zqC9W7mh7hbMLSIrXw",
},
{
"name": "GetPopulation",
"args": {"location": "New York, NY"},
"id": "call_6ghfKxV264jEfe1mRIkS3PE7",
},
]
Structured output:
.. code-block:: python
from typing import Optional
from pydantic import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
Joke(
setup="Why was the cat sitting on the computer?",
punchline="To keep an eye on the mouse!",
rating=None,
)
See ``AzureChatOpenAI.with_structured_output()`` for more.
JSON mode:
.. code-block:: python
json_llm = llm.bind(response_format={"type": "json_object"})
ai_msg = json_llm.invoke(
"Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]"
)
ai_msg.content
.. code-block:: python
'\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}'
Image input:
.. code-block:: python
import base64
import httpx
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
"The weather in the image appears to be quite pleasant. The sky is mostly clear"
Token usage:
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{"input_tokens": 28, "output_tokens": 5, "total_tokens": 33}
Logprobs:
.. code-block:: python
logprobs_llm = llm.bind(logprobs=True)
ai_msg = logprobs_llm.invoke(messages)
ai_msg.response_metadata["logprobs"]
.. code-block:: python
{
"content": [
{
"token": "J",
"bytes": [74],
"logprob": -4.9617593e-06,
"top_logprobs": [],
},
{
"token": "'adore",
"bytes": [39, 97, 100, 111, 114, 101],
"logprob": -0.25202933,
"top_logprobs": [],
},
{
"token": " la",
"bytes": [32, 108, 97],
"logprob": -0.20141791,
"top_logprobs": [],
},
{
"token": " programmation",
"bytes": [
32,
112,
114,
111,
103,
114,
97,
109,
109,
97,
116,
105,
111,
110,
],
"logprob": -1.9361265e-07,
"top_logprobs": [],
},
{
"token": ".",
"bytes": [46],
"logprob": -1.2233183e-05,
"top_logprobs": [],
},
]
}
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{
"token_usage": {
"completion_tokens": 6,
"prompt_tokens": 28,
"total_tokens": 34,
},
"model_name": "gpt-35-turbo",
"system_fingerprint": None,
"prompt_filter_results": [
{
"prompt_index": 0,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
}
],
"finish_reason": "stop",
"logprobs": None,
"content_filter_results": {
"hate": {"filtered": False, "severity": "safe"},
"self_harm": {"filtered": False, "severity": "safe"},
"sexual": {"filtered": False, "severity": "safe"},
"violence": {"filtered": False, "severity": "safe"},
},
}
""" # noqa: E501
azure_endpoint: Optional[str] = Field(
default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
)
"""Your Azure endpoint, including the resource.
Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
Example: `https://example-resource.azure.openai.com/`
"""
deployment_name: Union[str, None] = Field(default=None, alias="azure_deployment")
"""A model deployment.
If given sets the base client URL to include `/deployments/{azure_deployment}`.
Note: this means you won't be able to use non-deployment endpoints.
"""
openai_api_version: Optional[str] = Field(
alias="api_version",
default_factory=from_env("OPENAI_API_VERSION", default=None),
)
"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
# Check OPENAI_API_KEY for backwards compatibility.
# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
# other forms of azure credentials.
openai_api_key: Optional[SecretStr] = Field(
alias="api_key",
default_factory=secret_from_env(
["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
),
)
"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
azure_ad_token: Optional[SecretStr] = Field(
default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
)
"""Your Azure Active Directory token.
Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
For more:
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
"""
azure_ad_token_provider: Union[Callable[[], str], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every sync request. For async requests,
will be invoked if `azure_ad_async_token_provider` is not provided.
"""
azure_ad_async_token_provider: Union[Callable[[], Awaitable[str]], None] = None
"""A function that returns an Azure Active Directory token.
Will be invoked on every async request.
"""
model_version: str = ""
"""The version of the model (e.g. "0125" for gpt-3.5-0125).
Azure OpenAI doesn't return model version with the response by default so it must
be manually specified if you want to use this information downstream, e.g. when
calculating costs.
When you specify the version, it will be appended to the model name in the
response. Setting correct version will help you to calculate the cost properly.
Model version is not validated, so make sure you set it correctly to get the
correct cost.
"""
openai_api_type: Optional[str] = Field(
default_factory=from_env("OPENAI_API_TYPE", default="azure")
)
"""Legacy, for openai<1.0.0 support."""
validate_base_url: bool = True
"""If legacy arg openai_api_base is passed in, try to infer if it is a base_url or
azure_endpoint and update client params accordingly.
"""
model_name: Optional[str] = Field(default=None, alias="model") # type: ignore[assignment]
"""Name of the deployed OpenAI model, e.g. "gpt-4o", "gpt-35-turbo", etc.
Distinct from the Azure deployment name, which is set by the Azure user.
Used for tracing and token counting. Does NOT affect completion.
"""
disabled_params: Optional[Dict[str, Any]] = Field(default=None)
"""Parameters of the OpenAI client or chat.completions endpoint that should be
disabled for the given model.
Should be specified as ``{"param": None | ['val1', 'val2']}`` where the key is the
parameter and the value is either None, meaning that parameter should never be
used, or it's a list of disabled values for the parameter.
For example, older models may not support the 'parallel_tool_calls' parameter at
all, in which case ``disabled_params={"parallel_tool_calls: None}`` can ben passed
in.
If a parameter is disabled then it will not be used by default in any methods, e.g.
in
:meth:`~langchain_openai.chat_models.azure.AzureChatOpenAI.with_structured_output`.
However this does not prevent a user from directly passed in the parameter during
invocation.
By default, unless ``model_name="gpt-4o"`` is specified, then
'parallel_tools_calls' will be disabled.
"""
@classmethod
def get_lc_namespace(cls) -> List[str]:
"""Get the namespace of the langchain object."""
return ["langchain", "chat_models", "azure_openai"]
@property
def lc_secrets(self) -> Dict[str, str]:
return {
"openai_api_key": "AZURE_OPENAI_API_KEY",
"azure_ad_token": "AZURE_OPENAI_AD_TOKEN",
}
@classmethod
def is_lc_serializable(cls) -> bool:
return True
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.n > 1 and self.streaming:
raise ValueError("n must be 1 when streaming.")
if self.disabled_params is None:
# As of 09-17-2024 'parallel_tool_calls' param is only supported for gpt-4o.
if self.model_name and self.model_name == "gpt-4o":
pass
else:
self.disabled_params = {"parallel_tool_calls": None}
# Check OPENAI_ORGANIZATION for backwards compatibility.
self.openai_organization = (
self.openai_organization
or os.getenv("OPENAI_ORG_ID")
or os.getenv("OPENAI_ORGANIZATION")
)
# For backwards compatibility. Before openai v1, no distinction was made
# between azure_endpoint and base_url (openai_api_base).
openai_api_base = self.openai_api_base
if openai_api_base and self.validate_base_url:
if "/openai" not in openai_api_base:
raise ValueError(
"As of openai>=1.0.0, Azure endpoints should be specified via "
"the `azure_endpoint` param not `openai_api_base` "
"(or alias `base_url`)."
)
if self.deployment_name:
raise ValueError(
"As of openai>=1.0.0, if `azure_deployment` (or alias "
"`deployment_name`) is specified then "
"`base_url` (or alias `openai_api_base`) should not be. "
"If specifying `azure_deployment`/`deployment_name` then use "
"`azure_endpoint` instead of `base_url`.\n\n"
"For example, you could specify:\n\n"
'azure_endpoint="https://xxx.openai.azure.com/", '
'azure_deployment="my-deployment"\n\n'
"Or you can equivalently specify:\n\n"
'base_url="https://xxx.openai.azure.com/openai/deployments/my-deployment"'
)
client_params: dict = {
"api_version": self.openai_api_version,
"azure_endpoint": self.azure_endpoint,
"azure_deployment": self.deployment_name,
"api_key": (
self.openai_api_key.get_secret_value() if self.openai_api_key else None
),
"azure_ad_token": (
self.azure_ad_token.get_secret_value() if self.azure_ad_token else None
),
"azure_ad_token_provider": self.azure_ad_token_provider,
"organization": self.openai_organization,
"base_url": self.openai_api_base,
"timeout": self.request_timeout,
"max_retries": self.max_retries,
"default_headers": self.default_headers,
"default_query": self.default_query,
}
if not self.client:
sync_specific = {"http_client": self.http_client}
self.root_client = openai.AzureOpenAI(**client_params, **sync_specific) # type: ignore[arg-type]
self.client = self.root_client.chat.completions
if not self.async_client:
async_specific = {"http_client": self.http_async_client}
if self.azure_ad_async_token_provider:
client_params["azure_ad_token_provider"] = (
self.azure_ad_async_token_provider
)
self.root_async_client = openai.AsyncAzureOpenAI(
**client_params,
**async_specific, # type: ignore[arg-type]
)
self.async_client = self.root_async_client.chat.completions
return self
@property
def _identifying_params(self) -> Dict[str, Any]:
"""Get the identifying parameters."""
return {
**{"azure_deployment": self.deployment_name},
**super()._identifying_params,
}
@property
def _llm_type(self) -> str:
return "azure-openai-chat"
@property
def lc_attributes(self) -> Dict[str, Any]:
return {
"openai_api_type": self.openai_api_type,
"openai_api_version": self.openai_api_version,
}
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get the parameters used to invoke the model."""
params = super()._get_ls_params(stop=stop, **kwargs)
params["ls_provider"] = "azure"
if self.model_name:
if self.model_version and self.model_version not in self.model_name:
params["ls_model_name"] = (
self.model_name + "-" + self.model_version.lstrip("-")
)
else:
params["ls_model_name"] = self.model_name
elif self.deployment_name:
params["ls_model_name"] = self.deployment_name
return params
def _create_chat_result(
self,
response: Union[dict, openai.BaseModel],
generation_info: Optional[Dict] = None,
) -> ChatResult:
chat_result = super()._create_chat_result(response, generation_info)
if not isinstance(response, dict):
response = response.model_dump()
for res in response["choices"]:
if res.get("finish_reason", None) == "content_filter":
raise ValueError(
"Azure has not provided the response due to a content filter "
"being triggered"
)
if "model" in response:
model = response["model"]
if self.model_version:
model = f"{model}-{self.model_version}"
chat_result.llm_output = chat_result.llm_output or {}
chat_result.llm_output["model_name"] = model
if "prompt_filter_results" in response:
chat_result.llm_output = chat_result.llm_output or {}
chat_result.llm_output["prompt_filter_results"] = response[
"prompt_filter_results"
]
for chat_gen, response_choice in zip(
chat_result.generations, response["choices"]
):
chat_gen.generation_info = chat_gen.generation_info or {}
chat_gen.generation_info["content_filter_results"] = response_choice.get(
"content_filter_results", {}
)
return chat_result
|
0 | lc_public_repos/langchain/libs/partners/openai/langchain_openai | lc_public_repos/langchain/libs/partners/openai/langchain_openai/chat_models/__init__.py | from langchain_openai.chat_models.azure import AzureChatOpenAI
from langchain_openai.chat_models.base import ChatOpenAI
__all__ = ["ChatOpenAI", "AzureChatOpenAI"]
|
0 | lc_public_repos/langchain/libs/partners/openai/tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/test_compile.py | import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/llms/test_base.py | """Test OpenAI llm."""
from typing import Generator
import pytest
from langchain_core.callbacks import CallbackManager
from langchain_core.outputs import LLMResult
from langchain_openai import OpenAI
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
def test_stream() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI()
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token, str)
async def test_astream() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI()
async for token in llm.astream("I'm Pickle Rick"):
assert isinstance(token, str)
async def test_abatch() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI()
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
async def test_abatch_tags() -> None:
"""Test batch tokens from OpenAI."""
llm = OpenAI()
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token, str)
def test_batch() -> None:
"""Test batch tokens from OpenAI."""
llm = OpenAI()
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
async def test_ainvoke() -> None:
"""Test invoke tokens from OpenAI."""
llm = OpenAI()
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result, str)
def test_invoke() -> None:
"""Test invoke tokens from OpenAI."""
llm = OpenAI()
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result, str)
@pytest.mark.scheduled
def test_openai_call() -> None:
"""Test valid call to openai."""
llm = OpenAI()
output = llm.invoke("Say something nice:")
assert isinstance(output, str)
def test_openai_llm_output_contains_model_name() -> None:
"""Test llm_output contains model_name."""
llm = OpenAI(max_tokens=10)
llm_result = llm.generate(["Hello, how are you?"])
assert llm_result.llm_output is not None
assert llm_result.llm_output["model_name"] == llm.model_name
def test_openai_stop_valid() -> None:
"""Test openai stop logic on valid configuration."""
query = "write an ordered list of five items"
first_llm = OpenAI(stop="3", temperature=0) # type: ignore[call-arg]
first_output = first_llm.invoke(query)
second_llm = OpenAI(temperature=0)
second_output = second_llm.invoke(query, stop=["3"])
# Because it stops on new lines, shouldn't return anything
assert first_output == second_output
@pytest.mark.scheduled
def test_openai_streaming() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
generator = llm.stream("I'm Pickle Rick")
assert isinstance(generator, Generator)
for token in generator:
assert isinstance(token, str)
@pytest.mark.scheduled
async def test_openai_astream() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
async for token in llm.astream("I'm Pickle Rick"):
assert isinstance(token, str)
@pytest.mark.scheduled
async def test_openai_abatch() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
async def test_openai_abatch_tags() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token, str)
@pytest.mark.scheduled
def test_openai_batch() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
@pytest.mark.scheduled
async def test_openai_ainvoke() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result, str)
@pytest.mark.scheduled
def test_openai_invoke() -> None:
"""Test streaming tokens from OpenAI."""
llm = OpenAI(max_tokens=10)
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result, str)
@pytest.mark.scheduled
def test_openai_multiple_prompts() -> None:
"""Test completion with multiple prompts."""
llm = OpenAI(max_tokens=10)
output = llm.generate(["I'm Pickle Rick", "I'm Pickle Rick"])
assert isinstance(output, LLMResult)
assert isinstance(output.generations, list)
assert len(output.generations) == 2
def test_openai_streaming_best_of_error() -> None:
"""Test validation for streaming fails if best_of is not 1."""
with pytest.raises(ValueError):
OpenAI(best_of=2, streaming=True)
def test_openai_streaming_n_error() -> None:
"""Test validation for streaming fails if n is not 1."""
with pytest.raises(ValueError):
OpenAI(n=2, streaming=True)
def test_openai_streaming_multiple_prompts_error() -> None:
"""Test validation for streaming fails if multiple prompts are given."""
with pytest.raises(ValueError):
OpenAI(streaming=True).generate(["I'm Pickle Rick", "I'm Pickle Rick"])
@pytest.mark.scheduled
def test_openai_streaming_call() -> None:
"""Test valid call to openai."""
llm = OpenAI(max_tokens=10, streaming=True)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_openai_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = OpenAI(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
llm.invoke("Write me a sentence with 100 words.")
# new client sometimes passes 2 tokens at once
assert callback_handler.llm_streams >= 5
@pytest.mark.scheduled
async def test_openai_async_generate() -> None:
"""Test async generation."""
llm = OpenAI(max_tokens=10)
output = await llm.agenerate(["Hello, how are you?"])
assert isinstance(output, LLMResult)
async def test_openai_async_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = OpenAI(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
result = await llm.agenerate(["Write me a sentence with 100 words."])
# new client sometimes passes 2 tokens at once
assert callback_handler.llm_streams >= 5
assert isinstance(result, LLMResult)
def test_openai_modelname_to_contextsize_valid() -> None:
"""Test model name to context size on a valid model."""
assert OpenAI().modelname_to_contextsize("davinci") == 2049
def test_openai_modelname_to_contextsize_invalid() -> None:
"""Test model name to context size on an invalid model."""
with pytest.raises(ValueError):
OpenAI().modelname_to_contextsize("foobar")
@pytest.fixture
def mock_completion() -> dict:
return {
"id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
"object": "text_completion",
"created": 1689989000,
"model": "gpt-3.5-turbo-instruct",
"choices": [
{"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
],
"usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
}
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/llms/test_azure.py | """Test AzureOpenAI wrapper."""
import os
from typing import Any, Generator
import pytest
from langchain_core.callbacks import CallbackManager
from langchain_core.outputs import LLMResult
from langchain_openai import AzureOpenAI
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
DEPLOYMENT_NAME = os.environ.get(
"AZURE_OPENAI_DEPLOYMENT_NAME",
os.environ.get("AZURE_OPENAI_LLM_DEPLOYMENT_NAME", ""),
)
def _get_llm(**kwargs: Any) -> AzureOpenAI:
return AzureOpenAI( # type: ignore[call-arg, call-arg, call-arg]
deployment_name=DEPLOYMENT_NAME,
openai_api_version=OPENAI_API_VERSION,
azure_endpoint=OPENAI_API_BASE,
openai_api_key=OPENAI_API_KEY,
**kwargs,
)
@pytest.fixture
def llm() -> AzureOpenAI:
return _get_llm(max_tokens=10)
@pytest.mark.scheduled
def test_openai_call(llm: AzureOpenAI) -> None:
"""Test valid call to openai."""
output = llm.invoke("Say something nice:")
assert isinstance(output, str)
@pytest.mark.scheduled
def test_openai_streaming(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
generator = llm.stream("I'm Pickle Rick")
assert isinstance(generator, Generator)
full_response = ""
for token in generator:
assert isinstance(token, str)
full_response += token
assert full_response
@pytest.mark.scheduled
async def test_openai_astream(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
async for token in llm.astream("I'm Pickle Rick"):
assert isinstance(token, str)
@pytest.mark.scheduled
async def test_openai_abatch(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
async def test_openai_abatch_tags(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token, str)
@pytest.mark.scheduled
def test_openai_batch(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token, str)
@pytest.mark.scheduled
async def test_openai_ainvoke(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result, str)
@pytest.mark.scheduled
def test_openai_invoke(llm: AzureOpenAI) -> None:
"""Test streaming tokens from AzureOpenAI."""
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result, str)
@pytest.mark.scheduled
def test_openai_multiple_prompts(llm: AzureOpenAI) -> None:
"""Test completion with multiple prompts."""
output = llm.generate(["I'm Pickle Rick", "I'm Pickle Rick"])
assert isinstance(output, LLMResult)
assert isinstance(output.generations, list)
assert len(output.generations) == 2
def test_openai_streaming_best_of_error() -> None:
"""Test validation for streaming fails if best_of is not 1."""
with pytest.raises(ValueError):
_get_llm(best_of=2, streaming=True)
def test_openai_streaming_n_error() -> None:
"""Test validation for streaming fails if n is not 1."""
with pytest.raises(ValueError):
_get_llm(n=2, streaming=True)
def test_openai_streaming_multiple_prompts_error() -> None:
"""Test validation for streaming fails if multiple prompts are given."""
with pytest.raises(ValueError):
_get_llm(streaming=True).generate(["I'm Pickle Rick", "I'm Pickle Rick"])
@pytest.mark.scheduled
def test_openai_streaming_call() -> None:
"""Test valid call to openai."""
llm = _get_llm(max_tokens=10, streaming=True)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_openai_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = _get_llm(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
llm.invoke("Write me a sentence with 100 words.")
assert callback_handler.llm_streams == 11
@pytest.mark.scheduled
async def test_openai_async_generate() -> None:
"""Test async generation."""
llm = _get_llm(max_tokens=10)
output = await llm.agenerate(["Hello, how are you?"])
assert isinstance(output, LLMResult)
async def test_openai_async_streaming_callback() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
llm = _get_llm(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
result = await llm.agenerate(["Write me a sentence with 100 words."])
assert callback_handler.llm_streams == 11
assert isinstance(result, LLMResult)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/embeddings/test_base_standard.py | """Standard LangChain interface tests"""
from typing import Type
from langchain_core.embeddings import Embeddings
from langchain_tests.integration_tests.embeddings import EmbeddingsIntegrationTests
from langchain_openai import OpenAIEmbeddings
class TestOpenAIStandard(EmbeddingsIntegrationTests):
@property
def embeddings_class(self) -> Type[Embeddings]:
return OpenAIEmbeddings
@property
def embedding_model_params(self) -> dict:
return {"model": "text-embedding-3-small", "dimensions": 128}
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/embeddings/test_base.py | """Test OpenAI embeddings."""
import numpy as np
import openai
from langchain_openai.embeddings.base import OpenAIEmbeddings
def test_langchain_openai_embedding_documents() -> None:
"""Test openai embeddings."""
documents = ["foo bar"]
embedding = OpenAIEmbeddings()
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) > 0
def test_langchain_openai_embedding_query() -> None:
"""Test openai embeddings."""
document = "foo bar"
embedding = OpenAIEmbeddings()
output = embedding.embed_query(document)
assert len(output) > 0
def test_langchain_openai_embeddings_dimensions() -> None:
"""Test openai embeddings."""
documents = ["foo bar"]
embedding = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=128)
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 128
def test_langchain_openai_embeddings_equivalent_to_raw() -> None:
documents = ["disallowed special token '<|endoftext|>'"]
embedding = OpenAIEmbeddings()
lc_output = embedding.embed_documents(documents)[0]
direct_output = (
openai.OpenAI()
.embeddings.create(input=documents, model=embedding.model)
.data[0]
.embedding
)
assert np.allclose(lc_output, direct_output, atol=0.001)
async def test_langchain_openai_embeddings_equivalent_to_raw_async() -> None:
documents = ["disallowed special token '<|endoftext|>'"]
embedding = OpenAIEmbeddings()
lc_output = (await embedding.aembed_documents(documents))[0]
client = openai.AsyncOpenAI()
direct_output = (
(await client.embeddings.create(input=documents, model=embedding.model))
.data[0]
.embedding
)
assert np.allclose(lc_output, direct_output, atol=0.001)
def test_langchain_openai_embeddings_dimensions_large_num() -> None:
"""Test openai embeddings."""
documents = [f"foo bar {i}" for i in range(2000)]
embedding = OpenAIEmbeddings(model="text-embedding-3-small", dimensions=128)
output = embedding.embed_documents(documents)
assert len(output) == 2000
assert len(output[0]) == 128
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/embeddings/test_azure.py | """Test azure openai embeddings."""
import os
from typing import Any
import numpy as np
import openai
import pytest
from langchain_openai import AzureOpenAIEmbeddings
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
DEPLOYMENT_NAME = os.environ.get(
"AZURE_OPENAI_DEPLOYMENT_NAME",
os.environ.get("AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME", ""),
)
print
def _get_embeddings(**kwargs: Any) -> AzureOpenAIEmbeddings:
return AzureOpenAIEmbeddings( # type: ignore[call-arg]
azure_deployment=DEPLOYMENT_NAME,
api_version=OPENAI_API_VERSION,
azure_endpoint=OPENAI_API_BASE,
openai_api_key=OPENAI_API_KEY,
**kwargs,
)
@pytest.mark.scheduled
def test_azure_openai_embedding_documents() -> None:
"""Test openai embeddings."""
documents = ["foo bar"]
embedding = _get_embeddings()
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 1536
@pytest.mark.scheduled
def test_azure_openai_embedding_documents_multiple() -> None:
"""Test openai embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = _get_embeddings(chunk_size=2)
embedding.embedding_ctx_length = 8191
output = embedding.embed_documents(documents)
assert embedding.chunk_size == 2
assert len(output) == 3
assert len(output[0]) == 1536
assert len(output[1]) == 1536
assert len(output[2]) == 1536
@pytest.mark.scheduled
def test_azure_openai_embedding_documents_chunk_size() -> None:
"""Test openai embeddings."""
documents = ["foo bar"] * 20
embedding = _get_embeddings()
embedding.embedding_ctx_length = 8191
output = embedding.embed_documents(documents)
# Max 2048 chunks per batch on Azure OpenAI embeddings
assert embedding.chunk_size == 2048
assert len(output) == 20
assert all([len(out) == 1536 for out in output])
@pytest.mark.scheduled
async def test_azure_openai_embedding_documents_async_multiple() -> None:
"""Test openai embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = _get_embeddings(chunk_size=2)
embedding.embedding_ctx_length = 8191
output = await embedding.aembed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1536
assert len(output[1]) == 1536
assert len(output[2]) == 1536
@pytest.mark.scheduled
def test_azure_openai_embedding_query() -> None:
"""Test openai embeddings."""
document = "foo bar"
embedding = _get_embeddings()
output = embedding.embed_query(document)
assert len(output) == 1536
@pytest.mark.scheduled
async def test_azure_openai_embedding_async_query() -> None:
"""Test openai embeddings."""
document = "foo bar"
embedding = _get_embeddings()
output = await embedding.aembed_query(document)
assert len(output) == 1536
@pytest.mark.scheduled
def test_azure_openai_embedding_with_empty_string() -> None:
"""Test openai embeddings with empty string."""
document = ["", "abc"]
embedding = _get_embeddings()
output = embedding.embed_documents(document)
assert len(output) == 2
assert len(output[0]) == 1536
expected_output = (
openai.AzureOpenAI(
api_version=OPENAI_API_VERSION,
api_key=OPENAI_API_KEY,
azure_endpoint=OPENAI_API_BASE,
azure_deployment=DEPLOYMENT_NAME,
) # type: ignore
.embeddings.create(input="", model="text-embedding-ada-002")
.data[0]
.embedding
)
assert np.allclose(output[0], expected_output, atol=0.001)
assert len(output[1]) == 1536
@pytest.mark.scheduled
def test_embed_documents_normalized() -> None:
output = _get_embeddings().embed_documents(["foo walked to the market"])
assert np.isclose(np.linalg.norm(output[0]), 1.0)
@pytest.mark.scheduled
def test_embed_query_normalized() -> None:
output = _get_embeddings().embed_query("foo walked to the market")
assert np.isclose(np.linalg.norm(output), 1.0)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/chat_models/test_base_standard.py | """Standard LangChain interface tests"""
from pathlib import Path
from typing import Dict, List, Literal, Type, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import ChatOpenAI
REPO_ROOT_DIR = Path(__file__).parents[6]
class TestOpenAIStandard(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatOpenAI
@property
def chat_model_params(self) -> dict:
return {"model": "gpt-4o-mini", "stream_usage": True}
@property
def supports_image_inputs(self) -> bool:
return True
@property
def supported_usage_metadata_details(
self,
) -> Dict[
Literal["invoke", "stream"],
List[
Literal[
"audio_input",
"audio_output",
"reasoning_output",
"cache_read_input",
"cache_creation_input",
]
],
]:
return {"invoke": ["reasoning_output", "cache_read_input"], "stream": []}
def invoke_with_cache_read_input(self, *, stream: bool = False) -> AIMessage:
with open(REPO_ROOT_DIR / "README.md", "r") as f:
readme = f.read()
input_ = f"""What's langchain? Here's the langchain README:
{readme}
"""
llm = ChatOpenAI(model="gpt-4o-mini", stream_usage=True)
_invoke(llm, input_, stream)
# invoke twice so first invocation is cached
return _invoke(llm, input_, stream)
def invoke_with_reasoning_output(self, *, stream: bool = False) -> AIMessage:
llm = ChatOpenAI(model="o1-mini", stream_usage=True, temperature=1)
input_ = (
"explain the relationship between the 2008/9 economic crisis and the "
"startup ecosystem in the early 2010s"
)
return _invoke(llm, input_, stream)
def _invoke(llm: ChatOpenAI, input_: str, stream: bool) -> AIMessage:
if stream:
full = None
for chunk in llm.stream(input_):
full = full + chunk if full else chunk # type: ignore[operator]
return cast(AIMessage, full)
else:
return cast(AIMessage, llm.invoke(input_))
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/chat_models/test_base.py | """Test ChatOpenAI chat model."""
import base64
import json
from pathlib import Path
from textwrap import dedent
from typing import Any, AsyncIterator, List, Literal, Optional, cast
import httpx
import openai
import pytest
from langchain_core.callbacks import CallbackManager
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
HumanMessage,
SystemMessage,
ToolCall,
ToolMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
from langchain_core.prompts import ChatPromptTemplate
from langchain_tests.integration_tests.chat_models import _validate_tool_call_message
from langchain_tests.integration_tests.chat_models import (
magic_function as invalid_magic_function,
)
from pydantic import BaseModel, Field
from langchain_openai import ChatOpenAI
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
@pytest.mark.scheduled
def test_chat_openai() -> None:
"""Test ChatOpenAI wrapper."""
chat = ChatOpenAI(
temperature=0.7,
base_url=None,
organization=None,
openai_proxy=None,
timeout=10.0,
max_retries=3,
http_client=None,
n=1,
max_completion_tokens=10,
default_headers=None,
default_query=None,
)
message = HumanMessage(content="Hello")
response = chat.invoke([message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
def test_chat_openai_model() -> None:
"""Test ChatOpenAI wrapper handles model_name."""
chat = ChatOpenAI(model="foo")
assert chat.model_name == "foo"
chat = ChatOpenAI(model_name="bar") # type: ignore[call-arg]
assert chat.model_name == "bar"
def test_chat_openai_system_message() -> None:
"""Test ChatOpenAI wrapper with system message."""
chat = ChatOpenAI(max_completion_tokens=10)
system_message = SystemMessage(content="You are to chat with the user.")
human_message = HumanMessage(content="Hello")
response = chat.invoke([system_message, human_message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
@pytest.mark.scheduled
def test_chat_openai_generate() -> None:
"""Test ChatOpenAI wrapper with generate."""
chat = ChatOpenAI(max_completion_tokens=10, n=2)
message = HumanMessage(content="Hello")
response = chat.generate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
assert response.llm_output
for generations in response.generations:
assert len(generations) == 2
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
def test_chat_openai_multiple_completions() -> None:
"""Test ChatOpenAI wrapper with multiple completions."""
chat = ChatOpenAI(max_completion_tokens=10, n=5)
message = HumanMessage(content="Hello")
response = chat._generate([message])
assert isinstance(response, ChatResult)
assert len(response.generations) == 5
for generation in response.generations:
assert isinstance(generation.message, BaseMessage)
assert isinstance(generation.message.content, str)
@pytest.mark.scheduled
def test_chat_openai_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatOpenAI(
max_completion_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Hello")
response = chat.invoke([message])
assert callback_handler.llm_streams > 0
assert isinstance(response, BaseMessage)
@pytest.mark.scheduled
def test_chat_openai_streaming_generation_info() -> None:
"""Test that generation info is preserved when streaming."""
class _FakeCallback(FakeCallbackHandler):
saved_things: dict = {}
def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
# Save the generation
self.saved_things["generation"] = args[0]
callback = _FakeCallback()
callback_manager = CallbackManager([callback])
chat = ChatOpenAI(
max_completion_tokens=2, temperature=0, callback_manager=callback_manager
)
list(chat.stream("hi"))
generation = callback.saved_things["generation"]
# `Hello!` is two tokens, assert that that is what is returned
assert generation.generations[0][0].text == "Hello!"
def test_chat_openai_llm_output_contains_model_name() -> None:
"""Test llm_output contains model_name."""
chat = ChatOpenAI(max_completion_tokens=10)
message = HumanMessage(content="Hello")
llm_result = chat.generate([[message]])
assert llm_result.llm_output is not None
assert llm_result.llm_output["model_name"] == chat.model_name
def test_chat_openai_streaming_llm_output_contains_model_name() -> None:
"""Test llm_output contains model_name."""
chat = ChatOpenAI(max_completion_tokens=10, streaming=True)
message = HumanMessage(content="Hello")
llm_result = chat.generate([[message]])
assert llm_result.llm_output is not None
assert llm_result.llm_output["model_name"] == chat.model_name
def test_chat_openai_invalid_streaming_params() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
with pytest.raises(ValueError):
ChatOpenAI(max_completion_tokens=10, streaming=True, temperature=0, n=5)
@pytest.mark.scheduled
async def test_async_chat_openai() -> None:
"""Test async generation."""
chat = ChatOpenAI(max_completion_tokens=10, n=2)
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
assert response.llm_output
for generations in response.generations:
assert len(generations) == 2
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
async def test_async_chat_openai_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = ChatOpenAI(
max_completion_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert callback_handler.llm_streams > 0
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 1
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
async def test_async_chat_openai_bind_functions() -> None:
"""Test ChatOpenAI wrapper with multiple completions."""
class Person(BaseModel):
"""Identifying information about a person."""
name: str = Field(..., title="Name", description="The person's name")
age: int = Field(..., title="Age", description="The person's age")
fav_food: Optional[str] = Field(
default=None, title="Fav Food", description="The person's favorite food"
)
chat = ChatOpenAI(max_completion_tokens=30, n=1, streaming=True).bind_functions(
functions=[Person], function_call="Person"
)
prompt = ChatPromptTemplate.from_messages(
[("system", "Use the provided Person function"), ("user", "{input}")]
)
chain = prompt | chat
message = HumanMessage(content="Sally is 13 years old")
response = await chain.abatch([{"input": message}])
assert isinstance(response, list)
assert len(response) == 1
for generation in response:
assert isinstance(generation, AIMessage)
@pytest.mark.scheduled
def test_openai_streaming() -> None:
"""Test streaming tokens from OpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_astream() -> None:
"""Test streaming tokens from OpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
async for token in llm.astream("I'm Pickle Rick"):
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_abatch() -> None:
"""Test streaming tokens from ChatOpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_abatch_tags() -> None:
"""Test batch tokens from ChatOpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
def test_openai_batch() -> None:
"""Test batch tokens from ChatOpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_ainvoke() -> None:
"""Test invoke tokens from ChatOpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result.content, str)
@pytest.mark.scheduled
def test_openai_invoke() -> None:
"""Test invoke tokens from ChatOpenAI."""
llm = ChatOpenAI(max_completion_tokens=10)
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result.content, str)
# assert no response headers if include_response_headers is not set
assert "headers" not in result.response_metadata
def test_stream() -> None:
"""Test streaming tokens from OpenAI."""
llm = ChatOpenAI()
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream("I'm Pickle Rick"):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert full.response_metadata.get("finish_reason") is not None
assert full.response_metadata.get("model_name") is not None
# check token usage
aggregate: Optional[BaseMessageChunk] = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
for chunk in llm.stream("Hello", stream_usage=True):
assert isinstance(chunk.content, str)
aggregate = chunk if aggregate is None else aggregate + chunk
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
chunks_with_token_counts += 1
if chunk.response_metadata:
chunks_with_response_metadata += 1
if chunks_with_token_counts != 1 or chunks_with_response_metadata != 1:
raise AssertionError(
"Expected exactly one chunk with metadata. "
"AIMessageChunk aggregation can add these metadata. Check that "
"this is behaving properly."
)
assert isinstance(aggregate, AIMessageChunk)
assert aggregate.usage_metadata is not None
assert aggregate.usage_metadata["input_tokens"] > 0
assert aggregate.usage_metadata["output_tokens"] > 0
assert aggregate.usage_metadata["total_tokens"] > 0
async def test_astream() -> None:
"""Test streaming tokens from OpenAI."""
async def _test_stream(stream: AsyncIterator, expect_usage: bool) -> None:
full: Optional[BaseMessageChunk] = None
chunks_with_token_counts = 0
chunks_with_response_metadata = 0
async for chunk in stream:
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
chunks_with_token_counts += 1
if chunk.response_metadata:
chunks_with_response_metadata += 1
assert isinstance(full, AIMessageChunk)
if chunks_with_response_metadata != 1:
raise AssertionError(
"Expected exactly one chunk with metadata. "
"AIMessageChunk aggregation can add these metadata. Check that "
"this is behaving properly."
)
assert full.response_metadata.get("finish_reason") is not None
assert full.response_metadata.get("model_name") is not None
if expect_usage:
if chunks_with_token_counts != 1:
raise AssertionError(
"Expected exactly one chunk with token counts. "
"AIMessageChunk aggregation adds counts. Check that "
"this is behaving properly."
)
assert full.usage_metadata is not None
assert full.usage_metadata["input_tokens"] > 0
assert full.usage_metadata["output_tokens"] > 0
assert full.usage_metadata["total_tokens"] > 0
else:
assert chunks_with_token_counts == 0
assert full.usage_metadata is None
llm = ChatOpenAI(temperature=0, max_completion_tokens=5)
await _test_stream(llm.astream("Hello"), expect_usage=False)
await _test_stream(
llm.astream("Hello", stream_options={"include_usage": True}), expect_usage=True
)
await _test_stream(llm.astream("Hello", stream_usage=True), expect_usage=True)
llm = ChatOpenAI(
temperature=0,
max_completion_tokens=5,
model_kwargs={"stream_options": {"include_usage": True}},
)
await _test_stream(llm.astream("Hello"), expect_usage=True)
await _test_stream(
llm.astream("Hello", stream_options={"include_usage": False}),
expect_usage=False,
)
llm = ChatOpenAI(temperature=0, max_completion_tokens=5, stream_usage=True)
await _test_stream(llm.astream("Hello"), expect_usage=True)
await _test_stream(llm.astream("Hello", stream_usage=False), expect_usage=False)
async def test_abatch() -> None:
"""Test streaming tokens from ChatOpenAI."""
llm = ChatOpenAI()
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
async def test_abatch_tags() -> None:
"""Test batch tokens from ChatOpenAI."""
llm = ChatOpenAI()
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token.content, str)
def test_batch() -> None:
"""Test batch tokens from ChatOpenAI."""
llm = ChatOpenAI()
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
async def test_ainvoke() -> None:
"""Test invoke tokens from ChatOpenAI."""
llm = ChatOpenAI()
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result.content, str)
assert result.response_metadata.get("model_name") is not None
def test_invoke() -> None:
"""Test invoke tokens from ChatOpenAI."""
llm = ChatOpenAI()
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result.content, str)
assert result.response_metadata.get("model_name") is not None
def test_response_metadata() -> None:
llm = ChatOpenAI()
result = llm.invoke([HumanMessage(content="I'm PickleRick")], logprobs=True)
assert result.response_metadata
assert all(
k in result.response_metadata
for k in (
"token_usage",
"model_name",
"logprobs",
"system_fingerprint",
"finish_reason",
)
)
assert "content" in result.response_metadata["logprobs"]
async def test_async_response_metadata() -> None:
llm = ChatOpenAI()
result = await llm.ainvoke([HumanMessage(content="I'm PickleRick")], logprobs=True)
assert result.response_metadata
assert all(
k in result.response_metadata
for k in (
"token_usage",
"model_name",
"logprobs",
"system_fingerprint",
"finish_reason",
)
)
assert "content" in result.response_metadata["logprobs"]
def test_response_metadata_streaming() -> None:
llm = ChatOpenAI()
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream("I'm Pickle Rick", logprobs=True):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
assert all(
k in cast(BaseMessageChunk, full).response_metadata
for k in ("logprobs", "finish_reason")
)
assert "content" in cast(BaseMessageChunk, full).response_metadata["logprobs"]
async def test_async_response_metadata_streaming() -> None:
llm = ChatOpenAI()
full: Optional[BaseMessageChunk] = None
async for chunk in llm.astream("I'm Pickle Rick", logprobs=True):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
assert all(
k in cast(BaseMessageChunk, full).response_metadata
for k in ("logprobs", "finish_reason")
)
assert "content" in cast(BaseMessageChunk, full).response_metadata["logprobs"]
class GenerateUsername(BaseModel):
"Get a username based on someone's name and hair color."
name: str
hair_color: str
class MakeASandwich(BaseModel):
"Make a sandwich given a list of ingredients."
bread_type: str
cheese_type: str
condiments: List[str]
vegetables: List[str]
def test_tool_use() -> None:
llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
llm_with_tool = llm.bind_tools(tools=[GenerateUsername], tool_choice=True)
msgs: List = [HumanMessage("Sally has green hair, what would her username be?")]
ai_msg = llm_with_tool.invoke(msgs)
assert isinstance(ai_msg, AIMessage)
assert isinstance(ai_msg.tool_calls, list)
assert len(ai_msg.tool_calls) == 1
tool_call = ai_msg.tool_calls[0]
assert "args" in tool_call
tool_msg = ToolMessage(
"sally_green_hair", tool_call_id=ai_msg.additional_kwargs["tool_calls"][0]["id"]
)
msgs.extend([ai_msg, tool_msg])
llm_with_tool.invoke(msgs)
# Test streaming
ai_messages = llm_with_tool.stream(msgs)
first = True
for message in ai_messages:
if first:
gathered = message
first = False
else:
gathered = gathered + message # type: ignore
assert isinstance(gathered, AIMessageChunk)
assert isinstance(gathered.tool_call_chunks, list)
assert len(gathered.tool_call_chunks) == 1
tool_call_chunk = gathered.tool_call_chunks[0]
assert "args" in tool_call_chunk
streaming_tool_msg = ToolMessage(
"sally_green_hair",
tool_call_id=gathered.additional_kwargs["tool_calls"][0]["id"],
)
msgs.extend([gathered, streaming_tool_msg])
llm_with_tool.invoke(msgs)
def test_manual_tool_call_msg() -> None:
"""Test passing in manually construct tool call message."""
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm_with_tool = llm.bind_tools(tools=[GenerateUsername])
msgs: List = [
HumanMessage("Sally has green hair, what would her username be?"),
AIMessage(
content="",
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="foo",
)
],
),
ToolMessage("sally_green_hair", tool_call_id="foo"),
]
output: AIMessage = cast(AIMessage, llm_with_tool.invoke(msgs))
assert output.content
# Should not have called the tool again.
assert not output.tool_calls and not output.invalid_tool_calls
# OpenAI should error when tool call id doesn't match across AIMessage and
# ToolMessage
msgs = [
HumanMessage("Sally has green hair, what would her username be?"),
AIMessage(
content="",
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="bar",
)
],
),
ToolMessage("sally_green_hair", tool_call_id="foo"),
]
with pytest.raises(Exception):
llm_with_tool.invoke(msgs)
def test_bind_tools_tool_choice() -> None:
"""Test passing in manually construct tool call message."""
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
for tool_choice in ("any", "required"):
llm_with_tools = llm.bind_tools(
tools=[GenerateUsername, MakeASandwich], tool_choice=tool_choice
)
msg = cast(AIMessage, llm_with_tools.invoke("how are you"))
assert msg.tool_calls
llm_with_tools = llm.bind_tools(tools=[GenerateUsername, MakeASandwich])
msg = cast(AIMessage, llm_with_tools.invoke("how are you"))
assert not msg.tool_calls
def test_openai_structured_output() -> None:
class MyModel(BaseModel):
"""A Person"""
name: str
age: int
llm = ChatOpenAI().with_structured_output(MyModel)
result = llm.invoke("I'm a 27 year old named Erick")
assert isinstance(result, MyModel)
assert result.name == "Erick"
assert result.age == 27
def test_openai_proxy() -> None:
"""Test ChatOpenAI with proxy."""
chat_openai = ChatOpenAI(openai_proxy="http://localhost:8080")
mounts = chat_openai.client._client._client._mounts
assert len(mounts) == 1
for key, value in mounts.items():
proxy = value._pool._proxy_url.origin
assert proxy.scheme == b"http"
assert proxy.host == b"localhost"
assert proxy.port == 8080
async_client_mounts = chat_openai.async_client._client._client._mounts
assert len(async_client_mounts) == 1
for key, value in async_client_mounts.items():
proxy = value._pool._proxy_url.origin
assert proxy.scheme == b"http"
assert proxy.host == b"localhost"
assert proxy.port == 8080
def test_openai_response_headers() -> None:
"""Test ChatOpenAI response headers."""
chat_openai = ChatOpenAI(include_response_headers=True)
query = "I'm Pickle Rick"
result = chat_openai.invoke(query, max_completion_tokens=10)
headers = result.response_metadata["headers"]
assert headers
assert isinstance(headers, dict)
assert "content-type" in headers
# Stream
full: Optional[BaseMessageChunk] = None
for chunk in chat_openai.stream(query, max_completion_tokens=10):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessage)
headers = full.response_metadata["headers"]
assert headers
assert isinstance(headers, dict)
assert "content-type" in headers
async def test_openai_response_headers_async() -> None:
"""Test ChatOpenAI response headers."""
chat_openai = ChatOpenAI(include_response_headers=True)
query = "I'm Pickle Rick"
result = await chat_openai.ainvoke(query, max_completion_tokens=10)
headers = result.response_metadata["headers"]
assert headers
assert isinstance(headers, dict)
assert "content-type" in headers
# Stream
full: Optional[BaseMessageChunk] = None
async for chunk in chat_openai.astream(query, max_completion_tokens=10):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessage)
headers = full.response_metadata["headers"]
assert headers
assert isinstance(headers, dict)
assert "content-type" in headers
def test_image_token_counting_jpeg() -> None:
model = ChatOpenAI(model="gpt-4o", temperature=0)
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{"type": "image_url", "image_url": {"url": image_url}},
]
)
expected = cast(AIMessage, model.invoke([message])).usage_metadata[ # type: ignore[index]
"input_tokens"
]
actual = model.get_num_tokens_from_messages([message])
assert expected == actual
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
]
)
expected = cast(AIMessage, model.invoke([message])).usage_metadata[ # type: ignore[index]
"input_tokens"
]
actual = model.get_num_tokens_from_messages([message])
assert expected == actual
def test_image_token_counting_png() -> None:
model = ChatOpenAI(model="gpt-4o", temperature=0)
image_url = "https://upload.wikimedia.org/wikipedia/commons/4/47/PNG_transparency_demonstration_1.png"
message = HumanMessage(
content=[
{"type": "text", "text": "how many dice are in this image"},
{"type": "image_url", "image_url": {"url": image_url}},
]
)
expected = cast(AIMessage, model.invoke([message])).usage_metadata[ # type: ignore[index]
"input_tokens"
]
actual = model.get_num_tokens_from_messages([message])
assert expected == actual
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
message = HumanMessage(
content=[
{"type": "text", "text": "how many dice are in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{image_data}"},
},
]
)
expected = cast(AIMessage, model.invoke([message])).usage_metadata[ # type: ignore[index]
"input_tokens"
]
actual = model.get_num_tokens_from_messages([message])
assert expected == actual
def test_tool_calling_strict() -> None:
"""Test tool calling with strict=True."""
class magic_function(BaseModel):
"""Applies a magic function to an input."""
input: int
model = ChatOpenAI(model="gpt-4o", temperature=0)
model_with_tools = model.bind_tools([magic_function], strict=True)
# invalid_magic_function adds metadata to schema that isn't supported by OpenAI.
model_with_invalid_tool_schema = model.bind_tools(
[invalid_magic_function], strict=True
)
# Test invoke
query = "What is the value of magic_function(3)? Use the tool."
response = model_with_tools.invoke(query)
_validate_tool_call_message(response)
# Test invalid tool schema
with pytest.raises(openai.BadRequestError):
model_with_invalid_tool_schema.invoke(query)
# Test stream
full: Optional[BaseMessageChunk] = None
for chunk in model_with_tools.stream(query):
full = chunk if full is None else full + chunk # type: ignore
assert isinstance(full, AIMessage)
_validate_tool_call_message(full)
# Test invalid tool schema
with pytest.raises(openai.BadRequestError):
next(model_with_invalid_tool_schema.stream(query))
@pytest.mark.parametrize(
("model", "method", "strict"),
[("gpt-4o", "function_calling", True), ("gpt-4o-2024-08-06", "json_schema", None)],
)
def test_structured_output_strict(
model: str,
method: Literal["function_calling", "json_schema"],
strict: Optional[bool],
) -> None:
"""Test to verify structured output with strict=True."""
from pydantic import BaseModel as BaseModelProper
from pydantic import Field as FieldProper
llm = ChatOpenAI(model=model, temperature=0)
class Joke(BaseModelProper):
"""Joke to tell user."""
setup: str = FieldProper(description="question to set up a joke")
punchline: str = FieldProper(description="answer to resolve the joke")
# Pydantic class
# Type ignoring since the interface only officially supports pydantic 1
# or pydantic.v1.BaseModel but not pydantic.BaseModel from pydantic 2.
# We'll need to do a pass updating the type signatures.
chat = llm.with_structured_output(Joke, method=method, strict=strict)
result = chat.invoke("Tell me a joke about cats.")
assert isinstance(result, Joke)
for chunk in chat.stream("Tell me a joke about cats."):
assert isinstance(chunk, Joke)
# Schema
chat = llm.with_structured_output(
Joke.model_json_schema(), method=method, strict=strict
)
result = chat.invoke("Tell me a joke about cats.")
assert isinstance(result, dict)
assert set(result.keys()) == {"setup", "punchline"}
for chunk in chat.stream("Tell me a joke about cats."):
assert isinstance(chunk, dict)
assert isinstance(chunk, dict) # for mypy
assert set(chunk.keys()) == {"setup", "punchline"}
# Invalid schema with optional fields:
class InvalidJoke(BaseModelProper):
"""Joke to tell user."""
setup: str = FieldProper(description="question to set up a joke")
# Invalid field, can't have default value.
punchline: str = FieldProper(
default="foo", description="answer to resolve the joke"
)
chat = llm.with_structured_output(InvalidJoke, method=method, strict=strict)
with pytest.raises(openai.BadRequestError):
chat.invoke("Tell me a joke about cats.")
with pytest.raises(openai.BadRequestError):
next(chat.stream("Tell me a joke about cats."))
chat = llm.with_structured_output(
InvalidJoke.model_json_schema(), method=method, strict=strict
)
with pytest.raises(openai.BadRequestError):
chat.invoke("Tell me a joke about cats.")
with pytest.raises(openai.BadRequestError):
next(chat.stream("Tell me a joke about cats."))
@pytest.mark.parametrize(
("model", "method", "strict"), [("gpt-4o-2024-08-06", "json_schema", None)]
)
def test_nested_structured_output_strict(
model: str, method: Literal["json_schema"], strict: Optional[bool]
) -> None:
"""Test to verify structured output with strict=True for nested object."""
from typing import TypedDict
llm = ChatOpenAI(model=model, temperature=0)
class SelfEvaluation(TypedDict):
score: int
text: str
class JokeWithEvaluation(TypedDict):
"""Joke to tell user."""
setup: str
punchline: str
self_evaluation: SelfEvaluation
# Schema
chat = llm.with_structured_output(JokeWithEvaluation, method=method, strict=strict)
result = chat.invoke("Tell me a joke about cats.")
assert isinstance(result, dict)
assert set(result.keys()) == {"setup", "punchline", "self_evaluation"}
assert set(result["self_evaluation"].keys()) == {"score", "text"}
for chunk in chat.stream("Tell me a joke about cats."):
assert isinstance(chunk, dict)
assert isinstance(chunk, dict) # for mypy
assert set(chunk.keys()) == {"setup", "punchline", "self_evaluation"}
assert set(chunk["self_evaluation"].keys()) == {"score", "text"}
def test_json_mode() -> None:
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
response = llm.invoke(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
)
assert isinstance(response.content, str)
assert json.loads(response.content) == {"a": 1}
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, str)
assert json.loads(full.content) == {"a": 1}
async def test_json_mode_async() -> None:
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
response = await llm.ainvoke(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
)
assert isinstance(response.content, str)
assert json.loads(response.content) == {"a": 1}
# Test streaming
full: Optional[BaseMessageChunk] = None
async for chunk in llm.astream(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, str)
assert json.loads(full.content) == {"a": 1}
def test_audio_output_modality() -> None:
llm = ChatOpenAI(
model="gpt-4o-audio-preview",
temperature=0,
model_kwargs={
"modalities": ["text", "audio"],
"audio": {"voice": "alloy", "format": "wav"},
},
)
history: List[BaseMessage] = [
HumanMessage("Make me a short audio clip of you yelling")
]
output = llm.invoke(history)
assert isinstance(output, AIMessage)
assert "audio" in output.additional_kwargs
history.append(output)
history.append(HumanMessage("Make me a short audio clip of you whispering"))
output = llm.invoke(history)
assert isinstance(output, AIMessage)
assert "audio" in output.additional_kwargs
def test_audio_input_modality() -> None:
llm = ChatOpenAI(
model="gpt-4o-audio-preview",
temperature=0,
model_kwargs={
"modalities": ["text", "audio"],
"audio": {"voice": "alloy", "format": "wav"},
},
)
filepath = Path(__file__).parent / "audio_input.wav"
audio_data = filepath.read_bytes()
b64_audio_data = base64.b64encode(audio_data).decode("utf-8")
history: list[BaseMessage] = [
HumanMessage(
[
{"type": "text", "text": "What is happening in this audio clip"},
{
"type": "input_audio",
"input_audio": {"data": b64_audio_data, "format": "wav"},
},
]
)
]
output = llm.invoke(history)
assert isinstance(output, AIMessage)
assert "audio" in output.additional_kwargs
history.append(output)
history.append(HumanMessage("Why?"))
output = llm.invoke(history)
assert isinstance(output, AIMessage)
assert "audio" in output.additional_kwargs
def test_prediction_tokens() -> None:
code = dedent(
"""
/// <summary>
/// Represents a user with a first name, last name, and username.
/// </summary>
public class User
{
/// <summary>
/// Gets or sets the user's first name.
/// </summary>
public string FirstName { get; set; }
/// <summary>
/// Gets or sets the user's last name.
/// </summary>
public string LastName { get; set; }
/// <summary>
/// Gets or sets the user's username.
/// </summary>
public string Username { get; set; }
}
"""
)
llm = ChatOpenAI(model="gpt-4o")
query = (
"Replace the Username property with an Email property. "
"Respond only with code, and with no markdown formatting."
)
response = llm.invoke(
[{"role": "user", "content": query}, {"role": "user", "content": code}],
prediction={"type": "content", "content": code},
)
assert isinstance(response, AIMessage)
assert response.response_metadata is not None
output_token_details = response.response_metadata["token_usage"][
"completion_tokens_details"
]
assert output_token_details["accepted_prediction_tokens"] > 0
assert output_token_details["rejected_prediction_tokens"] > 0
def test_stream_o1() -> None:
list(ChatOpenAI(model="o1-mini").stream("how are you"))
async def test_astream_o1() -> None:
async for _ in ChatOpenAI(model="o1-mini").astream("how are you"):
pass
class Foo(BaseModel):
response: str
def test_stream_response_format() -> None:
list(ChatOpenAI(model="gpt-4o-mini").stream("how are ya", response_format=Foo))
async def test_astream_response_format() -> None:
async for _ in ChatOpenAI(model="gpt-4o-mini").astream(
"how are ya", response_format=Foo
):
pass
def test_o1_max_tokens() -> None:
response = ChatOpenAI(model="o1-mini", max_tokens=10).invoke("how are you") # type: ignore[call-arg]
assert isinstance(response, AIMessage)
response = ChatOpenAI(model="gpt-4o", max_completion_tokens=10).invoke(
"how are you"
)
assert isinstance(response, AIMessage)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/chat_models/test_azure_standard.py | """Standard LangChain interface tests"""
import os
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
class TestAzureOpenAIStandard(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return AzureChatOpenAI
@property
def chat_model_params(self) -> dict:
return {
"deployment_name": os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
"model": "gpt-4o",
"openai_api_version": OPENAI_API_VERSION,
"azure_endpoint": OPENAI_API_BASE,
}
@property
def supports_image_inputs(self) -> bool:
return True
@pytest.mark.xfail(reason="Not yet supported.")
def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
super().test_usage_metadata_streaming(model)
class TestAzureOpenAIStandardLegacy(ChatModelIntegrationTests):
"""Test a legacy model."""
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return AzureChatOpenAI
@property
def chat_model_params(self) -> dict:
return {
"deployment_name": os.environ["AZURE_OPENAI_LEGACY_CHAT_DEPLOYMENT_NAME"],
"openai_api_version": OPENAI_API_VERSION,
"azure_endpoint": OPENAI_API_BASE,
}
@pytest.mark.xfail(reason="Not yet supported.")
def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
super().test_usage_metadata_streaming(model)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests | lc_public_repos/langchain/libs/partners/openai/tests/integration_tests/chat_models/test_azure.py | """Test AzureChatOpenAI wrapper."""
import json
import os
from typing import Any, Optional
import pytest
from langchain_core.callbacks import CallbackManager
from langchain_core.messages import (
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
HumanMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
from langchain_openai import AzureChatOpenAI
from tests.unit_tests.fake.callbacks import FakeCallbackHandler
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.environ.get("AZURE_OPENAI_API_BASE", "")
OPENAI_API_KEY = os.environ.get("AZURE_OPENAI_API_KEY", "")
DEPLOYMENT_NAME = os.environ.get(
"AZURE_OPENAI_DEPLOYMENT_NAME",
os.environ.get("AZURE_OPENAI_CHAT_DEPLOYMENT_NAME", ""),
)
def _get_llm(**kwargs: Any) -> AzureChatOpenAI:
return AzureChatOpenAI( # type: ignore[call-arg, call-arg, call-arg]
deployment_name=DEPLOYMENT_NAME,
openai_api_version=OPENAI_API_VERSION,
azure_endpoint=OPENAI_API_BASE,
openai_api_key=OPENAI_API_KEY,
**kwargs,
)
@pytest.mark.scheduled
@pytest.fixture
def llm() -> AzureChatOpenAI:
return _get_llm(max_tokens=50)
def test_chat_openai(llm: AzureChatOpenAI) -> None:
"""Test AzureChatOpenAI wrapper."""
message = HumanMessage(content="Hello")
response = llm.invoke([message])
assert isinstance(response, BaseMessage)
assert isinstance(response.content, str)
@pytest.mark.scheduled
def test_chat_openai_generate() -> None:
"""Test AzureChatOpenAI wrapper with generate."""
chat = _get_llm(max_tokens=10, n=2)
message = HumanMessage(content="Hello")
response = chat.generate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 2
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
def test_chat_openai_multiple_completions() -> None:
"""Test AzureChatOpenAI wrapper with multiple completions."""
chat = _get_llm(max_tokens=10, n=5)
message = HumanMessage(content="Hello")
response = chat._generate([message])
assert isinstance(response, ChatResult)
assert len(response.generations) == 5
for generation in response.generations:
assert isinstance(generation.message, BaseMessage)
assert isinstance(generation.message.content, str)
@pytest.mark.scheduled
def test_chat_openai_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = _get_llm(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Hello")
response = chat.invoke([message])
assert callback_handler.llm_streams > 0
assert isinstance(response, BaseMessage)
@pytest.mark.scheduled
def test_chat_openai_streaming_generation_info() -> None:
"""Test that generation info is preserved when streaming."""
class _FakeCallback(FakeCallbackHandler):
saved_things: dict = {}
def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
# Save the generation
self.saved_things["generation"] = args[0]
callback = _FakeCallback()
callback_manager = CallbackManager([callback])
chat = _get_llm(max_tokens=2, temperature=0, callback_manager=callback_manager)
list(chat.stream("hi"))
generation = callback.saved_things["generation"]
# `Hello!` is two tokens, assert that that is what is returned
assert generation.generations[0][0].text == "Hello!"
@pytest.mark.scheduled
async def test_async_chat_openai() -> None:
"""Test async generation."""
chat = _get_llm(max_tokens=10, n=2)
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 2
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
async def test_async_chat_openai_streaming() -> None:
"""Test that streaming correctly invokes on_llm_new_token callback."""
callback_handler = FakeCallbackHandler()
callback_manager = CallbackManager([callback_handler])
chat = _get_llm(
max_tokens=10,
streaming=True,
temperature=0,
callback_manager=callback_manager,
verbose=True,
)
message = HumanMessage(content="Hello")
response = await chat.agenerate([[message], [message]])
assert callback_handler.llm_streams > 0
assert isinstance(response, LLMResult)
assert len(response.generations) == 2
for generations in response.generations:
assert len(generations) == 1
for generation in generations:
assert isinstance(generation, ChatGeneration)
assert isinstance(generation.text, str)
assert generation.text == generation.message.content
@pytest.mark.scheduled
def test_openai_streaming(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from OpenAI."""
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream("I'm Pickle Rick"):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert full.response_metadata.get("model_name") is not None
@pytest.mark.scheduled
async def test_openai_astream(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from OpenAI."""
full: Optional[BaseMessageChunk] = None
async for chunk in llm.astream("I'm Pickle Rick"):
assert isinstance(chunk.content, str)
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert full.response_metadata.get("model_name") is not None
@pytest.mark.scheduled
async def test_openai_abatch(llm: AzureChatOpenAI) -> None:
"""Test streaming tokens from AzureChatOpenAI."""
result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_abatch_tags(llm: AzureChatOpenAI) -> None:
"""Test batch tokens from AzureChatOpenAI."""
result = await llm.abatch(
["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]}
)
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
def test_openai_batch(llm: AzureChatOpenAI) -> None:
"""Test batch tokens from AzureChatOpenAI."""
result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"])
for token in result:
assert isinstance(token.content, str)
@pytest.mark.scheduled
async def test_openai_ainvoke(llm: AzureChatOpenAI) -> None:
"""Test invoke tokens from AzureChatOpenAI."""
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result.content, str)
assert result.response_metadata.get("model_name") is not None
@pytest.mark.scheduled
def test_openai_invoke(llm: AzureChatOpenAI) -> None:
"""Test invoke tokens from AzureChatOpenAI."""
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result.content, str)
assert result.response_metadata.get("model_name") is not None
def test_json_mode(llm: AzureChatOpenAI) -> None:
response = llm.invoke(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
)
assert isinstance(response.content, str)
assert json.loads(response.content) == {"a": 1}
# Test streaming
full: Optional[BaseMessageChunk] = None
for chunk in llm.stream(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, str)
assert json.loads(full.content) == {"a": 1}
async def test_json_mode_async(llm: AzureChatOpenAI) -> None:
response = await llm.ainvoke(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
)
assert isinstance(response.content, str)
assert json.loads(response.content) == {"a": 1}
# Test streaming
full: Optional[BaseMessageChunk] = None
async for chunk in llm.astream(
"Return this as json: {'a': 1}", response_format={"type": "json_object"}
):
full = chunk if full is None else full + chunk
assert isinstance(full, AIMessageChunk)
assert isinstance(full.content, str)
assert json.loads(full.content) == {"a": 1}
|
0 | lc_public_repos/langchain/libs/partners/openai/tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/test_token_counts.py | import pytest
from langchain_openai import ChatOpenAI, OpenAI
_EXPECTED_NUM_TOKENS = {
"ada": 17,
"babbage": 17,
"curie": 17,
"davinci": 17,
"gpt-4": 12,
"gpt-4-32k": 12,
"gpt-3.5-turbo": 12,
}
_MODELS = models = ["ada", "babbage", "curie", "davinci"]
_CHAT_MODELS = ["gpt-4", "gpt-4-32k", "gpt-3.5-turbo"]
@pytest.mark.xfail(reason="Old models require different tiktoken cached file")
@pytest.mark.parametrize("model", _MODELS)
def test_openai_get_num_tokens(model: str) -> None:
"""Test get_tokens."""
llm = OpenAI(model=model)
assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
@pytest.mark.parametrize("model", _CHAT_MODELS)
def test_chat_openai_get_num_tokens(model: str) -> None:
"""Test get_tokens."""
llm = ChatOpenAI(model=model)
assert llm.get_num_tokens("表情符号是\n🦜🔗") == _EXPECTED_NUM_TOKENS[model]
|
0 | lc_public_repos/langchain/libs/partners/openai/tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/test_secrets.py | from typing import Type, cast
import pytest
from langchain_core.load import dumpd
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_openai import (
AzureChatOpenAI,
AzureOpenAI,
AzureOpenAIEmbeddings,
ChatOpenAI,
OpenAI,
OpenAIEmbeddings,
)
def test_chat_openai_secrets() -> None:
o = ChatOpenAI(openai_api_key="foo") # type: ignore[call-arg]
s = str(o)
assert "foo" not in s
def test_openai_secrets() -> None:
o = OpenAI(openai_api_key="foo") # type: ignore[call-arg]
s = str(o)
assert "foo" not in s
def test_openai_embeddings_secrets() -> None:
o = OpenAIEmbeddings(openai_api_key="foo") # type: ignore[call-arg]
s = str(o)
assert "foo" not in s
def test_azure_chat_openai_secrets() -> None:
o = AzureChatOpenAI( # type: ignore[call-arg]
openai_api_key="foo1",
azure_endpoint="endpoint",
azure_ad_token="foo2", # type: ignore[arg-type]
api_version="version",
)
s = str(o)
assert "foo1" not in s
assert "foo2" not in s
def test_azure_openai_secrets() -> None:
o = AzureOpenAI( # type: ignore[call-arg]
openai_api_key="foo1",
azure_endpoint="endpoint",
azure_ad_token="foo2", # type: ignore[arg-type]
api_version="version",
)
s = str(o)
assert "foo1" not in s
assert "foo2" not in s
def test_azure_openai_embeddings_secrets() -> None:
o = AzureOpenAIEmbeddings( # type: ignore[call-arg]
openai_api_key="foo1",
azure_endpoint="endpoint",
azure_ad_token="foo2", # type: ignore[arg-type]
api_version="version",
)
s = str(o)
assert "foo1" not in s
assert "foo2" not in s
@pytest.mark.parametrize(
"model_class", [AzureChatOpenAI, AzureOpenAI, AzureOpenAIEmbeddings]
)
def test_azure_openai_api_key_is_secret_string(model_class: Type) -> None:
"""Test that the API key is stored as a SecretStr."""
model = model_class(
openai_api_key="secret-api-key",
azure_endpoint="endpoint",
azure_ad_token="secret-ad-token",
api_version="version",
)
assert isinstance(model.openai_api_key, SecretStr)
assert isinstance(model.azure_ad_token, SecretStr)
@pytest.mark.parametrize(
"model_class", [AzureChatOpenAI, AzureOpenAI, AzureOpenAIEmbeddings]
)
def test_azure_openai_api_key_masked_when_passed_from_env(
model_class: Type, monkeypatch: MonkeyPatch, capsys: CaptureFixture
) -> None:
"""Test that the API key is masked when passed from an environment variable."""
monkeypatch.setenv("AZURE_OPENAI_API_KEY", "secret-api-key")
monkeypatch.setenv("AZURE_OPENAI_AD_TOKEN", "secret-ad-token")
model = model_class(azure_endpoint="endpoint", api_version="version")
print(model.openai_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
print(model.azure_ad_token, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
@pytest.mark.parametrize(
"model_class", [AzureChatOpenAI, AzureOpenAI, AzureOpenAIEmbeddings]
)
def test_azure_openai_api_key_masked_when_passed_via_constructor(
model_class: Type, capsys: CaptureFixture
) -> None:
"""Test that the API key is masked when passed via the constructor."""
model = model_class(
openai_api_key="secret-api-key",
azure_endpoint="endpoint",
azure_ad_token="secret-ad-token",
api_version="version",
)
print(model.openai_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
print(model.azure_ad_token, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
@pytest.mark.parametrize(
"model_class", [AzureChatOpenAI, AzureOpenAI, AzureOpenAIEmbeddings]
)
def test_azure_openai_uses_actual_secret_value_from_secretstr(
model_class: Type,
) -> None:
"""Test that the actual secret value is correctly retrieved."""
model = model_class(
openai_api_key="secret-api-key",
azure_endpoint="endpoint",
azure_ad_token="secret-ad-token",
api_version="version",
)
assert cast(SecretStr, model.openai_api_key).get_secret_value() == "secret-api-key"
assert cast(SecretStr, model.azure_ad_token).get_secret_value() == "secret-ad-token"
@pytest.mark.parametrize("model_class", [ChatOpenAI, OpenAI, OpenAIEmbeddings])
def test_openai_api_key_is_secret_string(model_class: Type) -> None:
"""Test that the API key is stored as a SecretStr."""
model = model_class(openai_api_key="secret-api-key")
assert isinstance(model.openai_api_key, SecretStr)
@pytest.mark.parametrize("model_class", [ChatOpenAI, OpenAI, OpenAIEmbeddings])
def test_openai_api_key_masked_when_passed_from_env(
model_class: Type, monkeypatch: MonkeyPatch, capsys: CaptureFixture
) -> None:
"""Test that the API key is masked when passed from an environment variable."""
monkeypatch.setenv("OPENAI_API_KEY", "secret-api-key")
model = model_class()
print(model.openai_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
@pytest.mark.parametrize("model_class", [ChatOpenAI, OpenAI, OpenAIEmbeddings])
def test_openai_api_key_masked_when_passed_via_constructor(
model_class: Type, capsys: CaptureFixture
) -> None:
"""Test that the API key is masked when passed via the constructor."""
model = model_class(openai_api_key="secret-api-key")
print(model.openai_api_key, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
@pytest.mark.parametrize("model_class", [ChatOpenAI, OpenAI, OpenAIEmbeddings])
def test_openai_uses_actual_secret_value_from_secretstr(model_class: Type) -> None:
"""Test that the actual secret value is correctly retrieved."""
model = model_class(openai_api_key="secret-api-key")
assert cast(SecretStr, model.openai_api_key).get_secret_value() == "secret-api-key"
@pytest.mark.parametrize("model_class", [AzureChatOpenAI, AzureOpenAI])
def test_azure_serialized_secrets(model_class: Type) -> None:
"""Test that the actual secret value is correctly retrieved."""
model = model_class(
openai_api_key="secret-api-key", api_version="foo", azure_endpoint="foo"
)
serialized = dumpd(model)
assert serialized["kwargs"]["openai_api_key"]["id"] == ["AZURE_OPENAI_API_KEY"]
model = model_class(
azure_ad_token="secret-token", api_version="foo", azure_endpoint="foo"
)
serialized = dumpd(model)
assert serialized["kwargs"]["azure_ad_token"]["id"] == ["AZURE_OPENAI_AD_TOKEN"]
|
0 | lc_public_repos/langchain/libs/partners/openai/tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/test_imports.py | from langchain_openai import __all__
EXPECTED_ALL = [
"OpenAI",
"ChatOpenAI",
"OpenAIEmbeddings",
"AzureOpenAI",
"AzureChatOpenAI",
"AzureOpenAIEmbeddings",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/test_load.py | from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.load import load, loads
from langchain_openai import ChatOpenAI, OpenAI
def test_loads_openai_llm() -> None:
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello", top_p=0.8) # type: ignore[call-arg]
llm_string = dumps(llm)
llm2 = loads(llm_string, secrets_map={"OPENAI_API_KEY": "hello"})
assert llm2.dict() == llm.dict()
llm_string_2 = dumps(llm2)
assert llm_string_2 == llm_string
assert isinstance(llm2, OpenAI)
def test_load_openai_llm() -> None:
llm = OpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
llm_obj = dumpd(llm)
llm2 = load(llm_obj, secrets_map={"OPENAI_API_KEY": "hello"})
assert llm2.dict() == llm.dict()
assert dumpd(llm2) == llm_obj
assert isinstance(llm2, OpenAI)
def test_loads_openai_chat() -> None:
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
llm_string = dumps(llm)
llm2 = loads(llm_string, secrets_map={"OPENAI_API_KEY": "hello"})
assert llm2.dict() == llm.dict()
llm_string_2 = dumps(llm2)
assert llm_string_2 == llm_string
assert isinstance(llm2, ChatOpenAI)
def test_load_openai_chat() -> None:
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
llm_obj = dumpd(llm)
llm2 = load(llm_obj, secrets_map={"OPENAI_API_KEY": "hello"})
assert llm2.dict() == llm.dict()
assert dumpd(llm2) == llm_obj
assert isinstance(llm2, ChatOpenAI)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/fake/callbacks.py | """A fake callback handler for testing purposes."""
from itertools import chain
from typing import Any, Dict, List, Optional, Union
from uuid import UUID
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
from langchain_core.messages import BaseMessage
from pydantic import BaseModel
class BaseFakeCallbackHandler(BaseModel):
"""Base fake callback handler for testing."""
starts: int = 0
ends: int = 0
errors: int = 0
errors_args: List[Any] = []
text: int = 0
ignore_llm_: bool = False
ignore_chain_: bool = False
ignore_agent_: bool = False
ignore_retriever_: bool = False
ignore_chat_model_: bool = False
# to allow for similar callback handlers that are not technically equal
fake_id: Union[str, None] = None
# add finer-grained counters for easier debugging of failing tests
chain_starts: int = 0
chain_ends: int = 0
llm_starts: int = 0
llm_ends: int = 0
llm_streams: int = 0
tool_starts: int = 0
tool_ends: int = 0
agent_actions: int = 0
agent_ends: int = 0
chat_model_starts: int = 0
retriever_starts: int = 0
retriever_ends: int = 0
retriever_errors: int = 0
retries: int = 0
class BaseFakeCallbackHandlerMixin(BaseFakeCallbackHandler):
"""Base fake callback handler mixin for testing."""
def on_llm_start_common(self) -> None:
self.llm_starts += 1
self.starts += 1
def on_llm_end_common(self) -> None:
self.llm_ends += 1
self.ends += 1
def on_llm_error_common(self, *args: Any, **kwargs: Any) -> None:
self.errors += 1
self.errors_args.append({"args": args, "kwargs": kwargs})
def on_llm_new_token_common(self) -> None:
self.llm_streams += 1
def on_retry_common(self) -> None:
self.retries += 1
def on_chain_start_common(self) -> None:
self.chain_starts += 1
self.starts += 1
def on_chain_end_common(self) -> None:
self.chain_ends += 1
self.ends += 1
def on_chain_error_common(self) -> None:
self.errors += 1
def on_tool_start_common(self) -> None:
self.tool_starts += 1
self.starts += 1
def on_tool_end_common(self) -> None:
self.tool_ends += 1
self.ends += 1
def on_tool_error_common(self) -> None:
self.errors += 1
def on_agent_action_common(self) -> None:
self.agent_actions += 1
self.starts += 1
def on_agent_finish_common(self) -> None:
self.agent_ends += 1
self.ends += 1
def on_chat_model_start_common(self) -> None:
self.chat_model_starts += 1
self.starts += 1
def on_text_common(self) -> None:
self.text += 1
def on_retriever_start_common(self) -> None:
self.starts += 1
self.retriever_starts += 1
def on_retriever_end_common(self) -> None:
self.ends += 1
self.retriever_ends += 1
def on_retriever_error_common(self) -> None:
self.errors += 1
self.retriever_errors += 1
class FakeCallbackHandler(BaseCallbackHandler, BaseFakeCallbackHandlerMixin):
"""Fake callback handler for testing."""
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
@property
def ignore_retriever(self) -> bool:
"""Whether to ignore retriever callbacks."""
return self.ignore_retriever_
def on_llm_start(self, *args: Any, **kwargs: Any) -> Any:
self.on_llm_start_common()
def on_llm_new_token(self, *args: Any, **kwargs: Any) -> Any:
self.on_llm_new_token_common()
def on_llm_end(self, *args: Any, **kwargs: Any) -> Any:
self.on_llm_end_common()
def on_llm_error(self, *args: Any, **kwargs: Any) -> Any:
self.on_llm_error_common(*args, **kwargs)
def on_retry(self, *args: Any, **kwargs: Any) -> Any:
self.on_retry_common()
def on_chain_start(self, *args: Any, **kwargs: Any) -> Any:
self.on_chain_start_common()
def on_chain_end(self, *args: Any, **kwargs: Any) -> Any:
self.on_chain_end_common()
def on_chain_error(self, *args: Any, **kwargs: Any) -> Any:
self.on_chain_error_common()
def on_tool_start(self, *args: Any, **kwargs: Any) -> Any:
self.on_tool_start_common()
def on_tool_end(self, *args: Any, **kwargs: Any) -> Any:
self.on_tool_end_common()
def on_tool_error(self, *args: Any, **kwargs: Any) -> Any:
self.on_tool_error_common()
def on_agent_action(self, *args: Any, **kwargs: Any) -> Any:
self.on_agent_action_common()
def on_agent_finish(self, *args: Any, **kwargs: Any) -> Any:
self.on_agent_finish_common()
def on_text(self, *args: Any, **kwargs: Any) -> Any:
self.on_text_common()
def on_retriever_start(self, *args: Any, **kwargs: Any) -> Any:
self.on_retriever_start_common()
def on_retriever_end(self, *args: Any, **kwargs: Any) -> Any:
self.on_retriever_end_common()
def on_retriever_error(self, *args: Any, **kwargs: Any) -> Any:
self.on_retriever_error_common()
def __deepcopy__(self, memo: dict) -> "FakeCallbackHandler": # type: ignore[override]
return self
class FakeCallbackHandlerWithChatStart(FakeCallbackHandler):
def on_chat_model_start(
self,
serialized: Dict[str, Any],
messages: List[List[BaseMessage]],
*,
run_id: UUID,
parent_run_id: Optional[UUID] = None,
**kwargs: Any,
) -> Any:
assert all(isinstance(m, BaseMessage) for m in chain(*messages))
self.on_chat_model_start_common()
class FakeAsyncCallbackHandler(AsyncCallbackHandler, BaseFakeCallbackHandlerMixin):
"""Fake async callback handler for testing."""
@property
def ignore_llm(self) -> bool:
"""Whether to ignore LLM callbacks."""
return self.ignore_llm_
@property
def ignore_chain(self) -> bool:
"""Whether to ignore chain callbacks."""
return self.ignore_chain_
@property
def ignore_agent(self) -> bool:
"""Whether to ignore agent callbacks."""
return self.ignore_agent_
async def on_retry(self, *args: Any, **kwargs: Any) -> Any:
self.on_retry_common()
async def on_llm_start(self, *args: Any, **kwargs: Any) -> None:
self.on_llm_start_common()
async def on_llm_new_token(self, *args: Any, **kwargs: Any) -> None:
self.on_llm_new_token_common()
async def on_llm_end(self, *args: Any, **kwargs: Any) -> None:
self.on_llm_end_common()
async def on_llm_error(self, *args: Any, **kwargs: Any) -> None:
self.on_llm_error_common(*args, **kwargs)
async def on_chain_start(self, *args: Any, **kwargs: Any) -> None:
self.on_chain_start_common()
async def on_chain_end(self, *args: Any, **kwargs: Any) -> None:
self.on_chain_end_common()
async def on_chain_error(self, *args: Any, **kwargs: Any) -> None:
self.on_chain_error_common()
async def on_tool_start(self, *args: Any, **kwargs: Any) -> None:
self.on_tool_start_common()
async def on_tool_end(self, *args: Any, **kwargs: Any) -> None:
self.on_tool_end_common()
async def on_tool_error(self, *args: Any, **kwargs: Any) -> None:
self.on_tool_error_common()
async def on_agent_action(self, *args: Any, **kwargs: Any) -> None:
self.on_agent_action_common()
async def on_agent_finish(self, *args: Any, **kwargs: Any) -> None:
self.on_agent_finish_common()
async def on_text(self, *args: Any, **kwargs: Any) -> None:
self.on_text_common()
def __deepcopy__(self, memo: dict) -> "FakeAsyncCallbackHandler": # type: ignore[override]
return self
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/llms/test_base.py | import os
from typing import List
import pytest
from langchain_openai import OpenAI
os.environ["OPENAI_API_KEY"] = "foo"
def test_openai_model_param() -> None:
llm = OpenAI(model="foo")
assert llm.model_name == "foo"
llm = OpenAI(model_name="foo") # type: ignore[call-arg]
assert llm.model_name == "foo"
# Test standard tracing params
ls_params = llm._get_ls_params()
assert ls_params == {
"ls_provider": "openai",
"ls_model_type": "llm",
"ls_model_name": "foo",
"ls_temperature": 0.7,
"ls_max_tokens": 256,
}
def test_openai_model_kwargs() -> None:
llm = OpenAI(model_kwargs={"foo": "bar"})
assert llm.model_kwargs == {"foo": "bar"}
def test_openai_fields_in_model_kwargs() -> None:
"""Test that for backwards compatibility fields can be passed in as model_kwargs."""
llm = OpenAI(model_kwargs={"model_name": "foo"})
assert llm.model_name == "foo"
llm = OpenAI(model_kwargs={"model": "foo"})
assert llm.model_name == "foo"
def test_openai_incorrect_field() -> None:
with pytest.warns(match="not default parameter"):
llm = OpenAI(foo="bar") # type: ignore[call-arg]
assert llm.model_kwargs == {"foo": "bar"}
@pytest.fixture
def mock_completion() -> dict:
return {
"id": "cmpl-3evkmQda5Hu7fcZavknQda3SQ",
"object": "text_completion",
"created": 1689989000,
"model": "text-davinci-003",
"choices": [
{"text": "Bar Baz", "index": 0, "logprobs": None, "finish_reason": "length"}
],
"usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3},
}
@pytest.mark.parametrize("model", ["gpt-3.5-turbo-instruct"])
def test_get_token_ids(model: str) -> None:
OpenAI(model=model).get_token_ids("foo")
return
def test_custom_token_counting() -> None:
def token_encoder(text: str) -> List[int]:
return [1, 2, 3]
llm = OpenAI(custom_get_token_ids=token_encoder)
assert llm.get_token_ids("foo") == [1, 2, 3]
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/llms/test_imports.py | from langchain_openai.llms import __all__
EXPECTED_ALL = ["OpenAI", "AzureOpenAI"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/llms/test_azure.py | from typing import Any
from langchain_openai import AzureOpenAI
def test_azure_model_param(monkeypatch: Any) -> None:
monkeypatch.delenv("OPENAI_API_BASE", raising=False)
llm = AzureOpenAI(
openai_api_key="secret-api-key", # type: ignore[call-arg]
azure_endpoint="endpoint",
api_version="version",
azure_deployment="gpt-35-turbo-instruct",
)
# Test standard tracing params
ls_params = llm._get_ls_params()
assert ls_params == {
"ls_provider": "azure",
"ls_model_type": "llm",
"ls_model_name": "gpt-35-turbo-instruct",
"ls_temperature": 0.7,
"ls_max_tokens": 256,
}
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/embeddings/test_base_standard.py | """Standard LangChain interface tests"""
from typing import Tuple, Type
from langchain_core.embeddings import Embeddings
from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests
from langchain_openai import OpenAIEmbeddings
class TestOpenAIStandard(EmbeddingsUnitTests):
@property
def embeddings_class(self) -> Type[Embeddings]:
return OpenAIEmbeddings
@property
def init_from_env_params(self) -> Tuple[dict, dict, dict]:
return (
{
"OPENAI_API_KEY": "api_key",
"OPENAI_ORG_ID": "org_id",
"OPENAI_API_BASE": "api_base",
"OPENAI_PROXY": "https://proxy.com",
},
{},
{
"openai_api_key": "api_key",
"openai_organization": "org_id",
"openai_api_base": "api_base",
"openai_proxy": "https://proxy.com",
},
)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/embeddings/test_base.py | import os
import pytest
from langchain_openai import OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = "foo"
def test_openai_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
OpenAIEmbeddings(model_kwargs={"model": "foo"})
def test_openai_incorrect_field() -> None:
with pytest.warns(match="not default parameter"):
llm = OpenAIEmbeddings(foo="bar") # type: ignore[call-arg]
assert llm.model_kwargs == {"foo": "bar"}
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/embeddings/test_azure_embeddings.py | import os
from unittest import mock
from langchain_openai import AzureOpenAIEmbeddings
def test_initialize_azure_openai() -> None:
embeddings = AzureOpenAIEmbeddings( # type: ignore[call-arg]
model="text-embedding-large",
api_key="xyz", # type: ignore[arg-type]
azure_endpoint="my-base-url",
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",
)
assert embeddings.model == "text-embedding-large"
def test_intialize_azure_openai_with_base_set() -> None:
with mock.patch.dict(os.environ, {"OPENAI_API_BASE": "https://api.openai.com"}):
embeddings = AzureOpenAIEmbeddings( # type: ignore[call-arg, call-arg]
model="text-embedding-large",
api_key="xyz", # type: ignore[arg-type]
azure_endpoint="my-base-url",
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",
openai_api_base=None,
)
assert embeddings.model == "text-embedding-large"
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/embeddings/test_imports.py | from langchain_openai.embeddings import __all__
EXPECTED_ALL = ["OpenAIEmbeddings", "AzureOpenAIEmbeddings"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/embeddings/test_azure_standard.py | from typing import Tuple, Type
from langchain_core.embeddings import Embeddings
from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests
from langchain_openai import AzureOpenAIEmbeddings
class TestAzureOpenAIStandard(EmbeddingsUnitTests):
@property
def embeddings_class(self) -> Type[Embeddings]:
return AzureOpenAIEmbeddings
@property
def embedding_model_params(self) -> dict:
return {"api_key": "api_key", "azure_endpoint": "https://endpoint.com"}
@property
def init_from_env_params(self) -> Tuple[dict, dict, dict]:
return (
{
"AZURE_OPENAI_API_KEY": "api_key",
"AZURE_OPENAI_ENDPOINT": "https://endpoint.com",
"AZURE_OPENAI_AD_TOKEN": "token",
"OPENAI_ORG_ID": "org_id",
"OPENAI_API_VERSION": "yyyy-mm-dd",
"OPENAI_API_TYPE": "type",
},
{},
{
"openai_api_key": "api_key",
"azure_endpoint": "https://endpoint.com",
"azure_ad_token": "token",
"openai_organization": "org_id",
"openai_api_version": "yyyy-mm-dd",
"openai_api_type": "type",
},
)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/test_base_standard.py | """Standard LangChain interface tests"""
from typing import Tuple, Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_openai import ChatOpenAI
class TestOpenAIStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatOpenAI
@property
def init_from_env_params(self) -> Tuple[dict, dict, dict]:
return (
{
"OPENAI_API_KEY": "api_key",
"OPENAI_ORG_ID": "org_id",
"OPENAI_API_BASE": "api_base",
"OPENAI_PROXY": "https://proxy.com",
},
{},
{
"openai_api_key": "api_key",
"openai_organization": "org_id",
"openai_api_base": "api_base",
"openai_proxy": "https://proxy.com",
},
)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/test_base.py | """Test OpenAI Chat API wrapper."""
import json
from types import TracebackType
from typing import Any, Dict, List, Literal, Optional, Type, Union
from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
FunctionMessage,
HumanMessage,
InvalidToolCall,
SystemMessage,
ToolCall,
ToolMessage,
)
from langchain_core.messages.ai import UsageMetadata
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langchain_openai import ChatOpenAI
from langchain_openai.chat_models.base import (
_convert_dict_to_message,
_convert_message_to_dict,
_convert_to_openai_response_format,
_create_usage_metadata,
_format_message_content,
)
def test_openai_model_param() -> None:
llm = ChatOpenAI(model="foo")
assert llm.model_name == "foo"
llm = ChatOpenAI(model_name="foo") # type: ignore[call-arg]
assert llm.model_name == "foo"
llm = ChatOpenAI(max_tokens=10) # type: ignore[call-arg]
assert llm.max_tokens == 10
llm = ChatOpenAI(max_completion_tokens=10)
assert llm.max_tokens == 10
def test_openai_o1_temperature() -> None:
llm = ChatOpenAI(model="o1-preview")
assert llm.temperature == 1
llm = ChatOpenAI(model_name="o1-mini") # type: ignore[call-arg]
assert llm.temperature == 1
def test_function_message_dict_to_function_message() -> None:
content = json.dumps({"result": "Example #1"})
name = "test_function"
result = _convert_dict_to_message(
{"role": "function", "name": name, "content": content}
)
assert isinstance(result, FunctionMessage)
assert result.name == name
assert result.content == content
def test__convert_dict_to_message_human() -> None:
message = {"role": "user", "content": "foo"}
result = _convert_dict_to_message(message)
expected_output = HumanMessage(content="foo")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_human_with_name() -> None:
message = {"role": "user", "content": "foo", "name": "test"}
result = _convert_dict_to_message(message)
expected_output = HumanMessage(content="foo", name="test")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_ai() -> None:
message = {"role": "assistant", "content": "foo"}
result = _convert_dict_to_message(message)
expected_output = AIMessage(content="foo")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_ai_with_name() -> None:
message = {"role": "assistant", "content": "foo", "name": "test"}
result = _convert_dict_to_message(message)
expected_output = AIMessage(content="foo", name="test")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_system() -> None:
message = {"role": "system", "content": "foo"}
result = _convert_dict_to_message(message)
expected_output = SystemMessage(content="foo")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_system_with_name() -> None:
message = {"role": "system", "content": "foo", "name": "test"}
result = _convert_dict_to_message(message)
expected_output = SystemMessage(content="foo", name="test")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_tool() -> None:
message = {"role": "tool", "content": "foo", "tool_call_id": "bar"}
result = _convert_dict_to_message(message)
expected_output = ToolMessage(content="foo", tool_call_id="bar")
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
def test__convert_dict_to_message_tool_call() -> None:
raw_tool_call = {
"id": "call_wm0JY6CdwOMZ4eTxHWUThDNz",
"function": {
"arguments": '{"name": "Sally", "hair_color": "green"}',
"name": "GenerateUsername",
},
"type": "function",
}
message = {"role": "assistant", "content": None, "tool_calls": [raw_tool_call]}
result = _convert_dict_to_message(message)
expected_output = AIMessage(
content="",
additional_kwargs={"tool_calls": [raw_tool_call]},
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="call_wm0JY6CdwOMZ4eTxHWUThDNz",
type="tool_call",
)
],
)
assert result == expected_output
assert _convert_message_to_dict(expected_output) == message
# Test malformed tool call
raw_tool_calls: list = [
{
"id": "call_wm0JY6CdwOMZ4eTxHWUThDNz",
"function": {"arguments": "oops", "name": "GenerateUsername"},
"type": "function",
},
{
"id": "call_abc123",
"function": {
"arguments": '{"name": "Sally", "hair_color": "green"}',
"name": "GenerateUsername",
},
"type": "function",
},
]
raw_tool_calls = list(sorted(raw_tool_calls, key=lambda x: x["id"]))
message = {"role": "assistant", "content": None, "tool_calls": raw_tool_calls}
result = _convert_dict_to_message(message)
expected_output = AIMessage(
content="",
additional_kwargs={"tool_calls": raw_tool_calls},
invalid_tool_calls=[
InvalidToolCall(
name="GenerateUsername",
args="oops",
id="call_wm0JY6CdwOMZ4eTxHWUThDNz",
error=(
"Function GenerateUsername arguments:\n\noops\n\nare not "
"valid JSON. Received JSONDecodeError Expecting value: line 1 "
"column 1 (char 0)\nFor troubleshooting, visit: https://python"
".langchain.com/docs/troubleshooting/errors/OUTPUT_PARSING_FAILURE "
),
type="invalid_tool_call",
)
],
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="call_abc123",
type="tool_call",
)
],
)
assert result == expected_output
reverted_message_dict = _convert_message_to_dict(expected_output)
reverted_message_dict["tool_calls"] = list(
sorted(reverted_message_dict["tool_calls"], key=lambda x: x["id"])
)
assert reverted_message_dict == message
class MockAsyncContextManager:
def __init__(self, chunk_list: list):
self.current_chunk = 0
self.chunk_list = chunk_list
self.chunk_num = len(chunk_list)
async def __aenter__(self) -> "MockAsyncContextManager":
return self
async def __aexit__(
self,
exc_type: Optional[Type[BaseException]],
exc: Optional[BaseException],
tb: Optional[TracebackType],
) -> None:
pass
def __aiter__(self) -> "MockAsyncContextManager":
return self
async def __anext__(self) -> dict:
if self.current_chunk < self.chunk_num:
chunk = self.chunk_list[self.current_chunk]
self.current_chunk += 1
return chunk
else:
raise StopAsyncIteration
class MockSyncContextManager:
def __init__(self, chunk_list: list):
self.current_chunk = 0
self.chunk_list = chunk_list
self.chunk_num = len(chunk_list)
def __enter__(self) -> "MockSyncContextManager":
return self
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc: Optional[BaseException],
tb: Optional[TracebackType],
) -> None:
pass
def __iter__(self) -> "MockSyncContextManager":
return self
def __next__(self) -> dict:
if self.current_chunk < self.chunk_num:
chunk = self.chunk_list[self.current_chunk]
self.current_chunk += 1
return chunk
else:
raise StopIteration
GLM4_STREAM_META = """{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"\u4eba\u5de5\u667a\u80fd"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"\u52a9\u624b"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"，"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"\u4f60\u53ef\u4ee5"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"\u53eb\u6211"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"AI"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"\u52a9\u624b"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"delta":{"role":"assistant","content":"。"}}]}
{"id":"20240722102053e7277a4f94e848248ff9588ed37fb6e6","created":1721614853,"model":"glm-4","choices":[{"index":0,"finish_reason":"stop","delta":{"role":"assistant","content":""}}],"usage":{"prompt_tokens":13,"completion_tokens":10,"total_tokens":23}}
[DONE]""" # noqa: E501
@pytest.fixture
def mock_glm4_completion() -> list:
list_chunk_data = GLM4_STREAM_META.split("\n")
result_list = []
for msg in list_chunk_data:
if msg != "[DONE]":
result_list.append(json.loads(msg))
return result_list
async def test_glm4_astream(mock_glm4_completion: list) -> None:
llm_name = "glm-4"
llm = ChatOpenAI(model=llm_name, stream_usage=True)
mock_client = AsyncMock()
async def mock_create(*args: Any, **kwargs: Any) -> MockAsyncContextManager:
return MockAsyncContextManager(mock_glm4_completion)
mock_client.create = mock_create
usage_chunk = mock_glm4_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
with patch.object(llm, "async_client", mock_client):
async for chunk in llm.astream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
usage_metadata = chunk.usage_metadata
assert usage_metadata is not None
assert usage_metadata["input_tokens"] == usage_chunk["usage"]["prompt_tokens"]
assert usage_metadata["output_tokens"] == usage_chunk["usage"]["completion_tokens"]
assert usage_metadata["total_tokens"] == usage_chunk["usage"]["total_tokens"]
def test_glm4_stream(mock_glm4_completion: list) -> None:
llm_name = "glm-4"
llm = ChatOpenAI(model=llm_name, stream_usage=True)
mock_client = MagicMock()
def mock_create(*args: Any, **kwargs: Any) -> MockSyncContextManager:
return MockSyncContextManager(mock_glm4_completion)
mock_client.create = mock_create
usage_chunk = mock_glm4_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
with patch.object(llm, "client", mock_client):
for chunk in llm.stream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
usage_metadata = chunk.usage_metadata
assert usage_metadata is not None
assert usage_metadata["input_tokens"] == usage_chunk["usage"]["prompt_tokens"]
assert usage_metadata["output_tokens"] == usage_chunk["usage"]["completion_tokens"]
assert usage_metadata["total_tokens"] == usage_chunk["usage"]["total_tokens"]
DEEPSEEK_STREAM_DATA = """{"id":"d3610c24e6b42518a7883ea57c3ea2c3","choices":[{"index":0,"delta":{"content":"","role":"assistant"},"finish_reason":null,"logprobs":null}],"created":1721630271,"model":"deepseek-chat","system_fingerprint":"fp_7e0991cad4","object":"chat.completion.chunk","usage":null}
{"choices":[{"delta":{"content":"我是","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"Deep","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"Seek","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":" Chat","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"，","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"一个","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"由","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"深度","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"求","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"索","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"公司","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"开发的","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"智能","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"助手","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"。","role":"assistant"},"finish_reason":null,"index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":null}
{"choices":[{"delta":{"content":"","role":null},"finish_reason":"stop","index":0,"logprobs":null}],"created":1721630271,"id":"d3610c24e6b42518a7883ea57c3ea2c3","model":"deepseek-chat","object":"chat.completion.chunk","system_fingerprint":"fp_7e0991cad4","usage":{"completion_tokens":15,"prompt_tokens":11,"total_tokens":26}}
[DONE]""" # noqa: E501
@pytest.fixture
def mock_deepseek_completion() -> List[Dict]:
list_chunk_data = DEEPSEEK_STREAM_DATA.split("\n")
result_list = []
for msg in list_chunk_data:
if msg != "[DONE]":
result_list.append(json.loads(msg))
return result_list
async def test_deepseek_astream(mock_deepseek_completion: list) -> None:
llm_name = "deepseek-chat"
llm = ChatOpenAI(model=llm_name, stream_usage=True)
mock_client = AsyncMock()
async def mock_create(*args: Any, **kwargs: Any) -> MockAsyncContextManager:
return MockAsyncContextManager(mock_deepseek_completion)
mock_client.create = mock_create
usage_chunk = mock_deepseek_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
with patch.object(llm, "async_client", mock_client):
async for chunk in llm.astream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
usage_metadata = chunk.usage_metadata
assert usage_metadata is not None
assert usage_metadata["input_tokens"] == usage_chunk["usage"]["prompt_tokens"]
assert usage_metadata["output_tokens"] == usage_chunk["usage"]["completion_tokens"]
assert usage_metadata["total_tokens"] == usage_chunk["usage"]["total_tokens"]
def test_deepseek_stream(mock_deepseek_completion: list) -> None:
llm_name = "deepseek-chat"
llm = ChatOpenAI(model=llm_name, stream_usage=True)
mock_client = MagicMock()
def mock_create(*args: Any, **kwargs: Any) -> MockSyncContextManager:
return MockSyncContextManager(mock_deepseek_completion)
mock_client.create = mock_create
usage_chunk = mock_deepseek_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
with patch.object(llm, "client", mock_client):
for chunk in llm.stream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
usage_metadata = chunk.usage_metadata
assert usage_metadata is not None
assert usage_metadata["input_tokens"] == usage_chunk["usage"]["prompt_tokens"]
assert usage_metadata["output_tokens"] == usage_chunk["usage"]["completion_tokens"]
assert usage_metadata["total_tokens"] == usage_chunk["usage"]["total_tokens"]
OPENAI_STREAM_DATA = """{"id":"chatcmpl-9nhARrdUiJWEMd5plwV1Gc9NCjb9M","object":"chat.completion.chunk","created":1721631035,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_18cc0f1fa0","choices":[{"index":0,"delta":{"role":"assistant","content":""},"logprobs":null,"finish_reason":null}],"usage":null}
{"id":"chatcmpl-9nhARrdUiJWEMd5plwV1Gc9NCjb9M","object":"chat.completion.chunk","created":1721631035,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_18cc0f1fa0","choices":[{"index":0,"delta":{"content":"我是"},"logprobs":null,"finish_reason":null}],"usage":null}
{"id":"chatcmpl-9nhARrdUiJWEMd5plwV1Gc9NCjb9M","object":"chat.completion.chunk","created":1721631035,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_18cc0f1fa0","choices":[{"index":0,"delta":{"content":"助手"},"logprobs":null,"finish_reason":null}],"usage":null}
{"id":"chatcmpl-9nhARrdUiJWEMd5plwV1Gc9NCjb9M","object":"chat.completion.chunk","created":1721631035,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_18cc0f1fa0","choices":[{"index":0,"delta":{"content":"。"},"logprobs":null,"finish_reason":null}],"usage":null}
{"id":"chatcmpl-9nhARrdUiJWEMd5plwV1Gc9NCjb9M","object":"chat.completion.chunk","created":1721631035,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_18cc0f1fa0","choices":[{"index":0,"delta":{},"logprobs":null,"finish_reason":"stop"}],"usage":null}
{"id":"chatcmpl-9nhARrdUiJWEMd5plwV1Gc9NCjb9M","object":"chat.completion.chunk","created":1721631035,"model":"gpt-4o-2024-05-13","system_fingerprint":"fp_18cc0f1fa0","choices":[],"usage":{"prompt_tokens":14,"completion_tokens":3,"total_tokens":17}}
[DONE]""" # noqa: E501
@pytest.fixture
def mock_openai_completion() -> List[Dict]:
list_chunk_data = OPENAI_STREAM_DATA.split("\n")
result_list = []
for msg in list_chunk_data:
if msg != "[DONE]":
result_list.append(json.loads(msg))
return result_list
async def test_openai_astream(mock_openai_completion: list) -> None:
llm_name = "gpt-4o"
llm = ChatOpenAI(model=llm_name, stream_usage=True)
mock_client = AsyncMock()
async def mock_create(*args: Any, **kwargs: Any) -> MockAsyncContextManager:
return MockAsyncContextManager(mock_openai_completion)
mock_client.create = mock_create
usage_chunk = mock_openai_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
with patch.object(llm, "async_client", mock_client):
async for chunk in llm.astream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
usage_metadata = chunk.usage_metadata
assert usage_metadata is not None
assert usage_metadata["input_tokens"] == usage_chunk["usage"]["prompt_tokens"]
assert usage_metadata["output_tokens"] == usage_chunk["usage"]["completion_tokens"]
assert usage_metadata["total_tokens"] == usage_chunk["usage"]["total_tokens"]
def test_openai_stream(mock_openai_completion: list) -> None:
llm_name = "gpt-4o"
llm = ChatOpenAI(model=llm_name, stream_usage=True)
mock_client = MagicMock()
def mock_create(*args: Any, **kwargs: Any) -> MockSyncContextManager:
return MockSyncContextManager(mock_openai_completion)
mock_client.create = mock_create
usage_chunk = mock_openai_completion[-1]
usage_metadata: Optional[UsageMetadata] = None
with patch.object(llm, "client", mock_client):
for chunk in llm.stream("你的名字叫什么？只回答名字"):
assert isinstance(chunk, AIMessageChunk)
if chunk.usage_metadata is not None:
usage_metadata = chunk.usage_metadata
assert usage_metadata is not None
assert usage_metadata["input_tokens"] == usage_chunk["usage"]["prompt_tokens"]
assert usage_metadata["output_tokens"] == usage_chunk["usage"]["completion_tokens"]
assert usage_metadata["total_tokens"] == usage_chunk["usage"]["total_tokens"]
@pytest.fixture
def mock_completion() -> dict:
return {
"id": "chatcmpl-7fcZavknQda3SQ",
"object": "chat.completion",
"created": 1689989000,
"model": "gpt-3.5-turbo-0613",
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "Bar Baz", "name": "Erick"},
"finish_reason": "stop",
}
],
}
@pytest.fixture
def mock_client(mock_completion: dict) -> MagicMock:
rtn = MagicMock()
mock_create = MagicMock()
mock_resp = MagicMock()
mock_resp.headers = {"content-type": "application/json"}
mock_resp.parse.return_value = mock_completion
mock_create.return_value = mock_resp
rtn.with_raw_response.create = mock_create
rtn.create.return_value = mock_completion
return rtn
@pytest.fixture
def mock_async_client(mock_completion: dict) -> AsyncMock:
rtn = AsyncMock()
mock_create = AsyncMock()
mock_resp = MagicMock()
mock_resp.parse.return_value = mock_completion
mock_create.return_value = mock_resp
rtn.with_raw_response.create = mock_create
rtn.create.return_value = mock_completion
return rtn
def test_openai_invoke(mock_client: MagicMock) -> None:
llm = ChatOpenAI()
with patch.object(llm, "client", mock_client):
res = llm.invoke("bar")
assert res.content == "Bar Baz"
# headers are not in response_metadata if include_response_headers not set
assert "headers" not in res.response_metadata
assert mock_client.create.called
async def test_openai_ainvoke(mock_async_client: AsyncMock) -> None:
llm = ChatOpenAI()
with patch.object(llm, "async_client", mock_async_client):
res = await llm.ainvoke("bar")
assert res.content == "Bar Baz"
# headers are not in response_metadata if include_response_headers not set
assert "headers" not in res.response_metadata
assert mock_async_client.create.called
@pytest.mark.parametrize(
"model",
[
"gpt-3.5-turbo",
"gpt-4",
"gpt-3.5-0125",
"gpt-4-0125-preview",
"gpt-4-turbo-preview",
"gpt-4-vision-preview",
],
)
def test__get_encoding_model(model: str) -> None:
ChatOpenAI(model=model)._get_encoding_model()
return
def test_openai_invoke_name(mock_client: MagicMock) -> None:
llm = ChatOpenAI()
with patch.object(llm, "client", mock_client):
messages = [HumanMessage(content="Foo", name="Katie")]
res = llm.invoke(messages)
call_args, call_kwargs = mock_client.create.call_args
assert len(call_args) == 0 # no positional args
call_messages = call_kwargs["messages"]
assert len(call_messages) == 1
assert call_messages[0]["role"] == "user"
assert call_messages[0]["content"] == "Foo"
assert call_messages[0]["name"] == "Katie"
# check return type has name
assert res.content == "Bar Baz"
assert res.name == "Erick"
def test_custom_token_counting() -> None:
def token_encoder(text: str) -> List[int]:
return [1, 2, 3]
llm = ChatOpenAI(custom_get_token_ids=token_encoder)
assert llm.get_token_ids("foo") == [1, 2, 3]
def test_format_message_content() -> None:
content: Any = "hello"
assert content == _format_message_content(content)
content = None
assert content == _format_message_content(content)
content = []
assert content == _format_message_content(content)
content = [
{"type": "text", "text": "What is in this image?"},
{"type": "image_url", "image_url": {"url": "url.com"}},
]
assert content == _format_message_content(content)
content = [
{"type": "text", "text": "hello"},
{
"type": "tool_use",
"id": "toolu_01A09q90qw90lq917835lq9",
"name": "get_weather",
"input": {"location": "San Francisco, CA", "unit": "celsius"},
},
]
assert [{"type": "text", "text": "hello"}] == _format_message_content(content)
class GenerateUsername(BaseModel):
"Get a username based on someone's name and hair color."
name: str
hair_color: str
class MakeASandwich(BaseModel):
"Make a sandwich given a list of ingredients."
bread_type: str
cheese_type: str
condiments: List[str]
vegetables: List[str]
@pytest.mark.parametrize(
"tool_choice",
[
"any",
"none",
"auto",
"required",
"GenerateUsername",
{"type": "function", "function": {"name": "MakeASandwich"}},
False,
None,
],
)
@pytest.mark.parametrize("strict", [True, False, None])
def test_bind_tools_tool_choice(tool_choice: Any, strict: Optional[bool]) -> None:
"""Test passing in manually construct tool call message."""
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm.bind_tools(
tools=[GenerateUsername, MakeASandwich], tool_choice=tool_choice, strict=strict
)
@pytest.mark.parametrize(
"schema", [GenerateUsername, GenerateUsername.model_json_schema()]
)
@pytest.mark.parametrize("method", ["json_schema", "function_calling", "json_mode"])
@pytest.mark.parametrize("include_raw", [True, False])
@pytest.mark.parametrize("strict", [True, False, None])
def test_with_structured_output(
schema: Union[Type, Dict[str, Any], None],
method: Literal["function_calling", "json_mode", "json_schema"],
include_raw: bool,
strict: Optional[bool],
) -> None:
"""Test passing in manually construct tool call message."""
if method == "json_mode":
strict = None
llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0)
llm.with_structured_output(
schema, method=method, strict=strict, include_raw=include_raw
)
def test_get_num_tokens_from_messages() -> None:
llm = ChatOpenAI(model="gpt-4o")
messages = [
SystemMessage("you're a good assistant"),
HumanMessage("how are you"),
HumanMessage(
[
{"type": "text", "text": "what's in this image"},
{"type": "image_url", "image_url": {"url": "https://foobar.com"}},
{
"type": "image_url",
"image_url": {"url": "https://foobar.com", "detail": "low"},
},
]
),
AIMessage("a nice bird"),
AIMessage(
"",
tool_calls=[
ToolCall(id="foo", name="bar", args={"arg1": "arg1"}, type="tool_call")
],
),
AIMessage(
"",
additional_kwargs={
"function_call": {
"arguments": json.dumps({"arg1": "arg1"}),
"name": "fun",
}
},
),
AIMessage(
"text",
tool_calls=[
ToolCall(id="foo", name="bar", args={"arg1": "arg1"}, type="tool_call")
],
),
ToolMessage("foobar", tool_call_id="foo"),
]
expected = 176
actual = llm.get_num_tokens_from_messages(messages)
assert expected == actual
class Foo(BaseModel):
bar: int
# class FooV1(BaseModelV1):
# bar: int
@pytest.mark.parametrize(
"schema",
[
Foo
# FooV1
],
)
def test_schema_from_with_structured_output(schema: Type) -> None:
"""Test schema from with_structured_output."""
llm = ChatOpenAI()
structured_llm = llm.with_structured_output(
schema, method="json_schema", strict=True
)
expected = {
"properties": {"bar": {"title": "Bar", "type": "integer"}},
"required": ["bar"],
"title": schema.__name__,
"type": "object",
}
actual = structured_llm.get_output_schema().model_json_schema()
assert actual == expected
def test__create_usage_metadata() -> None:
usage_metadata = {
"completion_tokens": 15,
"prompt_tokens_details": None,
"completion_tokens_details": None,
"prompt_tokens": 11,
"total_tokens": 26,
}
result = _create_usage_metadata(usage_metadata)
assert result == UsageMetadata(
output_tokens=15,
input_tokens=11,
total_tokens=26,
input_token_details={},
output_token_details={},
)
def test__convert_to_openai_response_format() -> None:
# Test response formats that aren't tool-like.
response_format: dict = {
"type": "json_schema",
"json_schema": {
"name": "math_reasoning",
"schema": {
"type": "object",
"properties": {
"steps": {
"type": "array",
"items": {
"type": "object",
"properties": {
"explanation": {"type": "string"},
"output": {"type": "string"},
},
"required": ["explanation", "output"],
"additionalProperties": False,
},
},
"final_answer": {"type": "string"},
},
"required": ["steps", "final_answer"],
"additionalProperties": False,
},
"strict": True,
},
}
actual = _convert_to_openai_response_format(response_format)
assert actual == response_format
actual = _convert_to_openai_response_format(response_format["json_schema"])
assert actual == response_format
actual = _convert_to_openai_response_format(response_format, strict=True)
assert actual == response_format
with pytest.raises(ValueError):
_convert_to_openai_response_format(response_format, strict=False)
@pytest.mark.parametrize("method", ["function_calling", "json_schema"])
@pytest.mark.parametrize("strict", [True, None])
def test_structured_output_strict(
method: Literal["function_calling", "json_schema"], strict: Optional[bool]
) -> None:
"""Test to verify structured output with strict=True."""
llm = ChatOpenAI(model="gpt-4o-2024-08-06")
class Joke(BaseModel):
"""Joke to tell user."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
llm.with_structured_output(Joke, method=method, strict=strict)
# Schema
llm.with_structured_output(Joke.model_json_schema(), method=method, strict=strict)
def test_nested_structured_output_strict() -> None:
"""Test to verify structured output with strict=True for nested object."""
llm = ChatOpenAI(model="gpt-4o-2024-08-06")
class SelfEvaluation(TypedDict):
score: int
text: str
class JokeWithEvaluation(TypedDict):
"""Joke to tell user."""
setup: str
punchline: str
self_evaluation: SelfEvaluation
llm.with_structured_output(JokeWithEvaluation, method="json_schema")
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/test_imports.py | from langchain_openai.chat_models import __all__
EXPECTED_ALL = ["ChatOpenAI", "AzureChatOpenAI"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/test_azure_standard.py | """Standard LangChain interface tests"""
from typing import Tuple, Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_openai import AzureChatOpenAI
class TestOpenAIStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return AzureChatOpenAI
@property
def chat_model_params(self) -> dict:
return {
"deployment_name": "test",
"openai_api_version": "2021-10-01",
"azure_endpoint": "https://test.azure.com",
}
@pytest.mark.xfail(reason="AzureOpenAI does not support tool_choice='any'")
def test_bind_tool_pydantic(self, model: BaseChatModel) -> None:
super().test_bind_tool_pydantic(model)
@property
def init_from_env_params(self) -> Tuple[dict, dict, dict]:
return (
{
"AZURE_OPENAI_API_KEY": "api_key",
"AZURE_OPENAI_ENDPOINT": "https://endpoint.com",
"AZURE_OPENAI_AD_TOKEN": "token",
"OPENAI_ORG_ID": "org_id",
"OPENAI_API_VERSION": "yyyy-mm-dd",
"OPENAI_API_TYPE": "type",
},
{},
{
"openai_api_key": "api_key",
"azure_endpoint": "https://endpoint.com",
"azure_ad_token": "token",
"openai_organization": "org_id",
"openai_api_version": "yyyy-mm-dd",
"openai_api_type": "type",
},
)
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/test_azure.py | """Test Azure OpenAI Chat API wrapper."""
import os
from unittest import mock
from langchain_openai import AzureChatOpenAI
def test_initialize_azure_openai() -> None:
llm = AzureChatOpenAI( # type: ignore[call-arg]
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",
azure_endpoint="my-base-url",
)
assert llm.deployment_name == "35-turbo-dev"
assert llm.openai_api_version == "2023-05-15"
assert llm.azure_endpoint == "my-base-url"
def test_initialize_more() -> None:
llm = AzureChatOpenAI( # type: ignore[call-arg]
api_key="xyz", # type: ignore[arg-type]
azure_endpoint="my-base-url",
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",
temperature=0,
model="gpt-35-turbo",
model_version="0125",
)
assert llm.openai_api_key is not None
assert llm.openai_api_key.get_secret_value() == "xyz"
assert llm.azure_endpoint == "my-base-url"
assert llm.deployment_name == "35-turbo-dev"
assert llm.openai_api_version == "2023-05-15"
assert llm.temperature == 0
ls_params = llm._get_ls_params()
assert ls_params["ls_provider"] == "azure"
assert ls_params["ls_model_name"] == "gpt-35-turbo-0125"
def test_initialize_azure_openai_with_openai_api_base_set() -> None:
with mock.patch.dict(os.environ, {"OPENAI_API_BASE": "https://api.openai.com"}):
llm = AzureChatOpenAI( # type: ignore[call-arg, call-arg]
api_key="xyz", # type: ignore[arg-type]
azure_endpoint="my-base-url",
azure_deployment="35-turbo-dev",
openai_api_version="2023-05-15",
temperature=0,
openai_api_base=None,
)
assert llm.openai_api_key is not None
assert llm.openai_api_key.get_secret_value() == "xyz"
assert llm.azure_endpoint == "my-base-url"
assert llm.deployment_name == "35-turbo-dev"
assert llm.openai_api_version == "2023-05-15"
assert llm.temperature == 0
ls_params = llm._get_ls_params()
assert ls_params["ls_provider"] == "azure"
assert ls_params["ls_model_name"] == "35-turbo-dev"
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_base_standard.ambr | # serializer version: 1
# name: TestOpenAIStandard.test_serdes[serialized]
dict({
'id': list([
'langchain',
'chat_models',
'openai',
'ChatOpenAI',
]),
'kwargs': dict({
'max_retries': 2,
'max_tokens': 100,
'model_name': 'gpt-3.5-turbo',
'n': 1,
'openai_api_key': dict({
'id': list([
'OPENAI_API_KEY',
]),
'lc': 1,
'type': 'secret',
}),
'request_timeout': 60.0,
'stop': list([
]),
'temperature': 0.0,
}),
'lc': 1,
'name': 'ChatOpenAI',
'type': 'constructor',
})
# ---
|
0 | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models | lc_public_repos/langchain/libs/partners/openai/tests/unit_tests/chat_models/__snapshots__/test_azure_standard.ambr | # serializer version: 1
# name: TestOpenAIStandard.test_serdes[serialized]
dict({
'id': list([
'langchain',
'chat_models',
'azure_openai',
'AzureChatOpenAI',
]),
'kwargs': dict({
'azure_endpoint': 'https://test.azure.com',
'deployment_name': 'test',
'disabled_params': dict({
'parallel_tool_calls': None,
}),
'max_retries': 2,
'max_tokens': 100,
'n': 1,
'openai_api_key': dict({
'id': list([
'AZURE_OPENAI_API_KEY',
]),
'lc': 1,
'type': 'secret',
}),
'openai_api_type': 'azure',
'openai_api_version': '2021-10-01',
'request_timeout': 60.0,
'stop': list([
]),
'temperature': 0.0,
'validate_base_url': True,
}),
'lc': 1,
'name': 'AzureChatOpenAI',
'type': 'constructor',
})
# ---
|
0 | lc_public_repos/langchain/libs/partners/openai | lc_public_repos/langchain/libs/partners/openai/scripts/lint_imports.sh | #!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi
|
0 | lc_public_repos/langchain/libs/partners/openai | lc_public_repos/langchain/libs/partners/openai/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(file) # noqa: T201
traceback.print_exc()
print() # noqa: T201
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/unstructured/README.md | This package has moved!
https://github.com/langchain-ai/langchain-unstructured/tree/main/libs/unstructured
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/huggingface/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test integration_tests: TEST_FILE=tests/integration_tests/
test tests integration_test integration_tests:
poetry run pytest $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/huggingface --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_huggingface
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff check --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_huggingface -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/huggingface/LICENSE | MIT License
Copyright (c) 2023 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/huggingface/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "aiohappyeyeballs"
version = "2.4.3"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
name = "aiohttp"
version = "3.10.10"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
files = [
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huggingface-hub = ">=0.16.4,<1.0"
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[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
propcache = ">=0.2.0"
[[package]]
name = "zipp"
version = "3.20.2"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
{file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
]
[package.extras]
check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "2d468da765374084b6ad008d1b3e6d4d12f3c1d696cea84f2245f49012691272"
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/huggingface/README.md | # langchain-huggingface
This package contains the LangChain integrations for huggingface related classes.
## Installation and Setup
- Install the LangChain partner package
```bash
pip install langchain-huggingface
```
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/huggingface/pyproject.toml | [build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-huggingface"
version = "0.1.2"
description = "An integration package connecting Hugging Face and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.mypy]
disallow_untyped_defs = "True"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/huggingface"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-huggingface%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
tokenizers = ">=0.19.1"
transformers = ">=4.39.0"
sentence-transformers = ">=2.6.0"
huggingface-hub = ">=0.23.0"
[tool.ruff.lint]
select = ["E", "F", "I", "T201"]
[tool.coverage.run]
omit = ["tests/*"]
[tool.pytest.ini_options]
addopts = "--strict-markers --strict-config --durations=5"
markers = [
"requires: mark tests as requiring a specific library",
"compile: mark placeholder test used to compile integration tests without running them",
]
asyncio_mode = "auto"
[tool.poetry.group.test]
optional = true
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
optional = true
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.test.dependencies]
pytest = "^7.3.0"
pytest-asyncio = "^0.21.1"
pytest-watcher = "^0.3.4"
[[tool.poetry.group.test.dependencies.scipy]]
version = "^1"
python = "<3.12"
[[tool.poetry.group.test.dependencies.scipy]]
version = "^1.7.0"
python = ">=3.12"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1"
python = "<3.12"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.26.0"
python = ">=3.12"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
[tool.poetry.group.dev.dependencies]
ipykernel = "^6.29.2"
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
[tool.poetry.group.test.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.test.dependencies.langchain-tests]
path = "../../standard-tests"
develop = true
[tool.poetry.group.test.dependencies.langchain-community]
path = "../../community"
develop = true
[tool.poetry.group.dev.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.typing.dependencies.langchain-core]
path = "../../core"
develop = true
|
0 | lc_public_repos/langchain/libs/partners/huggingface/tests | lc_public_repos/langchain/libs/partners/huggingface/tests/integration_tests/test_standard.py | """Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
class TestHuggingFaceEndpoint(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatHuggingFace
@property
def chat_model_params(self) -> dict:
return {}
@pytest.fixture
def model(self) -> BaseChatModel:
llm = HuggingFaceEndpoint( # type: ignore[call-arg]
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
max_new_tokens=512,
do_sample=False,
repetition_penalty=1.03,
)
return self.chat_model_class(llm=llm) # type: ignore[call-arg]
@pytest.mark.xfail(reason=("Not implemented"))
def test_stream(self, model: BaseChatModel) -> None:
super().test_stream(model)
@pytest.mark.xfail(reason=("Not implemented"))
async def test_astream(self, model: BaseChatModel) -> None:
await super().test_astream(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_usage_metadata(self, model: BaseChatModel) -> None:
super().test_usage_metadata(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_usage_metadata_streaming(self, model: BaseChatModel) -> None:
super().test_usage_metadata_streaming(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_stop_sequence(self, model: BaseChatModel) -> None:
super().test_stop_sequence(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_tool_calling(self, model: BaseChatModel) -> None:
super().test_tool_calling(model)
@pytest.mark.xfail(reason=("Not implemented"))
async def test_tool_calling_async(self, model: BaseChatModel) -> None:
await super().test_tool_calling_async(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_tool_calling_with_no_arguments(self, model: BaseChatModel) -> None:
super().test_tool_calling_with_no_arguments(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_bind_runnables_as_tools(self, model: BaseChatModel) -> None:
super().test_bind_runnables_as_tools(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_structured_output(self, model: BaseChatModel) -> None:
super().test_structured_output(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_structured_output_async(self, model: BaseChatModel) -> None: # type: ignore[override]
super().test_structured_output(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_structured_output_pydantic_2_v1(self, model: BaseChatModel) -> None:
super().test_structured_output_pydantic_2_v1(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_structured_output_optional_param(self, model: BaseChatModel) -> None:
super().test_structured_output_optional_param(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_tool_message_histories_list_content(self, model: BaseChatModel) -> None:
super().test_tool_message_histories_list_content(model)
@pytest.mark.xfail(reason=("Not implemented"))
def test_structured_few_shot_examples(self, model: BaseChatModel) -> None:
super().test_structured_few_shot_examples(model)
|
0 | lc_public_repos/langchain/libs/partners/huggingface/tests | lc_public_repos/langchain/libs/partners/huggingface/tests/integration_tests/test_llms.py | from typing import Generator
from langchain_huggingface.llms import HuggingFacePipeline
def test_huggingface_pipeline_streaming() -> None:
"""Test streaming tokens from huggingface_pipeline."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}
)
generator = llm.stream("Q: How do you say 'hello' in German? A:'", stop=["."])
stream_results_string = ""
assert isinstance(generator, Generator)
for chunk in generator:
assert isinstance(chunk, str)
stream_results_string = chunk
assert len(stream_results_string.strip()) > 1
|
0 | lc_public_repos/langchain/libs/partners/huggingface/tests | lc_public_repos/langchain/libs/partners/huggingface/tests/integration_tests/test_compile.py | import pytest # type: ignore[import-not-found, import-not-found]
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
0 | lc_public_repos/langchain/libs/partners/huggingface/tests | lc_public_repos/langchain/libs/partners/huggingface/tests/integration_tests/test_embeddings_standard.py | """Test HuggingFace embeddings."""
from typing import Type
from langchain_tests.integration_tests import EmbeddingsIntegrationTests
from langchain_huggingface.embeddings import (
HuggingFaceEmbeddings,
HuggingFaceEndpointEmbeddings,
)
class TestHuggingFaceEmbeddings(EmbeddingsIntegrationTests):
@property
def embeddings_class(self) -> Type[HuggingFaceEmbeddings]:
return HuggingFaceEmbeddings
@property
def embedding_model_params(self) -> dict:
return {"model_name": "sentence-transformers/all-mpnet-base-v2"}
class TestHuggingFaceEndpointEmbeddings(EmbeddingsIntegrationTests):
@property
def embeddings_class(self) -> Type[HuggingFaceEndpointEmbeddings]:
return HuggingFaceEndpointEmbeddings
@property
def embedding_model_params(self) -> dict:
return {"model": "sentence-transformers/all-mpnet-base-v2"}
|
0 | lc_public_repos/langchain/libs/partners/huggingface/tests | lc_public_repos/langchain/libs/partners/huggingface/tests/unit_tests/test_chat_models.py | from typing import Any, Dict, List # type: ignore[import-not-found]
from unittest.mock import MagicMock, Mock, patch
import pytest # type: ignore[import-not-found]
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.outputs import ChatResult
from langchain_core.tools import BaseTool
from langchain_huggingface.chat_models import ( # type: ignore[import]
TGI_MESSAGE,
ChatHuggingFace,
_convert_message_to_chat_message,
_convert_TGI_message_to_LC_message,
)
from langchain_huggingface.llms.huggingface_endpoint import (
HuggingFaceEndpoint,
)
@pytest.mark.parametrize(
("message", "expected"),
[
(
SystemMessage(content="Hello"),
dict(role="system", content="Hello"),
),
(
HumanMessage(content="Hello"),
dict(role="user", content="Hello"),
),
(
AIMessage(content="Hello"),
dict(role="assistant", content="Hello", tool_calls=None),
),
(
ChatMessage(role="assistant", content="Hello"),
dict(role="assistant", content="Hello"),
),
],
)
def test_convert_message_to_chat_message(
message: BaseMessage, expected: Dict[str, str]
) -> None:
result = _convert_message_to_chat_message(message)
assert result == expected
@pytest.mark.parametrize(
("tgi_message", "expected"),
[
(
TGI_MESSAGE(role="assistant", content="Hello", tool_calls=[]),
AIMessage(content="Hello"),
),
(
TGI_MESSAGE(role="assistant", content="", tool_calls=[]),
AIMessage(content=""),
),
(
TGI_MESSAGE(
role="assistant",
content="",
tool_calls=[{"function": {"arguments": "'function string'"}}],
),
AIMessage(
content="",
additional_kwargs={
"tool_calls": [{"function": {"arguments": '"function string"'}}]
},
),
),
],
)
def test_convert_TGI_message_to_LC_message(
tgi_message: TGI_MESSAGE, expected: BaseMessage
) -> None:
result = _convert_TGI_message_to_LC_message(tgi_message)
assert result == expected
@pytest.fixture
def mock_llm() -> Mock:
llm = Mock(spec=HuggingFaceEndpoint)
llm.inference_server_url = "test endpoint url"
return llm
@pytest.fixture
@patch(
"langchain_huggingface.chat_models.huggingface.ChatHuggingFace._resolve_model_id"
)
def chat_hugging_face(mock_resolve_id: Any, mock_llm: Any) -> ChatHuggingFace:
chat_hf = ChatHuggingFace(llm=mock_llm, tokenizer=MagicMock())
return chat_hf
def test_create_chat_result(chat_hugging_face: Any) -> None:
mock_response = MagicMock()
mock_response.choices = [
MagicMock(
message=TGI_MESSAGE(
role="assistant", content="test message", tool_calls=[]
),
finish_reason="test finish reason",
)
]
mock_response.usage = {"tokens": 420}
result = chat_hugging_face._create_chat_result(mock_response)
assert isinstance(result, ChatResult)
assert result.generations[0].message.content == "test message"
assert (
result.generations[0].generation_info["finish_reason"] == "test finish reason" # type: ignore[index]
)
assert result.llm_output["token_usage"]["tokens"] == 420 # type: ignore[index]
assert result.llm_output["model"] == chat_hugging_face.llm.inference_server_url # type: ignore[index]
@pytest.mark.parametrize(
"messages, expected_error",
[
([], "At least one HumanMessage must be provided!"),
(
[HumanMessage(content="Hi"), AIMessage(content="Hello")],
"Last message must be a HumanMessage!",
),
],
)
def test_to_chat_prompt_errors(
chat_hugging_face: Any, messages: List[BaseMessage], expected_error: str
) -> None:
with pytest.raises(ValueError) as e:
chat_hugging_face._to_chat_prompt(messages)
assert expected_error in str(e.value)
def test_to_chat_prompt_valid_messages(chat_hugging_face: Any) -> None:
messages = [AIMessage(content="Hello"), HumanMessage(content="How are you?")]
expected_prompt = "Generated chat prompt"
chat_hugging_face.tokenizer.apply_chat_template.return_value = expected_prompt
result = chat_hugging_face._to_chat_prompt(messages)
assert result == expected_prompt
chat_hugging_face.tokenizer.apply_chat_template.assert_called_once_with(
[
{"role": "assistant", "content": "Hello"},
{"role": "user", "content": "How are you?"},
],
tokenize=False,
add_generation_prompt=True,
)
@pytest.mark.parametrize(
("message", "expected"),
[
(
SystemMessage(content="You are a helpful assistant."),
{"role": "system", "content": "You are a helpful assistant."},
),
(
AIMessage(content="How can I help you?"),
{"role": "assistant", "content": "How can I help you?"},
),
(
HumanMessage(content="Hello"),
{"role": "user", "content": "Hello"},
),
],
)
def test_to_chatml_format(
chat_hugging_face: Any, message: BaseMessage, expected: Dict[str, str]
) -> None:
result = chat_hugging_face._to_chatml_format(message)
assert result == expected
def test_to_chatml_format_with_invalid_type(chat_hugging_face: Any) -> None:
message = "Invalid message type"
with pytest.raises(ValueError) as e:
chat_hugging_face._to_chatml_format(message)
assert "Unknown message type:" in str(e.value)
def tool_mock() -> Dict:
return {"function": {"name": "test_tool"}}
@pytest.mark.parametrize(
"tools, tool_choice, expected_exception, expected_message",
[
([tool_mock()], ["invalid type"], ValueError, "Unrecognized tool_choice type."),
(
[tool_mock(), tool_mock()],
"test_tool",
ValueError,
"must provide exactly one tool.",
),
(
[tool_mock()],
{"type": "function", "function": {"name": "other_tool"}},
ValueError,
"Tool choice {'type': 'function', 'function': {'name': 'other_tool'}} "
"was specified, but the only provided tool was test_tool.",
),
],
)
def test_bind_tools_errors(
chat_hugging_face: Any,
tools: Dict[str, str],
tool_choice: Any,
expected_exception: Any,
expected_message: str,
) -> None:
with patch(
"langchain_huggingface.chat_models.huggingface.convert_to_openai_tool",
side_effect=lambda x: x,
):
with pytest.raises(expected_exception) as excinfo:
chat_hugging_face.bind_tools(tools, tool_choice=tool_choice)
assert expected_message in str(excinfo.value)
def test_bind_tools(chat_hugging_face: Any) -> None:
tools = [MagicMock(spec=BaseTool)]
with patch(
"langchain_huggingface.chat_models.huggingface.convert_to_openai_tool",
side_effect=lambda x: x,
), patch("langchain_core.runnables.base.Runnable.bind") as mock_super_bind:
chat_hugging_face.bind_tools(tools, tool_choice="auto")
mock_super_bind.assert_called_once()
_, kwargs = mock_super_bind.call_args
assert kwargs["tools"] == tools
assert kwargs["tool_choice"] == "auto"
|
0 | lc_public_repos/langchain/libs/partners/huggingface/tests | lc_public_repos/langchain/libs/partners/huggingface/tests/unit_tests/test_huggingface_pipeline.py | from unittest.mock import MagicMock, patch
from langchain_huggingface import HuggingFacePipeline
DEFAULT_MODEL_ID = "gpt2"
def test_initialization_default() -> None:
"""Test default initialization"""
llm = HuggingFacePipeline()
assert llm.model_id == DEFAULT_MODEL_ID
@patch("transformers.pipeline")
def test_initialization_with_pipeline(mock_pipeline: MagicMock) -> None:
"""Test initialization with a pipeline object"""
mock_pipe = MagicMock()
mock_pipe.model.name_or_path = "mock-model-id"
mock_pipeline.return_value = mock_pipe
llm = HuggingFacePipeline(pipeline=mock_pipe)
assert llm.model_id == "mock-model-id"
@patch("transformers.AutoTokenizer.from_pretrained")
@patch("transformers.AutoModelForCausalLM.from_pretrained")
@patch("transformers.pipeline")
def test_initialization_with_from_model_id(
mock_pipeline: MagicMock, mock_model: MagicMock, mock_tokenizer: MagicMock
) -> None:
"""Test initialization with the from_model_id method"""
mock_tokenizer.return_value = MagicMock(pad_token_id=0)
mock_model.return_value = MagicMock()
mock_pipe = MagicMock()
mock_pipe.task = "text-generation"
mock_pipe.model = mock_model.return_value
mock_pipeline.return_value = mock_pipe
llm = HuggingFacePipeline.from_model_id(
model_id="mock-model-id",
task="text-generation",
)
assert llm.model_id == "mock-model-id"
|
0 | lc_public_repos/langchain/libs/partners/huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/__init__.py | from langchain_huggingface.chat_models import (
ChatHuggingFace, # type: ignore[import-not-found]
)
from langchain_huggingface.embeddings import (
HuggingFaceEmbeddings,
HuggingFaceEndpointEmbeddings,
)
from langchain_huggingface.llms import (
HuggingFaceEndpoint,
HuggingFacePipeline,
)
__all__ = [
"ChatHuggingFace",
"HuggingFaceEndpointEmbeddings",
"HuggingFaceEmbeddings",
"HuggingFaceEndpoint",
"HuggingFacePipeline",
]
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/llms/huggingface_endpoint.py | import inspect
import json # type: ignore[import-not-found]
import logging
import os
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import LLM
from langchain_core.outputs import GenerationChunk
from langchain_core.utils import from_env, get_pydantic_field_names
from pydantic import ConfigDict, Field, model_validator
from typing_extensions import Self
logger = logging.getLogger(__name__)
VALID_TASKS = (
"text2text-generation",
"text-generation",
"summarization",
"conversational",
)
class HuggingFaceEndpoint(LLM):
"""
HuggingFace Endpoint.
To use this class, you should have installed the ``huggingface_hub`` package, and
the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token,
or given as a named parameter to the constructor.
Example:
.. code-block:: python
# Basic Example (no streaming)
llm = HuggingFaceEndpoint(
endpoint_url="http://localhost:8010/",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
huggingfacehub_api_token="my-api-key"
)
print(llm.invoke("What is Deep Learning?"))
# Streaming response example
from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
callbacks = [StreamingStdOutCallbackHandler()]
llm = HuggingFaceEndpoint(
endpoint_url="http://localhost:8010/",
max_new_tokens=512,
top_k=10,
top_p=0.95,
typical_p=0.95,
temperature=0.01,
repetition_penalty=1.03,
callbacks=callbacks,
streaming=True,
huggingfacehub_api_token="my-api-key"
)
print(llm.invoke("What is Deep Learning?"))
""" # noqa: E501
endpoint_url: Optional[str] = None
"""Endpoint URL to use. If repo_id is not specified then this needs to given or
should be pass as env variable in `HF_INFERENCE_ENDPOINT`"""
repo_id: Optional[str] = None
"""Repo to use. If endpoint_url is not specified then this needs to given"""
huggingfacehub_api_token: Optional[str] = Field(
default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
)
max_new_tokens: int = 512
"""Maximum number of generated tokens"""
top_k: Optional[int] = None
"""The number of highest probability vocabulary tokens to keep for
top-k-filtering."""
top_p: Optional[float] = 0.95
"""If set to < 1, only the smallest set of most probable tokens with probabilities
that add up to `top_p` or higher are kept for generation."""
typical_p: Optional[float] = 0.95
"""Typical Decoding mass. See [Typical Decoding for Natural Language
Generation](https://arxiv.org/abs/2202.00666) for more information."""
temperature: Optional[float] = 0.8
"""The value used to module the logits distribution."""
repetition_penalty: Optional[float] = None
"""The parameter for repetition penalty. 1.0 means no penalty.
See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details."""
return_full_text: bool = False
"""Whether to prepend the prompt to the generated text"""
truncate: Optional[int] = None
"""Truncate inputs tokens to the given size"""
stop_sequences: List[str] = Field(default_factory=list)
"""Stop generating tokens if a member of `stop_sequences` is generated"""
seed: Optional[int] = None
"""Random sampling seed"""
inference_server_url: str = ""
"""text-generation-inference instance base url"""
timeout: int = 120
"""Timeout in seconds"""
streaming: bool = False
"""Whether to generate a stream of tokens asynchronously"""
do_sample: bool = False
"""Activate logits sampling"""
watermark: bool = False
"""Watermarking with [A Watermark for Large Language Models]
(https://arxiv.org/abs/2301.10226)"""
server_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any text-generation-inference server parameters not explicitly specified"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for `call` not explicitly specified"""
model: str
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
task: Optional[str] = None
"""Task to call the model with.
Should be a task that returns `generated_text` or `summary_text`."""
model_config = ConfigDict(
extra="forbid",
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please make sure that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
# to correctly create the InferenceClient and AsyncInferenceClient
# in validate_environment, we need to populate values["model"].
# from InferenceClient docstring:
# model (`str`, `optional`):
# The model to run inference with. Can be a model id hosted on the Hugging
# Face Hub, e.g. `bigcode/starcoder`
# or a URL to a deployed Inference Endpoint. Defaults to None, in which
# case a recommended model is
# automatically selected for the task.
# this string could be in 3 places of descending priority:
# 2. values["model"] or values["endpoint_url"] or values["repo_id"]
# (equal priority - don't allow both set)
# 3. values["HF_INFERENCE_ENDPOINT"] (if none above set)
model = values.get("model")
endpoint_url = values.get("endpoint_url")
repo_id = values.get("repo_id")
if sum([bool(model), bool(endpoint_url), bool(repo_id)]) > 1:
raise ValueError(
"Please specify either a `model` OR an `endpoint_url` OR a `repo_id`,"
"not more than one."
)
values["model"] = (
model or endpoint_url or repo_id or os.environ.get("HF_INFERENCE_ENDPOINT")
)
if not values["model"]:
raise ValueError(
"Please specify a `model` or an `endpoint_url` or a `repo_id` for the "
"model."
)
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that package is installed and that the API token is valid."""
try:
from huggingface_hub import login # type: ignore[import]
except ImportError:
raise ImportError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv(
"HF_TOKEN"
)
if huggingfacehub_api_token is not None:
try:
login(token=huggingfacehub_api_token)
except Exception as e:
raise ValueError(
"Could not authenticate with huggingface_hub. "
"Please check your API token."
) from e
from huggingface_hub import AsyncInferenceClient, InferenceClient
# Instantiate clients with supported kwargs
sync_supported_kwargs = set(inspect.signature(InferenceClient).parameters)
self.client = InferenceClient(
model=self.model,
timeout=self.timeout,
token=huggingfacehub_api_token,
**{
key: value
for key, value in self.server_kwargs.items()
if key in sync_supported_kwargs
},
)
async_supported_kwargs = set(inspect.signature(AsyncInferenceClient).parameters)
self.async_client = AsyncInferenceClient(
model=self.model,
timeout=self.timeout,
token=huggingfacehub_api_token,
**{
key: value
for key, value in self.server_kwargs.items()
if key in async_supported_kwargs
},
)
ignored_kwargs = (
set(self.server_kwargs.keys())
- sync_supported_kwargs
- async_supported_kwargs
)
if len(ignored_kwargs) > 0:
logger.warning(
f"Ignoring following parameters as they are not supported by the "
f"InferenceClient or AsyncInferenceClient: {ignored_kwargs}."
)
return self
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling text generation inference API."""
return {
"max_new_tokens": self.max_new_tokens,
"top_k": self.top_k,
"top_p": self.top_p,
"typical_p": self.typical_p,
"temperature": self.temperature,
"repetition_penalty": self.repetition_penalty,
"return_full_text": self.return_full_text,
"truncate": self.truncate,
"stop_sequences": self.stop_sequences,
"seed": self.seed,
"do_sample": self.do_sample,
"watermark": self.watermark,
**self.model_kwargs,
}
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
_model_kwargs = self.model_kwargs or {}
return {
**{"endpoint_url": self.endpoint_url, "task": self.task},
**{"model_kwargs": _model_kwargs},
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "huggingface_endpoint"
def _invocation_params(
self, runtime_stop: Optional[List[str]], **kwargs: Any
) -> Dict[str, Any]:
params = {**self._default_params, **kwargs}
params["stop_sequences"] = params["stop_sequences"] + (runtime_stop or [])
return params
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint."""
invocation_params = self._invocation_params(stop, **kwargs)
if self.streaming:
completion = ""
for chunk in self._stream(prompt, stop, run_manager, **invocation_params):
completion += chunk.text
return completion
else:
invocation_params["stop"] = invocation_params[
"stop_sequences"
] # porting 'stop_sequences' into the 'stop' argument
response = self.client.post(
json={"inputs": prompt, "parameters": invocation_params},
stream=False,
task=self.task,
)
response_text = json.loads(response.decode())[0]["generated_text"]
# Maybe the generation has stopped at one of the stop sequences:
# then we remove this stop sequence from the end of the generated text
for stop_seq in invocation_params["stop_sequences"]:
if response_text[-len(stop_seq) :] == stop_seq:
response_text = response_text[: -len(stop_seq)]
return response_text
async def _acall(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
invocation_params = self._invocation_params(stop, **kwargs)
if self.streaming:
completion = ""
async for chunk in self._astream(
prompt, stop, run_manager, **invocation_params
):
completion += chunk.text
return completion
else:
invocation_params["stop"] = invocation_params["stop_sequences"]
response = await self.async_client.post(
json={"inputs": prompt, "parameters": invocation_params},
stream=False,
task=self.task,
)
response_text = json.loads(response.decode())[0]["generated_text"]
# Maybe the generation has stopped at one of the stop sequences:
# then remove this stop sequence from the end of the generated text
for stop_seq in invocation_params["stop_sequences"]:
if response_text[-len(stop_seq) :] == stop_seq:
response_text = response_text[: -len(stop_seq)]
return response_text
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
for response in self.client.text_generation(
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
for stop_seq in invocation_params["stop_sequences"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
text = response
# yield text, if any
if text:
chunk = GenerationChunk(text=text)
if run_manager:
run_manager.on_llm_new_token(chunk.text)
yield chunk
# break if stop sequence found
if stop_seq_found:
break
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
invocation_params = self._invocation_params(stop, **kwargs)
async for response in await self.async_client.text_generation(
prompt, **invocation_params, stream=True
):
# identify stop sequence in generated text, if any
stop_seq_found: Optional[str] = None
for stop_seq in invocation_params["stop_sequences"]:
if stop_seq in response:
stop_seq_found = stop_seq
# identify text to yield
text: Optional[str] = None
if stop_seq_found:
text = response[: response.index(stop_seq_found)]
else:
text = response
# yield text, if any
if text:
chunk = GenerationChunk(text=text)
if run_manager:
await run_manager.on_llm_new_token(chunk.text)
yield chunk
# break if stop sequence found
if stop_seq_found:
break
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py | from __future__ import annotations # type: ignore[import-not-found]
import importlib.util
import logging
from typing import Any, Dict, Iterator, List, Mapping, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from pydantic import ConfigDict, model_validator
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation"
VALID_TASKS = (
"text2text-generation",
"text-generation",
"summarization",
"translation",
)
DEFAULT_BATCH_SIZE = 4
logger = logging.getLogger(__name__)
class HuggingFacePipeline(BaseLLM):
"""HuggingFace Pipeline API.
To use, you should have the ``transformers`` python package installed.
Only supports `text-generation`, `text2text-generation`, `summarization` and
`translation` for now.
Example using from_model_id:
.. code-block:: python
from langchain_huggingface import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
Example passing pipeline in directly:
.. code-block:: python
from langchain_huggingface import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
"""
pipeline: Any = None #: :meta private:
model_id: Optional[str] = None
"""The model name. If not set explicitly by the user,
it will be inferred from the provided pipeline (if available).
If neither is provided, the DEFAULT_MODEL_ID will be used."""
model_kwargs: Optional[dict] = None
"""Keyword arguments passed to the model."""
pipeline_kwargs: Optional[dict] = None
"""Keyword arguments passed to the pipeline."""
batch_size: int = DEFAULT_BATCH_SIZE
"""Batch size to use when passing multiple documents to generate."""
model_config = ConfigDict(
extra="forbid",
)
@model_validator(mode="before")
@classmethod
def pre_init_validator(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""Ensure model_id is set either by pipeline or user input."""
if "model_id" not in values:
if "pipeline" in values and values["pipeline"]:
values["model_id"] = values["pipeline"].model.name_or_path
else:
values["model_id"] = DEFAULT_MODEL_ID
return values
@classmethod
def from_model_id(
cls,
model_id: str,
task: str,
backend: str = "default",
device: Optional[int] = None,
device_map: Optional[str] = None,
model_kwargs: Optional[dict] = None,
pipeline_kwargs: Optional[dict] = None,
batch_size: int = DEFAULT_BATCH_SIZE,
**kwargs: Any,
) -> HuggingFacePipeline:
"""Construct the pipeline object from model_id and task."""
try:
from transformers import ( # type: ignore[import]
AutoModelForCausalLM,
AutoModelForSeq2SeqLM,
AutoTokenizer,
)
from transformers import pipeline as hf_pipeline # type: ignore[import]
except ImportError:
raise ValueError(
"Could not import transformers python package. "
"Please install it with `pip install transformers`."
)
_model_kwargs = model_kwargs.copy() if model_kwargs else {}
if device_map is not None:
if device is not None:
raise ValueError(
"Both `device` and `device_map` are specified. "
"`device` will override `device_map`. "
"You will most likely encounter unexpected behavior."
"Please remove `device` and keep "
"`device_map`."
)
if "device_map" in _model_kwargs:
raise ValueError("`device_map` is already specified in `model_kwargs`.")
_model_kwargs["device_map"] = device_map
tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
try:
if task == "text-generation":
if backend == "openvino":
try:
from optimum.intel.openvino import ( # type: ignore[import]
OVModelForCausalLM,
)
except ImportError:
raise ValueError(
"Could not import optimum-intel python package. "
"Please install it with: "
"pip install 'optimum[openvino,nncf]' "
)
try:
# use local model
model = OVModelForCausalLM.from_pretrained(
model_id, **_model_kwargs
)
except Exception:
# use remote model
model = OVModelForCausalLM.from_pretrained(
model_id, export=True, **_model_kwargs
)
else:
model = AutoModelForCausalLM.from_pretrained(
model_id, **_model_kwargs
)
elif task in ("text2text-generation", "summarization", "translation"):
if backend == "openvino":
try:
from optimum.intel.openvino import OVModelForSeq2SeqLM
except ImportError:
raise ValueError(
"Could not import optimum-intel python package. "
"Please install it with: "
"pip install 'optimum[openvino,nncf]' "
)
try:
# use local model
model = OVModelForSeq2SeqLM.from_pretrained(
model_id, **_model_kwargs
)
except Exception:
# use remote model
model = OVModelForSeq2SeqLM.from_pretrained(
model_id, export=True, **_model_kwargs
)
else:
model = AutoModelForSeq2SeqLM.from_pretrained(
model_id, **_model_kwargs
)
else:
raise ValueError(
f"Got invalid task {task}, "
f"currently only {VALID_TASKS} are supported"
)
except ImportError as e:
raise ValueError(
f"Could not load the {task} model due to missing dependencies."
) from e
if tokenizer.pad_token is None:
tokenizer.pad_token_id = model.config.eos_token_id
if (
(
getattr(model, "is_loaded_in_4bit", False)
or getattr(model, "is_loaded_in_8bit", False)
)
and device is not None
and backend == "default"
):
logger.warning(
f"Setting the `device` argument to None from {device} to avoid "
"the error caused by attempting to move the model that was already "
"loaded on the GPU using the Accelerate module to the same or "
"another device."
)
device = None
if (
device is not None
and importlib.util.find_spec("torch") is not None
and backend == "default"
):
import torch
cuda_device_count = torch.cuda.device_count()
if device < -1 or (device >= cuda_device_count):
raise ValueError(
f"Got device=={device}, "
f"device is required to be within [-1, {cuda_device_count})"
)
if device_map is not None and device < 0:
device = None
if device is not None and device < 0 and cuda_device_count > 0:
logger.warning(
"Device has %d GPUs available. "
"Provide device={deviceId} to `from_model_id` to use available"
"GPUs for execution. deviceId is -1 (default) for CPU and "
"can be a positive integer associated with CUDA device id.",
cuda_device_count,
)
if device is not None and device_map is not None and backend == "openvino":
logger.warning("Please set device for OpenVINO through: `model_kwargs`")
if "trust_remote_code" in _model_kwargs:
_model_kwargs = {
k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"
}
_pipeline_kwargs = pipeline_kwargs or {}
pipeline = hf_pipeline(
task=task,
model=model,
tokenizer=tokenizer,
device=device,
batch_size=batch_size,
model_kwargs=_model_kwargs,
**_pipeline_kwargs,
)
if pipeline.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
return cls(
pipeline=pipeline,
model_id=model_id,
model_kwargs=_model_kwargs,
pipeline_kwargs=_pipeline_kwargs,
batch_size=batch_size,
**kwargs,
)
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {
"model_id": self.model_id,
"model_kwargs": self.model_kwargs,
"pipeline_kwargs": self.pipeline_kwargs,
}
@property
def _llm_type(self) -> str:
return "huggingface_pipeline"
def _generate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
# List to hold all results
text_generations: List[str] = []
pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
skip_prompt = kwargs.get("skip_prompt", False)
for i in range(0, len(prompts), self.batch_size):
batch_prompts = prompts[i : i + self.batch_size]
# Process batch of prompts
responses = self.pipeline(
batch_prompts,
**pipeline_kwargs,
)
# Process each response in the batch
for j, response in enumerate(responses):
if isinstance(response, list):
# if model returns multiple generations, pick the top one
response = response[0]
if self.pipeline.task == "text-generation":
text = response["generated_text"]
elif self.pipeline.task == "text2text-generation":
text = response["generated_text"]
elif self.pipeline.task == "summarization":
text = response["summary_text"]
elif self.pipeline.task in "translation":
text = response["translation_text"]
else:
raise ValueError(
f"Got invalid task {self.pipeline.task}, "
f"currently only {VALID_TASKS} are supported"
)
if skip_prompt:
text = text[len(batch_prompts[j]) :]
# Append the processed text to results
text_generations.append(text)
return LLMResult(
generations=[[Generation(text=text)] for text in text_generations]
)
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
from threading import Thread
import torch
from transformers import (
StoppingCriteria,
StoppingCriteriaList,
TextIteratorStreamer,
)
pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
skip_prompt = kwargs.get("skip_prompt", True)
if stop is not None:
stop = self.pipeline.tokenizer.convert_tokens_to_ids(stop)
stopping_ids_list = stop or []
class StopOnTokens(StoppingCriteria):
def __call__(
self,
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
**kwargs: Any,
) -> bool:
for stop_id in stopping_ids_list:
if input_ids[0][-1] == stop_id:
return True
return False
stopping_criteria = StoppingCriteriaList([StopOnTokens()])
streamer = TextIteratorStreamer(
self.pipeline.tokenizer,
timeout=60.0,
skip_prompt=skip_prompt,
skip_special_tokens=True,
)
generation_kwargs = dict(
text_inputs=prompt,
streamer=streamer,
stopping_criteria=stopping_criteria,
**pipeline_kwargs,
)
t1 = Thread(target=self.pipeline, kwargs=generation_kwargs)
t1.start()
for char in streamer:
chunk = GenerationChunk(text=char)
if run_manager:
run_manager.on_llm_new_token(chunk.text, chunk=chunk)
yield chunk
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/llms/__init__.py | from langchain_huggingface.llms.huggingface_endpoint import (
HuggingFaceEndpoint, # type: ignore[import-not-found]
)
from langchain_huggingface.llms.huggingface_pipeline import HuggingFacePipeline
__all__ = [
"HuggingFaceEndpoint",
"HuggingFacePipeline",
]
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface_endpoint.py | import json
import os
from typing import Any, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.utils import from_env
from pydantic import BaseModel, ConfigDict, Field, model_validator
from typing_extensions import Self
DEFAULT_MODEL = "sentence-transformers/all-mpnet-base-v2"
VALID_TASKS = ("feature-extraction",)
class HuggingFaceEndpointEmbeddings(BaseModel, Embeddings):
"""HuggingFaceHub embedding models.
To use, you should have the ``huggingface_hub`` python package installed, and the
environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass
it as a named parameter to the constructor.
Example:
.. code-block:: python
from langchain_huggingface import HuggingFaceEndpointEmbeddings
model = "sentence-transformers/all-mpnet-base-v2"
hf = HuggingFaceEndpointEmbeddings(
model=model,
task="feature-extraction",
huggingfacehub_api_token="my-api-key",
)
"""
client: Any = None #: :meta private:
async_client: Any = None #: :meta private:
model: Optional[str] = None
"""Model name to use."""
repo_id: Optional[str] = None
"""Huggingfacehub repository id, for backward compatibility."""
task: Optional[str] = "feature-extraction"
"""Task to call the model with."""
model_kwargs: Optional[dict] = None
"""Keyword arguments to pass to the model."""
huggingfacehub_api_token: Optional[str] = Field(
default_factory=from_env("HUGGINGFACEHUB_API_TOKEN", default=None)
)
model_config = ConfigDict(
extra="forbid",
protected_namespaces=(),
)
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
huggingfacehub_api_token = self.huggingfacehub_api_token or os.getenv(
"HF_TOKEN"
)
try:
from huggingface_hub import ( # type: ignore[import]
AsyncInferenceClient,
InferenceClient,
)
if self.model:
self.repo_id = self.model
elif self.repo_id:
self.model = self.repo_id
else:
self.model = DEFAULT_MODEL
self.repo_id = DEFAULT_MODEL
client = InferenceClient(
model=self.model,
token=huggingfacehub_api_token,
)
async_client = AsyncInferenceClient(
model=self.model,
token=huggingfacehub_api_token,
)
if self.task not in VALID_TASKS:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
self.client = client
self.async_client = async_client
except ImportError:
raise ImportError(
"Could not import huggingface_hub python package. "
"Please install it with `pip install huggingface_hub`."
)
return self
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Call out to HuggingFaceHub's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
# replace newlines, which can negatively affect performance.
texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
# api doc: https://huggingface.github.io/text-embeddings-inference/#/Text%20Embeddings%20Inference/embed
responses = self.client.post(
json={"inputs": texts, **_model_kwargs}, task=self.task
)
return json.loads(responses.decode())
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
"""Async Call to HuggingFaceHub's embedding endpoint for embedding search docs.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
# replace newlines, which can negatively affect performance.
texts = [text.replace("\n", " ") for text in texts]
_model_kwargs = self.model_kwargs or {}
responses = await self.async_client.post(
json={"inputs": texts, **_model_kwargs}, task=self.task
)
return json.loads(responses.decode())
def embed_query(self, text: str) -> List[float]:
"""Call out to HuggingFaceHub's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
response = self.embed_documents([text])[0]
return response
async def aembed_query(self, text: str) -> List[float]:
"""Async Call to HuggingFaceHub's embedding endpoint for embedding query text.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
response = (await self.aembed_documents([text]))[0]
return response
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/embeddings/huggingface.py | from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, Field
DEFAULT_MODEL_NAME = "sentence-transformers/all-mpnet-base-v2"
class HuggingFaceEmbeddings(BaseModel, Embeddings):
"""HuggingFace sentence_transformers embedding models.
To use, you should have the ``sentence_transformers`` python package installed.
Example:
.. code-block:: python
from langchain_huggingface import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
hf = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
"""
model_name: str = DEFAULT_MODEL_NAME
"""Model name to use."""
cache_folder: Optional[str] = None
"""Path to store models.
Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass to the Sentence Transformer model, such as `device`,
`prompts`, `default_prompt_name`, `revision`, `trust_remote_code`, or `token`.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer"""
encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method for the documents of
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
`precision`, `normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
query_encode_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Keyword arguments to pass when calling the `encode` method for the query of
the Sentence Transformer model, such as `prompt_name`, `prompt`, `batch_size`,
`precision`, `normalize_embeddings`, and more.
See also the Sentence Transformer documentation: https://sbert.net/docs/package_reference/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode"""
multi_process: bool = False
"""Run encode() on multiple GPUs."""
show_progress: bool = False
"""Whether to show a progress bar."""
def __init__(self, **kwargs: Any):
"""Initialize the sentence_transformer."""
super().__init__(**kwargs)
try:
import sentence_transformers # type: ignore[import]
except ImportError as exc:
raise ImportError(
"Could not import sentence_transformers python package. "
"Please install it with `pip install sentence-transformers`."
) from exc
self._client = sentence_transformers.SentenceTransformer(
self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
)
model_config = ConfigDict(
extra="forbid",
protected_namespaces=(),
)
def _embed(
self, texts: list[str], encode_kwargs: Dict[str, Any]
) -> List[List[float]]:
"""
Embed a text using the HuggingFace transformer model.
Args:
texts: The list of texts to embed.
encode_kwargs: Keyword arguments to pass when calling the
`encode` method for the documents of the SentenceTransformer
encode method.
Returns:
List of embeddings, one for each text.
"""
import sentence_transformers # type: ignore[import]
texts = list(map(lambda x: x.replace("\n", " "), texts))
if self.multi_process:
pool = self._client.start_multi_process_pool()
embeddings = self._client.encode_multi_process(texts, pool)
sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool)
else:
embeddings = self._client.encode(
texts,
show_progress_bar=self.show_progress,
**encode_kwargs, # type: ignore
)
if isinstance(embeddings, list):
raise TypeError(
"Expected embeddings to be a Tensor or a numpy array, "
"got a list instead."
)
return embeddings.tolist()
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Compute doc embeddings using a HuggingFace transformer model.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
return self._embed(texts, self.encode_kwargs)
def embed_query(self, text: str) -> List[float]:
"""Compute query embeddings using a HuggingFace transformer model.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
embed_kwargs = (
self.query_encode_kwargs
if len(self.query_encode_kwargs) > 0
else self.encode_kwargs
)
return self._embed([text], embed_kwargs)[0]
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/embeddings/__init__.py | from langchain_huggingface.embeddings.huggingface import (
HuggingFaceEmbeddings, # type: ignore[import-not-found]
)
from langchain_huggingface.embeddings.huggingface_endpoint import (
HuggingFaceEndpointEmbeddings,
)
__all__ = [
"HuggingFaceEmbeddings",
"HuggingFaceEndpointEmbeddings",
]
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/chat_models/huggingface.py | """Hugging Face Chat Wrapper."""
from dataclasses import dataclass
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Sequence,
Type,
Union,
cast,
)
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult, LLMResult
from langchain_core.runnables import Runnable
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_tool
from pydantic import model_validator
from typing_extensions import Self
from langchain_huggingface.llms.huggingface_endpoint import HuggingFaceEndpoint
from langchain_huggingface.llms.huggingface_pipeline import HuggingFacePipeline
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful, and honest assistant."""
@dataclass
class TGI_RESPONSE:
"""Response from the TextGenInference API."""
choices: List[Any]
usage: Dict
@dataclass
class TGI_MESSAGE:
"""Message to send to the TextGenInference API."""
role: str
content: str
tool_calls: List[Dict]
def _convert_message_to_chat_message(
message: BaseMessage,
) -> Dict:
if isinstance(message, ChatMessage):
return dict(role=message.role, content=message.content)
elif isinstance(message, HumanMessage):
return dict(role="user", content=message.content)
elif isinstance(message, AIMessage):
if "tool_calls" in message.additional_kwargs:
tool_calls = [
{
"function": {
"name": tc["function"]["name"],
"arguments": tc["function"]["arguments"],
}
}
for tc in message.additional_kwargs["tool_calls"]
]
else:
tool_calls = None
return {
"role": "assistant",
"content": message.content,
"tool_calls": tool_calls,
}
elif isinstance(message, SystemMessage):
return dict(role="system", content=message.content)
elif isinstance(message, ToolMessage):
return {
"role": "tool",
"content": message.content,
"name": message.name,
}
else:
raise ValueError(f"Got unknown type {message}")
def _convert_TGI_message_to_LC_message(
_message: TGI_MESSAGE,
) -> BaseMessage:
role = _message.role
assert role == "assistant", f"Expected role to be 'assistant', got {role}"
content = cast(str, _message.content)
if content is None:
content = ""
additional_kwargs: Dict = {}
if tool_calls := _message.tool_calls:
if "arguments" in tool_calls[0]["function"]:
functions_string = str(tool_calls[0]["function"].pop("arguments"))
corrected_functions = functions_string.replace("'", '"')
tool_calls[0]["function"]["arguments"] = corrected_functions
additional_kwargs["tool_calls"] = tool_calls
return AIMessage(content=content, additional_kwargs=additional_kwargs)
def _is_huggingface_hub(llm: Any) -> bool:
try:
from langchain_community.llms.huggingface_hub import ( # type: ignore[import-not-found]
HuggingFaceHub,
)
return isinstance(llm, HuggingFaceHub)
except ImportError:
# if no langchain community, it is not a HuggingFaceHub
return False
def _is_huggingface_textgen_inference(llm: Any) -> bool:
try:
from langchain_community.llms.huggingface_text_gen_inference import ( # type: ignore[import-not-found]
HuggingFaceTextGenInference,
)
return isinstance(llm, HuggingFaceTextGenInference)
except ImportError:
# if no langchain community, it is not a HuggingFaceTextGenInference
return False
def _is_huggingface_endpoint(llm: Any) -> bool:
return isinstance(llm, HuggingFaceEndpoint)
def _is_huggingface_pipeline(llm: Any) -> bool:
return isinstance(llm, HuggingFacePipeline)
class ChatHuggingFace(BaseChatModel):
"""Hugging Face LLM's as ChatModels.
Works with `HuggingFaceTextGenInference`, `HuggingFaceEndpoint`,
`HuggingFaceHub`, and `HuggingFacePipeline` LLMs.
Upon instantiating this class, the model_id is resolved from the url
provided to the LLM, and the appropriate tokenizer is loaded from
the HuggingFace Hub.
Setup:
Install ``langchain-huggingface`` and ensure your Hugging Face token
is saved.
.. code-block:: bash
pip install langchain-huggingface
.. code-block:: python
from huggingface_hub import login
login() # You will be prompted for your HF key, which will then be saved locally
Key init args — completion params:
llm: `HuggingFaceTextGenInference`, `HuggingFaceEndpoint`, `HuggingFaceHub`, or
'HuggingFacePipeline' LLM to be used.
Key init args — client params:
custom_get_token_ids: Optional[Callable[[str], List[int]]]
Optional encoder to use for counting tokens.
metadata: Optional[Dict[str, Any]]
Metadata to add to the run trace.
tags: Optional[List[str]]
Tags to add to the run trace.
tokenizer: Any
verbose: bool
Whether to print out response text.
See full list of supported init args and their descriptions in the params
section.
Instantiate:
.. code-block:: python
from langchain_huggingface import HuggingFaceEndpoint,
ChatHuggingFace
llm = HuggingFaceEndpoint(
repo_id="microsoft/Phi-3-mini-4k-instruct",
task="text-generation",
max_new_tokens=512,
do_sample=False,
repetition_penalty=1.03,
)
chat = ChatHuggingFace(llm=llm, verbose=True)
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user
sentence to French."),
("human", "I love programming."),
]
chat(...).invoke(messages)
.. code-block:: python
AIMessage(content='Je ai une passion pour le programme.\n\nIn
French, we use "ai" for masculine subjects and "a" for feminine
subjects. Since "programming" is gender-neutral in English, we
will go with the masculine "programme".\n\nConfirmation: "J\'aime
le programme." is more commonly used. The sentence above is
technically accurate, but less commonly used in spoken French as
"ai" is used less frequently in everyday speech.',
response_metadata={'token_usage': ChatCompletionOutputUsage
(completion_tokens=100, prompt_tokens=55, total_tokens=155),
'model': '', 'finish_reason': 'length'},
id='run-874c24b7-0272-4c99-b259-5d6d7facbc56-0')
Stream:
.. code-block:: python
for chunk in chat.stream(messages):
print(chunk)
.. code-block:: python
content='Je ai une passion pour le programme.\n\nIn French, we use
"ai" for masculine subjects and "a" for feminine subjects.
Since "programming" is gender-neutral in English,
we will go with the masculine "programme".\n\nConfirmation:
"J\'aime le programme." is more commonly used. The sentence
above is technically accurate, but less commonly used in spoken
French as "ai" is used less frequently in everyday speech.'
response_metadata={'token_usage': ChatCompletionOutputUsage
(completion_tokens=100, prompt_tokens=55, total_tokens=155),
'model': '', 'finish_reason': 'length'}
id='run-7d7b1967-9612-4f9a-911a-b2b5ca85046a-0'
Async:
.. code-block:: python
await chat.ainvoke(messages)
.. code-block:: python
AIMessage(content='Je déaime le programming.\n\nLittérale : Je
(j\'aime) déaime (le) programming.\n\nNote: "Programming" in
French is "programmation". But here, I used "programming" instead
of "programmation" because the user said "I love programming"
instead of "I love programming (in French)", which would be
"J\'aime la programmation". By translating the sentence
literally, I preserved the original meaning of the user\'s
sentence.', id='run-fd850318-e299-4735-b4c6-3496dc930b1d-0')
Tool calling:
.. code-block:: python
from pydantic import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state,
e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state,
e.g. San Francisco, CA")
chat_with_tools = chat.bind_tools([GetWeather, GetPopulation])
ai_msg = chat_with_tools.invoke("Which city is hotter today and
which is bigger: LA or NY?")
ai_msg.tool_calls
.. code-block:: python
[{'name': 'GetPopulation',
'args': {'location': 'Los Angeles, CA'},
'id': '0'}]
Response metadata
.. code-block:: python
ai_msg = chat.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
{'token_usage': ChatCompletionOutputUsage(completion_tokens=100,
prompt_tokens=8, total_tokens=108),
'model': '',
'finish_reason': 'length'}
""" # noqa: E501
llm: Any
"""LLM, must be of type HuggingFaceTextGenInference, HuggingFaceEndpoint,
HuggingFaceHub, or HuggingFacePipeline."""
# TODO: Is system_message used anywhere?
system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT)
tokenizer: Any = None
model_id: Optional[str] = None
def __init__(self, **kwargs: Any):
super().__init__(**kwargs)
from transformers import AutoTokenizer # type: ignore[import]
self._resolve_model_id()
self.tokenizer = (
AutoTokenizer.from_pretrained(self.model_id)
if self.tokenizer is None
else self.tokenizer
)
@model_validator(mode="after")
def validate_llm(self) -> Self:
if (
not _is_huggingface_hub(self.llm)
and not _is_huggingface_textgen_inference(self.llm)
and not _is_huggingface_endpoint(self.llm)
and not _is_huggingface_pipeline(self.llm)
):
raise TypeError(
"Expected llm to be one of HuggingFaceTextGenInference, "
"HuggingFaceEndpoint, HuggingFaceHub, HuggingFacePipeline "
f"received {type(self.llm)}"
)
return self
def _create_chat_result(self, response: TGI_RESPONSE) -> ChatResult:
generations = []
finish_reason = response.choices[0].finish_reason
gen = ChatGeneration(
message=_convert_TGI_message_to_LC_message(response.choices[0].message),
generation_info={"finish_reason": finish_reason},
)
generations.append(gen)
token_usage = response.usage
model_object = self.llm.inference_server_url
llm_output = {"token_usage": token_usage, "model": model_object}
return ChatResult(generations=generations, llm_output=llm_output)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if _is_huggingface_textgen_inference(self.llm):
message_dicts = self._create_message_dicts(messages, stop)
answer = self.llm.client.chat(messages=message_dicts, **kwargs)
return self._create_chat_result(answer)
elif _is_huggingface_endpoint(self.llm):
message_dicts = self._create_message_dicts(messages, stop)
answer = self.llm.client.chat_completion(messages=message_dicts, **kwargs)
return self._create_chat_result(answer)
else:
llm_input = self._to_chat_prompt(messages)
llm_result = self.llm._generate(
prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
)
return self._to_chat_result(llm_result)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if _is_huggingface_textgen_inference(self.llm):
message_dicts = self._create_message_dicts(messages, stop)
answer = await self.llm.async_client.chat(messages=message_dicts, **kwargs)
return self._create_chat_result(answer)
else:
llm_input = self._to_chat_prompt(messages)
llm_result = await self.llm._agenerate(
prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
)
return self._to_chat_result(llm_result)
def _to_chat_prompt(
self,
messages: List[BaseMessage],
) -> str:
"""Convert a list of messages into a prompt format expected by wrapped LLM."""
if not messages:
raise ValueError("At least one HumanMessage must be provided!")
if not isinstance(messages[-1], HumanMessage):
raise ValueError("Last message must be a HumanMessage!")
messages_dicts = [self._to_chatml_format(m) for m in messages]
return self.tokenizer.apply_chat_template(
messages_dicts, tokenize=False, add_generation_prompt=True
)
def _to_chatml_format(self, message: BaseMessage) -> dict:
"""Convert LangChain message to ChatML format."""
if isinstance(message, SystemMessage):
role = "system"
elif isinstance(message, AIMessage):
role = "assistant"
elif isinstance(message, HumanMessage):
role = "user"
else:
raise ValueError(f"Unknown message type: {type(message)}")
return {"role": role, "content": message.content}
@staticmethod
def _to_chat_result(llm_result: LLMResult) -> ChatResult:
chat_generations = []
for g in llm_result.generations[0]:
chat_generation = ChatGeneration(
message=AIMessage(content=g.text), generation_info=g.generation_info
)
chat_generations.append(chat_generation)
return ChatResult(
generations=chat_generations, llm_output=llm_result.llm_output
)
def _resolve_model_id(self) -> None:
"""Resolve the model_id from the LLM's inference_server_url"""
from huggingface_hub import list_inference_endpoints # type: ignore[import]
if _is_huggingface_hub(self.llm) or (
hasattr(self.llm, "repo_id") and self.llm.repo_id
):
self.model_id = self.llm.repo_id
return
elif _is_huggingface_textgen_inference(self.llm):
endpoint_url: Optional[str] = self.llm.inference_server_url
elif _is_huggingface_pipeline(self.llm):
self.model_id = self.llm.model_id
return
else:
endpoint_url = self.llm.endpoint_url
available_endpoints = list_inference_endpoints("*")
for endpoint in available_endpoints:
if endpoint.url == endpoint_url:
self.model_id = endpoint.repository
if not self.model_id:
raise ValueError(
"Failed to resolve model_id:"
f"Could not find model id for inference server: {endpoint_url}"
"Make sure that your Hugging Face token has access to the endpoint."
)
def bind_tools(
self,
tools: Sequence[Union[Dict[str, Any], Type, Callable, BaseTool]],
*,
tool_choice: Optional[Union[dict, str, Literal["auto", "none"], bool]] = None,
**kwargs: Any,
) -> Runnable[LanguageModelInput, BaseMessage]:
"""Bind tool-like objects to this chat model.
Assumes model is compatible with OpenAI tool-calling API.
Args:
tools: A list of tool definitions to bind to this chat model.
Supports any tool definition handled by
:meth:`langchain_core.utils.function_calling.convert_to_openai_tool`.
tool_choice: Which tool to require the model to call.
Must be the name of the single provided function or
"auto" to automatically determine which function to call
(if any), or a dict of the form:
{"type": "function", "function": {"name": <<tool_name>>}}.
**kwargs: Any additional parameters to pass to the
:class:`~langchain.runnable.Runnable` constructor.
"""
formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
if tool_choice is not None and tool_choice:
if len(formatted_tools) != 1:
raise ValueError(
"When specifying `tool_choice`, you must provide exactly one "
f"tool. Received {len(formatted_tools)} tools."
)
if isinstance(tool_choice, str):
if tool_choice not in ("auto", "none"):
tool_choice = {
"type": "function",
"function": {"name": tool_choice},
}
elif isinstance(tool_choice, bool):
tool_choice = formatted_tools[0]
elif isinstance(tool_choice, dict):
if (
formatted_tools[0]["function"]["name"]
!= tool_choice["function"]["name"]
):
raise ValueError(
f"Tool choice {tool_choice} was specified, but the only "
f"provided tool was {formatted_tools[0]['function']['name']}."
)
else:
raise ValueError(
f"Unrecognized tool_choice type. Expected str, bool or dict. "
f"Received: {tool_choice}"
)
kwargs["tool_choice"] = tool_choice
return super().bind(tools=formatted_tools, **kwargs)
def _create_message_dicts(
self, messages: List[BaseMessage], stop: Optional[List[str]]
) -> List[Dict[Any, Any]]:
message_dicts = [_convert_message_to_chat_message(m) for m in messages]
return message_dicts
@property
def _llm_type(self) -> str:
return "huggingface-chat-wrapper"
|
0 | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface | lc_public_repos/langchain/libs/partners/huggingface/langchain_huggingface/chat_models/__init__.py | from langchain_huggingface.chat_models.huggingface import ( # type: ignore[import-not-found]
TGI_MESSAGE,
TGI_RESPONSE,
ChatHuggingFace,
_convert_message_to_chat_message,
_convert_TGI_message_to_LC_message,
)
__all__ = [
"ChatHuggingFace",
"_convert_message_to_chat_message",
"_convert_TGI_message_to_LC_message",
"TGI_MESSAGE",
"TGI_RESPONSE",
]
|
0 | lc_public_repos/langchain/libs/partners/huggingface | lc_public_repos/langchain/libs/partners/huggingface/scripts/lint_imports.sh | #!/bin/bash
set -eu
# Initialize a variable to keep track of errors
errors=0
# make sure not importing from langchain or langchain_experimental
git --no-pager grep '^from langchain\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1))
git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1))
# Decide on an exit status based on the errors
if [ "$errors" -gt 0 ]; then
exit 1
else
exit 0
fi
|
0 | lc_public_repos/langchain/libs/partners/huggingface | lc_public_repos/langchain/libs/partners/huggingface/scripts/check_imports.py | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_faillure = True
print(file) # noqa: T201
traceback.print_exc()
print() # noqa: T201
sys.exit(1 if has_failure else 0)
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/box/README.md | This package has moved!
https://github.com/langchain-ai/langchain-box/tree/main/libs/box
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/mongodb/README.md | This package has moved!
https://github.com/langchain-ai/langchain-mongodb/tree/main/libs/mongodb
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/ibm/README.md | This package has moved!
https://github.com/langchain-ai/langchain-ibm/tree/main/libs/ibm |
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/ollama/Makefile | .PHONY: all format lint test tests integration_tests docker_tests help extended_tests
# Default target executed when no arguments are given to make.
all: help
# Define a variable for the test file path.
TEST_FILE ?= tests/unit_tests/
integration_test: TEST_FILE = tests/integration_tests/
# note: leaving out integration_tests (with s) command to skip release testing for now
# TODO(erick) configure ollama server to run in CI, in separate repo
# unit tests are run with the --disable-socket flag to prevent network calls
test tests:
poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
test_watch:
poetry run ptw --snapshot-update --now . -- -vv $(TEST_FILE)
# integration tests are run without the --disable-socket flag to allow network calls
integration_test:
poetry run pytest $(TEST_FILE)
# note: leaving out integration_tests (with s) command to skip release testing for now
# TODO(erick) configure ollama server to run in CI, in separate repo
######################
# LINTING AND FORMATTING
######################
# Define a variable for Python and notebook files.
PYTHON_FILES=.
MYPY_CACHE=.mypy_cache
lint format: PYTHON_FILES=.
lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ollama --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_ollama
lint_tests: PYTHON_FILES=tests
lint_tests: MYPY_CACHE=.mypy_cache_test
lint lint_diff lint_package lint_tests:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff
[ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE)
format format_diff:
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES)
[ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I --fix $(PYTHON_FILES)
spell_check:
poetry run codespell --toml pyproject.toml
spell_fix:
poetry run codespell --toml pyproject.toml -w
check_imports: $(shell find langchain_ollama -name '*.py')
poetry run python ./scripts/check_imports.py $^
######################
# HELP
######################
help:
@echo '----'
@echo 'check_imports - check imports'
@echo 'format - run code formatters'
@echo 'lint - run linters'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/ollama/LICENSE | MIT License
Copyright (c) 2024 LangChain, Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/ollama/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
[[package]]
name = "annotated-types"
version = "0.7.0"
description = "Reusable constraint types to use with typing.Annotated"
optional = false
python-versions = ">=3.8"
files = [
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]
[[package]]
name = "anyio"
version = "4.6.2.post1"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
{file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
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]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
[package.extras]
doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
trio = ["trio (>=0.26.1)"]
[[package]]
name = "certifi"
version = "2024.8.30"
description = "Python package for providing Mozilla's CA Bundle."
optional = false
python-versions = ">=3.6"
files = [
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[[package]]
name = "charset-normalizer"
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0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/ollama/README.md | # langchain-ollama
This package contains the LangChain integration with Ollama
## Installation
```bash
pip install -U langchain-ollama
```
You will also need to run the Ollama server locally.
You can download it [here](https://ollama.com/download).
## Chat Models
`ChatOllama` class exposes chat models from Ollama.
```python
from langchain_ollama import ChatOllama
llm = ChatOllama(model="llama3-groq-tool-use")
llm.invoke("Sing a ballad of LangChain.")
```
## Embeddings
`OllamaEmbeddings` class exposes embeddings from Ollama.
```python
from langchain_ollama import OllamaEmbeddings
embeddings = OllamaEmbeddings(model="llama3")
embeddings.embed_query("What is the meaning of life?")
```
## LLMs
`OllamaLLM` class exposes LLMs from Ollama.
```python
from langchain_ollama import OllamaLLM
llm = OllamaLLM(model="llama3")
llm.invoke("The meaning of life is")
```
|
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