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/mistralai | lc_public_repos/langchain/libs/partners/mistralai/langchain_mistralai/__init__.py | from langchain_mistralai.chat_models import ChatMistralAI
from langchain_mistralai.embeddings import MistralAIEmbeddings
__all__ = ["ChatMistralAI", "MistralAIEmbeddings"]
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/tests/integration_tests/test_standard.py | """Standard LangChain interface tests"""
from typing import Optional, Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ( # type: ignore[import-not-found]
ChatModelIntegrationTests, # type: ignore[import-not-found]
)
from langchain_mistralai import ChatMistralAI
class TestMistralStandard(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatMistralAI
@property
def chat_model_params(self) -> dict:
return {"model": "mistral-large-latest", "temperature": 0}
@property
def tool_choice_value(self) -> Optional[str]:
"""Value to use for tool choice when used in tests."""
return "any"
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/tests/integration_tests/test_chat_models.py | """Test ChatMistral chat model."""
import json
from typing import Any, Optional
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessageChunk,
HumanMessage,
)
from pydantic import BaseModel
from langchain_mistralai.chat_models import ChatMistralAI
def test_stream() -> None:
"""Test streaming tokens from ChatMistralAI."""
llm = ChatMistralAI()
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token.content, str)
async def test_astream() -> None:
"""Test streaming tokens from ChatMistralAI."""
llm = ChatMistralAI()
full: Optional[BaseMessageChunk] = None
chunks_with_token_counts = 0
async for token in llm.astream("I'm Pickle Rick"):
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"]
)
async def test_abatch() -> None:
"""Test streaming tokens from ChatMistralAI"""
llm = ChatMistralAI()
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 ChatMistralAI"""
llm = ChatMistralAI()
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 ChatMistralAI"""
llm = ChatMistralAI()
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 ChatMistralAI"""
llm = ChatMistralAI()
result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]})
assert isinstance(result.content, str)
def test_invoke() -> None:
"""Test invoke tokens from ChatMistralAI"""
llm = ChatMistralAI()
result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"]))
assert isinstance(result.content, str)
def test_chat_mistralai_llm_output_contains_model_name() -> None:
"""Test llm_output contains model_name."""
chat = ChatMistralAI(max_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
def test_chat_mistralai_streaming_llm_output_contains_model_name() -> None:
"""Test llm_output contains model_name."""
chat = ChatMistralAI(max_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
def test_chat_mistralai_llm_output_contains_token_usage() -> None:
"""Test llm_output contains model_name."""
chat = ChatMistralAI(max_tokens=10)
message = HumanMessage(content="Hello")
llm_result = chat.generate([[message]])
assert llm_result.llm_output is not None
assert "token_usage" in llm_result.llm_output
token_usage = llm_result.llm_output["token_usage"]
assert "prompt_tokens" in token_usage
assert "completion_tokens" in token_usage
assert "total_tokens" in token_usage
def test_chat_mistralai_streaming_llm_output_not_contain_token_usage() -> None:
"""Mistral currently doesn't return token usage when streaming."""
chat = ChatMistralAI(max_tokens=10, streaming=True)
message = HumanMessage(content="Hello")
llm_result = chat.generate([[message]])
assert llm_result.llm_output is not None
assert "token_usage" in llm_result.llm_output
token_usage = llm_result.llm_output["token_usage"]
assert not token_usage
def test_structured_output() -> None:
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
schema = {
"title": "AnswerWithJustification",
"description": (
"An answer to the user question along with justification for the answer."
),
"type": "object",
"properties": {
"answer": {"title": "Answer", "type": "string"},
"justification": {"title": "Justification", "type": "string"},
},
"required": ["answer", "justification"],
}
structured_llm = llm.with_structured_output(schema)
result = structured_llm.invoke(
"What weighs more a pound of bricks or a pound of feathers"
)
assert isinstance(result, dict)
def test_streaming_structured_output() -> None:
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
class Person(BaseModel):
name: str
age: int
structured_llm = llm.with_structured_output(Person)
strm = structured_llm.stream("Erick, 27 years old")
chunk_num = 0
for chunk in strm:
assert chunk_num == 0, "should only have one chunk with model"
assert isinstance(chunk, Person)
assert chunk.name == "Erick"
assert chunk.age == 27
chunk_num += 1
def test_tool_call() -> None:
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
class Person(BaseModel):
name: str
age: int
tool_llm = llm.bind_tools([Person])
result = tool_llm.invoke("Erick, 27 years old")
assert isinstance(result, AIMessage)
assert len(result.tool_calls) == 1
tool_call = result.tool_calls[0]
assert tool_call["name"] == "Person"
assert tool_call["args"] == {"name": "Erick", "age": 27}
def test_streaming_tool_call() -> None:
llm = ChatMistralAI(model="mistral-large-latest", temperature=0) # type: ignore[call-arg]
class Person(BaseModel):
name: str
age: int
tool_llm = llm.bind_tools([Person])
# where it calls the tool
strm = tool_llm.stream("Erick, 27 years old")
additional_kwargs = None
for chunk in strm:
assert isinstance(chunk, AIMessageChunk)
assert chunk.content == ""
additional_kwargs = chunk.additional_kwargs
assert additional_kwargs is not None
assert "tool_calls" in additional_kwargs
assert len(additional_kwargs["tool_calls"]) == 1
assert additional_kwargs["tool_calls"][0]["function"]["name"] == "Person"
assert json.loads(additional_kwargs["tool_calls"][0]["function"]["arguments"]) == {
"name": "Erick",
"age": 27,
}
assert isinstance(chunk, AIMessageChunk)
assert len(chunk.tool_call_chunks) == 1
tool_call_chunk = chunk.tool_call_chunks[0]
assert tool_call_chunk["name"] == "Person"
assert tool_call_chunk["args"] == '{"name": "Erick", "age": 27}'
# where it doesn't call the tool
strm = tool_llm.stream("What is 2+2?")
acc: Any = None
for chunk in strm:
assert isinstance(chunk, AIMessageChunk)
acc = chunk if acc is None else acc + chunk
assert acc.content != ""
assert "tool_calls" not in acc.additional_kwargs
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/tests/integration_tests/test_embeddings.py | """Test MistralAI Embedding"""
from langchain_mistralai import MistralAIEmbeddings
def test_mistralai_embedding_documents() -> None:
"""Test MistralAI embeddings for documents."""
documents = ["foo bar", "test document"]
embedding = MistralAIEmbeddings()
output = embedding.embed_documents(documents)
assert len(output) == 2
assert len(output[0]) == 1024
def test_mistralai_embedding_query() -> None:
"""Test MistralAI embeddings for query."""
document = "foo bar"
embedding = MistralAIEmbeddings()
output = embedding.embed_query(document)
assert len(output) == 1024
async def test_mistralai_embedding_documents_async() -> None:
"""Test MistralAI embeddings for documents."""
documents = ["foo bar", "test document"]
embedding = MistralAIEmbeddings()
output = await embedding.aembed_documents(documents)
assert len(output) == 2
assert len(output[0]) == 1024
async def test_mistralai_embedding_query_async() -> None:
"""Test MistralAI embeddings for query."""
document = "foo bar"
embedding = MistralAIEmbeddings()
output = await embedding.aembed_query(document)
assert len(output) == 1024
def test_mistralai_embedding_documents_long() -> None:
"""Test MistralAI embeddings for documents."""
documents = ["foo bar " * 1000, "test document " * 1000] * 5
embedding = MistralAIEmbeddings()
output = embedding.embed_documents(documents)
assert len(output) == 10
assert len(output[0]) == 1024
def test_mistralai_embed_query_character() -> None:
"""Test MistralAI embeddings for query."""
document = "😳"
embedding = MistralAIEmbeddings()
output = embedding.embed_query(document)
assert len(output) == 1024
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/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/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/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 import ( # type: ignore[import-not-found]
ChatModelUnitTests, # type: ignore[import-not-found]
)
from langchain_mistralai import ChatMistralAI
class TestMistralStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> Type[BaseChatModel]:
return ChatMistralAI
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/tests/unit_tests/test_chat_models.py | """Test MistralAI Chat API wrapper."""
import os
from typing import Any, AsyncGenerator, Dict, Generator, List, cast
from unittest.mock import patch
import pytest
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
InvalidToolCall,
SystemMessage,
ToolCall,
)
from pydantic import SecretStr
from langchain_mistralai.chat_models import ( # type: ignore[import]
ChatMistralAI,
_convert_message_to_mistral_chat_message,
_convert_mistral_chat_message_to_message,
_convert_tool_call_id_to_mistral_compatible,
_is_valid_mistral_tool_call_id,
)
os.environ["MISTRAL_API_KEY"] = "foo"
def test_mistralai_model_param() -> None:
llm = ChatMistralAI(model="foo") # type: ignore[call-arg]
assert llm.model == "foo"
def test_mistralai_initialization() -> None:
"""Test ChatMistralAI initialization."""
# Verify that ChatMistralAI can be initialized using a secret key provided
# as a parameter rather than an environment variable.
for model in [
ChatMistralAI(model="test", mistral_api_key="test"), # type: ignore[call-arg, call-arg]
ChatMistralAI(model="test", api_key="test"), # type: ignore[call-arg, arg-type]
]:
assert cast(SecretStr, model.mistral_api_key).get_secret_value() == "test"
@pytest.mark.parametrize(
"model,expected_url",
[
(ChatMistralAI(model="test"), "https://api.mistral.ai/v1"), # type: ignore[call-arg, arg-type]
(ChatMistralAI(model="test", endpoint="baz"), "baz"), # type: ignore[call-arg, arg-type]
],
)
def test_mistralai_initialization_baseurl(
model: ChatMistralAI, expected_url: str
) -> None:
"""Test ChatMistralAI initialization."""
# Verify that ChatMistralAI can be initialized providing endpoint, but also
# with default
assert model.endpoint == expected_url
@pytest.mark.parametrize(
"env_var_name",
[
("MISTRAL_BASE_URL"),
],
)
def test_mistralai_initialization_baseurl_env(env_var_name: str) -> None:
"""Test ChatMistralAI initialization."""
# Verify that ChatMistralAI can be initialized using env variable
import os
os.environ[env_var_name] = "boo"
model = ChatMistralAI(model="test") # type: ignore[call-arg]
assert model.endpoint == "boo"
@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"),
),
(
ChatMessage(role="assistant", content="Hello"),
dict(role="assistant", content="Hello"),
),
],
)
def test_convert_message_to_mistral_chat_message(
message: BaseMessage, expected: Dict
) -> None:
result = _convert_message_to_mistral_chat_message(message)
assert result == expected
def _make_completion_response_from_token(token: str) -> Dict:
return dict(
id="abc123",
model="fake_model",
choices=[
dict(
index=0,
delta=dict(content=token),
finish_reason=None,
)
],
)
def mock_chat_stream(*args: Any, **kwargs: Any) -> Generator:
def it() -> Generator:
for token in ["Hello", " how", " can", " I", " help", "?"]:
yield _make_completion_response_from_token(token)
return it()
async def mock_chat_astream(*args: Any, **kwargs: Any) -> AsyncGenerator:
async def it() -> AsyncGenerator:
for token in ["Hello", " how", " can", " I", " help", "?"]:
yield _make_completion_response_from_token(token)
return it()
class MyCustomHandler(BaseCallbackHandler):
last_token: str = ""
def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
self.last_token = token
@patch(
"langchain_mistralai.chat_models.ChatMistralAI.completion_with_retry",
new=mock_chat_stream,
)
def test_stream_with_callback() -> None:
callback = MyCustomHandler()
chat = ChatMistralAI(callbacks=[callback])
for token in chat.stream("Hello"):
assert callback.last_token == token.content
@patch("langchain_mistralai.chat_models.acompletion_with_retry", new=mock_chat_astream)
async def test_astream_with_callback() -> None:
callback = MyCustomHandler()
chat = ChatMistralAI(callbacks=[callback])
async for token in chat.astream("Hello"):
assert callback.last_token == token.content
def test__convert_dict_to_message_tool_call() -> None:
raw_tool_call = {
"id": "ssAbar4Dr",
"function": {
"arguments": '{"name": "Sally", "hair_color": "green"}',
"name": "GenerateUsername",
},
}
message = {"role": "assistant", "content": "", "tool_calls": [raw_tool_call]}
result = _convert_mistral_chat_message_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="ssAbar4Dr",
type="tool_call",
)
],
)
assert result == expected_output
assert _convert_message_to_mistral_chat_message(expected_output) == message
# Test malformed tool call
raw_tool_calls = [
{
"id": "pL5rEGzxe",
"function": {
"arguments": '{"name": "Sally", "hair_color": "green"}',
"name": "GenerateUsername",
},
},
{
"id": "ssAbar4Dr",
"function": {
"arguments": "oops",
"name": "GenerateUsername",
},
},
]
message = {"role": "assistant", "content": "", "tool_calls": raw_tool_calls}
result = _convert_mistral_chat_message_to_message(message)
expected_output = AIMessage(
content="",
additional_kwargs={"tool_calls": raw_tool_calls},
invalid_tool_calls=[
InvalidToolCall(
name="GenerateUsername",
args="oops",
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
id="ssAbar4Dr",
type="invalid_tool_call",
),
],
tool_calls=[
ToolCall(
name="GenerateUsername",
args={"name": "Sally", "hair_color": "green"},
id="pL5rEGzxe",
type="tool_call",
),
],
)
assert result == expected_output
assert _convert_message_to_mistral_chat_message(expected_output) == message
def test_custom_token_counting() -> None:
def token_encoder(text: str) -> List[int]:
return [1, 2, 3]
llm = ChatMistralAI(custom_get_token_ids=token_encoder)
assert llm.get_token_ids("foo") == [1, 2, 3]
def test_tool_id_conversion() -> None:
assert _is_valid_mistral_tool_call_id("ssAbar4Dr")
assert not _is_valid_mistral_tool_call_id("abc123")
assert not _is_valid_mistral_tool_call_id("call_JIIjI55tTipFFzpcP8re3BpM")
result_map = {
"ssAbar4Dr": "ssAbar4Dr",
"abc123": "pL5rEGzxe",
"call_JIIjI55tTipFFzpcP8re3BpM": "8kxAQvoED",
}
for input_id, expected_output in result_map.items():
assert _convert_tool_call_id_to_mistral_compatible(input_id) == expected_output
assert _is_valid_mistral_tool_call_id(expected_output)
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/tests/unit_tests/test_imports.py | from langchain_mistralai import __all__
EXPECTED_ALL = ["ChatMistralAI", "MistralAIEmbeddings"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests | lc_public_repos/langchain/libs/partners/mistralai/tests/unit_tests/test_embeddings.py | import os
from typing import cast
from pydantic import SecretStr
from langchain_mistralai import MistralAIEmbeddings
os.environ["MISTRAL_API_KEY"] = "foo"
def test_mistral_init() -> None:
for model in [
MistralAIEmbeddings(model="mistral-embed", mistral_api_key="test"), # type: ignore[call-arg]
MistralAIEmbeddings(model="mistral-embed", api_key="test"), # type: ignore[arg-type]
]:
assert model.model == "mistral-embed"
assert cast(SecretStr, model.mistral_api_key).get_secret_value() == "test"
|
0 | lc_public_repos/langchain/libs/partners/mistralai/tests/unit_tests | lc_public_repos/langchain/libs/partners/mistralai/tests/unit_tests/__snapshots__/test_standard.ambr | # serializer version: 1
# name: TestMistralStandard.test_serdes[serialized]
dict({
'id': list([
'langchain',
'chat_models',
'mistralai',
'ChatMistralAI',
]),
'kwargs': dict({
'endpoint': 'boo',
'max_concurrent_requests': 64,
'max_retries': 2,
'max_tokens': 100,
'mistral_api_key': dict({
'id': list([
'MISTRAL_API_KEY',
]),
'lc': 1,
'type': 'secret',
}),
'model': 'mistral-small',
'temperature': 0.0,
'timeout': 60,
'top_p': 1,
}),
'lc': 1,
'name': 'ChatMistralAI',
'type': 'constructor',
})
# ---
|
0 | lc_public_repos/langchain/libs/partners/mistralai | lc_public_repos/langchain/libs/partners/mistralai/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/mistralai | lc_public_repos/langchain/libs/partners/mistralai/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/chroma/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/chroma --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_chroma
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_chroma -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/chroma/LICENSE | MIT License
Copyright (c) 2024 LangChain, Inc.
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|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/chroma/poetry.lock | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
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[[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"
content-hash = "2d6bc4b9a18a322c326c3f7d5786c4b196a997458e6d2ca4043cb6b7a4a123b3"
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/chroma/README.md | # langchain-chroma
This package contains the LangChain integration with Chroma.
## Installation
```bash
pip install -U langchain-chroma
```
## Usage
The `Chroma` class exposes the connection to the Chroma vector store.
```python
from langchain_chroma import Chroma
embeddings = ... # use a LangChain Embeddings class
vectorstore = Chroma(embeddings=embeddings)
```
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/chroma/pyproject.toml | [build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-chroma"
version = "0.2.0"
description = "An integration package connecting Chroma 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/chroma"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-chroma%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4"
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14"
[[tool.poetry.dependencies.numpy]]
version = "^1.22.4"
python = "<3.12"
[[tool.poetry.dependencies.numpy]]
version = "^1.26.2"
python = ">=3.12"
[tool.ruff.lint]
select = ["E", "F", "I", "T201", "D"]
[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.dependencies.chromadb]
version = ">=0.4.0,<0.6.0,!=0.5.4,!=0.5.5,!=0.5.7,!=0.5.9,!=0.5.10,!=0.5.11,!=0.5.12"
[tool.poetry.dependencies.fastapi]
version = ">=0.95.2,<1"
optional = true
[tool.poetry.group.test]
optional = true
[tool.poetry.group.codespell]
optional = true
[tool.poetry.group.test_integration]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
optional = true
[tool.ruff.lint.pydocstyle]
convention = "google"
[tool.ruff.lint.per-file-ignores]
"tests/**" = ["D"]
[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"
# hack to make sure py3.9 compatible versionof onnxruntime is installed for testing
onnxruntime = [{version = "<1.20", python = "<3.10"}, {version = "*", python = ">=3.10"}]
[[tool.poetry.group.test.dependencies.langchain-core]]
path = "../../core"
develop = true
python = ">=3.9"
[[tool.poetry.group.test.dependencies.langchain-core]]
version = ">=0.1.40,<0.3"
python = "<3.9"
[[tool.poetry.group.test.dependencies.langchain-tests]]
path = "../../standard-tests"
develop = true
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
# hack to make sure py3.9 compatible versionof onnxruntime is installed for testing
onnxruntime = [{version = "<1.20", python = "<3.10"}, {version = "*", python = ">=3.10"}]
[tool.poetry.group.dev.dependencies]
[[tool.poetry.group.dev.dependencies.langchain-core]]
path = "../../core"
develop = true
python = ">=3.9"
[[tool.poetry.group.dev.dependencies.langchain-core]]
version = ">=0.1.40,<0.3"
python = "<3.9"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
types-requests = "^2.31.0.20240406"
[[tool.poetry.group.typing.dependencies.langchain-core]]
path = "../../core"
develop = true
python = ">=3.9"
[[tool.poetry.group.typing.dependencies.langchain-core]]
version = ">=0.1.40,<0.3"
python = "<3.9"
|
0 | lc_public_repos/langchain/libs/partners/chroma | lc_public_repos/langchain/libs/partners/chroma/langchain_chroma/vectorstores.py | """This is the langchain_chroma.vectorstores module.
It contains the Chroma class which is a vector store for handling various tasks.
"""
from __future__ import annotations
import base64
import logging
import uuid
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
import chromadb
import chromadb.config
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import xor_args
from langchain_core.vectorstores import VectorStore
if TYPE_CHECKING:
from chromadb.api.types import ID, OneOrMany, Where, WhereDocument
logger = logging.getLogger()
DEFAULT_K = 4 # Number of Documents to return.
def _results_to_docs(results: Any) -> List[Document]:
return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
return [
# TODO: Chroma can do batch querying,
# we shouldn't hard code to the 1st result
(
Document(page_content=result[0], metadata=result[1] or {}, id=result[2]),
result[3],
)
for result in zip(
results["documents"][0],
results["metadatas"][0],
results["ids"][0],
results["distances"][0],
)
]
def _results_to_docs_and_vectors(results: Any) -> List[Tuple[Document, np.ndarray]]:
return [
(Document(page_content=result[0], metadata=result[1] or {}), result[2])
for result in zip(
results["documents"][0],
results["metadatas"][0],
results["embeddings"][0],
)
]
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices.
Raises:
ValueError: If the number of columns in X and Y are not the same.
"""
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
"Number of columns in X and Y must be the same. X has shape"
f"{X.shape} "
f"and Y has shape {Y.shape}."
)
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance.
Args:
query_embedding: Query embedding.
embedding_list: List of embeddings to select from.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
k: Number of Documents to return. Defaults to 4.
Returns:
List of indices of embeddings selected by maximal marginal relevance.
"""
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs
class Chroma(VectorStore):
"""Chroma vector store integration.
Setup:
Install ``chromadb``, ``langchain-chroma`` packages:
.. code-block:: bash
pip install -qU chromadb langchain-chroma
Key init args — indexing params:
collection_name: str
Name of the collection.
embedding_function: Embeddings
Embedding function to use.
Key init args — client params:
client: Optional[Client]
Chroma client to use.
client_settings: Optional[chromadb.config.Settings]
Chroma client settings.
persist_directory: Optional[str]
Directory to persist the collection.
Instantiate:
.. code-block:: python
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
vector_store = Chroma(
collection_name="foo",
embedding_function=OpenAIEmbeddings(),
# other params...
)
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")
documents = [document_1, document_2, document_3]
ids = ["1", "2", "3"]
vector_store.add_documents(documents=documents, ids=ids)
Update Documents:
.. code-block:: python
updated_document = Document(
page_content="qux",
metadata={"bar": "baz"}
)
vector_store.update_documents(ids=["1"],documents=[updated_document])
Delete Documents:
.. code-block:: python
vector_store.delete(ids=["3"])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'baz': 'bar'}]
Search with filter:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1,filter={"baz": "bar"})
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* foo [{'baz': 'bar'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.000000] qux [{'bar': 'baz', 'baz': 'bar'}]
Async:
.. code-block:: python
# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)
# delete documents
# await vector_store.adelete(ids=["3"])
# search
# results = vector_store.asimilarity_search(query="thud",k=1)
# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.335463] foo [{'baz': 'bar'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")
.. code-block:: python
[Document(metadata={'baz': 'bar'}, page_content='thud')]
""" # noqa: E501
_LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
def __init__(
self,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
embedding_function: Optional[Embeddings] = None,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
collection_metadata: Optional[Dict] = None,
client: Optional[chromadb.ClientAPI] = None,
relevance_score_fn: Optional[Callable[[float], float]] = None,
create_collection_if_not_exists: Optional[bool] = True,
) -> None:
"""Initialize with a Chroma client.
Args:
collection_name: Name of the collection to create.
embedding_function: Embedding class object. Used to embed texts.
persist_directory: Directory to persist the collection.
client_settings: Chroma client settings
collection_metadata: Collection configurations.
client: Chroma client. Documentation:
https://docs.trychroma.com/reference/js-client#class:-chromaclient
relevance_score_fn: Function to calculate relevance score from distance.
Used only in `similarity_search_with_relevance_scores`
create_collection_if_not_exists: Whether to create collection
if it doesn't exist. Defaults to True.
"""
if client is not None:
self._client_settings = client_settings
self._client = client
self._persist_directory = persist_directory
else:
if client_settings:
# If client_settings is provided with persist_directory specified,
# then it is "in-memory and persisting to disk" mode.
client_settings.persist_directory = (
persist_directory or client_settings.persist_directory
)
_client_settings = client_settings
elif persist_directory:
_client_settings = chromadb.config.Settings(is_persistent=True)
_client_settings.persist_directory = persist_directory
else:
_client_settings = chromadb.config.Settings()
self._client_settings = _client_settings
self._client = chromadb.Client(_client_settings)
self._persist_directory = (
_client_settings.persist_directory or persist_directory
)
self._embedding_function = embedding_function
self._chroma_collection: Optional[chromadb.Collection] = None
self._collection_name = collection_name
self._collection_metadata = collection_metadata
if create_collection_if_not_exists:
self.__ensure_collection()
else:
self._chroma_collection = self._client.get_collection(name=collection_name)
self.override_relevance_score_fn = relevance_score_fn
def __ensure_collection(self) -> None:
"""Ensure that the collection exists or create it."""
self._chroma_collection = self._client.get_or_create_collection(
name=self._collection_name,
embedding_function=None,
metadata=self._collection_metadata,
)
@property
def _collection(self) -> chromadb.Collection:
"""Returns the underlying Chroma collection or throws an exception."""
if self._chroma_collection is None:
raise ValueError(
"Chroma collection not initialized. "
"Use `reset_collection` to re-create and initialize the collection. "
)
return self._chroma_collection
@property
def embeddings(self) -> Optional[Embeddings]:
"""Access the query embedding object."""
return self._embedding_function
@xor_args(("query_texts", "query_embeddings"))
def __query_collection(
self,
query_texts: Optional[List[str]] = None,
query_embeddings: Optional[List[List[float]]] = None,
n_results: int = 4,
where: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> Union[List[Document], chromadb.QueryResult]:
"""Query the chroma collection.
Args:
query_texts: List of query texts.
query_embeddings: List of query embeddings.
n_results: Number of results to return. Defaults to 4.
where: dict used to filter results by
e.g. {"color" : "red", "price": 4.20}.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of `n_results` nearest neighbor embeddings for provided
query_embeddings or query_texts.
See more: https://docs.trychroma.com/reference/py-collection#query
"""
return self._collection.query(
query_texts=query_texts,
query_embeddings=query_embeddings, # type: ignore
n_results=n_results,
where=where, # type: ignore
where_document=where_document, # type: ignore
**kwargs,
)
def encode_image(self, uri: str) -> str:
"""Get base64 string from image URI."""
with open(uri, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def add_images(
self,
uris: List[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more images through the embeddings and add to the vectorstore.
Args:
uris: File path to the image.
metadatas: Optional list of metadatas.
When querying, you can filter on this metadata.
ids: Optional list of IDs.
kwargs: Additional keyword arguments to pass.
Returns:
List of IDs of the added images.
Raises:
ValueError: When metadata is incorrect.
"""
# Map from uris to b64 encoded strings
b64_texts = [self.encode_image(uri=uri) for uri in uris]
# Populate IDs
if ids is None:
ids = [str(uuid.uuid4()) for _ in uris]
embeddings = None
# Set embeddings
if self._embedding_function is not None and hasattr(
self._embedding_function, "embed_image"
):
embeddings = self._embedding_function.embed_image(uris=uris)
if metadatas:
# fill metadatas with empty dicts if somebody
# did not specify metadata for all images
length_diff = len(uris) - len(metadatas)
if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids = []
for idx, m in enumerate(metadatas):
if m:
non_empty_ids.append(idx)
else:
empty_ids.append(idx)
if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
images_with_metadatas = [b64_texts[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
try:
self._collection.upsert(
metadatas=metadatas, # type: ignore
embeddings=embeddings_with_metadatas, # type: ignore
documents=images_with_metadatas,
ids=ids_with_metadata,
)
except ValueError as e:
if "Expected metadata value to be" in str(e):
msg = (
"Try filtering complex metadata using "
"langchain_community.vectorstores.utils.filter_complex_metadata."
)
raise ValueError(e.args[0] + "\n\n" + msg)
else:
raise e
if empty_ids:
images_without_metadatas = [b64_texts[j] for j in empty_ids]
embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas = [ids[j] for j in empty_ids]
self._collection.upsert(
embeddings=embeddings_without_metadatas,
documents=images_without_metadatas,
ids=ids_without_metadatas,
)
else:
self._collection.upsert(
embeddings=embeddings,
documents=b64_texts,
ids=ids,
)
return ids
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Texts to add to the vectorstore.
metadatas: Optional list of metadatas.
When querying, you can filter on this metadata.
ids: Optional list of IDs.
kwargs: Additional keyword arguments.
Returns:
List of IDs of the added texts.
Raises:
ValueError: When metadata is incorrect.
"""
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
else:
# Assign strings to any null IDs
for idx, _id in enumerate(ids):
if _id is None:
ids[idx] = str(uuid.uuid4())
embeddings = None
texts = list(texts)
if self._embedding_function is not None:
embeddings = self._embedding_function.embed_documents(texts)
if metadatas:
# fill metadatas with empty dicts if somebody
# did not specify metadata for all texts
length_diff = len(texts) - len(metadatas)
if length_diff:
metadatas = metadatas + [{}] * length_diff
empty_ids = []
non_empty_ids = []
for idx, m in enumerate(metadatas):
if m:
non_empty_ids.append(idx)
else:
empty_ids.append(idx)
if non_empty_ids:
metadatas = [metadatas[idx] for idx in non_empty_ids]
texts_with_metadatas = [texts[idx] for idx in non_empty_ids]
embeddings_with_metadatas = (
[embeddings[idx] for idx in non_empty_ids] if embeddings else None
)
ids_with_metadata = [ids[idx] for idx in non_empty_ids]
try:
self._collection.upsert(
metadatas=metadatas, # type: ignore
embeddings=embeddings_with_metadatas, # type: ignore
documents=texts_with_metadatas,
ids=ids_with_metadata,
)
except ValueError as e:
if "Expected metadata value to be" in str(e):
msg = (
"Try filtering complex metadata from the document using "
"langchain_community.vectorstores.utils.filter_complex_metadata."
)
raise ValueError(e.args[0] + "\n\n" + msg)
else:
raise e
if empty_ids:
texts_without_metadatas = [texts[j] for j in empty_ids]
embeddings_without_metadatas = (
[embeddings[j] for j in empty_ids] if embeddings else None
)
ids_without_metadatas = [ids[j] for j in empty_ids]
self._collection.upsert(
embeddings=embeddings_without_metadatas, # type: ignore
documents=texts_without_metadatas,
ids=ids_without_metadatas,
)
else:
self._collection.upsert(
embeddings=embeddings, # type: ignore
documents=texts,
ids=ids,
)
return ids
def similarity_search(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Run similarity search with Chroma.
Args:
query: Query text to search for.
k: Number of results to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of documents most similar to the query text.
"""
docs_and_scores = self.similarity_search_with_score(
query, k, filter=filter, **kwargs
)
return [doc for doc, _ in docs_and_scores]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of Documents most similar to the query vector.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
return _results_to_docs(results)
def similarity_search_by_vector_with_relevance_scores(
self,
embedding: List[float],
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector and similarity score.
Args:
embedding (List[float]): Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of documents most similar to the query text and relevance score
in float for each. Lower score represents more similarity.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
return _results_to_docs_and_scores(results)
def similarity_search_with_score(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Run similarity search with Chroma with distance.
Args:
query: Query text to search for.
k: Number of results to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of documents most similar to the query text and
distance in float for each. Lower score represents more similarity.
"""
if self._embedding_function is None:
results = self.__query_collection(
query_texts=[query],
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
else:
query_embedding = self._embedding_function.embed_query(query)
results = self.__query_collection(
query_embeddings=[query_embedding],
n_results=k,
where=filter,
where_document=where_document,
**kwargs,
)
return _results_to_docs_and_scores(results)
def similarity_search_with_vectors(
self,
query: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, np.ndarray]]:
"""Run similarity search with Chroma with vectors.
Args:
query: Query text to search for.
k: Number of results to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of documents most similar to the query text and
embedding vectors for each.
"""
include = ["documents", "metadatas", "embeddings"]
if self._embedding_function is None:
results = self.__query_collection(
query_texts=[query],
n_results=k,
where=filter,
where_document=where_document,
include=include,
**kwargs,
)
else:
query_embedding = self._embedding_function.embed_query(query)
results = self.__query_collection(
query_embeddings=[query_embedding],
n_results=k,
where=filter,
where_document=where_document,
include=include,
**kwargs,
)
return _results_to_docs_and_vectors(results)
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""Select the relevance score function based on collections distance metric.
The most similar documents will have the lowest relevance score. Default
relevance score function is euclidean distance. Distance metric must be
provided in `collection_metadata` during initialization of Chroma object.
Example: collection_metadata={"hnsw:space": "cosine"}. Available distance
metrics are: 'cosine', 'l2' and 'ip'.
Returns:
The relevance score function.
Raises:
ValueError: If the distance metric is not supported.
"""
if self.override_relevance_score_fn:
return self.override_relevance_score_fn
distance = "l2"
distance_key = "hnsw:space"
metadata = self._collection.metadata
if metadata and distance_key in metadata:
distance = metadata[distance_key]
if distance == "cosine":
return self._cosine_relevance_score_fn
elif distance == "l2":
return self._euclidean_relevance_score_fn
elif distance == "ip":
return self._max_inner_product_relevance_score_fn
else:
raise ValueError(
"No supported normalization function"
f" for distance metric of type: {distance}."
"Consider providing relevance_score_fn to Chroma constructor."
)
def similarity_search_by_image(
self,
uri: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Search for similar images based on the given image URI.
Args:
uri (str): URI of the image to search for.
k (int, optional): Number of results to return. Defaults to DEFAULT_K.
filter (Optional[Dict[str, str]], optional): Filter by metadata.
**kwargs (Any): Additional arguments to pass to function.
Returns:
List of Images most similar to the provided image.
Each element in list is a Langchain Document Object.
The page content is b64 encoded image, metadata is default or
as defined by user.
Raises:
ValueError: If the embedding function does not support image embeddings.
"""
if self._embedding_function is None or not hasattr(
self._embedding_function, "embed_image"
):
raise ValueError("The embedding function must support image embedding.")
# Obtain image embedding
# Assuming embed_image returns a single embedding
image_embedding = self._embedding_function.embed_image(uris=[uri])
# Perform similarity search based on the obtained embedding
results = self.similarity_search_by_vector(
embedding=image_embedding,
k=k,
filter=filter,
**kwargs,
)
return results
def similarity_search_by_image_with_relevance_score(
self,
uri: str,
k: int = DEFAULT_K,
filter: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Search for similar images based on the given image URI.
Args:
uri (str): URI of the image to search for.
k (int, optional): Number of results to return.
Defaults to DEFAULT_K.
filter (Optional[Dict[str, str]], optional): Filter by metadata.
**kwargs (Any): Additional arguments to pass to function.
Returns:
List[Tuple[Document, float]]: List of tuples containing documents similar
to the query image and their similarity scores.
0th element in each tuple is a Langchain Document Object.
The page content is b64 encoded img, metadata is default or defined by user.
Raises:
ValueError: If the embedding function does not support image embeddings.
"""
if self._embedding_function is None or not hasattr(
self._embedding_function, "embed_image"
):
raise ValueError("The embedding function must support image embedding.")
# Obtain image embedding
# Assuming embed_image returns a single embedding
image_embedding = self._embedding_function.embed_image(uris=[uri])
# Perform similarity search based on the obtained embedding
results = self.similarity_search_by_vector_with_relevance_scores(
embedding=image_embedding,
k=k,
filter=filter,
**kwargs,
)
return results
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm. Defaults to
20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.__query_collection(
query_embeddings=embedding,
n_results=fetch_k,
where=filter,
where_document=where_document,
include=["metadatas", "documents", "distances", "embeddings"],
**kwargs,
)
mmr_selected = maximal_marginal_relevance(
np.array(embedding, dtype=np.float32),
results["embeddings"][0],
k=k,
lambda_mult=lambda_mult,
)
candidates = _results_to_docs(results)
selected_results = [r for i, r in enumerate(candidates) if i in mmr_selected]
return selected_results
def max_marginal_relevance_search(
self,
query: str,
k: int = DEFAULT_K,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[Dict[str, str]] = None,
where_document: Optional[Dict[str, str]] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
where_document: dict used to filter by the documents.
E.g. {$contains: {"text": "hello"}}.
kwargs: Additional keyword arguments to pass to Chroma collection query.
Returns:
List of Documents selected by maximal marginal relevance.
Raises:
ValueError: If the embedding function is not provided.
"""
if self._embedding_function is None:
raise ValueError(
"For MMR search, you must specify an embedding function on" "creation."
)
embedding = self._embedding_function.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
embedding,
k,
fetch_k,
lambda_mult=lambda_mult,
filter=filter,
where_document=where_document,
)
def delete_collection(self) -> None:
"""Delete the collection."""
self._client.delete_collection(self._collection.name)
self._chroma_collection = None
def reset_collection(self) -> None:
"""Resets the collection.
Resets the collection by deleting the collection and recreating an empty one.
"""
self.delete_collection()
self.__ensure_collection()
def get(
self,
ids: Optional[OneOrMany[ID]] = None,
where: Optional[Where] = None,
limit: Optional[int] = None,
offset: Optional[int] = None,
where_document: Optional[WhereDocument] = None,
include: Optional[List[str]] = None,
) -> Dict[str, Any]:
"""Gets the collection.
Args:
ids: The ids of the embeddings to get. Optional.
where: A Where type dict used to filter results by.
E.g. `{"$and": [{"color": "red"}, {"price": 4.20}]}` Optional.
limit: The number of documents to return. Optional.
offset: The offset to start returning results from.
Useful for paging results with limit. Optional.
where_document: A WhereDocument type dict used to filter by the documents.
E.g. `{$contains: "hello"}`. Optional.
include: A list of what to include in the results.
Can contain `"embeddings"`, `"metadatas"`, `"documents"`.
Ids are always included.
Defaults to `["metadatas", "documents"]`. Optional.
Return:
A dict with the keys `"ids"`, `"embeddings"`, `"metadatas"`, `"documents"`.
"""
kwargs = {
"ids": ids,
"where": where,
"limit": limit,
"offset": offset,
"where_document": where_document,
}
if include is not None:
kwargs["include"] = include
return self._collection.get(**kwargs) # type: ignore
def get_by_ids(self, ids: Sequence[str], /) -> list[Document]:
"""Get documents by their IDs.
The returned documents are expected to have the ID field set to the ID of the
document in the vector store.
Fewer documents may be returned than requested if some IDs are not found or
if there are duplicated IDs.
Users should not assume that the order of the returned documents matches
the order of the input IDs. Instead, users should rely on the ID field of the
returned documents.
This method should **NOT** raise exceptions if no documents are found for
some IDs.
Args:
ids: List of ids to retrieve.
Returns:
List of Documents.
.. versionadded:: 0.2.1
"""
results = self.get(ids=list(ids))
return [
Document(page_content=doc, metadata=meta, id=doc_id)
for doc, meta, doc_id in zip(
results["documents"], results["metadatas"], results["ids"]
)
]
def update_document(self, document_id: str, document: Document) -> None:
"""Update a document in the collection.
Args:
document_id: ID of the document to update.
document: Document to update.
"""
return self.update_documents([document_id], [document])
# type: ignore
def update_documents(self, ids: List[str], documents: List[Document]) -> None:
"""Update a document in the collection.
Args:
ids: List of ids of the document to update.
documents: List of documents to update.
Raises:
ValueError: If the embedding function is not provided.
"""
text = [document.page_content for document in documents]
metadata = [document.metadata for document in documents]
if self._embedding_function is None:
raise ValueError(
"For update, you must specify an embedding function on creation."
)
embeddings = self._embedding_function.embed_documents(text)
if hasattr(
self._collection._client, "get_max_batch_size"
) or hasattr( # for Chroma 0.5.1 and above
self._collection._client, "max_batch_size"
): # for Chroma 0.4.10 and above
from chromadb.utils.batch_utils import create_batches
for batch in create_batches(
api=self._collection._client,
ids=ids,
metadatas=metadata, # type: ignore
documents=text,
embeddings=embeddings, # type: ignore
):
self._collection.update(
ids=batch[0],
embeddings=batch[1],
documents=batch[3],
metadatas=batch[2],
)
else:
self._collection.update(
ids=ids,
embeddings=embeddings, # type: ignore
documents=text,
metadatas=metadata, # type: ignore
)
@classmethod
def from_texts(
cls: Type[Chroma],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.ClientAPI] = None,
collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a raw documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
texts: List of texts to add to the collection.
collection_name: Name of the collection to create.
persist_directory: Directory to persist the collection.
embedding: Embedding function. Defaults to None.
metadatas: List of metadatas. Defaults to None.
ids: List of document IDs. Defaults to None.
client_settings: Chroma client settings.
client: Chroma client. Documentation:
https://docs.trychroma.com/reference/js-client#class:-chromaclient
collection_metadata: Collection configurations.
Defaults to None.
kwargs: Additional keyword arguments to initialize a Chroma client.
Returns:
Chroma: Chroma vectorstore.
"""
chroma_collection = cls(
collection_name=collection_name,
embedding_function=embedding,
persist_directory=persist_directory,
client_settings=client_settings,
client=client,
collection_metadata=collection_metadata,
**kwargs,
)
if ids is None:
ids = [str(uuid.uuid4()) for _ in texts]
if hasattr(
chroma_collection._client, "get_max_batch_size"
) or hasattr( # for Chroma 0.5.1 and above
chroma_collection._client, "max_batch_size"
): # for Chroma 0.4.10 and above
from chromadb.utils.batch_utils import create_batches
for batch in create_batches(
api=chroma_collection._client,
ids=ids,
metadatas=metadatas, # type: ignore
documents=texts,
):
chroma_collection.add_texts(
texts=batch[3] if batch[3] else [],
metadatas=batch[2] if batch[2] else None, # type: ignore
ids=batch[0],
)
else:
chroma_collection.add_texts(texts=texts, metadatas=metadatas, ids=ids)
return chroma_collection
@classmethod
def from_documents(
cls: Type[Chroma],
documents: List[Document],
embedding: Optional[Embeddings] = None,
ids: Optional[List[str]] = None,
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
persist_directory: Optional[str] = None,
client_settings: Optional[chromadb.config.Settings] = None,
client: Optional[chromadb.ClientAPI] = None, # Add this line
collection_metadata: Optional[Dict] = None,
**kwargs: Any,
) -> Chroma:
"""Create a Chroma vectorstore from a list of documents.
If a persist_directory is specified, the collection will be persisted there.
Otherwise, the data will be ephemeral in-memory.
Args:
collection_name: Name of the collection to create.
persist_directory: Directory to persist the collection.
ids : List of document IDs. Defaults to None.
documents: List of documents to add to the vectorstore.
embedding: Embedding function. Defaults to None.
client_settings: Chroma client settings.
client: Chroma client. Documentation:
https://docs.trychroma.com/reference/js-client#class:-chromaclient
collection_metadata: Collection configurations.
Defaults to None.
kwargs: Additional keyword arguments to initialize a Chroma client.
Returns:
Chroma: Chroma vectorstore.
"""
texts = [doc.page_content for doc in documents]
metadatas = [doc.metadata for doc in documents]
if ids is None:
ids = [doc.id if doc.id else str(uuid.uuid4()) for doc in documents]
return cls.from_texts(
texts=texts,
embedding=embedding,
metadatas=metadatas,
ids=ids,
collection_name=collection_name,
persist_directory=persist_directory,
client_settings=client_settings,
client=client,
collection_metadata=collection_metadata,
**kwargs,
)
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
"""Delete by vector IDs.
Args:
ids: List of ids to delete.
kwargs: Additional keyword arguments.
"""
self._collection.delete(ids=ids, **kwargs)
|
0 | lc_public_repos/langchain/libs/partners/chroma | lc_public_repos/langchain/libs/partners/chroma/langchain_chroma/__init__.py | """This is the langchain_chroma package.
It contains the Chroma class for handling various tasks.
"""
from langchain_chroma.vectorstores import Chroma
__all__ = [
"Chroma",
]
|
0 | lc_public_repos/langchain/libs/partners/chroma/tests | lc_public_repos/langchain/libs/partners/chroma/tests/integration_tests/test_vectorstores.py | """Test Chroma functionality."""
import uuid
from typing import (
Generator,
cast,
)
import chromadb
import pytest # type: ignore[import-not-found]
import requests
from chromadb.api.client import SharedSystemClient
from chromadb.api.types import Embeddable
from langchain_core.documents import Document
from langchain_core.embeddings.fake import FakeEmbeddings as Fak
from langchain_chroma.vectorstores import Chroma
from tests.integration_tests.fake_embeddings import (
ConsistentFakeEmbeddings,
FakeEmbeddings,
)
class MyEmbeddingFunction:
def __init__(self, fak: Fak):
self.fak = fak
def __call__(self, input: Embeddable) -> list[list[float]]:
texts = cast(list[str], input)
return self.fak.embed_documents(texts=texts)
@pytest.fixture()
def client() -> Generator[chromadb.ClientAPI, None, None]:
SharedSystemClient.clear_system_cache()
client = chromadb.Client(chromadb.config.Settings())
yield client
def test_chroma() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id is not None
def test_from_documents() -> None:
"""Test init using .from_documents."""
documents = [
Document(page_content="foo"),
Document(page_content="bar"),
Document(page_content="baz"),
]
docsearch = Chroma.from_documents(documents=documents, embedding=FakeEmbeddings())
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id is not None
def test_chroma_with_ids() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
ids=ids,
)
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id == "id_0"
async def test_chroma_async() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = await docsearch.asimilarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id is not None
async def test_chroma_async_with_ids() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
ids=ids,
)
output = await docsearch.asimilarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id == "id_0"
def test_chroma_with_metadatas() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].metadata == {"page": "0"}
assert output[0].id is not None
def test_chroma_with_metadatas_and_ids() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
)
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].metadata == {"page": "0"}
assert output[0].id == "id_0"
def test_chroma_with_metadatas_with_scores_and_ids() -> None:
"""Test end to end construction and scored search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
)
output = docsearch.similarity_search_with_score("foo", k=1)
docsearch.delete_collection()
assert output == [
(Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0)
]
def test_chroma_with_metadatas_with_vectors() -> None:
"""Test end to end construction and scored search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
embeddings = ConsistentFakeEmbeddings()
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=embeddings,
metadatas=metadatas,
)
vec_1 = embeddings.embed_query(texts[0])
output = docsearch.similarity_search_with_vectors("foo", k=1)
docsearch.delete_collection()
assert output[0][0] == Document(page_content="foo", metadata={"page": "0"})
assert (output[0][1] == vec_1).all()
def test_chroma_with_metadatas_with_scores_using_vector() -> None:
"""Test end to end construction and scored search, using embedding vector."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
ids = [f"id_{i}" for i in range(len(texts))]
embeddings = FakeEmbeddings()
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=embeddings,
metadatas=metadatas,
ids=ids,
)
embedded_query = embeddings.embed_query("foo")
output = docsearch.similarity_search_by_vector_with_relevance_scores(
embedding=embedded_query, k=1
)
docsearch.delete_collection()
assert output == [
(Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0)
]
def test_chroma_search_filter() -> None:
"""Test end to end construction and search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
)
output1 = docsearch.similarity_search("far", k=1, filter={"first_letter": "f"})
output2 = docsearch.similarity_search("far", k=1, filter={"first_letter": "b"})
docsearch.delete_collection()
assert output1 == [
Document(page_content="far", metadata={"first_letter": "f"}, id="id_0")
]
assert output2 == [
Document(page_content="bar", metadata={"first_letter": "b"}, id="id_1")
]
def test_chroma_search_filter_with_scores() -> None:
"""Test end to end construction and scored search with metadata filtering."""
texts = ["far", "bar", "baz"]
metadatas = [{"first_letter": "{}".format(text[0])} for text in texts]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
)
output1 = docsearch.similarity_search_with_score(
"far", k=1, filter={"first_letter": "f"}
)
output2 = docsearch.similarity_search_with_score(
"far", k=1, filter={"first_letter": "b"}
)
docsearch.delete_collection()
assert output1 == [
(Document(page_content="far", metadata={"first_letter": "f"}, id="id_0"), 0.0)
]
assert output2 == [
(Document(page_content="bar", metadata={"first_letter": "b"}, id="id_1"), 1.0)
]
def test_chroma_with_persistence() -> None:
"""Test end to end construction and search, with persistence."""
chroma_persist_dir = "./tests/persist_dir"
collection_name = "test_collection"
texts = ["foo", "bar", "baz"]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name=collection_name,
texts=texts,
embedding=FakeEmbeddings(),
persist_directory=chroma_persist_dir,
ids=ids,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", id="id_0")]
# Get a new VectorStore from the persisted directory
docsearch = Chroma(
collection_name=collection_name,
embedding_function=FakeEmbeddings(),
persist_directory=chroma_persist_dir,
)
output = docsearch.similarity_search("foo", k=1)
assert output == [Document(page_content="foo", id="id_0")]
# Clean up
docsearch.delete_collection()
# Persist doesn't need to be called again
# Data will be automatically persisted on object deletion
# Or on program exit
def test_chroma_mmr() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output = docsearch.max_marginal_relevance_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id is not None
def test_chroma_mmr_by_vector() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
embeddings = FakeEmbeddings()
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=embeddings
)
embedded_query = embeddings.embed_query("foo")
output = docsearch.max_marginal_relevance_search_by_vector(embedded_query, k=1)
docsearch.delete_collection()
assert len(output) == 1
assert output[0].page_content == "foo"
assert output[0].id is not None
def test_chroma_with_include_parameter() -> None:
"""Test end to end construction and include parameter."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
output1 = docsearch.get(include=["embeddings"])
output2 = docsearch.get()
docsearch.delete_collection()
assert output1["embeddings"] is not None
assert output2["embeddings"] is None
def test_chroma_update_document() -> None:
"""Test the update_document function in the Chroma class.
Uses an external document id.
"""
# Make a consistent embedding
embedding = ConsistentFakeEmbeddings()
# Initial document content and id
initial_content = "foo"
document_id = "doc1"
# Create an instance of Document with initial content and metadata
original_doc = Document(page_content=initial_content, metadata={"page": "0"})
# Initialize a Chroma instance with the original document
docsearch = Chroma.from_documents(
collection_name="test_collection",
documents=[original_doc],
embedding=embedding,
ids=[document_id],
)
old_embedding = docsearch._collection.peek()["embeddings"][ # type: ignore
docsearch._collection.peek()["ids"].index(document_id)
]
# Define updated content for the document
updated_content = "updated foo"
# Create a new Document instance with the updated content and the same id
updated_doc = Document(page_content=updated_content, metadata={"page": "0"})
# Update the document in the Chroma instance
docsearch.update_document(document_id=document_id, document=updated_doc)
# Perform a similarity search with the updated content
output = docsearch.similarity_search(updated_content, k=1)
# Assert that the new embedding is correct
new_embedding = docsearch._collection.peek()["embeddings"][ # type: ignore
docsearch._collection.peek()["ids"].index(document_id)
]
docsearch.delete_collection()
# Assert that the updated document is returned by the search
assert output == [
Document(page_content=updated_content, metadata={"page": "0"}, id=document_id)
]
assert list(new_embedding) == list(embedding.embed_documents([updated_content])[0])
assert list(new_embedding) != list(old_embedding)
def test_chroma_update_document_with_id() -> None:
"""Test the update_document function in the Chroma class.
Uses an internal document id.
"""
# Make a consistent embedding
embedding = ConsistentFakeEmbeddings()
# Initial document content and id
initial_content = "foo"
document_id = "doc1"
# Create an instance of Document with initial content and metadata
original_doc = Document(
page_content=initial_content, metadata={"page": "0"}, id=document_id
)
# Initialize a Chroma instance with the original document
docsearch = Chroma.from_documents(
collection_name="test_collection",
documents=[original_doc],
embedding=embedding,
)
old_embedding = docsearch._collection.peek()["embeddings"][ # type: ignore
docsearch._collection.peek()["ids"].index(document_id)
]
# Define updated content for the document
updated_content = "updated foo"
# Create a new Document instance with the updated content and the same id
updated_doc = Document(
page_content=updated_content, metadata={"page": "0"}, id=document_id
)
# Update the document in the Chroma instance
docsearch.update_document(document_id=document_id, document=updated_doc)
# Perform a similarity search with the updated content
output = docsearch.similarity_search(updated_content, k=1)
# Assert that the new embedding is correct
new_embedding = docsearch._collection.peek()["embeddings"][ # type: ignore
docsearch._collection.peek()["ids"].index(document_id)
]
docsearch.delete_collection()
# Assert that the updated document is returned by the search
assert output == [
Document(page_content=updated_content, metadata={"page": "0"}, id=document_id)
]
assert list(new_embedding) == list(embedding.embed_documents([updated_content])[0])
assert list(new_embedding) != list(old_embedding)
# TODO: RELEVANCE SCORE IS BROKEN. FIX TEST
def test_chroma_with_relevance_score_custom_normalization_fn() -> None:
"""Test searching with relevance score and custom normalization function."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
ids = [f"id_{i}" for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test1_collection",
texts=texts,
embedding=FakeEmbeddings(),
metadatas=metadatas,
ids=ids,
relevance_score_fn=lambda d: d * 0,
collection_metadata={"hnsw:space": "l2"},
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
docsearch.delete_collection()
assert output == [
(Document(page_content="foo", metadata={"page": "0"}, id="id_0"), 0.0),
(Document(page_content="bar", metadata={"page": "1"}, id="id_1"), 0.0),
(Document(page_content="baz", metadata={"page": "2"}, id="id_2"), 0.0),
]
def test_init_from_client(client: chromadb.ClientAPI) -> None:
Chroma(client=client)
def test_init_from_client_settings() -> None:
import chromadb
client_settings = chromadb.config.Settings()
Chroma(client_settings=client_settings)
def test_chroma_add_documents_no_metadata() -> None:
db = Chroma(embedding_function=FakeEmbeddings())
db.add_documents([Document(page_content="foo")])
db.delete_collection()
def test_chroma_add_documents_mixed_metadata() -> None:
db = Chroma(embedding_function=FakeEmbeddings())
docs = [
Document(page_content="foo", id="0"),
Document(page_content="bar", metadata={"baz": 1}, id="1"),
]
ids = ["0", "1"]
actual_ids = db.add_documents(docs)
search = db.similarity_search("foo bar")
db.delete_collection()
assert actual_ids == ids
assert sorted(search, key=lambda d: d.page_content) == sorted(
docs, key=lambda d: d.page_content
)
def is_api_accessible(url: str) -> bool:
try:
response = requests.get(url)
return response.status_code == 200
except Exception:
return False
def batch_support_chroma_version() -> bool:
major, minor, patch = chromadb.__version__.split(".")
if int(major) == 0 and int(minor) >= 4 and int(patch) >= 10:
return True
return False
@pytest.mark.requires("chromadb")
@pytest.mark.skipif(
not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
reason="API not accessible",
)
@pytest.mark.skipif(
not batch_support_chroma_version(),
reason="ChromaDB version does not support batching",
)
def test_chroma_large_batch() -> None:
client = chromadb.HttpClient()
embedding_function = MyEmbeddingFunction(fak=Fak(size=255))
col = client.get_or_create_collection(
"my_collection",
embedding_function=embedding_function, # type: ignore
)
docs = ["This is a test document"] * (client.get_max_batch_size() + 100) # type: ignore
db = Chroma.from_texts(
client=client,
collection_name=col.name,
texts=docs,
embedding=embedding_function.fak,
ids=[str(uuid.uuid4()) for _ in range(len(docs))],
)
db.delete_collection()
@pytest.mark.requires("chromadb")
@pytest.mark.skipif(
not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
reason="API not accessible",
)
@pytest.mark.skipif(
not batch_support_chroma_version(),
reason="ChromaDB version does not support batching",
)
def test_chroma_large_batch_update() -> None:
client = chromadb.HttpClient()
embedding_function = MyEmbeddingFunction(fak=Fak(size=255))
col = client.get_or_create_collection(
"my_collection",
embedding_function=embedding_function, # type: ignore
)
docs = ["This is a test document"] * (client.get_max_batch_size() + 100) # type: ignore
ids = [str(uuid.uuid4()) for _ in range(len(docs))]
db = Chroma.from_texts(
client=client,
collection_name=col.name,
texts=docs,
embedding=embedding_function.fak,
ids=ids,
)
new_docs = [
Document(
page_content="This is a new test document", metadata={"doc_id": f"{i}"}
)
for i in range(len(docs) - 10)
]
new_ids = [_id for _id in ids[: len(new_docs)]]
db.update_documents(ids=new_ids, documents=new_docs)
db.delete_collection()
@pytest.mark.requires("chromadb")
@pytest.mark.skipif(
not is_api_accessible("http://localhost:8000/api/v1/heartbeat"),
reason="API not accessible",
)
@pytest.mark.skipif(
batch_support_chroma_version(), reason="ChromaDB version does not support batching"
)
def test_chroma_legacy_batching() -> None:
client = chromadb.HttpClient()
embedding_function = Fak(size=255)
col = client.get_or_create_collection(
"my_collection",
embedding_function=MyEmbeddingFunction, # type: ignore
)
docs = ["This is a test document"] * 100
db = Chroma.from_texts(
client=client,
collection_name=col.name,
texts=docs,
embedding=embedding_function,
ids=[str(uuid.uuid4()) for _ in range(len(docs))],
)
db.delete_collection()
def test_create_collection_if_not_exist_default() -> None:
"""Tests existing behaviour without the new create_collection_if_not_exists flag."""
texts = ["foo", "bar", "baz"]
docsearch = Chroma.from_texts(
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
)
assert docsearch._client.get_collection("test_collection") is not None
docsearch.delete_collection()
def test_create_collection_if_not_exist_true_existing(
client: chromadb.ClientAPI,
) -> None:
"""Tests create_collection_if_not_exists=True and collection already existing."""
client.create_collection("test_collection")
vectorstore = Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
create_collection_if_not_exists=True,
)
assert vectorstore._client.get_collection("test_collection") is not None
vectorstore.delete_collection()
def test_create_collection_if_not_exist_false_existing(
client: chromadb.ClientAPI,
) -> None:
"""Tests create_collection_if_not_exists=False and collection already existing."""
client.create_collection("test_collection")
vectorstore = Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
create_collection_if_not_exists=False,
)
assert vectorstore._client.get_collection("test_collection") is not None
vectorstore.delete_collection()
def test_create_collection_if_not_exist_false_non_existing(
client: chromadb.ClientAPI,
) -> None:
"""Tests create_collection_if_not_exists=False and collection not-existing,
should raise."""
with pytest.raises(Exception, match="does not exist"):
Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
create_collection_if_not_exists=False,
)
def test_create_collection_if_not_exist_true_non_existing(
client: chromadb.ClientAPI,
) -> None:
"""Tests create_collection_if_not_exists=True and collection non-existing. ."""
vectorstore = Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
create_collection_if_not_exists=True,
)
assert vectorstore._client.get_collection("test_collection") is not None
vectorstore.delete_collection()
def test_collection_none_after_delete(
client: chromadb.ClientAPI,
) -> None:
"""Tests create_collection_if_not_exists=True and collection non-existing. ."""
vectorstore = Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
)
assert vectorstore._client.get_collection("test_collection") is not None
vectorstore.delete_collection()
assert vectorstore._chroma_collection is None
with pytest.raises(Exception, match="Chroma collection not initialized"):
_ = vectorstore._collection
with pytest.raises(Exception, match="does not exist"):
vectorstore._client.get_collection("test_collection")
with pytest.raises(Exception):
vectorstore.similarity_search("foo")
def test_reset_collection(client: chromadb.ClientAPI) -> None:
"""Tests ensure_collection method."""
vectorstore = Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
)
vectorstore.add_documents([Document(page_content="foo")])
assert vectorstore._collection.count() == 1
vectorstore.reset_collection()
assert vectorstore._chroma_collection is not None
assert vectorstore._client.get_collection("test_collection") is not None
assert vectorstore._collection.name == "test_collection"
assert vectorstore._collection.count() == 0
# Clean up
vectorstore.delete_collection()
def test_delete_where_clause(client: chromadb.ClientAPI) -> None:
"""Tests delete_where_clause method."""
vectorstore = Chroma(
client=client,
collection_name="test_collection",
embedding_function=FakeEmbeddings(),
)
vectorstore.add_documents(
[
Document(page_content="foo", metadata={"test": "bar"}),
Document(page_content="bar", metadata={"test": "foo"}),
]
)
assert vectorstore._collection.count() == 2
vectorstore.delete(where={"test": "bar"})
assert vectorstore._collection.count() == 1
# Clean up
vectorstore.delete_collection()
|
0 | lc_public_repos/langchain/libs/partners/chroma/tests | lc_public_repos/langchain/libs/partners/chroma/tests/integration_tests/test_standard.py | from typing import AsyncGenerator, Generator
import pytest
from langchain_core.vectorstores import VectorStore
from langchain_tests.integration_tests.vectorstores import (
AsyncReadWriteTestSuite,
ReadWriteTestSuite,
)
from langchain_chroma import Chroma
class TestSync(ReadWriteTestSuite):
@pytest.fixture()
def vectorstore(self) -> Generator[VectorStore, None, None]: # type: ignore
"""Get an empty vectorstore for unit tests."""
store = Chroma(embedding_function=self.get_embeddings())
try:
yield store
finally:
store.delete_collection()
pass
class TestAsync(AsyncReadWriteTestSuite):
@pytest.fixture()
async def vectorstore(self) -> AsyncGenerator[VectorStore, None]: # type: ignore
"""Get an empty vectorstore for unit tests."""
store = Chroma(embedding_function=self.get_embeddings())
try:
yield store
finally:
store.delete_collection()
pass
|
0 | lc_public_repos/langchain/libs/partners/chroma/tests | lc_public_repos/langchain/libs/partners/chroma/tests/integration_tests/fake_embeddings.py | """Fake Embedding class for testing purposes."""
import math
from typing import List
from langchain_core.embeddings import Embeddings
fake_texts = ["foo", "bar", "baz"]
class FakeEmbeddings(Embeddings):
"""Fake embeddings functionality for testing."""
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return simple embeddings.
Embeddings encode each text as its index."""
return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
return self.embed_documents(texts)
def embed_query(self, text: str) -> List[float]:
"""Return constant query embeddings.
Embeddings are identical to embed_documents(texts)[0].
Distance to each text will be that text's index,
as it was passed to embed_documents."""
return [float(1.0)] * 9 + [float(0.0)]
async def aembed_query(self, text: str) -> List[float]:
return self.embed_query(text)
class ConsistentFakeEmbeddings(FakeEmbeddings):
"""Fake embeddings which remember all the texts seen so far to return consistent
vectors for the same texts."""
def __init__(self, dimensionality: int = 10) -> None:
self.known_texts: List[str] = []
self.dimensionality = dimensionality
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return consistent embeddings for each text seen so far."""
out_vectors = []
for text in texts:
if text not in self.known_texts:
self.known_texts.append(text)
vector = [float(1.0)] * (self.dimensionality - 1) + [
float(self.known_texts.index(text))
]
out_vectors.append(vector)
return out_vectors
def embed_query(self, text: str) -> List[float]:
"""Return consistent embeddings for the text, if seen before, or a constant
one if the text is unknown."""
return self.embed_documents([text])[0]
class AngularTwoDimensionalEmbeddings(Embeddings):
"""
From angles (as strings in units of pi) to unit embedding vectors on a circle.
"""
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Make a list of texts into a list of embedding vectors.
"""
return [self.embed_query(text) for text in texts]
def embed_query(self, text: str) -> List[float]:
"""
Convert input text to a 'vector' (list of floats).
If the text is a number, use it as the angle for the
unit vector in units of pi.
Any other input text becomes the singular result [0, 0] !
"""
try:
angle = float(text)
return [math.cos(angle * math.pi), math.sin(angle * math.pi)]
except ValueError:
# Assume: just test string, no attention is paid to values.
return [0.0, 0.0]
|
0 | lc_public_repos/langchain/libs/partners/chroma/tests | lc_public_repos/langchain/libs/partners/chroma/tests/integration_tests/test_compile.py | import pytest # type: ignore[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/chroma/tests | lc_public_repos/langchain/libs/partners/chroma/tests/unit_tests/test_vectorstores.py | from langchain_core.embeddings.fake import (
FakeEmbeddings,
)
from langchain_chroma.vectorstores import Chroma
def test_initialization() -> None:
"""Test integration vectorstore initialization."""
texts = ["foo", "bar", "baz"]
Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(size=10),
)
def test_similarity_search() -> None:
"""Test similarity search by Chroma."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": str(i)} for i in range(len(texts))]
docsearch = Chroma.from_texts(
collection_name="test_collection",
texts=texts,
embedding=FakeEmbeddings(size=10),
metadatas=metadatas,
)
output = docsearch.similarity_search("foo", k=1)
docsearch.delete_collection()
assert len(output) == 1
|
0 | lc_public_repos/langchain/libs/partners/chroma/tests | lc_public_repos/langchain/libs/partners/chroma/tests/unit_tests/test_imports.py | from langchain_chroma import __all__
EXPECTED_ALL = [
"Chroma",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/chroma | lc_public_repos/langchain/libs/partners/chroma/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/chroma | lc_public_repos/langchain/libs/partners/chroma/scripts/check_imports.py | """This module checks if the given python files can be imported without error."""
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/qdrant/Makefile | .PHONY: all format lint test tests integration_test integration_tests help
# 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/qdrant --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_qdrant
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_qdrant -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 'lint_tests - run linters on tests'
@echo 'test - run unit tests'
@echo 'tests - run unit tests'
@echo 'test TEST_FILE=<test_file> - run all tests in file'
@echo 'integration_test - run integration tests'
@echo 'integration_tests - run integration tests'
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/qdrant/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/qdrant/poetry.lock | # This file is automatically @generated by Poetry 1.8.4 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 = [
{file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
]
[[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"},
{file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
]
[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 = [
{file = "certifi-2024.8.30-py3-none-any.whl", hash = "sha256:922820b53db7a7257ffbda3f597266d435245903d80737e34f8a45ff3e3230d8"},
{file = "certifi-2024.8.30.tar.gz", hash = "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"},
]
[[package]]
name = "charset-normalizer"
version = "3.4.0"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
optional = false
python-versions = ">=3.7.0"
files = [
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[metadata]
lock-version = "2.0"
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|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/qdrant/README.md | # langchain-qdrant
This package contains the LangChain integration with [Qdrant](https://qdrant.tech/).
## Installation
```bash
pip install -U langchain-qdrant
```
## Usage
The `Qdrant` class exposes the connection to the Qdrant vector store.
```python
from langchain_qdrant import Qdrant
embeddings = ... # use a LangChain Embeddings class
vectorstore = Qdrant.from_existing_collection(
embeddings=embeddings,
collection_name="<COLLECTION_NAME>",
url="http://localhost:6333",
)
```
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/qdrant/pyproject.toml | [build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-qdrant"
version = "0.2.0"
description = "An integration package connecting Qdrant and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.ruff]
select = ["E", "F", "I"]
[tool.mypy]
disallow_untyped_defs = true
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/qdrant"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-qdrant%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4"
qdrant-client = "^1.10.1"
fastembed = { version = "^0.3.3", python = ">=3.9,<3.13", optional = true }
pydantic = "^2.7.4"
langchain-core = ">=0.2.43,<0.4.0,!=0.3.0,!=0.3.1,!=0.3.2,!=0.3.3,!=0.3.4,!=0.3.5,!=0.3.6,!=0.3.7,!=0.3.8,!=0.3.9,!=0.3.10,!=0.3.11,!=0.3.12,!=0.3.13,!=0.3.14"
[tool.poetry.extras]
fastembed = ["fastembed"]
[tool.coverage.run]
omit = ["tests/*"]
[tool.pytest.ini_options]
addopts = "--snapshot-warn-unused --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.test_integration]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
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"
requests = "^2.31.0"
[[tool.poetry.group.test.dependencies.langchain-core]]
path = "../../core"
develop = true
python = ">=3.9"
[[tool.poetry.group.test.dependencies.langchain-core]]
version = ">=0.1.40,<0.3"
python = "<3.9"
[tool.poetry.group.dev.dependencies]
[[tool.poetry.group.dev.dependencies.langchain-core]]
path = "../../core"
develop = true
python = ">=3.9"
[[tool.poetry.group.dev.dependencies.langchain-core]]
version = ">=0.1.52,<0.3"
python = "<3.9"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint.dependencies]
ruff = "^0.5"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
simsimd = "^6.0.0"
[[tool.poetry.group.typing.dependencies.langchain-core]]
path = "../../core"
develop = true
python = ">=3.9"
[[tool.poetry.group.typing.dependencies.langchain-core]]
version = ">=0.1.52,<0.3"
python = "<3.9"
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/langchain_qdrant/_utils.py | from typing import List, Union
import numpy as np
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance."""
if min(k, len(embedding_list)) <= 0:
return []
if query_embedding.ndim == 1:
query_embedding = np.expand_dims(query_embedding, axis=0)
similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0]
most_similar = int(np.argmax(similarity_to_query))
idxs = [most_similar]
selected = np.array([embedding_list[most_similar]])
while len(idxs) < min(k, len(embedding_list)):
best_score = -np.inf
idx_to_add = -1
similarity_to_selected = cosine_similarity(embedding_list, selected)
for i, query_score in enumerate(similarity_to_query):
if i in idxs:
continue
redundant_score = max(similarity_to_selected[i])
equation_score = (
lambda_mult * query_score - (1 - lambda_mult) * redundant_score
)
if equation_score > best_score:
best_score = equation_score
idx_to_add = i
idxs.append(idx_to_add)
selected = np.append(selected, [embedding_list[idx_to_add]], axis=0)
return idxs
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-width matrices."""
if len(X) == 0 or len(Y) == 0:
return np.array([])
X = np.array(X)
Y = np.array(Y)
if X.shape[1] != Y.shape[1]:
raise ValueError(
f"Number of columns in X and Y must be the same. X has shape {X.shape} "
f"and Y has shape {Y.shape}."
)
try:
import simsimd as simd
X = np.array(X, dtype=np.float32)
Y = np.array(Y, dtype=np.float32)
Z = 1 - np.array(simd.cdist(X, Y, metric="cosine"))
return Z
except ImportError:
X_norm = np.linalg.norm(X, axis=1)
Y_norm = np.linalg.norm(Y, axis=1)
# Ignore divide by zero errors run time warnings as those are handled below.
with np.errstate(divide="ignore", invalid="ignore"):
similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm)
similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0
return similarity
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py | from abc import ABC, abstractmethod
from typing import List
from langchain_core.runnables.config import run_in_executor
from pydantic import BaseModel, Field
class SparseVector(BaseModel, extra="forbid"):
"""
Sparse vector structure
"""
indices: List[int] = Field(..., description="indices must be unique")
values: List[float] = Field(
..., description="values and indices must be the same length"
)
class SparseEmbeddings(ABC):
"""An interface for sparse embedding models to use with Qdrant."""
@abstractmethod
def embed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Embed search docs."""
@abstractmethod
def embed_query(self, text: str) -> SparseVector:
"""Embed query text."""
async def aembed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Asynchronous Embed search docs."""
return await run_in_executor(None, self.embed_documents, texts)
async def aembed_query(self, text: str) -> SparseVector:
"""Asynchronous Embed query text."""
return await run_in_executor(None, self.embed_query, text)
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py | from typing import Any, List, Optional, Sequence
from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector
class FastEmbedSparse(SparseEmbeddings):
"""An interface for sparse embedding models to use with Qdrant."""
def __init__(
self,
model_name: str = "Qdrant/bm25",
batch_size: int = 256,
cache_dir: Optional[str] = None,
threads: Optional[int] = None,
providers: Optional[Sequence[Any]] = None,
parallel: Optional[int] = None,
**kwargs: Any,
) -> None:
"""
Sparse encoder implementation using FastEmbed - https://qdrant.github.io/fastembed/
For a list of available models, see https://qdrant.github.io/fastembed/examples/Supported_Models/
Args:
model_name (str): The name of the model to use. Defaults to `"Qdrant/bm25"`.
batch_size (int): Batch size for encoding. Defaults to 256.
cache_dir (str, optional): The path to the model cache directory.\
Can also be set using the\
`FASTEMBED_CACHE_PATH` env variable.
threads (int, optional): The number of threads onnxruntime session can use.
providers (Sequence[Any], optional): List of ONNX execution providers.\
parallel (int, optional): If `>1`, data-parallel encoding will be used, r\
Recommended for encoding of large datasets.\
If `0`, use all available cores.\
If `None`, don't use data-parallel processing,\
use default onnxruntime threading instead.\
Defaults to None.
kwargs: Additional options to pass to fastembed.SparseTextEmbedding
Raises:
ValueError: If the model_name is not supported in SparseTextEmbedding.
"""
try:
from fastembed import SparseTextEmbedding # type: ignore
except ImportError:
raise ValueError(
"The 'fastembed' package is not installed. "
"Please install it with "
"`pip install fastembed` or `pip install fastembed-gpu`."
)
self._batch_size = batch_size
self._parallel = parallel
self._model = SparseTextEmbedding(
model_name=model_name,
cache_dir=cache_dir,
threads=threads,
providers=providers,
**kwargs,
)
def embed_documents(self, texts: List[str]) -> List[SparseVector]:
results = self._model.embed(
texts, batch_size=self._batch_size, parallel=self._parallel
)
return [
SparseVector(indices=result.indices.tolist(), values=result.values.tolist())
for result in results
]
def embed_query(self, text: str) -> SparseVector:
result = next(self._model.query_embed(text))
return SparseVector(
indices=result.indices.tolist(), values=result.values.tolist()
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/langchain_qdrant/vectorstores.py | from __future__ import annotations
import functools
import os
import uuid
import warnings
from itertools import islice
from operator import itemgetter
from typing import (
TYPE_CHECKING,
Any,
AsyncGenerator,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
import numpy as np
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.runnables.config import run_in_executor
from langchain_core.vectorstores import VectorStore
from qdrant_client import AsyncQdrantClient, QdrantClient
from qdrant_client.http import models
from qdrant_client.local.async_qdrant_local import AsyncQdrantLocal
from langchain_qdrant._utils import maximal_marginal_relevance
if TYPE_CHECKING:
DictFilter = Dict[str, Union[str, int, bool, dict, list]]
MetadataFilter = Union[DictFilter, models.Filter]
class QdrantException(Exception):
"""`Qdrant` related exceptions."""
def sync_call_fallback(method: Callable) -> Callable:
"""
Decorator to call the synchronous method of the class if the async method is not
implemented. This decorator might be only used for the methods that are defined
as async in the class.
"""
@functools.wraps(method)
async def wrapper(self: Any, *args: Any, **kwargs: Any) -> Any:
try:
return await method(self, *args, **kwargs)
except NotImplementedError:
# If the async method is not implemented, call the synchronous method
# by removing the first letter from the method name. For example,
# if the async method is called ``aadd_texts``, the synchronous method
# will be called ``aad_texts``.
return await run_in_executor(
None, getattr(self, method.__name__[1:]), *args, **kwargs
)
return wrapper
@deprecated(since="0.1.2", alternative="QdrantVectorStore", removal="0.5.0")
class Qdrant(VectorStore):
"""`Qdrant` vector store.
Example:
.. code-block:: python
from qdrant_client import QdrantClient
from langchain_qdrant import Qdrant
client = QdrantClient()
collection_name = "MyCollection"
qdrant = Qdrant(client, collection_name, embedding_function)
"""
CONTENT_KEY: str = "page_content"
METADATA_KEY: str = "metadata"
VECTOR_NAME: Optional[str] = None
def __init__(
self,
client: Any,
collection_name: str,
embeddings: Optional[Embeddings] = None,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
distance_strategy: str = "COSINE",
vector_name: Optional[str] = VECTOR_NAME,
async_client: Optional[Any] = None,
embedding_function: Optional[Callable] = None, # deprecated
):
"""Initialize with necessary components."""
if not isinstance(client, QdrantClient):
raise ValueError(
f"client should be an instance of qdrant_client.QdrantClient, "
f"got {type(client)}"
)
if async_client is not None and not isinstance(async_client, AsyncQdrantClient):
raise ValueError(
f"async_client should be an instance of qdrant_client.AsyncQdrantClient"
f"got {type(async_client)}"
)
if embeddings is None and embedding_function is None:
raise ValueError(
"`embeddings` value can't be None. Pass `Embeddings` instance."
)
if embeddings is not None and embedding_function is not None:
raise ValueError(
"Both `embeddings` and `embedding_function` are passed. "
"Use `embeddings` only."
)
self._embeddings = embeddings
self._embeddings_function = embedding_function
self.client: QdrantClient = client
self.async_client: Optional[AsyncQdrantClient] = async_client
self.collection_name = collection_name
self.content_payload_key = content_payload_key or self.CONTENT_KEY
self.metadata_payload_key = metadata_payload_key or self.METADATA_KEY
self.vector_name = vector_name or self.VECTOR_NAME
if embedding_function is not None:
warnings.warn(
"Using `embedding_function` is deprecated. "
"Pass `Embeddings` instance to `embeddings` instead."
)
if not isinstance(embeddings, Embeddings):
warnings.warn(
"`embeddings` should be an instance of `Embeddings`."
"Using `embeddings` as `embedding_function` which is deprecated"
)
self._embeddings_function = embeddings
self._embeddings = None
self.distance_strategy = distance_strategy.upper()
@property
def embeddings(self) -> Optional[Embeddings]:
return self._embeddings
def add_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size:
How many vectors upload per-request.
Default: 64
Returns:
List of ids from adding the texts into the vectorstore.
"""
added_ids = []
for batch_ids, points in self._generate_rest_batches(
texts, metadatas, ids, batch_size
):
self.client.upsert(
collection_name=self.collection_name, points=points, **kwargs
)
added_ids.extend(batch_ids)
return added_ids
@sync_call_fallback
async def aadd_texts(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
batch_size:
How many vectors upload per-request.
Default: 64
Returns:
List of ids from adding the texts into the vectorstore.
"""
if self.async_client is None or isinstance(
self.async_client._client, AsyncQdrantLocal
):
raise NotImplementedError(
"QdrantLocal cannot interoperate with sync and async clients"
)
added_ids = []
async for batch_ids, points in self._agenerate_rest_batches(
texts, metadatas, ids, batch_size
):
await self.async_client.upsert(
collection_name=self.collection_name, points=points, **kwargs
)
added_ids.extend(batch_ids)
return added_ids
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score(
query,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def asimilarity_search(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
Returns:
List of Documents most similar to the query.
"""
results = await self.asimilarity_search_with_score(query, k, filter, **kwargs)
return list(map(itemgetter(0), results))
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of documents most similar to the query text and distance for each.
"""
return self.similarity_search_with_score_by_vector(
self._embed_query(query),
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
@sync_call_fallback
async def asimilarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of documents most similar to the query text and distance for each.
"""
query_embedding = await self._aembed_query(query)
return await self.asimilarity_search_with_score_by_vector(
query_embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def asimilarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of Documents most similar to the query.
"""
results = await self.asimilarity_search_with_score_by_vector(
embedding,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def similarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of documents most similar to the query text and distance for each.
"""
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, embedding) # type: ignore[assignment]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
result.score,
)
for result in results
]
@sync_call_fallback
async def asimilarity_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to embedding vector.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
offset:
Offset of the first result to return.
May be used to paginate results.
Note: large offset values may cause performance issues.
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of documents most similar to the query text and distance for each.
"""
if self.async_client is None or isinstance(
self.async_client._client, AsyncQdrantLocal
):
raise NotImplementedError(
"QdrantLocal cannot interoperate with sync and async clients"
)
if filter is not None and isinstance(filter, dict):
warnings.warn(
"Using dict as a `filter` is deprecated. Please use qdrant-client "
"filters directly: "
"https://qdrant.tech/documentation/concepts/filtering/",
DeprecationWarning,
)
qdrant_filter = self._qdrant_filter_from_dict(filter)
else:
qdrant_filter = filter
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, embedding) # type: ignore[assignment]
results = await self.async_client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False, # Langchain does not expect vectors to be returned
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return [
(
self._document_from_scored_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
result.score,
)
for result in results
]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = self._embed_query(query)
return self.max_marginal_relevance_search_by_vector(
query_embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
@sync_call_fallback
async def amax_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
query: Text to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of Documents selected by maximal marginal relevance.
"""
query_embedding = await self._aembed_query(query)
return await self.amax_marginal_relevance_search_by_vector(
query_embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.max_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
@sync_call_fallback
async def amax_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to
AsyncQdrantClient.Search().
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
results = await self.amax_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
filter: Filter by metadata. Defaults to None.
search_params: Additional search params
score_threshold:
Define a minimal score threshold for the result.
If defined, less similar results will not be returned.
Score of the returned result might be higher or smaller than the
threshold depending on the Distance function used.
E.g. for cosine similarity only higher scores will be returned.
consistency:
Read consistency of the search. Defines how many replicas should be
queried before returning the result.
Values:
- int - number of replicas to query, values should present in all
queried replicas
- 'majority' - query all replicas, but return values present in the
majority of replicas
- 'quorum' - query the majority of replicas, return values present in
all of them
- 'all' - query all replicas, and return values present in all replicas
**kwargs:
Any other named arguments to pass through to QdrantClient.search()
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, query_vector) # type: ignore[assignment]
results = self.client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=filter,
search_params=search_params,
limit=fetch_k,
with_payload=True,
with_vectors=True,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
embeddings = [
result.vector.get(self.vector_name) # type: ignore[index, union-attr]
if self.vector_name is not None
else result.vector
for result in results
]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
(
self._document_from_scored_point(
results[i],
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
results[i].score,
)
for i in mmr_selected
]
@sync_call_fallback
async def amax_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[MetadataFilter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Args:
embedding: Embedding vector to look up documents similar to.
k: Number of Documents to return. Defaults to 4.
fetch_k: Number of Documents to fetch to pass to MMR algorithm.
Defaults to 20.
lambda_mult: Number between 0 and 1 that determines the degree
of diversity among the results with 0 corresponding
to maximum diversity and 1 to minimum diversity.
Defaults to 0.5.
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
if self.async_client is None or isinstance(
self.async_client._client, AsyncQdrantLocal
):
raise NotImplementedError(
"QdrantLocal cannot interoperate with sync and async clients"
)
query_vector = embedding
if self.vector_name is not None:
query_vector = (self.vector_name, query_vector) # type: ignore[assignment]
results = await self.async_client.search(
collection_name=self.collection_name,
query_vector=query_vector,
query_filter=filter,
search_params=search_params,
limit=fetch_k,
with_payload=True,
with_vectors=True,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
embeddings = [
result.vector.get(self.vector_name) # type: ignore[index, union-attr]
if self.vector_name is not None
else result.vector
for result in results
]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
(
self._document_from_scored_point(
results[i],
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
results[i].score,
)
for i in mmr_selected
]
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
True if deletion is successful, False otherwise.
"""
result = self.client.delete(
collection_name=self.collection_name,
points_selector=ids,
)
return result.status == models.UpdateStatus.COMPLETED
@sync_call_fallback
async def adelete(
self, ids: Optional[List[str]] = None, **kwargs: Any
) -> Optional[bool]:
"""Delete by vector ID or other criteria.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
True if deletion is successful, False otherwise.
"""
if self.async_client is None or isinstance(
self.async_client._client, AsyncQdrantLocal
):
raise NotImplementedError(
"QdrantLocal cannot interoperate with sync and async clients"
)
result = await self.async_client.delete(
collection_name=self.collection_name,
points_selector=ids,
)
return result.status == models.UpdateStatus.COMPLETED
@classmethod
def from_texts(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
batch_size: int = 64,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[models.HnswConfigDiff] = None,
optimizers_config: Optional[models.OptimizersConfigDiff] = None,
wal_config: Optional[models.WalConfigDiff] = None,
quantization_config: Optional[models.QuantizationConfig] = None,
init_from: Optional[models.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
"""Construct Qdrant wrapper from a list of texts.
Args:
texts: A list of texts to be indexed in Qdrant.
embedding: A subclass of `Embeddings`, responsible for text vectorization.
metadatas:
An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location:
If ':memory:' - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefix]". Default: `None`
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key:
API key for authentication in Qdrant Cloud. Default: None
Can also be set via environment variable `QDRANT_API_KEY`.
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host:
Host name of Qdrant service. If url and host are None, set to
'localhost'. Default: None
path:
Path in which the vectors will be stored while using local mode.
Default: None
collection_name:
Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content"
metadata_payload_key:
A payload key used to store the metadata of the document.
Default: "metadata"
vector_name:
Name of the vector to be used internally in Qdrant.
Default: None
batch_size:
How many vectors upload per-request.
Default: 64
shard_number: Number of shards in collection. Default is 1, minimum is 1.
replication_factor:
Replication factor for collection. Default is 1, minimum is 1.
Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor:
Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any performance impact.
Have effect only in distributed mode.
on_disk_payload:
If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config: Params for HNSW index
optimizers_config: Params for optimizer
wal_config: Params for Write-Ahead-Log
quantization_config:
Params for quantization, if None - quantization will be disabled
init_from:
Use data stored in another collection to initialize this collection
force_recreate:
Force recreating the collection
**kwargs:
Additional arguments passed directly into REST client initialization
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, "localhost")
"""
qdrant = cls.construct_instance(
texts,
embedding,
location,
url,
port,
grpc_port,
prefer_grpc,
https,
api_key,
prefix,
timeout,
host,
path,
collection_name,
distance_func,
content_payload_key,
metadata_payload_key,
vector_name,
shard_number,
replication_factor,
write_consistency_factor,
on_disk_payload,
hnsw_config,
optimizers_config,
wal_config,
quantization_config,
init_from,
on_disk,
force_recreate,
**kwargs,
)
qdrant.add_texts(texts, metadatas, ids, batch_size)
return qdrant
@classmethod
def from_existing_collection(
cls: Type[Qdrant],
embedding: Embeddings,
path: Optional[str] = None,
collection_name: Optional[str] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
distance_strategy: str = "COSINE",
vector_name: Optional[str] = VECTOR_NAME,
**kwargs: Any,
) -> Qdrant:
"""
Get instance of an existing Qdrant collection.
This method will return the instance of the store without inserting any new
embeddings
"""
if collection_name is None:
raise ValueError("Must specify collection_name. Received None.")
client, async_client = cls._generate_clients(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
return cls(
client=client,
async_client=async_client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_strategy,
vector_name=vector_name,
)
@classmethod
@sync_call_fallback
async def afrom_texts(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
batch_size: int = 64,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[models.HnswConfigDiff] = None,
optimizers_config: Optional[models.OptimizersConfigDiff] = None,
wal_config: Optional[models.WalConfigDiff] = None,
quantization_config: Optional[models.QuantizationConfig] = None,
init_from: Optional[models.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
"""Construct Qdrant wrapper from a list of texts.
Args:
texts: A list of texts to be indexed in Qdrant.
embedding: A subclass of `Embeddings`, responsible for text vectorization.
metadatas:
An optional list of metadata. If provided it has to be of the same
length as a list of texts.
ids:
Optional list of ids to associate with the texts. Ids have to be
uuid-like strings.
location:
If ':memory:' - use in-memory Qdrant instance.
If `str` - use it as a `url` parameter.
If `None` - fallback to relying on `host` and `port` parameters.
url: either host or str of "Optional[scheme], host, Optional[port],
Optional[prefix]". Default: `None`
port: Port of the REST API interface. Default: 6333
grpc_port: Port of the gRPC interface. Default: 6334
prefer_grpc:
If true - use gPRC interface whenever possible in custom methods.
Default: False
https: If true - use HTTPS(SSL) protocol. Default: None
api_key:
API key for authentication in Qdrant Cloud. Default: None
Can also be set via environment variable `QDRANT_API_KEY`.
prefix:
If not None - add prefix to the REST URL path.
Example: service/v1 will result in
http://localhost:6333/service/v1/{qdrant-endpoint} for REST API.
Default: None
timeout:
Timeout for REST and gRPC API requests.
Default: 5.0 seconds for REST and unlimited for gRPC
host:
Host name of Qdrant service. If url and host are None, set to
'localhost'. Default: None
path:
Path in which the vectors will be stored while using local mode.
Default: None
collection_name:
Name of the Qdrant collection to be used. If not provided,
it will be created randomly. Default: None
distance_func:
Distance function. One of: "Cosine" / "Euclid" / "Dot".
Default: "Cosine"
content_payload_key:
A payload key used to store the content of the document.
Default: "page_content"
metadata_payload_key:
A payload key used to store the metadata of the document.
Default: "metadata"
vector_name:
Name of the vector to be used internally in Qdrant.
Default: None
batch_size:
How many vectors upload per-request.
Default: 64
shard_number: Number of shards in collection. Default is 1, minimum is 1.
replication_factor:
Replication factor for collection. Default is 1, minimum is 1.
Defines how many copies of each shard will be created.
Have effect only in distributed mode.
write_consistency_factor:
Write consistency factor for collection. Default is 1, minimum is 1.
Defines how many replicas should apply the operation for us to consider
it successful. Increasing this number will make the collection more
resilient to inconsistencies, but will also make it fail if not enough
replicas are available.
Does not have any performance impact.
Have effect only in distributed mode.
on_disk_payload:
If true - point`s payload will not be stored in memory.
It will be read from the disk every time it is requested.
This setting saves RAM by (slightly) increasing the response time.
Note: those payload values that are involved in filtering and are
indexed - remain in RAM.
hnsw_config: Params for HNSW index
optimizers_config: Params for optimizer
wal_config: Params for Write-Ahead-Log
quantization_config:
Params for quantization, if None - quantization will be disabled
init_from:
Use data stored in another collection to initialize this collection
force_recreate:
Force recreating the collection
**kwargs:
Additional arguments passed directly into REST client initialization
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Initializes the Qdrant database as an in-memory docstore by default
(and overridable to a remote docstore)
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = await Qdrant.afrom_texts(texts, embeddings, "localhost")
"""
qdrant = await cls.aconstruct_instance(
texts,
embedding,
location,
url,
port,
grpc_port,
prefer_grpc,
https,
api_key,
prefix,
timeout,
host,
path,
collection_name,
distance_func,
content_payload_key,
metadata_payload_key,
vector_name,
shard_number,
replication_factor,
write_consistency_factor,
on_disk_payload,
hnsw_config,
optimizers_config,
wal_config,
quantization_config,
init_from,
on_disk,
force_recreate,
**kwargs,
)
await qdrant.aadd_texts(texts, metadatas, ids, batch_size)
return qdrant
@classmethod
def construct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[models.HnswConfigDiff] = None,
optimizers_config: Optional[models.OptimizersConfigDiff] = None,
wal_config: Optional[models.WalConfigDiff] = None,
quantization_config: Optional[models.QuantizationConfig] = None,
init_from: Optional[models.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
# Just do a single quick embedding to get vector size
partial_embeddings = embedding.embed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client, async_client = cls._generate_clients(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
collection_exists = client.collection_exists(collection_name)
if collection_exists and force_recreate:
client.delete_collection(collection_name)
collection_exists = False
if collection_exists:
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is raised.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}."
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
assert isinstance(current_vector_config, models.VectorParams), (
"Expected current_vector_config to be an instance of "
f"models.VectorParams, but got {type(current_vector_config)}"
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size:
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} "
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_distance_func} similarity, but requested "
f"{distance_func}. Please set `distance_func` parameter to "
f"`{current_distance_func}` if you want to reuse it. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
else:
vectors_config = models.VectorParams(
size=vector_size,
distance=models.Distance[distance_func],
on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.create_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
qdrant = cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_func,
vector_name=vector_name,
async_client=async_client,
)
return qdrant
@classmethod
async def aconstruct_instance(
cls: Type[Qdrant],
texts: List[str],
embedding: Embeddings,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
collection_name: Optional[str] = None,
distance_func: str = "Cosine",
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: Optional[str] = VECTOR_NAME,
shard_number: Optional[int] = None,
replication_factor: Optional[int] = None,
write_consistency_factor: Optional[int] = None,
on_disk_payload: Optional[bool] = None,
hnsw_config: Optional[models.HnswConfigDiff] = None,
optimizers_config: Optional[models.OptimizersConfigDiff] = None,
wal_config: Optional[models.WalConfigDiff] = None,
quantization_config: Optional[models.QuantizationConfig] = None,
init_from: Optional[models.InitFrom] = None,
on_disk: Optional[bool] = None,
force_recreate: bool = False,
**kwargs: Any,
) -> Qdrant:
# Just do a single quick embedding to get vector size
partial_embeddings = await embedding.aembed_documents(texts[:1])
vector_size = len(partial_embeddings[0])
collection_name = collection_name or uuid.uuid4().hex
distance_func = distance_func.upper()
client, async_client = cls._generate_clients(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
collection_exists = client.collection_exists(collection_name)
if collection_exists and force_recreate:
client.delete_collection(collection_name)
collection_exists = False
if collection_exists:
# Get the vector configuration of the existing collection and vector, if it
# was specified. If the old configuration does not match the current one,
# an exception is raised.
collection_info = client.get_collection(collection_name=collection_name)
current_vector_config = collection_info.config.params.vectors
if isinstance(current_vector_config, dict) and vector_name is not None:
if vector_name not in current_vector_config:
raise QdrantException(
f"Existing Qdrant collection {collection_name} does not "
f"contain vector named {vector_name}. Did you mean one of the "
f"existing vectors: {', '.join(current_vector_config.keys())}? "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_vector_config = current_vector_config.get(vector_name) # type: ignore[assignment]
elif isinstance(current_vector_config, dict) and vector_name is None:
raise QdrantException(
f"Existing Qdrant collection {collection_name} uses named vectors. "
f"If you want to reuse it, please set `vector_name` to any of the "
f"existing named vectors: "
f"{', '.join(current_vector_config.keys())}."
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
elif (
not isinstance(current_vector_config, dict) and vector_name is not None
):
raise QdrantException(
f"Existing Qdrant collection {collection_name} doesn't use named "
f"vectors. If you want to reuse it, please set `vector_name` to "
f"`None`. If you want to recreate the collection, set "
f"`force_recreate` parameter to `True`."
)
assert isinstance(current_vector_config, models.VectorParams), (
"Expected current_vector_config to be an instance of "
f"models.VectorParams, but got {type(current_vector_config)}"
)
# Check if the vector configuration has the same dimensionality.
if current_vector_config.size != vector_size:
raise QdrantException(
f"Existing Qdrant collection is configured for vectors with "
f"{current_vector_config.size} "
f"dimensions. Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
current_distance_func = (
current_vector_config.distance.name.upper() # type: ignore[union-attr]
)
if current_distance_func != distance_func:
raise QdrantException(
f"Existing Qdrant collection is configured for "
f"{current_vector_config.distance} " # type: ignore[union-attr]
f"similarity. Please set `distance_func` parameter to "
f"`{distance_func}` if you want to reuse it. If you want to "
f"recreate the collection, set `force_recreate` parameter to "
f"`True`."
)
else:
vectors_config = models.VectorParams(
size=vector_size,
distance=models.Distance[distance_func],
on_disk=on_disk,
)
# If vector name was provided, we're going to use the named vectors feature
# with just a single vector.
if vector_name is not None:
vectors_config = { # type: ignore[assignment]
vector_name: vectors_config,
}
client.create_collection(
collection_name=collection_name,
vectors_config=vectors_config,
shard_number=shard_number,
replication_factor=replication_factor,
write_consistency_factor=write_consistency_factor,
on_disk_payload=on_disk_payload,
hnsw_config=hnsw_config,
optimizers_config=optimizers_config,
wal_config=wal_config,
quantization_config=quantization_config,
init_from=init_from,
timeout=timeout, # type: ignore[arg-type]
)
qdrant = cls(
client=client,
collection_name=collection_name,
embeddings=embedding,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance_strategy=distance_func,
vector_name=vector_name,
async_client=async_client,
)
return qdrant
@staticmethod
def _cosine_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
return (distance + 1.0) / 2.0
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance_strategy == "COSINE":
return self._cosine_relevance_score_fn
elif self.distance_strategy == "DOT":
return self._max_inner_product_relevance_score_fn
elif self.distance_strategy == "EUCLID":
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be cosine, "
"max_inner_product, or euclidean"
)
def _similarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return self.similarity_search_with_score(query, k, **kwargs)
@sync_call_fallback
async def _asimilarity_search_with_relevance_scores(
self,
query: str,
k: int = 4,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs and relevance scores in the range [0, 1].
0 is dissimilar, 1 is most similar.
Args:
query: input text
k: Number of Documents to return. Defaults to 4.
**kwargs: kwargs to be passed to similarity search. Should include:
score_threshold: Optional, a floating point value between 0 to 1 to
filter the resulting set of retrieved docs
Returns:
List of Tuples of (doc, similarity_score)
"""
return await self.asimilarity_search_with_score(query, k, **kwargs)
@classmethod
def _build_payloads(
cls,
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,
metadata_payload_key: str,
) -> List[dict]:
payloads = []
for i, text in enumerate(texts):
if text is None:
raise ValueError(
"At least one of the texts is None. Please remove it before "
"calling .from_texts or .add_texts on Qdrant instance."
)
metadata = metadatas[i] if metadatas is not None else None
payloads.append(
{
content_payload_key: text,
metadata_payload_key: metadata,
}
)
return payloads
@classmethod
def _document_from_scored_point(
cls,
scored_point: Any,
collection_name: str,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
metadata = scored_point.payload.get(metadata_payload_key) or {}
metadata["_id"] = scored_point.id
metadata["_collection_name"] = collection_name
return Document(
page_content=scored_point.payload.get(content_payload_key, ""),
metadata=metadata,
)
def _build_condition(self, key: str, value: Any) -> List[models.FieldCondition]:
out = []
if isinstance(value, dict):
for _key, value in value.items():
out.extend(self._build_condition(f"{key}.{_key}", value))
elif isinstance(value, list):
for _value in value:
if isinstance(_value, dict):
out.extend(self._build_condition(f"{key}[]", _value))
else:
out.extend(self._build_condition(f"{key}", _value))
else:
out.append(
models.FieldCondition(
key=f"{self.metadata_payload_key}.{key}",
match=models.MatchValue(value=value),
)
)
return out
def _qdrant_filter_from_dict(
self, filter: Optional[DictFilter]
) -> Optional[models.Filter]:
if not filter:
return None
return models.Filter(
must=[
condition
for key, value in filter.items()
for condition in self._build_condition(key, value)
]
)
def _embed_query(self, query: str) -> List[float]:
"""Embed query text.
Used to provide backward compatibility with `embedding_function` argument.
Args:
query: Query text.
Returns:
List of floats representing the query embedding.
"""
if self.embeddings is not None:
embedding = self.embeddings.embed_query(query)
else:
if self._embeddings_function is not None:
embedding = self._embeddings_function(query)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
async def _aembed_query(self, query: str) -> List[float]:
"""Embed query text asynchronously.
Used to provide backward compatibility with `embedding_function` argument.
Args:
query: Query text.
Returns:
List of floats representing the query embedding.
"""
if self.embeddings is not None:
embedding = await self.embeddings.aembed_query(query)
else:
if self._embeddings_function is not None:
embedding = self._embeddings_function(query)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embedding.tolist() if hasattr(embedding, "tolist") else embedding
def _embed_texts(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = self.embeddings.embed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embeddings = embeddings.tolist()
elif self._embeddings_function is not None:
embeddings = []
for text in texts:
embedding = self._embeddings_function(text)
if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
async def _aembed_texts(self, texts: Iterable[str]) -> List[List[float]]:
"""Embed search texts.
Used to provide backward compatibility with `embedding_function` argument.
Args:
texts: Iterable of texts to embed.
Returns:
List of floats representing the texts embedding.
"""
if self.embeddings is not None:
embeddings = await self.embeddings.aembed_documents(list(texts))
if hasattr(embeddings, "tolist"):
embeddings = embeddings.tolist()
elif self._embeddings_function is not None:
embeddings = []
for text in texts:
embedding = self._embeddings_function(text)
if hasattr(embeddings, "tolist"):
embedding = embedding.tolist()
embeddings.append(embedding)
else:
raise ValueError("Neither of embeddings or embedding_function is set")
return embeddings
def _generate_rest_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
) -> Generator[Tuple[List[str], List[models.PointStruct]], None, None]:
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
# Generate the embeddings for all the texts in a batch
batch_embeddings = self._embed_texts(batch_texts)
points = [
models.PointStruct(
id=point_id,
vector=vector # type: ignore[arg-type]
if self.vector_name is None
else {self.vector_name: vector},
payload=payload,
)
for point_id, vector, payload in zip(
batch_ids,
batch_embeddings,
self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
)
]
yield batch_ids, points
async def _agenerate_rest_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str]] = None,
batch_size: int = 64,
) -> AsyncGenerator[Tuple[List[str], List[models.PointStruct]], None]:
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
# Take the corresponding metadata and id for each text in a batch
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
# Generate the embeddings for all the texts in a batch
batch_embeddings = await self._aembed_texts(batch_texts)
points = [
models.PointStruct(
id=point_id,
vector=vector # type: ignore[arg-type]
if self.vector_name is None
else {self.vector_name: vector},
payload=payload,
)
for point_id, vector, payload in zip(
batch_ids,
batch_embeddings,
self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
)
]
yield batch_ids, points
@staticmethod
def _generate_clients(
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
**kwargs: Any,
) -> Tuple[QdrantClient, Optional[AsyncQdrantClient]]:
if api_key is None:
api_key = os.getenv("QDRANT_API_KEY")
sync_client = QdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
if location == ":memory:" or path is not None:
# Local Qdrant cannot co-exist with Sync and Async clients
# We fallback to sync operations in this case
async_client = None
else:
async_client = AsyncQdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
return sync_client, async_client
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/langchain_qdrant/qdrant.py | from __future__ import annotations
import uuid
from enum import Enum
from itertools import islice
from operator import itemgetter
from typing import (
Any,
Callable,
Dict,
Generator,
Iterable,
List,
Optional,
Sequence,
Tuple,
Type,
Union,
)
import numpy as np
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.vectorstores import VectorStore
from qdrant_client import QdrantClient, models
from langchain_qdrant._utils import maximal_marginal_relevance
from langchain_qdrant.sparse_embeddings import SparseEmbeddings
class QdrantVectorStoreError(Exception):
"""`QdrantVectorStore` related exceptions."""
class RetrievalMode(str, Enum):
DENSE = "dense"
SPARSE = "sparse"
HYBRID = "hybrid"
class QdrantVectorStore(VectorStore):
"""Qdrant vector store integration.
Setup:
Install ``langchain-qdrant`` package.
.. code-block:: bash
pip install -qU langchain-qdrant
Key init args — indexing params:
collection_name: str
Name of the collection.
embedding: Embeddings
Embedding function to use.
sparse_embedding: SparseEmbeddings
Optional sparse embedding function to use.
Key init args — client params:
client: QdrantClient
Qdrant client to use.
retrieval_mode: RetrievalMode
Retrieval mode to use.
Instantiate:
.. code-block:: python
from langchain_qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
from langchain_openai import OpenAIEmbeddings
client = QdrantClient(":memory:")
client.create_collection(
collection_name="demo_collection",
vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
)
vector_store = QdrantVectorStore(
client=client,
collection_name="demo_collection",
embedding=OpenAIEmbeddings(),
)
Add Documents:
.. code-block:: python
from langchain_core.documents import Document
from uuid import uuid4
document_1 = Document(page_content="foo", metadata={"baz": "bar"})
document_2 = Document(page_content="thud", metadata={"bar": "baz"})
document_3 = Document(page_content="i will be deleted :(")
documents = [document_1, document_2, document_3]
ids = [str(uuid4()) for _ in range(len(documents))]
vector_store.add_documents(documents=documents, ids=ids)
Delete Documents:
.. code-block:: python
vector_store.delete(ids=[ids[-1]])
Search:
.. code-block:: python
results = vector_store.similarity_search(query="thud",k=1)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}]
Search with filter:
.. code-block:: python
from qdrant_client.http import models
results = vector_store.similarity_search(query="thud",k=1,filter=models.Filter(must=[models.FieldCondition(key="metadata.bar", match=models.MatchValue(value="baz"),)]))
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* thud [{'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}]
Search with score:
.. code-block:: python
results = vector_store.similarity_search_with_score(query="qux",k=1)
for doc, score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]
Async:
.. code-block:: python
# add documents
# await vector_store.aadd_documents(documents=documents, ids=ids)
# delete documents
# await vector_store.adelete(ids=["3"])
# search
# results = vector_store.asimilarity_search(query="thud",k=1)
# search with score
results = await vector_store.asimilarity_search_with_score(query="qux",k=1)
for doc,score in results:
print(f"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]")
.. code-block:: python
* [SIM=0.832268] foo [{'baz': 'bar', '_id': '44ec7094-b061-45ac-8fbf-014b0f18e8aa', '_collection_name': 'demo_collection'}]
Use as Retriever:
.. code-block:: python
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 1, "fetch_k": 2, "lambda_mult": 0.5},
)
retriever.invoke("thud")
.. code-block:: python
[Document(metadata={'bar': 'baz', '_id': '0d706099-6dd9-412a-9df6-a71043e020de', '_collection_name': 'demo_collection'}, page_content='thud')]
""" # noqa: E501
CONTENT_KEY: str = "page_content"
METADATA_KEY: str = "metadata"
VECTOR_NAME: str = "" # The default/unnamed vector - https://qdrant.tech/documentation/concepts/collections/#create-a-collection
SPARSE_VECTOR_NAME: str = "langchain-sparse"
def __init__(
self,
client: QdrantClient,
collection_name: str,
embedding: Optional[Embeddings] = None,
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
vector_name: str = VECTOR_NAME,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
distance: models.Distance = models.Distance.COSINE,
sparse_embedding: Optional[SparseEmbeddings] = None,
sparse_vector_name: str = SPARSE_VECTOR_NAME,
validate_embeddings: bool = True,
validate_collection_config: bool = True,
):
"""Initialize a new instance of `QdrantVectorStore`.
Example:
.. code-block:: python
qdrant = Qdrant(
client=client,
collection_name="my-collection",
embedding=OpenAIEmbeddings(),
retrieval_mode=RetrievalMode.HYBRID,
sparse_embedding=FastEmbedSparse(),
)
"""
if validate_embeddings:
self._validate_embeddings(retrieval_mode, embedding, sparse_embedding)
if validate_collection_config:
self._validate_collection_config(
client,
collection_name,
retrieval_mode,
vector_name,
sparse_vector_name,
distance,
embedding,
)
self._client = client
self.collection_name = collection_name
self._embeddings = embedding
self.retrieval_mode = retrieval_mode
self.vector_name = vector_name
self.content_payload_key = content_payload_key
self.metadata_payload_key = metadata_payload_key
self.distance = distance
self._sparse_embeddings = sparse_embedding
self.sparse_vector_name = sparse_vector_name
@property
def client(self) -> QdrantClient:
"""Get the Qdrant client instance that is being used.
Returns:
QdrantClient: An instance of `QdrantClient`.
"""
return self._client
@property
def embeddings(self) -> Embeddings:
"""Get the dense embeddings instance that is being used.
Raises:
ValueError: If embeddings are `None`.
Returns:
Embeddings: An instance of `Embeddings`.
"""
if self._embeddings is None:
raise ValueError(
"Embeddings are `None`. Please set using the `embedding` parameter."
)
return self._embeddings
@property
def sparse_embeddings(self) -> SparseEmbeddings:
"""Get the sparse embeddings instance that is being used.
Raises:
ValueError: If sparse embeddings are `None`.
Returns:
SparseEmbeddings: An instance of `SparseEmbeddings`.
"""
if self._sparse_embeddings is None:
raise ValueError(
"Sparse embeddings are `None`. "
"Please set using the `sparse_embedding` parameter."
)
return self._sparse_embeddings
@classmethod
def from_texts(
cls: Type[QdrantVectorStore],
texts: List[str],
embedding: Optional[Embeddings] = None,
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str | int]] = None,
collection_name: Optional[str] = None,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
distance: models.Distance = models.Distance.COSINE,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: str = VECTOR_NAME,
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
sparse_embedding: Optional[SparseEmbeddings] = None,
sparse_vector_name: str = SPARSE_VECTOR_NAME,
collection_create_options: Dict[str, Any] = {},
vector_params: Dict[str, Any] = {},
sparse_vector_params: Dict[str, Any] = {},
batch_size: int = 64,
force_recreate: bool = False,
validate_embeddings: bool = True,
validate_collection_config: bool = True,
**kwargs: Any,
) -> QdrantVectorStore:
"""Construct an instance of `QdrantVectorStore` from a list of texts.
This is a user-friendly interface that:
1. Creates embeddings, one for each text
2. Creates a Qdrant collection if it doesn't exist.
3. Adds the text embeddings to the Qdrant database
This is intended to be a quick way to get started.
Example:
.. code-block:: python
from langchain_qdrant import Qdrant
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
qdrant = Qdrant.from_texts(texts, embeddings, url="http://localhost:6333")
"""
client_options = {
"location": location,
"url": url,
"port": port,
"grpc_port": grpc_port,
"prefer_grpc": prefer_grpc,
"https": https,
"api_key": api_key,
"prefix": prefix,
"timeout": timeout,
"host": host,
"path": path,
**kwargs,
}
qdrant = cls.construct_instance(
embedding,
retrieval_mode,
sparse_embedding,
client_options,
collection_name,
distance,
content_payload_key,
metadata_payload_key,
vector_name,
sparse_vector_name,
force_recreate,
collection_create_options,
vector_params,
sparse_vector_params,
validate_embeddings,
validate_collection_config,
)
qdrant.add_texts(texts, metadatas, ids, batch_size)
return qdrant
@classmethod
def from_existing_collection(
cls: Type[QdrantVectorStore],
collection_name: str,
embedding: Optional[Embeddings] = None,
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
location: Optional[str] = None,
url: Optional[str] = None,
port: Optional[int] = 6333,
grpc_port: int = 6334,
prefer_grpc: bool = False,
https: Optional[bool] = None,
api_key: Optional[str] = None,
prefix: Optional[str] = None,
timeout: Optional[int] = None,
host: Optional[str] = None,
path: Optional[str] = None,
distance: models.Distance = models.Distance.COSINE,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: str = VECTOR_NAME,
sparse_vector_name: str = SPARSE_VECTOR_NAME,
sparse_embedding: Optional[SparseEmbeddings] = None,
validate_embeddings: bool = True,
validate_collection_config: bool = True,
**kwargs: Any,
) -> QdrantVectorStore:
"""Construct an instance of `QdrantVectorStore` from an existing collection
without adding any data.
Returns:
QdrantVectorStore: A new instance of `QdrantVectorStore`.
"""
client = QdrantClient(
location=location,
url=url,
port=port,
grpc_port=grpc_port,
prefer_grpc=prefer_grpc,
https=https,
api_key=api_key,
prefix=prefix,
timeout=timeout,
host=host,
path=path,
**kwargs,
)
return cls(
client=client,
collection_name=collection_name,
embedding=embedding,
retrieval_mode=retrieval_mode,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance=distance,
vector_name=vector_name,
sparse_embedding=sparse_embedding,
sparse_vector_name=sparse_vector_name,
validate_embeddings=validate_embeddings,
validate_collection_config=validate_collection_config,
)
def add_texts( # type: ignore
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str | int]] = None,
batch_size: int = 64,
**kwargs: Any,
) -> List[str | int]:
"""Add texts with embeddings to the vectorstore.
Returns:
List of ids from adding the texts into the vectorstore.
"""
added_ids = []
for batch_ids, points in self._generate_batches(
texts, metadatas, ids, batch_size
):
self.client.upsert(
collection_name=self.collection_name, points=points, **kwargs
)
added_ids.extend(batch_ids)
return added_ids
def similarity_search(
self,
query: str,
k: int = 4,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
hybrid_fusion: Optional[models.FusionQuery] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to query.
Returns:
List of Documents most similar to the query.
"""
results = self.similarity_search_with_score(
query,
k,
filter=filter,
search_params=search_params,
offset=offset,
score_threshold=score_threshold,
consistency=consistency,
hybrid_fusion=hybrid_fusion,
**kwargs,
)
return list(map(itemgetter(0), results))
def similarity_search_with_score(
self,
query: str,
k: int = 4,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
hybrid_fusion: Optional[models.FusionQuery] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs most similar to query.
Returns:
List of documents most similar to the query text and distance for each.
"""
query_options = {
"collection_name": self.collection_name,
"query_filter": filter,
"search_params": search_params,
"limit": k,
"offset": offset,
"with_payload": True,
"with_vectors": False,
"score_threshold": score_threshold,
"consistency": consistency,
**kwargs,
}
if self.retrieval_mode == RetrievalMode.DENSE:
query_dense_embedding = self.embeddings.embed_query(query)
results = self.client.query_points(
query=query_dense_embedding,
using=self.vector_name,
**query_options,
).points
elif self.retrieval_mode == RetrievalMode.SPARSE:
query_sparse_embedding = self.sparse_embeddings.embed_query(query)
results = self.client.query_points(
query=models.SparseVector(
indices=query_sparse_embedding.indices,
values=query_sparse_embedding.values,
),
using=self.sparse_vector_name,
**query_options,
).points
elif self.retrieval_mode == RetrievalMode.HYBRID:
query_dense_embedding = self.embeddings.embed_query(query)
query_sparse_embedding = self.sparse_embeddings.embed_query(query)
results = self.client.query_points(
prefetch=[
models.Prefetch(
using=self.vector_name,
query=query_dense_embedding,
filter=filter,
limit=k,
params=search_params,
),
models.Prefetch(
using=self.sparse_vector_name,
query=models.SparseVector(
indices=query_sparse_embedding.indices,
values=query_sparse_embedding.values,
),
filter=filter,
limit=k,
params=search_params,
),
],
query=hybrid_fusion or models.FusionQuery(fusion=models.Fusion.RRF),
**query_options,
).points
else:
raise ValueError(f"Invalid retrieval mode. {self.retrieval_mode}.")
return [
(
self._document_from_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
result.score,
)
for result in results
]
def similarity_search_by_vector(
self,
embedding: List[float],
k: int = 4,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
offset: int = 0,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs most similar to embedding vector.
Returns:
List of Documents most similar to the query.
"""
qdrant_filter = filter
self._validate_collection_for_dense(
client=self.client,
collection_name=self.collection_name,
vector_name=self.vector_name,
distance=self.distance,
dense_embeddings=embedding,
)
results = self.client.query_points(
collection_name=self.collection_name,
query=embedding,
using=self.vector_name,
query_filter=qdrant_filter,
search_params=search_params,
limit=k,
offset=offset,
with_payload=True,
with_vectors=False,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
).points
return [
self._document_from_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
)
for result in results
]
def max_marginal_relevance_search(
self,
query: str,
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance with dense vectors.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Returns:
List of Documents selected by maximal marginal relevance.
"""
self._validate_collection_for_dense(
self.client,
self.collection_name,
self.vector_name,
self.distance,
self.embeddings,
)
query_embedding = self.embeddings.embed_query(query)
return self.max_marginal_relevance_search_by_vector(
query_embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
def max_marginal_relevance_search_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Document]:
"""Return docs selected using the maximal marginal relevance with dense vectors.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Returns:
List of Documents selected by maximal marginal relevance.
"""
results = self.max_marginal_relevance_search_with_score_by_vector(
embedding,
k=k,
fetch_k=fetch_k,
lambda_mult=lambda_mult,
filter=filter,
search_params=search_params,
score_threshold=score_threshold,
consistency=consistency,
**kwargs,
)
return list(map(itemgetter(0), results))
def max_marginal_relevance_search_with_score_by_vector(
self,
embedding: List[float],
k: int = 4,
fetch_k: int = 20,
lambda_mult: float = 0.5,
filter: Optional[models.Filter] = None,
search_params: Optional[models.SearchParams] = None,
score_threshold: Optional[float] = None,
consistency: Optional[models.ReadConsistency] = None,
**kwargs: Any,
) -> List[Tuple[Document, float]]:
"""Return docs selected using the maximal marginal relevance.
Maximal marginal relevance optimizes for similarity to query AND diversity
among selected documents.
Returns:
List of Documents selected by maximal marginal relevance and distance for
each.
"""
results = self.client.query_points(
collection_name=self.collection_name,
query=embedding,
query_filter=filter,
search_params=search_params,
limit=fetch_k,
with_payload=True,
with_vectors=True,
score_threshold=score_threshold,
consistency=consistency,
using=self.vector_name,
**kwargs,
).points
embeddings = [
result.vector
if isinstance(result.vector, list)
else result.vector.get(self.vector_name) # type: ignore
for result in results
]
mmr_selected = maximal_marginal_relevance(
np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
)
return [
(
self._document_from_point(
results[i],
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
),
results[i].score,
)
for i in mmr_selected
]
def delete( # type: ignore
self,
ids: Optional[List[str | int]] = None,
**kwargs: Any,
) -> Optional[bool]:
"""Delete documents by their ids.
Args:
ids: List of ids to delete.
**kwargs: Other keyword arguments that subclasses might use.
Returns:
True if deletion is successful, False otherwise.
"""
result = self.client.delete(
collection_name=self.collection_name,
points_selector=ids,
)
return result.status == models.UpdateStatus.COMPLETED
def get_by_ids(self, ids: Sequence[str | int], /) -> List[Document]:
results = self.client.retrieve(self.collection_name, ids, with_payload=True)
return [
self._document_from_point(
result,
self.collection_name,
self.content_payload_key,
self.metadata_payload_key,
)
for result in results
]
@classmethod
def construct_instance(
cls: Type[QdrantVectorStore],
embedding: Optional[Embeddings] = None,
retrieval_mode: RetrievalMode = RetrievalMode.DENSE,
sparse_embedding: Optional[SparseEmbeddings] = None,
client_options: Dict[str, Any] = {},
collection_name: Optional[str] = None,
distance: models.Distance = models.Distance.COSINE,
content_payload_key: str = CONTENT_KEY,
metadata_payload_key: str = METADATA_KEY,
vector_name: str = VECTOR_NAME,
sparse_vector_name: str = SPARSE_VECTOR_NAME,
force_recreate: bool = False,
collection_create_options: Dict[str, Any] = {},
vector_params: Dict[str, Any] = {},
sparse_vector_params: Dict[str, Any] = {},
validate_embeddings: bool = True,
validate_collection_config: bool = True,
) -> QdrantVectorStore:
if validate_embeddings:
cls._validate_embeddings(retrieval_mode, embedding, sparse_embedding)
collection_name = collection_name or uuid.uuid4().hex
client = QdrantClient(**client_options)
collection_exists = client.collection_exists(collection_name)
if collection_exists and force_recreate:
client.delete_collection(collection_name)
collection_exists = False
if collection_exists:
if validate_collection_config:
cls._validate_collection_config(
client,
collection_name,
retrieval_mode,
vector_name,
sparse_vector_name,
distance,
embedding,
)
else:
vectors_config, sparse_vectors_config = {}, {}
if retrieval_mode == RetrievalMode.DENSE:
partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore
vector_params["size"] = len(partial_embeddings[0])
vector_params["distance"] = distance
vectors_config = {
vector_name: models.VectorParams(
**vector_params,
)
}
elif retrieval_mode == RetrievalMode.SPARSE:
sparse_vectors_config = {
sparse_vector_name: models.SparseVectorParams(
**sparse_vector_params
)
}
elif retrieval_mode == RetrievalMode.HYBRID:
partial_embeddings = embedding.embed_documents(["dummy_text"]) # type: ignore
vector_params["size"] = len(partial_embeddings[0])
vector_params["distance"] = distance
vectors_config = {
vector_name: models.VectorParams(
**vector_params,
)
}
sparse_vectors_config = {
sparse_vector_name: models.SparseVectorParams(
**sparse_vector_params
)
}
collection_create_options["collection_name"] = collection_name
collection_create_options["vectors_config"] = vectors_config
collection_create_options["sparse_vectors_config"] = sparse_vectors_config
client.create_collection(**collection_create_options)
qdrant = cls(
client=client,
collection_name=collection_name,
embedding=embedding,
retrieval_mode=retrieval_mode,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
distance=distance,
vector_name=vector_name,
sparse_embedding=sparse_embedding,
sparse_vector_name=sparse_vector_name,
validate_embeddings=False,
validate_collection_config=False,
)
return qdrant
@staticmethod
def _cosine_relevance_score_fn(distance: float) -> float:
"""Normalize the distance to a score on a scale [0, 1]."""
return (distance + 1.0) / 2.0
def _select_relevance_score_fn(self) -> Callable[[float], float]:
"""
The 'correct' relevance function
may differ depending on a few things, including:
- the distance / similarity metric used by the VectorStore
- the scale of your embeddings (OpenAI's are unit normed. Many others are not!)
- embedding dimensionality
- etc.
"""
if self.distance == models.Distance.COSINE:
return self._cosine_relevance_score_fn
elif self.distance == models.Distance.DOT:
return self._max_inner_product_relevance_score_fn
elif self.distance == models.Distance.EUCLID:
return self._euclidean_relevance_score_fn
else:
raise ValueError(
"Unknown distance strategy, must be COSINE, DOT, or EUCLID."
)
@classmethod
def _document_from_point(
cls,
scored_point: Any,
collection_name: str,
content_payload_key: str,
metadata_payload_key: str,
) -> Document:
metadata = scored_point.payload.get(metadata_payload_key) or {}
metadata["_id"] = scored_point.id
metadata["_collection_name"] = collection_name
return Document(
page_content=scored_point.payload.get(content_payload_key, ""),
metadata=metadata,
)
def _generate_batches(
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
ids: Optional[Sequence[str | int]] = None,
batch_size: int = 64,
) -> Generator[tuple[list[str | int], list[models.PointStruct]], Any, None]:
texts_iterator = iter(texts)
metadatas_iterator = iter(metadatas or [])
ids_iterator = iter(ids or [uuid.uuid4().hex for _ in iter(texts)])
while batch_texts := list(islice(texts_iterator, batch_size)):
batch_metadatas = list(islice(metadatas_iterator, batch_size)) or None
batch_ids = list(islice(ids_iterator, batch_size))
points = [
models.PointStruct(
id=point_id,
vector=vector,
payload=payload,
)
for point_id, vector, payload in zip(
batch_ids,
self._build_vectors(batch_texts),
self._build_payloads(
batch_texts,
batch_metadatas,
self.content_payload_key,
self.metadata_payload_key,
),
)
]
yield batch_ids, points
@staticmethod
def _build_payloads(
texts: Iterable[str],
metadatas: Optional[List[dict]],
content_payload_key: str,
metadata_payload_key: str,
) -> List[dict]:
payloads = []
for i, text in enumerate(texts):
if text is None:
raise ValueError(
"At least one of the texts is None. Please remove it before "
"calling .from_texts or .add_texts."
)
metadata = metadatas[i] if metadatas is not None else None
payloads.append(
{
content_payload_key: text,
metadata_payload_key: metadata,
}
)
return payloads
def _build_vectors(
self,
texts: Iterable[str],
) -> List[models.VectorStruct]:
if self.retrieval_mode == RetrievalMode.DENSE:
batch_embeddings = self.embeddings.embed_documents(list(texts))
return [
{
self.vector_name: vector,
}
for vector in batch_embeddings
]
elif self.retrieval_mode == RetrievalMode.SPARSE:
batch_sparse_embeddings = self.sparse_embeddings.embed_documents(
list(texts)
)
return [
{
self.sparse_vector_name: models.SparseVector(
values=vector.values, indices=vector.indices
)
}
for vector in batch_sparse_embeddings
]
elif self.retrieval_mode == RetrievalMode.HYBRID:
dense_embeddings = self.embeddings.embed_documents(list(texts))
sparse_embeddings = self.sparse_embeddings.embed_documents(list(texts))
assert len(dense_embeddings) == len(
sparse_embeddings
), "Mismatched length between dense and sparse embeddings."
return [
{
self.vector_name: dense_vector,
self.sparse_vector_name: models.SparseVector(
values=sparse_vector.values, indices=sparse_vector.indices
),
}
for dense_vector, sparse_vector in zip(
dense_embeddings, sparse_embeddings
)
]
else:
raise ValueError(
f"Unknown retrieval mode. {self.retrieval_mode} to build vectors."
)
@classmethod
def _validate_collection_config(
cls: Type[QdrantVectorStore],
client: QdrantClient,
collection_name: str,
retrieval_mode: RetrievalMode,
vector_name: str,
sparse_vector_name: str,
distance: models.Distance,
embedding: Optional[Embeddings],
) -> None:
if retrieval_mode == RetrievalMode.DENSE:
cls._validate_collection_for_dense(
client, collection_name, vector_name, distance, embedding
)
elif retrieval_mode == RetrievalMode.SPARSE:
cls._validate_collection_for_sparse(
client, collection_name, sparse_vector_name
)
elif retrieval_mode == RetrievalMode.HYBRID:
cls._validate_collection_for_dense(
client, collection_name, vector_name, distance, embedding
)
cls._validate_collection_for_sparse(
client, collection_name, sparse_vector_name
)
@classmethod
def _validate_collection_for_dense(
cls: Type[QdrantVectorStore],
client: QdrantClient,
collection_name: str,
vector_name: str,
distance: models.Distance,
dense_embeddings: Union[Embeddings, List[float], None],
) -> None:
collection_info = client.get_collection(collection_name=collection_name)
vector_config = collection_info.config.params.vectors
if isinstance(vector_config, Dict):
# vector_config is a Dict[str, VectorParams]
if vector_name not in vector_config:
raise QdrantVectorStoreError(
f"Existing Qdrant collection {collection_name} does not "
f"contain dense vector named {vector_name}. "
"Did you mean one of the "
f"existing vectors: {', '.join(vector_config.keys())}? " # type: ignore
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
# Get the VectorParams object for the specified vector_name
vector_config = vector_config[vector_name] # type: ignore
else:
# vector_config is an instance of VectorParams
# Case of a collection with single/unnamed vector.
if vector_name != "":
raise QdrantVectorStoreError(
f"Existing Qdrant collection {collection_name} is built "
"with unnamed dense vector. "
f"If you want to reuse it, set `vector_name` to ''(empty string)."
f"If you want to recreate the collection, "
"set `force_recreate` to `True`."
)
assert vector_config is not None, "VectorParams is None"
if isinstance(dense_embeddings, Embeddings):
vector_size = len(dense_embeddings.embed_documents(["dummy_text"])[0])
elif isinstance(dense_embeddings, list):
vector_size = len(dense_embeddings)
else:
raise ValueError("Invalid `embeddings` type.")
if vector_config.size != vector_size:
raise QdrantVectorStoreError(
f"Existing Qdrant collection is configured for dense vectors with "
f"{vector_config.size} dimensions. "
f"Selected embeddings are {vector_size}-dimensional. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
if vector_config.distance != distance:
raise QdrantVectorStoreError(
f"Existing Qdrant collection is configured for "
f"{vector_config.distance.name} similarity, but requested "
f"{distance.upper()}. Please set `distance` parameter to "
f"`{vector_config.distance.name}` if you want to reuse it. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
@classmethod
def _validate_collection_for_sparse(
cls: Type[QdrantVectorStore],
client: QdrantClient,
collection_name: str,
sparse_vector_name: str,
) -> None:
collection_info = client.get_collection(collection_name=collection_name)
sparse_vector_config = collection_info.config.params.sparse_vectors
if (
sparse_vector_config is None
or sparse_vector_name not in sparse_vector_config
):
raise QdrantVectorStoreError(
f"Existing Qdrant collection {collection_name} does not "
f"contain sparse vectors named {sparse_vector_config}. "
f"If you want to recreate the collection, set `force_recreate` "
f"parameter to `True`."
)
@classmethod
def _validate_embeddings(
cls: Type[QdrantVectorStore],
retrieval_mode: RetrievalMode,
embedding: Optional[Embeddings],
sparse_embedding: Optional[SparseEmbeddings],
) -> None:
if retrieval_mode == RetrievalMode.DENSE and embedding is None:
raise ValueError(
"'embedding' cannot be None when retrieval mode is 'dense'"
)
elif retrieval_mode == RetrievalMode.SPARSE and sparse_embedding is None:
raise ValueError(
"'sparse_embedding' cannot be None when retrieval mode is 'sparse'"
)
elif retrieval_mode == RetrievalMode.HYBRID and any(
[embedding is None, sparse_embedding is None]
):
raise ValueError(
"Both 'embedding' and 'sparse_embedding' cannot be None "
"when retrieval mode is 'hybrid'"
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/langchain_qdrant/__init__.py | from langchain_qdrant.fastembed_sparse import FastEmbedSparse
from langchain_qdrant.qdrant import QdrantVectorStore, RetrievalMode
from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector
from langchain_qdrant.vectorstores import Qdrant
__all__ = [
"Qdrant",
"QdrantVectorStore",
"SparseEmbeddings",
"SparseVector",
"FastEmbedSparse",
"RetrievalMode",
]
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/common.py | from typing import List
import requests # type: ignore
from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_qdrant import SparseEmbeddings, SparseVector
def qdrant_running_locally() -> bool:
"""Check if Qdrant is running at http://localhost:6333."""
try:
response = requests.get("http://localhost:6333", timeout=10.0)
response_json = response.json()
return response_json.get("title") == "qdrant - vector search engine"
except (requests.exceptions.ConnectionError, requests.exceptions.Timeout):
return False
def assert_documents_equals(actual: List[Document], expected: List[Document]): # type: ignore[no-untyped-def]
assert len(actual) == len(expected)
for actual_doc, expected_doc in zip(actual, expected):
assert actual_doc.page_content == expected_doc.page_content
assert "_id" in actual_doc.metadata
assert "_collection_name" in actual_doc.metadata
actual_doc.metadata.pop("_id")
actual_doc.metadata.pop("_collection_name")
assert actual_doc.metadata == expected_doc.metadata
class ConsistentFakeEmbeddings(Embeddings):
"""Fake embeddings which remember all the texts seen so far to return consistent
vectors for the same texts."""
def __init__(self, dimensionality: int = 10) -> None:
self.known_texts: List[str] = []
self.dimensionality = dimensionality
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Return consistent embeddings for each text seen so far."""
out_vectors = []
for text in texts:
if text not in self.known_texts:
self.known_texts.append(text)
vector = [float(1.0)] * (self.dimensionality - 1) + [
float(self.known_texts.index(text))
]
out_vectors.append(vector)
return out_vectors
def embed_query(self, text: str) -> List[float]:
"""Return consistent embeddings for the text, if seen before, or a constant
one if the text is unknown."""
return self.embed_documents([text])[0]
class ConsistentFakeSparseEmbeddings(SparseEmbeddings):
"""Fake sparse embeddings which remembers all the texts seen so far "
"to return consistent vectors for the same texts."""
def __init__(self, dimensionality: int = 25) -> None:
self.known_texts: List[str] = []
self.dimensionality = 25
def embed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Return consistent embeddings for each text seen so far."""
out_vectors = []
for text in texts:
if text not in self.known_texts:
self.known_texts.append(text)
index = self.known_texts.index(text)
indices = [i + index for i in range(self.dimensionality)]
values = [1.0] * (self.dimensionality - 1) + [float(index)]
out_vectors.append(SparseVector(indices=indices, values=values))
return out_vectors
def embed_query(self, text: str) -> SparseVector:
"""Return consistent embeddings for the text, "
"if seen before, or a constant one if the text is unknown."""
return self.embed_documents([text])[0]
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_from_texts.py | import tempfile
import uuid
from typing import Optional
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from langchain_qdrant.vectorstores import QdrantException
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations
def test_qdrant_from_texts_stores_duplicated_texts() -> None:
"""Test end to end Qdrant.from_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["abc", "abc"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_from_texts_stores_ids(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end Qdrant.from_texts stores provided ids."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
vec_store = Qdrant.from_texts(
["abc", "def"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
path=str(tmpdir),
batch_size=batch_size,
vector_name=vector_name,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_from_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
"""Test end to end Qdrant.from_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
def test_qdrant_from_texts_reuses_same_collection(vector_name: Optional[str]) -> None:
"""Test if Qdrant.from_texts reuses the same collection"""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
embeddings = ConsistentFakeEmbeddings()
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
embeddings,
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
vec_store = Qdrant.from_texts(
["foo", "bar"],
embeddings,
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 7 == client.count(collection_name).count
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
def test_qdrant_from_texts_raises_error_on_different_dimensionality(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
with pytest.raises(QdrantException):
Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
@pytest.mark.parametrize(
["first_vector_name", "second_vector_name"],
[
(None, "custom-vector"),
("custom-vector", None),
("my-first-vector", "my-second_vector"),
],
)
def test_qdrant_from_texts_raises_error_on_different_vector_name(
first_vector_name: Optional[str],
second_vector_name: Optional[str],
) -> None:
"""Test if Qdrant.from_texts raises an exception if vector name does not match"""
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
path=str(tmpdir),
vector_name=first_vector_name,
)
del vec_store
with pytest.raises(QdrantException):
Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
path=str(tmpdir),
vector_name=second_vector_name,
)
def test_qdrant_from_texts_raises_error_on_different_distance() -> None:
"""Test if Qdrant.from_texts raises an exception if distance does not match"""
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
distance_func="Cosine",
)
del vec_store
with pytest.raises(QdrantException) as excinfo:
Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
path=str(tmpdir),
distance_func="Euclid",
)
expected_message = (
"configured for COSINE similarity, but requested EUCLID. Please set "
"`distance_func` parameter to `COSINE`"
)
assert expected_message in str(excinfo.value)
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
def test_qdrant_from_texts_recreates_collection_on_force_recreate(
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.from_texts recreates the collection even if config mismatches"""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
vec_store = Qdrant.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
)
del vec_store
vec_store = Qdrant.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
path=str(tmpdir),
vector_name=vector_name,
force_recreate=True,
)
del vec_store
client = QdrantClient(path=str(tmpdir))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
def test_qdrant_from_texts_stores_metadatas(
batch_size: int, content_payload_key: str, metadata_payload_key: str
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
)
output = docsearch.similarity_search("foo", k=1)
assert_documents_equals(
output, [Document(page_content="foo", metadata={"page": 0})]
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
def test_from_texts_passed_optimizers_config_and_on_disk_payload(location: str) -> None:
from qdrant_client import models
collection_name = uuid.uuid4().hex
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
optimizers_config = models.OptimizersConfigDiff(memmap_threshold=1000)
vec_store = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
optimizers_config=optimizers_config,
on_disk_payload=True,
on_disk=True,
collection_name=collection_name,
location=location,
)
collection_info = vec_store.client.get_collection(collection_name)
assert collection_info.config.params.vectors.on_disk is True # type: ignore
assert collection_info.config.optimizer_config.memmap_threshold == 1000
assert collection_info.config.params.on_disk_payload is True
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_add_texts.py | import uuid
from typing import Optional
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_documents_extends_existing_collection(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch: Qdrant = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
batch_size=batch_size,
vector_name=vector_name,
)
new_texts = ["foobar", "foobaz"]
docsearch.add_documents(
[Document(page_content=content) for content in new_texts], batch_size=batch_size
)
output = docsearch.similarity_search("foobar", k=1)
# ConsistentFakeEmbeddings return the same query embedding as the first document
# embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the
# same as "foobar" embedding
assert_documents_equals(output, [Document(page_content="foobar")])
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_returns_all_ids(batch_size: int) -> None:
"""Test end to end Qdrant.add_texts returns unique ids."""
docsearch: Qdrant = Qdrant.from_texts(
["foobar"],
ConsistentFakeEmbeddings(),
location=":memory:",
batch_size=batch_size,
)
ids = docsearch.add_texts(["foo", "bar", "baz"])
assert 3 == len(ids)
assert 3 == len(set(ids))
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_add_texts_stores_duplicated_texts(vector_name: Optional[str]) -> None:
"""Test end to end Qdrant.add_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
if vector_name is not None:
vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
client.recreate_collection(collection_name, vectors_config=vectors_config)
vec_store = Qdrant(
client,
collection_name,
embeddings=ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
assert 2 == len(set(ids))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_stores_ids(batch_size: int) -> None:
"""Test end to end Qdrant.add_texts stores provided ids."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
client.recreate_collection(
collection_name,
vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
)
vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
returned_ids = vec_store.add_texts(["abc", "def"], ids=ids, batch_size=batch_size)
assert all(first == second for first, second in zip(ids, returned_ids))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_add_texts_stores_embeddings_as_named_vectors(vector_name: str) -> None:
"""Test end to end Qdrant.add_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
collection_name = uuid.uuid4().hex
client = QdrantClient(":memory:")
client.recreate_collection(
collection_name,
vectors_config={
vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
},
)
vec_store = Qdrant(
client,
collection_name,
ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
vec_store.add_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_similarity_search.py | from typing import Optional
import numpy as np
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
output = docsearch.similarity_search("foo", k=1)
assert_documents_equals(actual=output, expected=[Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = docsearch.similarity_search_by_vector(embeddings, k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_score_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1)
assert len(output) == 1
document, score = output[0]
assert_documents_equals(actual=[document], expected=[Document(page_content="foo")])
assert score >= 0
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_filters(
batch_size: int, vector_name: Optional[str]
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
batch_size=batch_size,
vector_name=vector_name,
)
output = docsearch.similarity_search(
"foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
)
assert_documents_equals(
actual=output,
expected=[
Document(
page_content="bar",
metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_score_no_threshold(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
output = docsearch.similarity_search_with_relevance_scores(
"foo", k=3, score_threshold=None
)
assert len(output) == 3
for i in range(len(output)):
assert round(output[i][1], 2) >= 0
assert round(output[i][1], 2) <= 1
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_score_with_threshold(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
score_threshold = 0.98
kwargs = {"score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_score_with_threshold_and_filter(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
score_threshold = 0.99 # for almost exact match
# test negative filter condition
negative_filter = {"page": 1, "metadata": {"page": 2, "pages": [3]}}
kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 0
# test positive filter condition
positive_filter = {"page": 0, "metadata": {"page": 1, "pages": [2]}}
kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_filters_with_qdrant_filters(
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
from qdrant_client.http import models as rest
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
vector_name=vector_name,
)
qdrant_filter = rest.Filter(
must=[
rest.FieldCondition(
key="metadata.page",
match=rest.MatchValue(value=1),
),
rest.FieldCondition(
key="metadata.details.page",
match=rest.MatchValue(value=2),
),
rest.FieldCondition(
key="metadata.details.pages",
match=rest.MatchAny(any=[3]),
),
]
)
output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
assert_documents_equals(
actual=output,
expected=[
Document(
page_content="bar",
metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_similarity_search_with_relevance_scores(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
output = docsearch.similarity_search_with_relevance_scores("foo", k=3)
assert all(
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_max_marginal_relevance.py | from typing import Optional
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "test_content"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "test_metadata"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
def test_qdrant_max_marginal_relevance_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
) -> None:
"""Test end to end construction and MRR search."""
from qdrant_client import models
filter = models.Filter(
must=[
models.FieldCondition(
key=f"{metadata_payload_key}.page",
match=models.MatchValue(
value=2,
),
),
],
)
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=":memory:",
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
distance_func="EUCLID", # Euclid distance used to avoid normalization
)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert_documents_equals(
output,
[
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="baz", metadata={"page": 2}),
],
)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter
)
assert_documents_equals(
output,
[Document(page_content="baz", metadata={"page": 2})],
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_embedding_interface.py | import uuid
from typing import Callable, Optional
import pytest # type: ignore[import-not-found]
from langchain_core.embeddings import Embeddings
from langchain_qdrant import Qdrant
from tests.integration_tests.common import ConsistentFakeEmbeddings
@pytest.mark.parametrize(
["embeddings", "embedding_function"],
[
(ConsistentFakeEmbeddings(), None),
(ConsistentFakeEmbeddings().embed_query, None),
(None, ConsistentFakeEmbeddings().embed_query),
],
)
def test_qdrant_embedding_interface(
embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
) -> None:
"""Test Qdrant may accept different types for embeddings."""
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
Qdrant(
client,
collection_name,
embeddings=embeddings,
embedding_function=embedding_function,
)
@pytest.mark.parametrize(
["embeddings", "embedding_function"],
[
(ConsistentFakeEmbeddings(), ConsistentFakeEmbeddings().embed_query),
(None, None),
],
)
def test_qdrant_embedding_interface_raises_value_error(
embeddings: Optional[Embeddings], embedding_function: Optional[Callable]
) -> None:
"""Test Qdrant requires only one method for embeddings."""
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
collection_name = uuid.uuid4().hex
with pytest.raises(ValueError):
Qdrant(
client,
collection_name,
embeddings=embeddings,
embedding_function=embedding_function,
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/fixtures.py | import logging
import os
from typing import List
from langchain_qdrant.qdrant import RetrievalMode
from tests.integration_tests.common import qdrant_running_locally
logger = logging.getLogger(__name__)
def qdrant_locations(use_in_memory: bool = True) -> List[str]:
locations = []
if use_in_memory:
logger.info("Running Qdrant tests with in-memory mode.")
locations.append(":memory:")
if qdrant_running_locally():
logger.info("Running Qdrant tests with local Qdrant instance.")
locations.append("http://localhost:6333")
if qdrant_url := os.getenv("QDRANT_URL"):
logger.info(f"Running Qdrant tests with Qdrant instance at {qdrant_url}.")
locations.append(qdrant_url)
return locations
def retrieval_modes(
*, dense: bool = True, sparse: bool = True, hybrid: bool = True
) -> List[RetrievalMode]:
modes = []
if dense:
modes.append(RetrievalMode.DENSE)
if sparse:
modes.append(RetrievalMode.SPARSE)
if hybrid:
modes.append(RetrievalMode.HYBRID)
return modes
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_compile.py | import pytest # type: ignore[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/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/test_from_existing_collection.py | import tempfile
import uuid
import pytest # type: ignore[import-not-found]
from langchain_qdrant import Qdrant
from tests.integration_tests.common import ConsistentFakeEmbeddings
@pytest.mark.parametrize("vector_name", ["custom-vector"])
def test_qdrant_from_existing_collection_uses_same_collection(vector_name: str) -> None:
"""Test if the Qdrant.from_existing_collection reuses the same collection."""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
with tempfile.TemporaryDirectory() as tmpdir:
docs = ["foo"]
qdrant = Qdrant.from_texts(
docs,
embedding=ConsistentFakeEmbeddings(),
path=str(tmpdir),
collection_name=collection_name,
vector_name=vector_name,
)
del qdrant
qdrant = Qdrant.from_existing_collection(
embedding=ConsistentFakeEmbeddings(),
path=str(tmpdir),
collection_name=collection_name,
vector_name=vector_name,
)
qdrant.add_texts(["baz", "bar"])
del qdrant
client = QdrantClient(path=str(tmpdir))
assert 3 == client.count(collection_name).count
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/conftest.py | import os
from qdrant_client import QdrantClient
from tests.integration_tests.fixtures import qdrant_locations
def pytest_runtest_teardown() -> None:
"""Clean up all collections after the each test."""
for location in qdrant_locations():
client = QdrantClient(location=location, api_key=os.getenv("QDRANT_API_KEY"))
collections = client.get_collections().collections
for collection in collections:
client.delete_collection(collection.name)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/fastembed/test_fastembed_sparse.py | import numpy as np
import pytest
from langchain_qdrant import FastEmbedSparse
pytest.importorskip("fastembed", reason="'fastembed' package is not installed")
@pytest.mark.parametrize(
"model_name", ["Qdrant/bm25", "Qdrant/bm42-all-minilm-l6-v2-attentions"]
)
def test_attention_embeddings(model_name: str) -> None:
model = FastEmbedSparse(model_name=model_name)
query_output = model.embed_query("Stay, steady and sprint.")
assert len(query_output.indices) == len(query_output.values)
assert np.allclose(query_output.values, np.ones(len(query_output.values)))
texts = [
"The journey of a thousand miles begins with a single step.",
"Be yourself in a world that is constantly trying to make you something else",
"In the end, we only regret the chances we didn't take.",
"Every moment is a fresh beginning.",
"Not all those who wander are lost.",
"Do not go where the path may lead, go elsewhere and leave a trail.",
"Life is what happens when you're busy making other plans.",
"The only limit to our realization of tomorrow is our doubts of today.",
]
output = model.embed_documents(texts)
assert len(output) == len(texts)
for result in output:
assert len(result.indices) == len(result.values)
assert len(result.indices) > 0
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_texts.py | import uuid
from typing import List, Union
import pytest
from langchain_core.documents import Document
from qdrant_client import models
from langchain_qdrant import QdrantVectorStore, RetrievalMode
from langchain_qdrant.qdrant import QdrantVectorStoreError
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
ConsistentFakeSparseEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
def test_vectorstore_from_texts(location: str, retrieval_mode: RetrievalMode) -> None:
"""Test end to end Qdrant.from_texts stores texts."""
collection_name = uuid.uuid4().hex
vec_store = QdrantVectorStore.from_texts(
["Lorem ipsum dolor sit amet", "Ipsum dolor sit amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=location,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
assert 2 == vec_store.client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
def test_qdrant_from_texts_stores_ids(
batch_size: int,
vector_name: str,
sparse_vector_name: str,
location: str,
retrieval_mode: RetrievalMode,
) -> None:
"""Test end to end Qdrant.from_texts stores provided ids."""
collection_name = uuid.uuid4().hex
ids: List[Union[str, int]] = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
786,
]
vec_store = QdrantVectorStore.from_texts(
["abc", "def"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
location=location,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
batch_size=batch_size,
vector_name=vector_name,
sparse_vector_name=sparse_vector_name,
)
assert 2 == vec_store.client.count(collection_name).count
stored_ids = [point.id for point in vec_store.client.retrieve(collection_name, ids)]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_qdrant_from_texts_stores_embeddings_as_named_vectors(
location: str,
retrieval_mode: RetrievalMode,
vector_name: str,
sparse_vector_name: str,
) -> None:
"""Test end to end Qdrant.from_texts stores named vectors if name is provided."""
collection_name = uuid.uuid4().hex
vec_store = QdrantVectorStore.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
assert 5 == vec_store.client.count(collection_name).count
if retrieval_mode in retrieval_modes(sparse=False):
assert all(
(vector_name in point.vector or isinstance(point.vector, list)) # type: ignore
for point in vec_store.client.scroll(collection_name, with_vectors=True)[0]
)
if retrieval_mode in retrieval_modes(dense=False):
assert all(
sparse_vector_name in point.vector # type: ignore
for point in vec_store.client.scroll(collection_name, with_vectors=True)[0]
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_qdrant_from_texts_reuses_same_collection(
location: str,
retrieval_mode: RetrievalMode,
vector_name: str,
sparse_vector_name: str,
) -> None:
"""Test if Qdrant.from_texts reuses the same collection"""
collection_name = uuid.uuid4().hex
embeddings = ConsistentFakeEmbeddings()
sparse_embeddings = ConsistentFakeSparseEmbeddings()
vec_store = QdrantVectorStore.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
embeddings,
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=sparse_embeddings,
)
del vec_store
vec_store = QdrantVectorStore.from_texts(
["foo", "bar"],
embeddings,
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=sparse_embeddings,
)
assert 7 == vec_store.client.count(collection_name).count
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
def test_qdrant_from_texts_raises_error_on_different_dimensionality(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
) -> None:
"""Test if Qdrant.from_texts raises an exception if dimensionality does not match"""
collection_name = uuid.uuid4().hex
QdrantVectorStore.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
with pytest.raises(QdrantVectorStoreError) as excinfo:
QdrantVectorStore.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
expected_message = "collection is configured for dense vectors "
"with 10 dimensions. Selected embeddings are 5-dimensional"
assert expected_message in str(excinfo.value)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize(
["first_vector_name", "second_vector_name"],
[
("", "custom-vector"),
("custom-vector", ""),
("my-first-vector", "my-second_vector"),
],
)
@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
def test_qdrant_from_texts_raises_error_on_different_vector_name(
location: str,
first_vector_name: str,
second_vector_name: str,
retrieval_mode: RetrievalMode,
) -> None:
"""Test if Qdrant.from_texts raises an exception if vector name does not match"""
collection_name = uuid.uuid4().hex
QdrantVectorStore.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
location=location,
vector_name=first_vector_name,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
with pytest.raises(QdrantVectorStoreError) as excinfo:
QdrantVectorStore.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
location=location,
vector_name=second_vector_name,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
expected_message = "does not contain dense vector named"
assert expected_message in str(excinfo.value)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
def test_qdrant_from_texts_raises_error_on_different_distance(
location: str, vector_name: str, retrieval_mode: RetrievalMode
) -> None:
"""Test if Qdrant.from_texts raises an exception if distance does not match"""
collection_name = uuid.uuid4().hex
QdrantVectorStore.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=location,
vector_name=vector_name,
distance=models.Distance.COSINE,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
with pytest.raises(QdrantVectorStoreError) as excinfo:
QdrantVectorStore.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=location,
vector_name=vector_name,
distance=models.Distance.EUCLID,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
expected_message = "configured for COSINE similarity, but requested EUCLID"
assert expected_message in str(excinfo.value)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_qdrant_from_texts_recreates_collection_on_force_recreate(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
) -> None:
collection_name = uuid.uuid4().hex
vec_store = QdrantVectorStore.from_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
vec_store = QdrantVectorStore.from_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
force_recreate=True,
)
assert 2 == vec_store.client.count(collection_name).count
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
)
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_qdrant_from_texts_stores_metadatas(
location: str,
content_payload_key: str,
metadata_payload_key: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
) -> None:
"""Test end to end construction and search."""
texts = ["fabrin", "barizda"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=location,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
output = docsearch.similarity_search("fabrin", k=1)
assert_documents_equals(
output, [Document(page_content="fabrin", metadata={"page": 0})]
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_from_texts_passed_optimizers_config_and_on_disk_payload(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
) -> None:
collection_name = uuid.uuid4().hex
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
optimizers_config = models.OptimizersConfigDiff(memmap_threshold=1000)
vec_store = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
collection_create_options={
"on_disk_payload": True,
"optimizers_config": optimizers_config,
},
vector_params={
"on_disk": True,
},
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
collection_info = vec_store.client.get_collection(collection_name)
assert collection_info.config.params.vectors[vector_name].on_disk is True # type: ignore
assert collection_info.config.optimizer_config.memmap_threshold == 1000
assert collection_info.config.params.on_disk_payload is True
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_add_texts.py | import uuid
from typing import List, Union
import pytest
from langchain_core.documents import Document
from qdrant_client import QdrantClient, models
from langchain_qdrant import QdrantVectorStore, RetrievalMode
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
ConsistentFakeSparseEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_qdrant_add_documents_extends_existing_collection(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
new_texts = ["foobar", "foobaz"]
docsearch.add_documents([Document(page_content=content) for content in new_texts])
output = docsearch.similarity_search("foobar", k=1)
assert_documents_equals(output, [Document(page_content="foobar")])
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_returns_all_ids(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
batch_size: int,
) -> None:
"""Test end to end Qdrant.add_texts returns unique ids."""
docsearch = QdrantVectorStore.from_texts(
["foobar"],
ConsistentFakeEmbeddings(),
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
batch_size=batch_size,
)
ids = docsearch.add_texts(["foo", "bar", "baz"])
assert 3 == len(ids)
assert 3 == len(set(ids))
assert 3 == len(docsearch.get_by_ids(ids))
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
def test_qdrant_add_texts_stores_duplicated_texts(
location: str,
vector_name: str,
) -> None:
"""Test end to end Qdrant.add_texts stores duplicated texts separately."""
client = QdrantClient(location)
collection_name = uuid.uuid4().hex
vectors_config = {
vector_name: models.VectorParams(size=10, distance=models.Distance.COSINE)
}
client.recreate_collection(collection_name, vectors_config=vectors_config)
vec_store = QdrantVectorStore(
client,
collection_name,
embedding=ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
ids = vec_store.add_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
assert 2 == len(set(ids))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
@pytest.mark.parametrize("batch_size", [1, 64])
def test_qdrant_add_texts_stores_ids(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
batch_size: int,
) -> None:
"""Test end to end Qdrant.add_texts stores provided ids."""
ids: List[Union[str, int]] = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
432,
432145435,
]
collection_name = uuid.uuid4().hex
vec_store = QdrantVectorStore.from_texts(
["abc", "def", "ghi"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
batch_size=batch_size,
)
assert 3 == vec_store.client.count(collection_name).count
stored_ids = [point.id for point in vec_store.client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
assert 3 == len(vec_store.get_by_ids(ids))
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_mmr.py | import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from qdrant_client import models
from langchain_qdrant import QdrantVectorStore, RetrievalMode
from langchain_qdrant.qdrant import QdrantVectorStoreError
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
ConsistentFakeSparseEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
# MMR is supported when dense embeddings are available
# i.e. In Dense and Hybrid retrieval modes
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize(
"content_payload_key", [QdrantVectorStore.CONTENT_KEY, "test_content"]
)
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "test_metadata"]
)
@pytest.mark.parametrize("retrieval_mode", retrieval_modes(sparse=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
def test_qdrant_mmr_search(
location: str,
content_payload_key: str,
metadata_payload_key: str,
retrieval_mode: RetrievalMode,
vector_name: str,
) -> None:
"""Test end to end construction and MRR search."""
filter = models.Filter(
must=[
models.FieldCondition(
key=f"{metadata_payload_key}.page",
match=models.MatchValue(
value=2,
),
),
],
)
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
location=location,
retrieval_mode=retrieval_mode,
vector_name=vector_name,
distance=models.Distance.EUCLID,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert_documents_equals(
output,
[
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="baz", metadata={"page": 2}),
],
)
output = docsearch.max_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter
)
assert_documents_equals(
output,
[Document(page_content="baz", metadata={"page": 2})],
)
# MMR shouldn't work with only sparse retrieval mode
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize(
"content_payload_key", [QdrantVectorStore.CONTENT_KEY, "test_content"]
)
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "test_metadata"]
)
@pytest.mark.parametrize("retrieval_mode", retrieval_modes(dense=False, hybrid=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
def test_invalid_qdrant_mmr_with_sparse(
location: str,
content_payload_key: str,
metadata_payload_key: str,
retrieval_mode: RetrievalMode,
vector_name: str,
) -> None:
"""Test end to end construction and MRR search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
location=location,
retrieval_mode=retrieval_mode,
vector_name=vector_name,
distance=models.Distance.EUCLID,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
with pytest.raises(QdrantVectorStoreError) as excinfo:
docsearch.max_marginal_relevance_search("foo", k=2, fetch_k=3, lambda_mult=0.0)
expected_message = "does not contain dense vector named"
assert expected_message in str(excinfo.value)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_search.py | import pytest
from langchain_core.documents import Document
from qdrant_client import models
from langchain_qdrant import QdrantVectorStore, RetrievalMode
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
ConsistentFakeSparseEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize("batch_size", [1, 64])
def test_similarity_search(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
batch_size: int,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=location,
batch_size=batch_size,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
output = docsearch.similarity_search("foo", k=1)
assert_documents_equals(actual=output, expected=[Document(page_content="foo")])
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
)
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("batch_size", [1, 64])
def test_similarity_search_by_vector(
location: str,
content_payload_key: str,
metadata_payload_key: str,
vector_name: str,
batch_size: int,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=location,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = docsearch.similarity_search_by_vector(embeddings, k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
)
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
def test_similarity_search_filters(
location: str,
metadata_payload_key: str,
retrieval_mode: RetrievalMode,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=location,
metadata_payload_key=metadata_payload_key,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
qdrant_filter = models.Filter(
must=[
models.FieldCondition(
key=f"{metadata_payload_key}.page", match=models.MatchValue(value=1)
)
]
)
output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
assert_documents_equals(
actual=output,
expected=[
Document(
page_content="bar",
metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
def test_similarity_relevance_search_no_threshold(
location: str,
vector_name: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=location,
vector_name=vector_name,
)
output = docsearch.similarity_search_with_relevance_scores(
"foo", k=3, score_threshold=None
)
assert len(output) == 3
for i in range(len(output)):
assert round(output[i][1], 2) >= 0
assert round(output[i][1], 2) <= 1
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
def test_relevance_search_with_threshold(
location: str,
vector_name: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=location,
vector_name=vector_name,
)
score_threshold = 0.99
kwargs = {"score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
)
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
def test_relevance_search_with_threshold_and_filter(
location: str,
content_payload_key: str,
metadata_payload_key: str,
vector_name: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
location=location,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
vector_name=vector_name,
)
score_threshold = 0.99 # for almost exact match
negative_filter = models.Filter(
must=[
models.FieldCondition(
key=f"{metadata_payload_key}.page", match=models.MatchValue(value=1)
)
]
)
kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 0
positive_filter = models.Filter(
must=[
models.FieldCondition(
key=f"{metadata_payload_key}.page", match=models.MatchValue(value=0)
)
]
)
kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("location", qdrant_locations())
@pytest.mark.parametrize("content_payload_key", [QdrantVectorStore.CONTENT_KEY, "foo"])
@pytest.mark.parametrize(
"metadata_payload_key", [QdrantVectorStore.METADATA_KEY, "bar"]
)
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
def test_similarity_search_filters_with_qdrant_filters(
location: str,
content_payload_key: str,
metadata_payload_key: str,
retrieval_mode: RetrievalMode,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = QdrantVectorStore.from_texts(
texts,
ConsistentFakeEmbeddings(),
location=location,
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
retrieval_mode=retrieval_mode,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
qdrant_filter = models.Filter(
must=[
models.FieldCondition(
key=content_payload_key, match=models.MatchValue(value="bar")
),
models.FieldCondition(
key=f"{metadata_payload_key}.page",
match=models.MatchValue(value=1),
),
models.FieldCondition(
key=f"{metadata_payload_key}.details.page",
match=models.MatchValue(value=2),
),
models.FieldCondition(
key=f"{metadata_payload_key}.details.pages",
match=models.MatchAny(any=[3]),
),
]
)
output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter)
assert_documents_equals(
actual=output,
expected=[
Document(
page_content="bar",
metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
)
],
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/qdrant_vector_store/test_from_existing.py | import uuid
import pytest
from langchain_qdrant.qdrant import QdrantVectorStore, RetrievalMode
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
ConsistentFakeSparseEmbeddings,
)
from tests.integration_tests.fixtures import qdrant_locations, retrieval_modes
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", ["", "my-vector"])
@pytest.mark.parametrize("retrieval_mode", retrieval_modes())
@pytest.mark.parametrize(
"sparse_vector_name", ["my-sparse-vector", "another-sparse-vector"]
)
def test_qdrant_from_existing_collection_uses_same_collection(
location: str,
vector_name: str,
retrieval_mode: RetrievalMode,
sparse_vector_name: str,
) -> None:
"""Test if the QdrantVectorStore.from_existing_collection reuses the collection."""
collection_name = uuid.uuid4().hex
docs = ["foo"]
QdrantVectorStore.from_texts(
docs,
embedding=ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
qdrant = QdrantVectorStore.from_existing_collection(
collection_name,
embedding=ConsistentFakeEmbeddings(),
location=location,
vector_name=vector_name,
retrieval_mode=retrieval_mode,
sparse_vector_name=sparse_vector_name,
sparse_embedding=ConsistentFakeSparseEmbeddings(),
)
qdrant.add_texts(["baz", "bar"])
assert 3 == qdrant.client.count(collection_name).count
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/async_api/test_from_texts.py | import os
import uuid
from typing import Optional
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from langchain_qdrant.vectorstores import QdrantException
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import (
qdrant_locations,
)
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_duplicated_texts(qdrant_location: str) -> None:
"""Test end to end Qdrant.afrom_texts stores duplicated texts separately."""
collection_name = uuid.uuid4().hex
vec_store = await Qdrant.afrom_texts(
["abc", "abc"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
location=qdrant_location,
)
client = vec_store.client
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_ids(
batch_size: int, vector_name: Optional[str], qdrant_location: str
) -> None:
"""Test end to end Qdrant.afrom_texts stores provided ids."""
collection_name = uuid.uuid4().hex
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
vec_store = await Qdrant.afrom_texts(
["abc", "def"],
ConsistentFakeEmbeddings(),
ids=ids,
collection_name=collection_name,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
client = vec_store.client
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_embeddings_as_named_vectors(
vector_name: str,
qdrant_location: str,
) -> None:
"""Test end to end Qdrant.afrom_texts stores named vectors if name is provided."""
collection_name = uuid.uuid4().hex
vec_store = await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(),
collection_name=collection_name,
vector_name=vector_name,
location=qdrant_location,
)
client = vec_store.client
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
async def test_qdrant_from_texts_reuses_same_collection(
location: str, vector_name: Optional[str]
) -> None:
"""Test if Qdrant.afrom_texts reuses the same collection"""
collection_name = uuid.uuid4().hex
embeddings = ConsistentFakeEmbeddings()
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
embeddings,
collection_name=collection_name,
vector_name=vector_name,
location=location,
)
vec_store = await Qdrant.afrom_texts(
["foo", "bar"],
embeddings,
collection_name=collection_name,
vector_name=vector_name,
location=location,
)
client = vec_store.client
assert 7 == client.count(collection_name).count
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
async def test_qdrant_from_texts_raises_error_on_different_dimensionality(
location: str,
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts raises an exception if dimensionality does not
match"""
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
vector_name=vector_name,
location=location,
)
with pytest.raises(QdrantException):
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
vector_name=vector_name,
location=location,
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize(
["first_vector_name", "second_vector_name"],
[
(None, "custom-vector"),
("custom-vector", None),
("my-first-vector", "my-second_vector"),
],
)
async def test_qdrant_from_texts_raises_error_on_different_vector_name(
location: str,
first_vector_name: Optional[str],
second_vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts raises an exception if vector name does not match"""
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
vector_name=first_vector_name,
location=location,
)
with pytest.raises(QdrantException):
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
vector_name=second_vector_name,
location=location,
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
async def test_qdrant_from_texts_raises_error_on_different_distance(
location: str,
) -> None:
"""Test if Qdrant.afrom_texts raises an exception if distance does not match"""
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
distance_func="Cosine",
location=location,
)
with pytest.raises(QdrantException):
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
distance_func="Euclid",
location=location,
)
@pytest.mark.parametrize("location", qdrant_locations(use_in_memory=False))
@pytest.mark.parametrize("vector_name", [None, "custom-vector"])
async def test_qdrant_from_texts_recreates_collection_on_force_recreate(
location: str,
vector_name: Optional[str],
) -> None:
"""Test if Qdrant.afrom_texts recreates the collection even if config mismatches"""
from qdrant_client import QdrantClient
collection_name = uuid.uuid4().hex
await Qdrant.afrom_texts(
["lorem", "ipsum", "dolor", "sit", "amet"],
ConsistentFakeEmbeddings(dimensionality=10),
collection_name=collection_name,
vector_name=vector_name,
location=location,
)
await Qdrant.afrom_texts(
["foo", "bar"],
ConsistentFakeEmbeddings(dimensionality=5),
collection_name=collection_name,
vector_name=vector_name,
force_recreate=True,
location=location,
)
client = QdrantClient(location=location, api_key=os.getenv("QDRANT_API_KEY"))
assert 2 == client.count(collection_name).count
vector_params = client.get_collection(collection_name).config.params.vectors
if vector_name is not None:
vector_params = vector_params[vector_name] # type: ignore[index]
assert 5 == vector_params.size # type: ignore[union-attr]
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_from_texts_stores_metadatas(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = await Qdrant.afrom_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
location=qdrant_location,
)
output = await docsearch.asimilarity_search("foo", k=1)
assert_documents_equals(
output, [Document(page_content="foo", metadata={"page": 0})]
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/async_api/test_add_texts.py | import os
import uuid
from typing import Optional
import pytest # type: ignore[import-not-found]
from langchain_qdrant import Qdrant
from tests.integration_tests.common import ConsistentFakeEmbeddings
from tests.integration_tests.fixtures import qdrant_locations
API_KEY = os.getenv("QDRANT_API_KEY")
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_returns_all_ids(
batch_size: int, qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts returns unique ids."""
docsearch: Qdrant = Qdrant.from_texts(
["foobar"],
ConsistentFakeEmbeddings(),
batch_size=batch_size,
location=qdrant_location,
)
ids = await docsearch.aadd_texts(["foo", "bar", "baz"])
assert 3 == len(ids)
assert 3 == len(set(ids))
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_stores_duplicated_texts(
vector_name: Optional[str], qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts stores duplicated texts separately."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
client = QdrantClient(location=qdrant_location, api_key=API_KEY)
collection_name = uuid.uuid4().hex
vectors_config = rest.VectorParams(size=10, distance=rest.Distance.COSINE)
if vector_name is not None:
vectors_config = {vector_name: vectors_config} # type: ignore[assignment]
client.recreate_collection(collection_name, vectors_config=vectors_config)
vec_store = Qdrant(
client,
collection_name,
embeddings=ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
ids = await vec_store.aadd_texts(["abc", "abc"], [{"a": 1}, {"a": 2}])
assert 2 == len(set(ids))
assert 2 == client.count(collection_name).count
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_stores_ids(
batch_size: int, qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts stores provided ids."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
ids = [
"fa38d572-4c31-4579-aedc-1960d79df6df",
"cdc1aa36-d6ab-4fb2-8a94-56674fd27484",
]
client = QdrantClient(location=qdrant_location, api_key=API_KEY)
collection_name = uuid.uuid4().hex
client.recreate_collection(
collection_name,
vectors_config=rest.VectorParams(size=10, distance=rest.Distance.COSINE),
)
vec_store = Qdrant(client, collection_name, ConsistentFakeEmbeddings())
returned_ids = await vec_store.aadd_texts(
["abc", "def"], ids=ids, batch_size=batch_size
)
assert all(first == second for first, second in zip(ids, returned_ids))
assert 2 == client.count(collection_name).count
stored_ids = [point.id for point in client.scroll(collection_name)[0]]
assert set(ids) == set(stored_ids)
@pytest.mark.parametrize("vector_name", ["custom-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_aadd_texts_stores_embeddings_as_named_vectors(
vector_name: str, qdrant_location: str
) -> None:
"""Test end to end Qdrant.aadd_texts stores named vectors if name is provided."""
from qdrant_client import QdrantClient
from qdrant_client.http import models as rest
collection_name = uuid.uuid4().hex
client = QdrantClient(location=qdrant_location, api_key=API_KEY)
client.recreate_collection(
collection_name,
vectors_config={
vector_name: rest.VectorParams(size=10, distance=rest.Distance.COSINE)
},
)
vec_store = Qdrant(
client,
collection_name,
ConsistentFakeEmbeddings(),
vector_name=vector_name,
)
await vec_store.aadd_texts(["lorem", "ipsum", "dolor", "sit", "amet"])
assert 5 == client.count(collection_name).count
assert all(
vector_name in point.vector # type: ignore[operator]
for point in client.scroll(collection_name, with_vectors=True)[0]
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/async_api/test_similarity_search.py | from typing import Optional
import numpy as np
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import qdrant_locations
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search("foo", k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = await docsearch.asimilarity_search_by_vector(embeddings, k=1)
assert_documents_equals(output, [Document(page_content="foo")])
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_score_by_vector(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
embeddings = ConsistentFakeEmbeddings().embed_query("foo")
output = await docsearch.asimilarity_search_with_score_by_vector(embeddings, k=1)
assert len(output) == 1
document, score = output[0]
assert_documents_equals([document], [Document(page_content="foo")])
assert score >= 0
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_filters(
batch_size: int, vector_name: Optional[str], qdrant_location: str
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search(
"foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}}
)
assert_documents_equals(
output,
[
Document(
page_content="bar",
metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_relevance_score_no_threshold(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search_with_relevance_scores(
"foo", k=3, score_threshold=None
)
assert len(output) == 3
for i in range(len(output)):
assert round(output[i][1], 2) >= 0
assert round(output[i][1], 2) <= 1
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_relevance_score_with_threshold(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
score_threshold = 0.98
kwargs = {"score_threshold": score_threshold}
output = await docsearch.asimilarity_search_with_relevance_scores(
"foo", k=3, **kwargs
)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_similarity_search_with_relevance_score_with_threshold_and_filter(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "metadata": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
score_threshold = 0.99 # for almost exact match
# test negative filter condition
negative_filter = {"page": 1, "metadata": {"page": 2, "pages": [3]}}
kwargs = {"filter": negative_filter, "score_threshold": score_threshold}
output = docsearch.similarity_search_with_relevance_scores("foo", k=3, **kwargs)
assert len(output) == 0
# test positive filter condition
positive_filter = {"page": 0, "metadata": {"page": 1, "pages": [2]}}
kwargs = {"filter": positive_filter, "score_threshold": score_threshold}
output = await docsearch.asimilarity_search_with_relevance_scores(
"foo", k=3, **kwargs
)
assert len(output) == 1
assert all([score >= score_threshold for _, score in output])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_filters_with_qdrant_filters(
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
from qdrant_client.http import models as rest
texts = ["foo", "bar", "baz"]
metadatas = [
{"page": i, "details": {"page": i + 1, "pages": [i + 2, -1]}}
for i in range(len(texts))
]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
vector_name=vector_name,
location=qdrant_location,
)
qdrant_filter = rest.Filter(
must=[
rest.FieldCondition(
key="metadata.page",
match=rest.MatchValue(value=1),
),
rest.FieldCondition(
key="metadata.details.page",
match=rest.MatchValue(value=2),
),
rest.FieldCondition(
key="metadata.details.pages",
match=rest.MatchAny(any=[3]),
),
]
)
output = await docsearch.asimilarity_search("foo", k=1, filter=qdrant_filter)
assert_documents_equals(
output,
[
Document(
page_content="bar",
metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}},
)
],
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "foo"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "bar"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_similarity_search_with_relevance_scores(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: str,
qdrant_location: str,
) -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
)
output = await docsearch.asimilarity_search_with_relevance_scores("foo", k=3)
assert all(
(1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests | lc_public_repos/langchain/libs/partners/qdrant/tests/integration_tests/async_api/test_max_marginal_relevance.py | from typing import Optional
import pytest # type: ignore[import-not-found]
from langchain_core.documents import Document
from langchain_qdrant import Qdrant
from tests.integration_tests.common import (
ConsistentFakeEmbeddings,
assert_documents_equals,
)
from tests.integration_tests.fixtures import (
qdrant_locations,
)
@pytest.mark.parametrize("batch_size", [1, 64])
@pytest.mark.parametrize("content_payload_key", [Qdrant.CONTENT_KEY, "test_content"])
@pytest.mark.parametrize("metadata_payload_key", [Qdrant.METADATA_KEY, "test_metadata"])
@pytest.mark.parametrize("vector_name", [None, "my-vector"])
@pytest.mark.parametrize("qdrant_location", qdrant_locations())
async def test_qdrant_max_marginal_relevance_search(
batch_size: int,
content_payload_key: str,
metadata_payload_key: str,
vector_name: Optional[str],
qdrant_location: str,
) -> None:
"""Test end to end construction and MRR search."""
texts = ["foo", "bar", "baz"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = Qdrant.from_texts(
texts,
ConsistentFakeEmbeddings(),
metadatas=metadatas,
content_payload_key=content_payload_key,
metadata_payload_key=metadata_payload_key,
batch_size=batch_size,
vector_name=vector_name,
location=qdrant_location,
distance_func="EUCLID", # Euclid distance used to avoid normalization
)
output = await docsearch.amax_marginal_relevance_search(
"foo", k=2, fetch_k=3, lambda_mult=0.0
)
assert_documents_equals(
output,
[
Document(page_content="foo", metadata={"page": 0}),
Document(page_content="baz", metadata={"page": 2}),
],
)
|
0 | lc_public_repos/langchain/libs/partners/qdrant/tests | lc_public_repos/langchain/libs/partners/qdrant/tests/unit_tests/test_imports.py | from langchain_qdrant import __all__
EXPECTED_ALL = [
"Qdrant",
"QdrantVectorStore",
"SparseEmbeddings",
"SparseVector",
"FastEmbedSparse",
"RetrievalMode",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/qdrant | lc_public_repos/langchain/libs/partners/qdrant/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/qdrant | lc_public_repos/langchain/libs/partners/qdrant/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/voyageai/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/voyageai --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_voyageai
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_voyageai -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/voyageai/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/voyageai/poetry.lock | # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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name = "aiohappyeyeballs"
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{file = "aiohappyeyeballs-2.4.0.tar.gz", hash = "sha256:55a1714f084e63d49639800f95716da97a1f173d46a16dfcfda0016abb93b6b2"},
]
[[package]]
name = "aiohttp"
version = "3.10.5"
description = "Async http client/server framework (asyncio)"
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
idna = ">=2.0"
multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "c037eee2ed1ce2ed484992fb08ee187a0db037bf8c84cf69d8715c7c62a59bfe"
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/voyageai/README.md | # langchain-voyageai
This package contains the LangChain integrations for VoyageAI through their `voyageai` client package.
## Installation and Setup
- Install the LangChain partner package
```bash
pip install langchain-voyageai
```
- Get an VoyageAI api key and set it as an environment variable (`VOYAGE_API_KEY`) or use the API key as a parameter in the Client.
## Text Embedding Model
See a [usage example](https://python.langchain.com/docs/integrations/text_embedding/voyageai)
```python
from langchain_voyageai import VoyageAIEmbeddings
```
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/voyageai/pyproject.toml | [build-system]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-voyageai"
version = "0.1.3"
description = "An integration package connecting VoyageAI 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/voyageai"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-voyageai%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
voyageai = ">=0.2.1,<1"
pydantic = ">=2,<3"
[tool.ruff.lint]
select = [ "E", "F", "I",]
[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.test_integration]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
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"
[[tool.poetry.group.test.dependencies.numpy]]
version = "^1.24.0"
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.test_integration.dependencies]
[tool.poetry.group.lint.dependencies]
ruff = "^0.1.5"
[tool.poetry.group.typing.dependencies]
mypy = "^1.10"
[tool.poetry.group.test.dependencies.langchain-core]
path = "../../core"
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/voyageai/tests | lc_public_repos/langchain/libs/partners/voyageai/tests/integration_tests/test_rerank.py | """Test the voyageai reranker."""
import os
from langchain_core.documents import Document
from langchain_voyageai.rerank import VoyageAIRerank
def test_voyageai_reranker_init() -> None:
"""Test the voyageai reranker initializes correctly."""
VoyageAIRerank(voyage_api_key="foo", model="foo") # type: ignore[arg-type]
def test_sync() -> None:
rerank = VoyageAIRerank(
voyage_api_key=os.environ["VOYAGE_API_KEY"], # type: ignore[arg-type]
model="rerank-lite-1",
)
doc_list = [
"The Mediterranean diet emphasizes fish, olive oil, and vegetables"
", believed to reduce chronic diseases.",
"Photosynthesis in plants converts light energy into glucose and "
"produces essential oxygen.",
"20th-century innovations, from radios to smartphones, centered "
"on electronic advancements.",
"Rivers provide water, irrigation, and habitat for aquatic species, "
"vital for ecosystems.",
"Apple’s conference call to discuss fourth fiscal quarter results and "
"business updates is scheduled for Thursday, November 2, 2023 at 2:00 "
"p.m. PT / 5:00 p.m. ET.",
"Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' "
"endure in literature.",
]
documents = [Document(page_content=x) for x in doc_list]
result = rerank.compress_documents(
query="When is the Apple's conference call scheduled?", documents=documents
)
assert len(doc_list) == len(result)
async def test_async() -> None:
rerank = VoyageAIRerank(
voyage_api_key=os.environ["VOYAGE_API_KEY"], # type: ignore[arg-type]
model="rerank-lite-1",
)
doc_list = [
"The Mediterranean diet emphasizes fish, olive oil, and vegetables"
", believed to reduce chronic diseases.",
"Photosynthesis in plants converts light energy into glucose and "
"produces essential oxygen.",
"20th-century innovations, from radios to smartphones, centered "
"on electronic advancements.",
"Rivers provide water, irrigation, and habitat for aquatic species, "
"vital for ecosystems.",
"Apple’s conference call to discuss fourth fiscal quarter results and "
"business updates is scheduled for Thursday, November 2, 2023 at 2:00 "
"p.m. PT / 5:00 p.m. ET.",
"Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' "
"endure in literature.",
]
documents = [Document(page_content=x) for x in doc_list]
result = await rerank.acompress_documents(
query="When is the Apple's conference call scheduled?", documents=documents
)
assert len(doc_list) == len(result)
|
0 | lc_public_repos/langchain/libs/partners/voyageai/tests | lc_public_repos/langchain/libs/partners/voyageai/tests/integration_tests/test_embeddings.py | """Test VoyageAI embeddings."""
from langchain_voyageai import VoyageAIEmbeddings
# Please set VOYAGE_API_KEY in the environment variables
MODEL = "voyage-2"
def test_langchain_voyageai_embedding_documents() -> None:
"""Test voyage embeddings."""
documents = ["foo bar"]
embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg]
output = embedding.embed_documents(documents)
assert len(output) == 1
assert len(output[0]) == 1024
def test_langchain_voyageai_embedding_documents_multiple() -> None:
"""Test voyage embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = VoyageAIEmbeddings(model=MODEL, batch_size=2)
output = embedding.embed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1024
assert len(output[1]) == 1024
assert len(output[2]) == 1024
def test_langchain_voyageai_embedding_query() -> None:
"""Test voyage embeddings."""
document = "foo bar"
embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg]
output = embedding.embed_query(document)
assert len(output) == 1024
async def test_langchain_voyageai_async_embedding_documents_multiple() -> None:
"""Test voyage embeddings."""
documents = ["foo bar", "bar foo", "foo"]
embedding = VoyageAIEmbeddings(model=MODEL, batch_size=2)
output = await embedding.aembed_documents(documents)
assert len(output) == 3
assert len(output[0]) == 1024
assert len(output[1]) == 1024
assert len(output[2]) == 1024
async def test_langchain_voyageai_async_embedding_query() -> None:
"""Test voyage embeddings."""
document = "foo bar"
embedding = VoyageAIEmbeddings(model=MODEL) # type: ignore[call-arg]
output = await embedding.aembed_query(document)
assert len(output) == 1024
|
0 | lc_public_repos/langchain/libs/partners/voyageai/tests | lc_public_repos/langchain/libs/partners/voyageai/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/voyageai/tests | lc_public_repos/langchain/libs/partners/voyageai/tests/unit_tests/test_rerank.py | from collections import namedtuple
from typing import Any
import pytest # type: ignore
from langchain_core.documents import Document
from voyageai.api_resources import VoyageResponse # type: ignore
from voyageai.object import RerankingObject # type: ignore
from langchain_voyageai.rerank import VoyageAIRerank
doc_list = [
"The Mediterranean diet emphasizes fish, olive oil, and vegetables"
", believed to reduce chronic diseases.",
"Photosynthesis in plants converts light energy into glucose and "
"produces essential oxygen.",
"20th-century innovations, from radios to smartphones, centered "
"on electronic advancements.",
"Rivers provide water, irrigation, and habitat for aquatic species, "
"vital for ecosystems.",
"Apple’s conference call to discuss fourth fiscal quarter results and "
"business updates is scheduled for Thursday, November 2, 2023 at 2:00 "
"p.m. PT / 5:00 p.m. ET.",
"Shakespeare's works, like 'Hamlet' and 'A Midsummer Night's Dream,' "
"endure in literature.",
]
documents = [Document(page_content=x) for x in doc_list]
@pytest.mark.requires("voyageai")
def test_init() -> None:
VoyageAIRerank(
voyage_api_key="foo", # type: ignore[arg-type]
model="rerank-lite-1",
)
def get_mock_rerank_result() -> RerankingObject:
VoyageResultItem = namedtuple("VoyageResultItem", ["index", "relevance_score"])
Usage = namedtuple("Usage", ["total_tokens"])
voyage_response = VoyageResponse()
voyage_response.data = [
VoyageResultItem(index=1, relevance_score=0.9),
VoyageResultItem(index=0, relevance_score=0.8),
]
voyage_response.usage = Usage(total_tokens=255)
return RerankingObject(response=voyage_response, documents=doc_list)
@pytest.mark.requires("voyageai")
def test_rerank_unit_test(mocker: Any) -> None:
mocker.patch("voyageai.Client.rerank").return_value = get_mock_rerank_result()
expected_result = [
Document(
page_content="Photosynthesis in plants converts light energy into "
"glucose and produces essential oxygen.",
metadata={"relevance_score": 0.9},
),
Document(
page_content="The Mediterranean diet emphasizes fish, olive oil, and "
"vegetables, believed to reduce chronic diseases.",
metadata={"relevance_score": 0.8},
),
]
rerank = VoyageAIRerank(
voyage_api_key="foo", # type: ignore[arg-type]
model="rerank-lite-1",
)
result = rerank.compress_documents(
documents=documents, query="When is the Apple's conference call scheduled?"
)
assert expected_result == result
def test_rerank_empty_input() -> None:
rerank = VoyageAIRerank(
voyage_api_key="foo", # type: ignore[arg-type]
model="rerank-lite-1",
)
result = rerank.compress_documents(
documents=[], query="When is the Apple's conference call scheduled?"
)
assert len(result) == 0
|
0 | lc_public_repos/langchain/libs/partners/voyageai/tests | lc_public_repos/langchain/libs/partners/voyageai/tests/unit_tests/test_imports.py | from langchain_voyageai import __all__
EXPECTED_ALL = [
"VoyageAIEmbeddings",
"VoyageAIRerank",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/voyageai/tests | lc_public_repos/langchain/libs/partners/voyageai/tests/unit_tests/test_embeddings.py | """Test embedding model integration."""
from langchain_core.embeddings import Embeddings
from langchain_voyageai import VoyageAIEmbeddings
MODEL = "voyage-2"
def test_initialization_voyage_2() -> None:
"""Test embedding model initialization."""
emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model=MODEL) # type: ignore
assert isinstance(emb, Embeddings)
assert emb.batch_size == 72
assert emb.model == MODEL
assert emb._client is not None
def test_initialization_voyage_2_with_full_api_key_name() -> None:
"""Test embedding model initialization."""
# Testing that we can initialize the model using `voyage_api_key`
# instead of `api_key`
emb = VoyageAIEmbeddings(voyage_api_key="NOT_A_VALID_KEY", model=MODEL) # type: ignore
assert isinstance(emb, Embeddings)
assert emb.batch_size == 72
assert emb.model == MODEL
assert emb._client is not None
def test_initialization_voyage_1() -> None:
"""Test embedding model initialization."""
emb = VoyageAIEmbeddings(api_key="NOT_A_VALID_KEY", model="voyage-01") # type: ignore
assert isinstance(emb, Embeddings)
assert emb.batch_size == 7
assert emb.model == "voyage-01"
assert emb._client is not None
def test_initialization_voyage_1_batch_size() -> None:
"""Test embedding model initialization."""
emb = VoyageAIEmbeddings(
api_key="NOT_A_VALID_KEY", # type: ignore
model="voyage-01",
batch_size=15,
)
assert isinstance(emb, Embeddings)
assert emb.batch_size == 15
assert emb.model == "voyage-01"
assert emb._client is not None
|
0 | lc_public_repos/langchain/libs/partners/voyageai | lc_public_repos/langchain/libs/partners/voyageai/langchain_voyageai/rerank.py | from __future__ import annotations
import os
from copy import deepcopy
from typing import Any, Dict, Optional, Sequence, Union
import voyageai # type: ignore
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import Document
from langchain_core.documents.compressor import BaseDocumentCompressor
from langchain_core.utils import convert_to_secret_str
from pydantic import ConfigDict, SecretStr, model_validator
from voyageai.object import RerankingObject # type: ignore
class VoyageAIRerank(BaseDocumentCompressor):
"""Document compressor that uses `VoyageAI Rerank API`."""
client: voyageai.Client = None
aclient: voyageai.AsyncClient = None
"""VoyageAI clients to use for compressing documents."""
voyage_api_key: Optional[SecretStr] = None
"""VoyageAI API key. Must be specified directly or via environment variable
VOYAGE_API_KEY."""
model: str
"""Model to use for reranking."""
top_k: Optional[int] = None
"""Number of documents to return."""
truncation: bool = True
model_config = ConfigDict(
arbitrary_types_allowed=True,
)
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate that api key exists in environment."""
voyage_api_key = values.get("voyage_api_key") or os.getenv(
"VOYAGE_API_KEY", None
)
if voyage_api_key:
api_key_secretstr = convert_to_secret_str(voyage_api_key)
values["voyage_api_key"] = api_key_secretstr
api_key_str = api_key_secretstr.get_secret_value()
else:
api_key_str = None
values["client"] = voyageai.Client(api_key=api_key_str)
values["aclient"] = voyageai.AsyncClient(api_key=api_key_str)
return values
def _rerank(
self,
documents: Sequence[Union[str, Document]],
query: str,
) -> RerankingObject:
"""Returns an ordered list of documents ordered by their relevance
to the provided query.
Args:
query: The query to use for reranking.
documents: A sequence of documents to rerank.
"""
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
return self.client.rerank(
query=query,
documents=docs,
model=self.model,
top_k=self.top_k,
truncation=self.truncation,
)
async def _arerank(
self,
documents: Sequence[Union[str, Document]],
query: str,
) -> RerankingObject:
"""Returns an ordered list of documents ordered by their relevance
to the provided query.
Args:
query: The query to use for reranking.
documents: A sequence of documents to rerank.
"""
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
return await self.aclient.rerank(
query=query,
documents=docs,
model=self.model,
top_k=self.top_k,
truncation=self.truncation,
)
def compress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
"""
Compress documents using VoyageAI's rerank API.
Args:
documents: A sequence of documents to compress.
query: The query to use for compressing the documents.
callbacks: Callbacks to run during the compression process.
Returns:
A sequence of compressed documents in relevance_score order.
"""
if len(documents) == 0:
return []
compressed = []
for res in self._rerank(documents, query).results:
doc = documents[res.index]
doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
doc_copy.metadata["relevance_score"] = res.relevance_score
compressed.append(doc_copy)
return compressed
async def acompress_documents(
self,
documents: Sequence[Document],
query: str,
callbacks: Optional[Callbacks] = None,
) -> Sequence[Document]:
"""
Compress documents using VoyageAI's rerank API.
Args:
documents: A sequence of documents to compress.
query: The query to use for compressing the documents.
callbacks: Callbacks to run during the compression process.
Returns:
A sequence of compressed documents in relevance_score order.
"""
if len(documents) == 0:
return []
compressed = []
for res in (await self._arerank(documents, query)).results:
doc = documents[res.index]
doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
doc_copy.metadata["relevance_score"] = res.relevance_score
compressed.append(doc_copy)
return compressed
|
0 | lc_public_repos/langchain/libs/partners/voyageai | lc_public_repos/langchain/libs/partners/voyageai/langchain_voyageai/embeddings.py | import logging
from typing import Any, Iterable, List, Optional
import voyageai # type: ignore
from langchain_core.embeddings import Embeddings
from langchain_core.utils import secret_from_env
from pydantic import (
BaseModel,
ConfigDict,
Field,
PrivateAttr,
SecretStr,
model_validator,
)
from typing_extensions import Self
logger = logging.getLogger(__name__)
DEFAULT_VOYAGE_2_BATCH_SIZE = 72
DEFAULT_VOYAGE_3_LITE_BATCH_SIZE = 30
DEFAULT_VOYAGE_3_BATCH_SIZE = 10
DEFAULT_BATCH_SIZE = 7
class VoyageAIEmbeddings(BaseModel, Embeddings):
"""VoyageAIEmbeddings embedding model.
Example:
.. code-block:: python
from langchain_voyageai import VoyageAIEmbeddings
model = VoyageAIEmbeddings()
"""
_client: voyageai.Client = PrivateAttr()
_aclient: voyageai.client_async.AsyncClient = PrivateAttr()
model: str
batch_size: int
show_progress_bar: bool = False
truncation: Optional[bool] = None
voyage_api_key: SecretStr = Field(
alias="api_key",
default_factory=secret_from_env(
"VOYAGE_API_KEY",
error_message="Must set `VOYAGE_API_KEY` environment variable or "
"pass `api_key` to VoyageAIEmbeddings constructor.",
),
)
model_config = ConfigDict(
extra="forbid",
populate_by_name=True,
)
@model_validator(mode="before")
@classmethod
def default_values(cls, values: dict) -> Any:
"""Set default batch size based on model"""
model = values.get("model")
batch_size = values.get("batch_size")
if batch_size is None:
values["batch_size"] = (
DEFAULT_VOYAGE_2_BATCH_SIZE
if model in ["voyage-2", "voyage-02"]
else (
DEFAULT_VOYAGE_3_LITE_BATCH_SIZE
if model == "voyage-3-lite"
else (
DEFAULT_VOYAGE_3_BATCH_SIZE
if model == "voyage-3"
else DEFAULT_BATCH_SIZE
)
)
)
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that VoyageAI credentials exist in environment."""
api_key_str = self.voyage_api_key.get_secret_value()
self._client = voyageai.Client(api_key=api_key_str)
self._aclient = voyageai.client_async.AsyncClient(api_key=api_key_str)
return self
def _get_batch_iterator(self, texts: List[str]) -> Iterable:
if self.show_progress_bar:
try:
from tqdm.auto import tqdm # type: ignore
except ImportError as e:
raise ImportError(
"Must have tqdm installed if `show_progress_bar` is set to True. "
"Please install with `pip install tqdm`."
) from e
_iter = tqdm(range(0, len(texts), self.batch_size))
else:
_iter = range(0, len(texts), self.batch_size) # type: ignore
return _iter
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""Embed search docs."""
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
embeddings.extend(
self._client.embed(
texts[i : i + self.batch_size],
model=self.model,
input_type="document",
truncation=self.truncation,
).embeddings
)
return embeddings
def embed_query(self, text: str) -> List[float]:
"""Embed query text."""
return self._client.embed(
[text], model=self.model, input_type="query", truncation=self.truncation
).embeddings[0]
async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
embeddings: List[List[float]] = []
_iter = self._get_batch_iterator(texts)
for i in _iter:
r = await self._aclient.embed(
texts[i : i + self.batch_size],
model=self.model,
input_type="document",
truncation=self.truncation,
)
embeddings.extend(r.embeddings)
return embeddings
async def aembed_query(self, text: str) -> List[float]:
r = await self._aclient.embed(
[text],
model=self.model,
input_type="query",
truncation=self.truncation,
)
return r.embeddings[0]
|
0 | lc_public_repos/langchain/libs/partners/voyageai | lc_public_repos/langchain/libs/partners/voyageai/langchain_voyageai/__init__.py | from langchain_voyageai.embeddings import VoyageAIEmbeddings
from langchain_voyageai.rerank import VoyageAIRerank
__all__ = ["VoyageAIEmbeddings", "VoyageAIRerank"]
|
0 | lc_public_repos/langchain/libs/partners/voyageai | lc_public_repos/langchain/libs/partners/voyageai/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/voyageai | lc_public_repos/langchain/libs/partners/voyageai/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/astradb/README.md | This package has moved!
https://github.com/langchain-ai/langchain-datastax/tree/main/libs/astradb |
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/prompty/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/
test:
poetry run pytest $(TEST_FILE)
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/prompty --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$')
lint_package: PYTHON_FILES=langchain_prompty
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_prompty -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/prompty/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/prompty/poetry.lock | # This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
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name = "aiohappyeyeballs"
version = "2.4.0"
description = "Happy Eyeballs for asyncio"
optional = false
python-versions = ">=3.8"
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]
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name = "aiohttp"
version = "3.10.5"
description = "Async http client/server framework (asyncio)"
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python-versions = ">=3.8"
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multidict = ">=4.0"
[metadata]
lock-version = "2.0"
python-versions = ">=3.9,<4.0"
content-hash = "320579abcfcbf377b14b4dd467dd33c0e7daf1de42fa53f81e2a226bbc00e4ba"
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/prompty/README.md | # langchain-prompty
This package contains the LangChain integration with Microsoft Prompty.
## Installation
```bash
pip install -U langchain-prompty
```
## Usage
Use the `create_chat_prompt` function to load `prompty` file as prompt.
```python
from langchain_prompty import create_chat_prompt
prompt = create_chat_prompt('<your .prompty file path>')
```
Then you can use the prompt for next steps.
Here is an example .prompty file:
```prompty
---
name: Basic Prompt
description: A basic prompt that uses the GPT-3 chat API to answer questions
authors:
- author_1
- author_2
model:
api: chat
configuration:
azure_deployment: gpt-35-turbo
sample:
firstName: Jane
lastName: Doe
question: What is the meaning of life?
chat_history: []
---
system:
You are an AI assistant who helps people find information.
As the assistant, you answer questions briefly, succinctly,
and in a personable manner using markdown and even add some personal flair with appropriate emojis.
{% for item in chat_history %}
{{item.role}}:
{{item.content}}
{% endfor %}
user:
{{input}}
```
|
0 | lc_public_repos/langchain/libs/partners | lc_public_repos/langchain/libs/partners/prompty/pyproject.toml | [build-system]
requires = [ "poetry-core>=1.0.0",]
build-backend = "poetry.core.masonry.api"
[tool.poetry]
name = "langchain-prompty"
version = "0.1.1"
description = "An integration package connecting Prompty and LangChain"
authors = []
readme = "README.md"
repository = "https://github.com/langchain-ai/langchain"
license = "MIT"
[tool.ruff]
select = [ "E", "F", "I",]
[tool.mypy]
disallow_untyped_defs = "True"
[tool.poetry.urls]
"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/prompty"
"Release Notes" = "https://github.com/langchain-ai/langchain/releases?q=tag%3A%22langchain-prompty%3D%3D0%22&expanded=true"
[tool.poetry.dependencies]
python = ">=3.9,<4.0"
langchain-core = "^0.3.15"
pyyaml = "^6.0.1"
[tool.coverage.run]
omit = [ "tests/*",]
[tool.pytest.ini_options]
addopts = "--snapshot-warn-unused --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.test_integration]
optional = true
[tool.poetry.group.lint]
optional = true
[tool.poetry.group.dev]
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"
[tool.poetry.group.codespell.dependencies]
codespell = "^2.2.0"
[tool.poetry.group.test_integration.dependencies]
[tool.poetry.group.lint.dependencies]
ruff = "^0.1.5"
[tool.poetry.group.dev.dependencies]
types-pyyaml = "^6.0.12.20240311"
[tool.poetry.group.typing.dependencies]
mypy = "^0.991"
types-pyyaml = "^6.0.12.20240311"
[tool.poetry.group.test.dependencies.langchain-core]
path = "../../core"
develop = true
[tool.poetry.group.test.dependencies.langchain]
path = "../../langchain"
develop = true
[tool.poetry.group.test.dependencies.langchain-text-splitters]
path = "../../text-splitters"
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/prompty | lc_public_repos/langchain/libs/partners/prompty/langchain_prompty/renderers.py | from langchain_core.utils import mustache
from pydantic import BaseModel
from .core import Invoker, Prompty, SimpleModel
class MustacheRenderer(Invoker):
"""Render a mustache template."""
def __init__(self, prompty: Prompty) -> None:
self.prompty = prompty
def invoke(self, data: BaseModel) -> BaseModel:
assert isinstance(data, SimpleModel)
generated = mustache.render(self.prompty.content, data.item)
return SimpleModel[str](item=generated)
|
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/langchain_prompty/core.py | from __future__ import annotations
import abc
import json
import os
import re
from pathlib import Path
from typing import Any, Dict, Generic, List, Literal, Optional, Type, TypeVar, Union
import yaml
from pydantic import BaseModel, ConfigDict, Field, FilePath
T = TypeVar("T")
class SimpleModel(BaseModel, Generic[T]):
"""Simple model for a single item."""
item: T
class PropertySettings(BaseModel):
"""Property settings for a prompty model."""
model_config = ConfigDict(arbitrary_types_allowed=True)
type: Literal["string", "number", "array", "object", "boolean"]
default: Union[str, int, float, List, Dict, bool] = Field(default=None)
description: str = Field(default="")
class ModelSettings(BaseModel):
"""Model settings for a prompty model."""
api: str = Field(default="")
configuration: dict = Field(default={})
parameters: dict = Field(default={})
response: dict = Field(default={})
def model_dump_safe(self) -> dict:
d = self.model_dump()
d["configuration"] = {
k: "*" * len(v) if "key" in k.lower() or "secret" in k.lower() else v
for k, v in d["configuration"].items()
}
return d
class TemplateSettings(BaseModel):
"""Template settings for a prompty model."""
type: str = Field(default="mustache")
parser: str = Field(default="")
class Prompty(BaseModel):
"""Base Prompty model."""
# metadata
name: str = Field(default="")
description: str = Field(default="")
authors: List[str] = Field(default=[])
tags: List[str] = Field(default=[])
version: str = Field(default="")
base: str = Field(default="")
basePrompty: Optional[Prompty] = Field(default=None)
# model
model: ModelSettings = Field(default_factory=ModelSettings)
# sample
sample: dict = Field(default={})
# input / output
inputs: Dict[str, PropertySettings] = Field(default={})
outputs: Dict[str, PropertySettings] = Field(default={})
# template
template: TemplateSettings
file: FilePath = Field(default="")
content: str = Field(default="")
def to_safe_dict(self) -> Dict[str, Any]:
d = {}
for k, v in self:
if v != "" and v != {} and v != [] and v is not None:
if k == "model":
d[k] = v.model_dump_safe()
elif k == "template":
d[k] = v.model_dump()
elif k == "inputs" or k == "outputs":
d[k] = {k: v.model_dump() for k, v in v.items()}
elif k == "file":
d[k] = (
str(self.file.as_posix())
if isinstance(self.file, Path)
else self.file
)
elif k == "basePrompty":
# no need to serialize basePrompty
continue
else:
d[k] = v
return d
# generate json representation of the prompty
def to_safe_json(self) -> str:
d = self.to_safe_dict()
return json.dumps(d)
@staticmethod
def normalize(attribute: Any, parent: Path, env_error: bool = True) -> Any:
if isinstance(attribute, str):
attribute = attribute.strip()
if attribute.startswith("${") and attribute.endswith("}"):
variable = attribute[2:-1].split(":")
if variable[0] in os.environ.keys():
return os.environ[variable[0]]
else:
if len(variable) > 1:
return variable[1]
else:
if env_error:
raise ValueError(
f"Variable {variable[0]} not found in environment"
)
else:
return ""
elif (
attribute.startswith("file:")
and Path(parent / attribute.split(":")[1]).exists()
):
with open(parent / attribute.split(":")[1], "r") as f:
items = json.load(f)
if isinstance(items, list):
return [Prompty.normalize(value, parent) for value in items]
elif isinstance(items, dict):
return {
key: Prompty.normalize(value, parent)
for key, value in items.items()
}
else:
return items
else:
return attribute
elif isinstance(attribute, list):
return [Prompty.normalize(value, parent) for value in attribute]
elif isinstance(attribute, dict):
return {
key: Prompty.normalize(value, parent)
for key, value in attribute.items()
}
else:
return attribute
def param_hoisting(
top: Dict[str, Any], bottom: Dict[str, Any], top_key: Any = None
) -> Dict[str, Any]:
"""Merge two dictionaries with hoisting of parameters from bottom to top.
Args:
top: The top dictionary.
bottom: The bottom dictionary.
top_key: The key to hoist from the bottom to the top.
Returns:
The merged dictionary.
"""
if top_key:
new_dict = {**top[top_key]} if top_key in top else {}
else:
new_dict = {**top}
for key, value in bottom.items():
if key not in new_dict:
new_dict[key] = value
return new_dict
class Invoker(abc.ABC):
"""Base class for all invokers."""
def __init__(self, prompty: Prompty) -> None:
self.prompty = prompty
@abc.abstractmethod
def invoke(self, data: BaseModel) -> BaseModel:
pass
def __call__(self, data: BaseModel) -> BaseModel:
return self.invoke(data)
class NoOpParser(Invoker):
"""NoOp parser for invokers."""
def invoke(self, data: BaseModel) -> BaseModel:
return data
class InvokerFactory(object):
"""Factory for creating invokers."""
_instance = None
_renderers: Dict[str, Type[Invoker]] = {}
_parsers: Dict[str, Type[Invoker]] = {}
_executors: Dict[str, Type[Invoker]] = {}
_processors: Dict[str, Type[Invoker]] = {}
def __new__(cls) -> InvokerFactory:
if cls._instance is None:
cls._instance = super(InvokerFactory, cls).__new__(cls)
# Add NOOP invokers
cls._renderers["NOOP"] = NoOpParser
cls._parsers["NOOP"] = NoOpParser
cls._executors["NOOP"] = NoOpParser
cls._processors["NOOP"] = NoOpParser
return cls._instance
def register(
self,
type: Literal["renderer", "parser", "executor", "processor"],
name: str,
invoker: Type[Invoker],
) -> None:
if type == "renderer":
self._renderers[name] = invoker
elif type == "parser":
self._parsers[name] = invoker
elif type == "executor":
self._executors[name] = invoker
elif type == "processor":
self._processors[name] = invoker
else:
raise ValueError(f"Invalid type {type}")
def register_renderer(self, name: str, renderer_class: Any) -> None:
self.register("renderer", name, renderer_class)
def register_parser(self, name: str, parser_class: Any) -> None:
self.register("parser", name, parser_class)
def register_executor(self, name: str, executor_class: Any) -> None:
self.register("executor", name, executor_class)
def register_processor(self, name: str, processor_class: Any) -> None:
self.register("processor", name, processor_class)
def __call__(
self,
type: Literal["renderer", "parser", "executor", "processor"],
name: str,
prompty: Prompty,
data: BaseModel,
) -> Any:
if type == "renderer":
return self._renderers[name](prompty)(data)
elif type == "parser":
return self._parsers[name](prompty)(data)
elif type == "executor":
return self._executors[name](prompty)(data)
elif type == "processor":
return self._processors[name](prompty)(data)
else:
raise ValueError(f"Invalid type {type}")
def to_dict(self) -> Dict[str, Any]:
return {
"renderers": {
k: f"{v.__module__}.{v.__name__}" for k, v in self._renderers.items()
},
"parsers": {
k: f"{v.__module__}.{v.__name__}" for k, v in self._parsers.items()
},
"executors": {
k: f"{v.__module__}.{v.__name__}" for k, v in self._executors.items()
},
"processors": {
k: f"{v.__module__}.{v.__name__}" for k, v in self._processors.items()
},
}
def to_json(self) -> str:
return json.dumps(self.to_dict())
class Frontmatter:
"""Class for reading frontmatter from a string or file."""
_yaml_delim = r"(?:---|\+\+\+)"
_yaml = r"(.*?)"
_content = r"\s*(.+)$"
_re_pattern = r"^\s*" + _yaml_delim + _yaml + _yaml_delim + _content
_regex = re.compile(_re_pattern, re.S | re.M)
@classmethod
def read_file(cls, path: str) -> dict[str, Any]:
"""Reads file at path and returns dict with separated frontmatter.
See read() for more info on dict return value.
"""
with open(path, encoding="utf-8") as file:
file_contents = file.read()
return cls.read(file_contents)
@classmethod
def read(cls, string: str) -> dict[str, Any]:
"""Returns dict with separated frontmatter from string.
Returned dict keys:
attributes -- extracted YAML attributes in dict form.
body -- string contents below the YAML separators
frontmatter -- string representation of YAML
"""
fmatter = ""
body = ""
result = cls._regex.search(string)
if result:
fmatter = result.group(1)
body = result.group(2)
return {
"attributes": yaml.load(fmatter, Loader=yaml.FullLoader),
"body": body,
"frontmatter": fmatter,
}
|
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/langchain_prompty/langchain.py | from typing import Any, Dict
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable, RunnableLambda
from .parsers import RoleMap
from .utils import load, prepare
def create_chat_prompt(
path: str,
input_name_agent_scratchpad: str = "agent_scratchpad",
) -> Runnable[Dict[str, Any], ChatPromptTemplate]:
"""Create a chat prompt from a Langchain schema."""
def runnable_chat_lambda(inputs: Dict[str, Any]) -> ChatPromptTemplate:
p = load(path)
parsed = prepare(p, inputs)
# Parsed messages have been templated
# Convert to Message objects to avoid templating attempts in ChatPromptTemplate
lc_messages = []
for message in parsed:
message_class = RoleMap.get_message_class(message["role"])
lc_messages.append(message_class(content=message["content"]))
lc_messages.append(
MessagesPlaceholder(
variable_name=input_name_agent_scratchpad, optional=True
) # type: ignore[arg-type]
)
lc_p = ChatPromptTemplate.from_messages(lc_messages)
lc_p = lc_p.partial(**p.inputs)
return lc_p
return RunnableLambda(runnable_chat_lambda)
|
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/langchain_prompty/utils.py | import traceback
from pathlib import Path
from typing import Any, Dict, List, Union
from .core import (
Frontmatter,
InvokerFactory,
ModelSettings,
Prompty,
PropertySettings,
SimpleModel,
TemplateSettings,
param_hoisting,
)
def load(prompt_path: str, configuration: str = "default") -> Prompty:
"""Load a prompty file and return a Prompty object.
Args:
prompt_path: The path to the prompty file.
configuration: The configuration to use. Defaults to "default".
Returns:
The Prompty object.
"""
file_path = Path(prompt_path)
if not file_path.is_absolute():
# get caller's path (take into account trace frame)
caller = Path(traceback.extract_stack()[-3].filename)
file_path = Path(caller.parent / file_path).resolve().absolute()
# load dictionary from prompty file
matter = Frontmatter.read_file(file_path.__fspath__())
attributes = matter["attributes"]
content = matter["body"]
# normalize attribute dictionary resolve keys and files
attributes = Prompty.normalize(attributes, file_path.parent)
# load global configuration
if "model" not in attributes:
attributes["model"] = {}
# pull model settings out of attributes
try:
model = ModelSettings(**attributes.pop("model"))
except Exception as e:
raise ValueError(f"Error in model settings: {e}")
# pull template settings
try:
if "template" in attributes:
t = attributes.pop("template")
if isinstance(t, dict):
template = TemplateSettings(**t)
# has to be a string denoting the type
else:
template = TemplateSettings(type=t, parser="prompty")
else:
template = TemplateSettings(type="mustache", parser="prompty")
except Exception as e:
raise ValueError(f"Error in template loader: {e}")
# formalize inputs and outputs
if "inputs" in attributes:
try:
inputs = {
k: PropertySettings(**v) for (k, v) in attributes.pop("inputs").items()
}
except Exception as e:
raise ValueError(f"Error in inputs: {e}")
else:
inputs = {}
if "outputs" in attributes:
try:
outputs = {
k: PropertySettings(**v) for (k, v) in attributes.pop("outputs").items()
}
except Exception as e:
raise ValueError(f"Error in outputs: {e}")
else:
outputs = {}
# recursive loading of base prompty
if "base" in attributes:
# load the base prompty from the same directory as the current prompty
base = load(file_path.parent / attributes["base"])
# hoist the base prompty's attributes to the current prompty
model.api = base.model.api if model.api == "" else model.api
model.configuration = param_hoisting(
model.configuration, base.model.configuration
)
model.parameters = param_hoisting(model.parameters, base.model.parameters)
model.response = param_hoisting(model.response, base.model.response)
attributes["sample"] = param_hoisting(attributes, base.sample, "sample")
p = Prompty(
**attributes,
model=model,
inputs=inputs,
outputs=outputs,
template=template,
content=content,
file=file_path,
basePrompty=base,
)
else:
p = Prompty(
**attributes,
model=model,
inputs=inputs,
outputs=outputs,
template=template,
content=content,
file=file_path,
)
return p
def prepare(
prompt: Prompty,
inputs: Dict[str, Any] = {},
) -> Any:
"""Prepare the inputs for the prompty.
Args:
prompt: The Prompty object.
inputs: The inputs to the prompty. Defaults to {}.
Returns:
The prepared inputs.
"""
invoker = InvokerFactory()
inputs = param_hoisting(inputs, prompt.sample)
if prompt.template.type == "NOOP":
render = prompt.content
else:
# render
result = invoker(
"renderer",
prompt.template.type,
prompt,
SimpleModel(item=inputs),
)
render = result.item
if prompt.template.parser == "NOOP":
result = render
else:
# parse
result = invoker(
"parser",
f"{prompt.template.parser}.{prompt.model.api}",
prompt,
SimpleModel(item=result.item),
)
if isinstance(result, SimpleModel):
return result.item
else:
return result
def run(
prompt: Prompty,
content: Union[Dict, List, str],
configuration: Dict[str, Any] = {},
parameters: Dict[str, Any] = {},
raw: bool = False,
) -> Any:
"""Run the prompty.
Args:
prompt: The Prompty object.
content: The content to run the prompty on.
configuration: The configuration to use. Defaults to {}.
parameters: The parameters to use. Defaults to {}.
raw: Whether to return the raw output. Defaults to False.
Returns:
The result of running the prompty.
"""
invoker = InvokerFactory()
if configuration != {}:
prompt.model.configuration = param_hoisting(
configuration, prompt.model.configuration
)
if parameters != {}:
prompt.model.parameters = param_hoisting(parameters, prompt.model.parameters)
# execute
result = invoker(
"executor",
prompt.model.configuration["type"],
prompt,
SimpleModel(item=content),
)
# skip?
if not raw:
# process
result = invoker(
"processor",
prompt.model.configuration["type"],
prompt,
result,
)
if isinstance(result, SimpleModel):
return result.item
else:
return result
def execute(
prompt: Union[str, Prompty],
configuration: Dict[str, Any] = {},
parameters: Dict[str, Any] = {},
inputs: Dict[str, Any] = {},
raw: bool = False,
connection: str = "default",
) -> Any:
"""Execute a prompty.
Args:
prompt: The prompt to execute.
Can be a path to a prompty file or a Prompty object.
configuration: The configuration to use. Defaults to {}.
parameters: The parameters to use. Defaults to {}.
inputs: The inputs to the prompty. Defaults to {}.
raw: Whether to return the raw output. Defaults to False.
connection: The connection to use. Defaults to "default".
Returns:
The result of executing the prompty.
"""
if isinstance(prompt, str):
prompt = load(prompt, connection)
# prepare content
content = prepare(prompt, inputs)
# run LLM model
result = run(prompt, content, configuration, parameters, raw)
return result
|
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/langchain_prompty/__init__.py | from langchain_prompty.core import InvokerFactory
from langchain_prompty.langchain import create_chat_prompt
from langchain_prompty.parsers import PromptyChatParser
from langchain_prompty.renderers import MustacheRenderer
InvokerFactory().register_renderer("mustache", MustacheRenderer)
InvokerFactory().register_parser("prompty.chat", PromptyChatParser)
__all__ = ["create_chat_prompt"]
|
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/langchain_prompty/parsers.py | import base64
import re
from typing import Dict, List, Type, Union
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from pydantic import BaseModel
from .core import Invoker, Prompty, SimpleModel
class RoleMap:
_ROLE_MAP: Dict[str, Type[BaseMessage]] = {
"system": SystemMessage,
"user": HumanMessage,
"human": HumanMessage,
"assistant": AIMessage,
"ai": AIMessage,
"function": FunctionMessage,
}
ROLES = _ROLE_MAP.keys()
@classmethod
def get_message_class(cls, role: str) -> Type[BaseMessage]:
return cls._ROLE_MAP[role]
class PromptyChatParser(Invoker):
"""Parse a chat prompt into a list of messages."""
def __init__(self, prompty: Prompty) -> None:
self.prompty = prompty
self.roles = RoleMap.ROLES
self.path = self.prompty.file.parent
def inline_image(self, image_item: str) -> str:
# pass through if it's a url or base64 encoded
if image_item.startswith("http") or image_item.startswith("data"):
return image_item
# otherwise, it's a local file - need to base64 encode it
else:
image_path = self.path / image_item
with open(image_path, "rb") as f:
base64_image = base64.b64encode(f.read()).decode("utf-8")
if image_path.suffix == ".png":
return f"data:image/png;base64,{base64_image}"
elif image_path.suffix == ".jpg":
return f"data:image/jpeg;base64,{base64_image}"
elif image_path.suffix == ".jpeg":
return f"data:image/jpeg;base64,{base64_image}"
else:
raise ValueError(
f"Invalid image format {image_path.suffix} - currently only .png "
"and .jpg / .jpeg are supported."
)
def parse_content(self, content: str) -> Union[str, List]:
"""for parsing inline images"""
# regular expression to parse markdown images
image = r"(?P<alt>!\[[^\]]*\])\((?P<filename>.*?)(?=\"|\))\)"
matches = re.findall(image, content, flags=re.MULTILINE)
if len(matches) > 0:
content_items = []
content_chunks = re.split(image, content, flags=re.MULTILINE)
current_chunk = 0
for i in range(len(content_chunks)):
# image entry
if (
current_chunk < len(matches)
and content_chunks[i] == matches[current_chunk][0]
):
content_items.append(
{
"type": "image_url",
"image_url": {
"url": self.inline_image(
matches[current_chunk][1].split(" ")[0].strip()
)
},
}
)
# second part of image entry
elif (
current_chunk < len(matches)
and content_chunks[i] == matches[current_chunk][1]
):
current_chunk += 1
# text entry
else:
if len(content_chunks[i].strip()) > 0:
content_items.append(
{"type": "text", "text": content_chunks[i].strip()}
)
return content_items
else:
return content
def invoke(self, data: BaseModel) -> BaseModel:
assert isinstance(data, SimpleModel)
messages = []
separator = r"(?i)^\s*#?\s*(" + "|".join(self.roles) + r")\s*:\s*\n"
# get valid chunks - remove empty items
chunks = [
item
for item in re.split(separator, data.item, flags=re.MULTILINE)
if len(item.strip()) > 0
]
# if no starter role, then inject system role
if chunks[0].strip().lower() not in self.roles:
chunks.insert(0, "system")
# if last chunk is role entry, then remove (no content?)
if chunks[-1].strip().lower() in self.roles:
chunks.pop()
if len(chunks) % 2 != 0:
raise ValueError("Invalid prompt format")
# create messages
for i in range(0, len(chunks), 2):
role = chunks[i].strip().lower()
content = chunks[i + 1].strip()
messages.append({"role": role, "content": self.parse_content(content)})
return SimpleModel[list](item=messages)
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/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/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/test_imports.py | from langchain_prompty import __all__
EXPECTED_ALL = ["create_chat_prompt"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/fake_callback_handler.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 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
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
input_prompts: List[str] = []
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) -> None:
self.errors += 1
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."""
def __init__(self) -> None:
super().__init__()
self.input_prompts = []
@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, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
) -> Any:
self.input_prompts = prompts
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()
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, serialized: Dict[str, Any], prompts: List[str], **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()
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/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/test_templating.py | from pathlib import Path
import pytest
from langchain_prompty import create_chat_prompt
PROMPT_DIR = Path(__file__).parent / "prompts"
def test_double_templating() -> None:
"""
Assess whether double templating occurs when invoking a chat prompt.
If it does, an error is thrown and the test fails.
"""
prompt_path = PROMPT_DIR / "double_templating.prompty"
templated_prompt = create_chat_prompt(str(prompt_path))
query = "What do you think of this JSON object: {'key': 7}?"
try:
templated_prompt.invoke(input={"user_input": query})
except KeyError as e:
pytest.fail("Double templating occurred: " + str(e))
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/test_prompty_serialization.py | import json
import os
from typing import List, Tuple
from langchain.agents.format_scratchpad import format_to_openai_function_messages
from langchain.tools import tool
from langchain_core.language_models import FakeListLLM
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.utils.function_calling import convert_to_openai_function
from pydantic import BaseModel, Field
import langchain_prompty
from .fake_callback_handler import FakeCallbackHandler
from .fake_chat_model import FakeEchoPromptChatModel
from .fake_output_parser import FakeOutputParser
prompty_folder_relative = "./prompts/"
# Get the directory of the current script
current_script_dir = os.path.dirname(__file__)
# Combine the current script directory with the relative path
prompty_folder = os.path.abspath(
os.path.join(current_script_dir, prompty_folder_relative)
)
def test_prompty_basic_chain() -> None:
prompt = langchain_prompty.create_chat_prompt(f"{prompty_folder}/chat.prompty")
model = FakeEchoPromptChatModel()
chain = prompt | model
parsed_prompts = chain.invoke(
{
"firstName": "fakeFirstName",
"lastName": "fakeLastName",
"input": "fakeQuestion",
}
)
if isinstance(parsed_prompts.content, str):
msgs = json.loads(str(parsed_prompts.content))
else:
msgs = parsed_prompts.content
print(msgs)
assert len(msgs) == 2
# Test for system and user entries
system_message = msgs[0]
user_message = msgs[1]
# Check the types of the messages
assert (
system_message["type"] == "system"
), "The first message should be of type 'system'."
assert (
user_message["type"] == "human"
), "The second message should be of type 'human'."
# Test for existence of fakeFirstName and fakeLastName in the system message
assert (
"fakeFirstName" in system_message["content"]
), "The string 'fakeFirstName' should be in the system message content."
assert (
"fakeLastName" in system_message["content"]
), "The string 'fakeLastName' should be in the system message content."
# Test for existence of fakeQuestion in the user message
assert (
"fakeQuestion" in user_message["content"]
), "The string 'fakeQuestion' should be in the user message content."
def test_prompty_used_in_agent() -> None:
prompt = langchain_prompty.create_chat_prompt(f"{prompty_folder}/chat.prompty")
tool_name = "search"
responses = [
f"FooBarBaz\nAction: {tool_name}\nAction Input: fakeSearch",
"Oh well\nFinal Answer: fakefinalresponse",
]
callbackHandler = FakeCallbackHandler()
llm = FakeListLLM(responses=responses, callbacks=[callbackHandler])
@tool
def search(query: str) -> str:
"""Look up things."""
return "FakeSearchResponse"
tools = [search]
llm_with_tools = llm.bind(functions=[convert_to_openai_function(t) for t in tools])
agent = (
{ # type: ignore[var-annotated]
"firstName": lambda x: x["firstName"],
"lastName": lambda x: x["lastName"],
"input": lambda x: x["input"],
"chat_history": lambda x: x["chat_history"],
"agent_scratchpad": lambda x: (
format_to_openai_function_messages(x["intermediate_steps"])
if "intermediate_steps" in x
else []
),
}
| prompt
| llm_with_tools
| FakeOutputParser()
)
from langchain.agents import AgentExecutor
class AgentInput(BaseModel):
input: str
chat_history: List[Tuple[str, str]] = Field(
...,
extra={"widget": {"type": "chat", "input": "input", "output": "output"}},
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types(
input_type=AgentInput # type: ignore[arg-type]
)
agent_executor.invoke(
{
"firstName": "fakeFirstName",
"lastName": "fakeLastName",
"input": "fakeQuestion",
"chat_history": [
AIMessage(content="chat_history_1_ai"),
HumanMessage(content="chat_history_1_human"),
],
}
)
print(callbackHandler)
input_prompt = callbackHandler.input_prompts[0]
# Test for existence of fakeFirstName and fakeLastName in the system message
assert "fakeFirstName" in input_prompt
assert "fakeLastName" in input_prompt
assert "chat_history_1_ai" in input_prompt
assert "chat_history_1_human" in input_prompt
assert "fakeQuestion" in input_prompt
assert "fakeSearch" in input_prompt
def test_all_prompty_can_run() -> None:
exclusions = ["embedding.prompty", "groundedness.prompty"]
prompty_files = [
f
for f in os.listdir(prompty_folder)
if os.path.isfile(os.path.join(prompty_folder, f))
and f.endswith(".prompty")
and f not in exclusions
]
for file in prompty_files:
file_path = os.path.join(prompty_folder, file)
print(f"==========\nTesting Prompty file: {file_path}")
prompt = langchain_prompty.create_chat_prompt(file_path)
model = FakeEchoPromptChatModel()
chain = prompt | model
output = chain.invoke({})
print(f"{file_path}, {output}")
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/fake_chat_model.py | """Fake Chat Model wrapper for testing purposes."""
import json
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import SimpleChatModel
from langchain_core.messages import AIMessage, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatResult
class FakeEchoPromptChatModel(SimpleChatModel):
"""Fake Chat Model wrapper for testing purposes."""
def _call(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
return json.dumps([message.model_dump() for message in messages])
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
output_str = "fake response 2"
message = AIMessage(content=output_str)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
@property
def _llm_type(self) -> str:
return "fake-echo-prompt-chat-model"
@property
def _identifying_params(self) -> Dict[str, Any]:
return {"key": "fake"}
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/fake_output_parser.py | from typing import Optional, Tuple, Union
from langchain.agents import AgentOutputParser
from langchain_core.agents import AgentAction, AgentFinish
def extract_action_details(text: str) -> Tuple[Optional[str], Optional[str]]:
# Split the text into lines and strip whitespace
lines = [line.strip() for line in text.strip().split("\n")]
# Initialize variables to hold the extracted values
action = None
action_input = None
# Iterate through the lines to find and extract the desired information
for line in lines:
if line.startswith("Action:"):
action = line.split(":", 1)[1].strip()
elif line.startswith("Action Input:"):
action_input = line.split(":", 1)[1].strip()
return action, action_input
class FakeOutputParser(AgentOutputParser):
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
print("FakeOutputParser", text)
action, input = extract_action_details(text)
if action:
log = f"\nInvoking: `{action}` with `{input}"
return AgentAction(tool=action, tool_input=(input or ""), log=log)
elif "Final Answer" in text:
return AgentFinish({"output": text}, text)
return AgentAction(
"Intermediate Answer", "after_colon", "Final Answer: This should end"
)
@property
def _type(self) -> str:
return "self_ask"
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/prompts/chat.prompty | ---
name: Basic Prompt
description: A basic prompt that uses the GPT-3 chat API to answer questions
authors:
- author_1
- author_2
model:
api: chat
configuration:
azure_deployment: gpt-35-turbo
sample:
firstName: Jane
lastName: Doe
input: What is the meaning of life?
chat_history: []
---
system:
You are an AI assistant who helps people find information.
As the assistant, you answer questions briefly, succinctly,
and in a personable manner using markdown and even add some personal flair with appropriate emojis.
# Customer
You are helping {{firstName}} {{lastName}} to find answers to their questions.
Use their name to address them in your responses.
{{#chat_history}}
{{type}}:
{{content}}
{{/chat_history}}
user:
{{input}} |
0 | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/prompts/basic_chat.prompty | ---
name: Basic Prompt
description: A basic prompt that uses the GPT-3 chat API to answer questions
authors:
- author_1
- author_2
model:
api: chat
configuration:
azure_deployment: gpt-35-turbo
sample:
firstName: Jane
lastName: Doe
question: What is the meaning of life?
chat_history: []
---
system:
You are an AI assistant who helps people find information.
As the assistant, you answer questions briefly, succinctly,
and in a personable manner using markdown and even add some personal flair with appropriate emojis.
{{#chat_history}}
{{role}}:
{{content}}
{{/chat_history}}
user:
{{input}}
|
0 | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests | lc_public_repos/langchain/libs/partners/prompty/tests/unit_tests/prompts/double_templating.prompty | ---
name: IssuePrompt
description: A prompt used to detect if double templating occurs
model:
api: chat
template: mustache
---
user:
{{user_input}} |
0 | lc_public_repos/langchain/libs/partners/prompty | lc_public_repos/langchain/libs/partners/prompty/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
|
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