id stringlengths 14 16 | text stringlengths 36 2.73k | source stringlengths 49 117 |
|---|---|---|
b61218194d0e-1 | """Total probability mass of tokens to consider at each step."""
top_k: int = 50
"""The number of highest probability tokens to keep for top-k filtering."""
repetition_penalty: float = 1.0
"""Penalizes repeated tokens. 1.0 means no penalty."""
length_penalty: float = 1.0
"""Exponential penalty t... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
b61218194d0e-2 | @property
def _default_params(self) -> Mapping[str, Any]:
"""Get the default parameters for calling NLPCloud API."""
return {
"temperature": self.temperature,
"min_length": self.min_length,
"max_length": self.max_length,
"length_no_input": self.length_... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
b61218194d0e-3 | The string generated by the model.
Example:
.. code-block:: python
response = nlpcloud("Tell me a joke.")
"""
if stop and len(stop) > 1:
raise ValueError(
"NLPCloud only supports a single stop sequence per generation."
"Pass... | https://python.langchain.com/en/latest/_modules/langchain/llms/nlpcloud.html |
0c3a20467387-0 | Source code for langchain.llms.ctransformers
"""Wrapper around the C Transformers library."""
from typing import Any, Dict, Optional, Sequence
from pydantic import root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
[docs]class CTransformers(LLM):
"""W... | https://python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
0c3a20467387-1 | "config": self.config,
}
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "ctransformers"
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that ``ctransformers`` package is installed."""
try:
from... | https://python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
0c3a20467387-2 | text.append(chunk)
_run_manager.on_llm_new_token(chunk, verbose=self.verbose)
return "".join(text)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/llms/ctransformers.html |
6cbaabe87e86-0 | Source code for langchain.llms.huggingface_endpoint
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
6cbaabe87e86-1 | huggingfacehub_api_token: Optional[str] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
hugging... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
6cbaabe87e86-2 | return "huggingface_endpoint"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
) -> str:
"""Call out to HuggingFace Hub's inference endpoint.
Args:
prompt: The prompt to pass into t... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
6cbaabe87e86-3 | elif self.task == "summarization":
text = generated_text[0]["summary_text"]
else:
raise ValueError(
f"Got invalid task {self.task}, "
f"currently only {VALID_TASKS} are supported"
)
if stop is not None:
# This is a bit hacky... | https://python.langchain.com/en/latest/_modules/langchain/llms/huggingface_endpoint.html |
ec486957182e-0 | Source code for langchain.llms.anthropic
"""Wrapper around Anthropic APIs."""
import re
import warnings
from typing import Any, Callable, Dict, Generator, List, Mapping, Optional, Tuple, Union
from pydantic import BaseModel, Extra, root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMR... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
ec486957182e-1 | anthropic_api_key = get_from_dict_or_env(
values, "anthropic_api_key", "ANTHROPIC_API_KEY"
)
try:
import anthropic
values["client"] = anthropic.Client(
api_key=anthropic_api_key,
default_request_timeout=values["default_request_timeout"]... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
ec486957182e-2 | if stop is None:
stop = []
# Never want model to invent new turns of Human / Assistant dialog.
stop.extend([self.HUMAN_PROMPT])
return stop
[docs]class Anthropic(LLM, _AnthropicCommon):
r"""Wrapper around Anthropic's large language models.
To use, you should have the ``anthro... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
ec486957182e-3 | extra = Extra.forbid
@property
def _llm_type(self) -> str:
"""Return type of llm."""
return "anthropic-llm"
def _wrap_prompt(self, prompt: str) -> str:
if not self.HUMAN_PROMPT or not self.AI_PROMPT:
raise NameError("Please ensure the anthropic package is loaded")
... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
ec486957182e-4 | if self.streaming:
stream_resp = self.client.completion_stream(
prompt=self._wrap_prompt(prompt),
stop_sequences=stop,
**self._default_params,
)
current_completion = ""
for data in stream_resp:
delta = data["... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
ec486957182e-5 | **self._default_params,
)
return response["completion"]
[docs] def stream(self, prompt: str, stop: Optional[List[str]] = None) -> Generator:
r"""Call Anthropic completion_stream and return the resulting generator.
BETA: this is a beta feature while we figure out the right abstraction.... | https://python.langchain.com/en/latest/_modules/langchain/llms/anthropic.html |
3f9b725500cf-0 | Source code for langchain.llms.rwkv
"""Wrapper for the RWKV model.
Based on https://github.com/saharNooby/rwkv.cpp/blob/master/rwkv/chat_with_bot.py
https://github.com/BlinkDL/ChatRWKV/blob/main/v2/chat.py
"""
from typing import Any, Dict, List, Mapping, Optional, Set
from pydantic import BaseModel, Extra, roo... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
3f9b725500cf-1 | """Positive values penalize new tokens based on their existing frequency
in the text so far, decreasing the model's likelihood to repeat the same
line verbatim.."""
penalty_alpha_presence: float = 0.4
"""Positive values penalize new tokens based on whether they appear
in the text so far, increasing ... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
3f9b725500cf-2 | """Validate that the python package exists in the environment."""
try:
import tokenizers
except ImportError:
raise ImportError(
"Could not import tokenizers python package. "
"Please install it with `pip install tokenizers`."
)
... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
3f9b725500cf-3 | AVOID_REPEAT_TOKENS = []
AVOID_REPEAT = "，：？！"
for i in AVOID_REPEAT:
dd = self.pipeline.encode(i)
assert len(dd) == 1
AVOID_REPEAT_TOKENS += dd
tokens = [int(x) for x in _tokens]
self.model_tokens += tokens
out: Any = None
while len(to... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
3f9b725500cf-4 | occurrence[token] += 1
logits = self.run_rnn([token])
xxx = self.tokenizer.decode(self.model_tokens[out_last:])
if "\ufffd" not in xxx: # avoid utf-8 display issues
decoded += xxx
out_last = begin + i + 1
if i >= self.max_tokens_per_ge... | https://python.langchain.com/en/latest/_modules/langchain/llms/rwkv.html |
9a5feace4e4c-0 | Source code for langchain.chat_models.azure_openai
"""Azure OpenAI chat wrapper."""
from __future__ import annotations
import logging
from typing import Any, Dict, Mapping
from pydantic import root_validator
from langchain.chat_models.openai import ChatOpenAI
from langchain.schema import ChatResult
from langchain.utils... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9a5feace4e4c-1 | openai_api_base: str = ""
openai_api_version: str = ""
openai_api_key: str = ""
openai_organization: str = ""
openai_proxy: str = ""
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
values... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9a5feace4e4c-2 | except AttributeError:
raise ValueError(
"`openai` has no `ChatCompletion` attribute, this is likely "
"due to an old version of the openai package. Try upgrading it "
"with `pip install --upgrade openai`."
)
if values["n"] < 1:
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
9a5feace4e4c-3 | )
return super()._create_chat_result(response)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chat_models/azure_openai.html |
ee32f1594022-0 | Source code for langchain.chat_models.promptlayer_openai
"""PromptLayer wrapper."""
import datetime
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models import ChatOpenAI
from langchain.sch... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
ee32f1594022-1 | ) -> ChatResult:
"""Call ChatOpenAI generate and then call PromptLayer API to log the request."""
from promptlayer.utils import get_api_key, promptlayer_api_request
request_start_time = datetime.datetime.now().timestamp()
generated_responses = super()._generate(messages, stop, run_manage... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
ee32f1594022-2 | generated_responses = await super()._agenerate(messages, stop, run_manager)
request_end_time = datetime.datetime.now().timestamp()
message_dicts, params = super()._create_message_dicts(messages, stop)
for i, generation in enumerate(generated_responses.generations):
response_dict, par... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/promptlayer_openai.html |
0c1e4b78ffca-0 | Source code for langchain.chat_models.vertexai
"""Wrapper around Google VertexAI chat-based models."""
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from pydantic import root_validator
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForL... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
0c1e4b78ffca-1 | """
if not history:
return _ChatHistory()
first_message = history[0]
system_message = first_message if isinstance(first_message, SystemMessage) else None
chat_history = _ChatHistory(system_message=system_message)
messages_left = history[1:] if system_message else history
if len(messages_... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
0c1e4b78ffca-2 | ) -> ChatResult:
"""Generate next turn in the conversation.
Args:
messages: The history of the conversation as a list of messages.
stop: The list of stop words (optional).
run_manager: The Callbackmanager for LLM run, it's not used at the moment.
Returns:
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
0c1e4b78ffca-3 | By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/chat_models/vertexai.html |
ef05ca6fd5bd-0 | Source code for langchain.chat_models.openai
"""OpenAI chat wrapper."""
from __future__ import annotations
import logging
import sys
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Mapping,
Optional,
Tuple,
Union,
)
from pydantic import Extra, Field, root_validator
fro... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-1 | return retry(
reraise=True,
stop=stop_after_attempt(llm.max_retries),
wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds),
retry=(
retry_if_exception_type(openai.error.Timeout)
| retry_if_exception_type(openai.error.APIError)
| retry_... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-2 | elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
elif isinstance(message, SystemMessage):
message_dict = {"role": "system", "content": ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-3 | leave blank if not using a proxy or service emulator."""
openai_api_base: Optional[str] = None
openai_organization: Optional[str] = None
# to support explicit proxy for OpenAI
openai_proxy: Optional[str] = None
request_timeout: Optional[Union[float, Tuple[float, float]]] = None
"""Timeout for re... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-4 | if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator(... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-5 | )
if values["n"] < 1:
raise ValueError("n must be at least 1.")
if values["n"] > 1 and values["streaming"]:
raise ValueError("n must be 1 when streaming.")
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters f... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-6 | """Use tenacity to retry the completion call."""
retry_decorator = self._create_retry_decorator()
@retry_decorator
def _completion_with_retry(**kwargs: Any) -> Any:
return self.client.create(**kwargs)
return _completion_with_retry(**kwargs)
def _combine_llm_outputs(self, ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-7 | {"content": inner_completion, "role": role}
)
return ChatResult(generations=[ChatGeneration(message=message)])
response = self.completion_with_retry(messages=message_dicts, **params)
return self._create_chat_result(response)
def _create_message_dicts(
self, messages: ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-8 | async for stream_resp in await acompletion_with_retry(
self, messages=message_dicts, **params
):
role = stream_resp["choices"][0]["delta"].get("role", role)
token = stream_resp["choices"][0]["delta"].get("content", "")
inner_completion += token... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-9 | """Return type of chat model."""
return "openai-chat"
def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]:
tiktoken_ = _import_tiktoken()
model = self.model_name
if model == "gpt-3.5-turbo":
# gpt-3.5-turbo may change over time.
# Returning num toke... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
ef05ca6fd5bd-10 | Official documentation: https://github.com/openai/openai-cookbook/blob/
main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
if sys.version_info[1] <= 7:
return super().get_num_tokens_from_messages(messages)
model, encoding = self._get_encoding_model()
if model == "g... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/openai.html |
9c94f72abc3f-0 | Source code for langchain.chat_models.google_palm
"""Wrapper around Google's PaLM Chat API."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
9c94f72abc3f-1 | raise ChatGooglePalmError("ChatResponse must have at least one candidate.")
generations: List[ChatGeneration] = []
for candidate in response.candidates:
author = candidate.get("author")
if author is None:
raise ChatGooglePalmError(f"ChatResponse must have an author: {candidate}")
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
9c94f72abc3f-2 | raise ChatGooglePalmError("System message must be first input message.")
context = input_message.content
elif isinstance(input_message, HumanMessage) and input_message.example:
if messages:
raise ChatGooglePalmError(
"Message examples must come before ... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
9c94f72abc3f-3 | context=context,
examples=examples,
messages=messages,
)
def _create_retry_decorator() -> Callable[[Any], Any]:
"""Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions"""
import google.api_core.exceptions
multiplier = 2
min_seconds = 1
max_seconds = 60
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
9c94f72abc3f-4 | return await _achat_with_retry(**kwargs)
[docs]class ChatGooglePalm(BaseChatModel, BaseModel):
"""Wrapper around Google's PaLM Chat API.
To use you must have the google.generativeai Python package installed and
either:
1. The ``GOOGLE_API_KEY``` environment varaible set with your API key, or
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
9c94f72abc3f-5 | """Validate api key, python package exists, temperature, top_p, and top_k."""
google_api_key = get_from_dict_or_env(
values, "google_api_key", "GOOGLE_API_KEY"
)
try:
import google.generativeai as genai
genai.configure(api_key=google_api_key)
except Im... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
9c94f72abc3f-6 | )
return _response_to_result(response, stop)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
) -> ChatResult:
prompt = _messages_to_prompt_dict(messages)
... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/google_palm.html |
e92bf4bdc192-0 | Source code for langchain.chat_models.anthropic
from typing import Any, Dict, List, Optional
from pydantic import Extra
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain.chat_models.base import BaseChatModel
from langchain.llms.anthropic import _... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
e92bf4bdc192-1 | elif isinstance(message, AIMessage):
message_text = f"{self.AI_PROMPT} {message.content}"
elif isinstance(message, SystemMessage):
message_text = f"{self.HUMAN_PROMPT} <admin>{message.content}</admin>"
else:
raise ValueError(f"Got unknown type {message}")
retu... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
e92bf4bdc192-2 | ) -> ChatResult:
prompt = self._convert_messages_to_prompt(messages)
params: Dict[str, Any] = {"prompt": prompt, **self._default_params}
if stop:
params["stop_sequences"] = stop
if self.streaming:
completion = ""
stream_resp = self.client.completion_st... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
e92bf4bdc192-3 | completion = response["completion"]
message = AIMessage(content=completion)
return ChatResult(generations=[ChatGeneration(message=message)])
[docs] def get_num_tokens(self, text: str) -> int:
"""Calculate number of tokens."""
if not self.count_tokens:
raise NameError("Plea... | https://python.langchain.com/en/latest/_modules/langchain/chat_models/anthropic.html |
fc6046c4dbfd-0 | Source code for langchain.prompts.few_shot
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
StringPromptTemplate,
check_valid_template,
)
from langcha... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
fc6046c4dbfd-1 | """Check that one and only one of examples/example_selector are provided."""
examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_select... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
fc6046c4dbfd-2 | # Get the examples to use.
examples = self._get_examples(**kwargs)
examples = [
{k: e[k] for k in self.example_prompt.input_variables} for e in examples
]
# Format the examples.
example_strings = [
self.example_prompt.format(**example) for example in examp... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot.html |
96fca020a6dd-0 | Source code for langchain.prompts.chat
"""Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, List, Sequence, Tuple, Type, TypeVar, Union
from pydantic import BaseModel, Field
from langchain.memory.buffer import get_b... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
96fca020a6dd-1 | """Input variables for this prompt template."""
return [self.variable_name]
MessagePromptTemplateT = TypeVar(
"MessagePromptTemplateT", bound="BaseStringMessagePromptTemplate"
)
class BaseStringMessagePromptTemplate(BaseMessagePromptTemplate, ABC):
prompt: StringPromptTemplate
additional_kwargs: dic... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
96fca020a6dd-2 | )
class HumanMessagePromptTemplate(BaseStringMessagePromptTemplate):
def format(self, **kwargs: Any) -> BaseMessage:
text = self.prompt.format(**kwargs)
return HumanMessage(content=text, additional_kwargs=self.additional_kwargs)
class AIMessagePromptTemplate(BaseStringMessagePromptTemplate):
def... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
96fca020a6dd-3 | messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
@classmethod
def from_template(cls, template: str, **kwargs: Any) -> ChatPromptTemplate:
prompt_template = PromptTemplate.from_template(template, **kwargs)
message = HumanMessagePromptTemplate(prompt=prompt_template)
return cl... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
96fca020a6dd-4 | result = []
for message_template in self.messages:
if isinstance(message_template, BaseMessage):
result.extend([message_template])
elif isinstance(message_template, BaseMessagePromptTemplate):
rel_params = {
k: v
for... | https://python.langchain.com/en/latest/_modules/langchain/prompts/chat.html |
9d63fb169c0b-0 | Source code for langchain.prompts.base
"""BasePrompt schema definition."""
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
import yaml
from pydantic import BaseModel, Extra, Field, roo... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9d63fb169c0b-1 | "Please install it with `pip install jinja2`."
)
env = Environment()
ast = env.parse(template)
variables = meta.find_undeclared_variables(ast)
return variables
DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
"f-string": formatter.format,
"jinja2": jinja2_formatter,
}
DEFAULT_VALIDATOR... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9d63fb169c0b-2 | """A list of the names of the variables the prompt template expects."""
output_parser: Optional[BaseOutputParser] = None
"""How to parse the output of calling an LLM on this formatted prompt."""
partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field(
default_factory=dict
)
cl... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9d63fb169c0b-3 | prompt_dict["partial_variables"] = {**self.partial_variables, **kwargs}
return type(self)(**prompt_dict)
def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]:
# Get partial params:
partial_kwargs = {
k: v if isinstance(v, str) else v()
for k, v... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
9d63fb169c0b-4 | save_path = Path(file_path)
else:
save_path = file_path
directory_path = save_path.parent
directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
prompt_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w... | https://python.langchain.com/en/latest/_modules/langchain/prompts/base.html |
b8ee990c8f3f-0 | Source code for langchain.prompts.prompt
"""Prompt schema definition."""
from __future__ import annotations
from pathlib import Path
from string import Formatter
from typing import Any, Dict, List, Union
from pydantic import Extra, root_validator
from langchain.prompts.base import (
DEFAULT_FORMATTER_MAPPING,
S... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
b8ee990c8f3f-1 | """
kwargs = self._merge_partial_and_user_variables(**kwargs)
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
@root_validator()
def template_is_valid(cls, values: Dict) -> Dict:
"""Check that template and input variables are consistent."""
if value... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
b8ee990c8f3f-2 | [docs] @classmethod
def from_file(
cls, template_file: Union[str, Path], input_variables: List[str], **kwargs: Any
) -> PromptTemplate:
"""Load a prompt from a file.
Args:
template_file: The path to the file containing the prompt template.
input_variables: A li... | https://python.langchain.com/en/latest/_modules/langchain/prompts/prompt.html |
8eccc9cc9134-0 | Source code for langchain.prompts.few_shot_with_templates
"""Prompt template that contains few shot examples."""
from typing import Any, Dict, List, Optional
from pydantic import Extra, root_validator
from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, StringPromptTemplate
from langchain.prompts.example_selec... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
8eccc9cc9134-1 | examples = values.get("examples", None)
example_selector = values.get("example_selector", None)
if examples and example_selector:
raise ValueError(
"Only one of 'examples' and 'example_selector' should be provided"
)
if examples is None and example_selecto... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
8eccc9cc9134-2 | Args:
kwargs: Any arguments to be passed to the prompt template.
Returns:
A formatted string.
Example:
.. code-block:: python
prompt.format(variable1="foo")
"""
kwargs = self._merge_partial_and_user_variables(**kwargs)
# Get the example... | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
8eccc9cc9134-3 | """Return a dictionary of the prompt."""
if self.example_selector:
raise ValueError("Saving an example selector is not currently supported")
return super().dict(**kwargs)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/few_shot_with_templates.html |
3f574573f4a2-0 | Source code for langchain.prompts.loading
"""Load prompts from disk."""
import importlib
import json
import logging
from pathlib import Path
from typing import Union
import yaml
from langchain.output_parsers.regex import RegexParser
from langchain.prompts.base import BasePromptTemplate
from langchain.prompts.few_shot i... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
3f574573f4a2-1 | with open(template_path) as f:
template = f.read()
else:
raise ValueError
# Set the template variable to the extracted variable.
config[var_name] = template
return config
def _load_examples(config: dict) -> dict:
"""Load examples if necessary."""
if isinst... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
3f574573f4a2-2 | config = _load_template("prefix", config)
# Load the example prompt.
if "example_prompt_path" in config:
if "example_prompt" in config:
raise ValueError(
"Only one of example_prompt and example_prompt_path should "
"be specified."
)
config[... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
3f574573f4a2-3 | with open(file_path) as f:
config = json.load(f)
elif file_path.suffix == ".yaml":
with open(file_path, "r") as f:
config = yaml.safe_load(f)
elif file_path.suffix == ".py":
spec = importlib.util.spec_from_loader(
"prompt", loader=None, origin=str(file_path)
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/loading.html |
aed627abb0c0-0 | Source code for langchain.prompts.example_selector.length_based
"""Select examples based on length."""
import re
from typing import Callable, Dict, List
from pydantic import BaseModel, validator
from langchain.prompts.example_selector.base import BaseExampleSelector
from langchain.prompts.prompt import PromptTemplate
d... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
aed627abb0c0-1 | get_text_length = values["get_text_length"]
string_examples = [example_prompt.format(**eg) for eg in values["examples"]]
return [get_text_length(eg) for eg in string_examples]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use base... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/length_based.html |
68742d912109-0 | Source code for langchain.prompts.example_selector.semantic_similarity
"""Example selector that selects examples based on SemanticSimilarity."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Type
from pydantic import BaseModel, Extra
from langchain.embeddings.base import Embeddings
fr... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
68742d912109-1 | return ids[0]
[docs] def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on semantic similarity."""
# Get the docs with the highest similarity.
if self.input_keys:
input_variables = {key: input_variables[key] for key in s... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
68742d912109-2 | instead of all variables.
vectorstore_cls_kwargs: optional kwargs containing url for vector store
Returns:
The ExampleSelector instantiated, backed by a vector store.
"""
if input_keys:
string_examples = [
" ".join(sorted_values({k: eg[k] for k... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
68742d912109-3 | examples = [dict(e.metadata) for e in example_docs]
# If example keys are provided, filter examples to those keys.
if self.example_keys:
examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
return examples
[docs] @classmethod
def from_examples(
cls,
... | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
68742d912109-4 | )
return cls(vectorstore=vectorstore, k=k, fetch_k=fetch_k, input_keys=input_keys)
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Jun 11, 2023. | https://python.langchain.com/en/latest/_modules/langchain/prompts/example_selector/semantic_similarity.html |
b1c978110697-0 | Source code for langchain.agents.initialize
"""Load agent."""
from typing import Any, Optional, Sequence
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_types import AgentType
from langchain.agents.loading import AGENT_TO_CLASS, load_agent
from langchain.base_language import BaseLanguageMod... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
b1c978110697-1 | "but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
raise ValueError(
f"Got unknown agent type: {agent}. "
f"Valid types are: {AGENT_TO_CLASS.keys()}."
)
agent_cls = AGENT_TO_CLASS[agent]
ag... | https://python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
c9638ff90a75-0 | Source code for langchain.agents.agent
"""Chain that takes in an input and produces an action and action input."""
from __future__ import annotations
import asyncio
import json
import logging
import time
from abc import abstractmethod
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Sequ... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-1 | return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Ste... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-2 | # `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
raise ValueError(
f"Got unsupported early_stopping_method `{early_stopping_method}`"
)
[docs]... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-3 | directory_path.mkdir(parents=True, exist_ok=True)
# Fetch dictionary to save
agent_dict = self.dict()
if save_path.suffix == ".json":
with open(file_path, "w") as f:
json.dump(agent_dict, f, indent=4)
elif save_path.suffix == ".yaml":
with open(fil... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-4 | **kwargs: Any,
) -> Union[List[AgentAction], AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has taken to date,
along with observations
callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-5 | Example:
.. code-block:: python
# If working with agent executor
agent.agent.save(file_path="path/agent.yaml")
"""
# Convert file to Path object.
if isinstance(file_path, str):
save_path = Path(file_path)
else:
save_path = file_path... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-6 | return _dict
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has take... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-7 | }
[docs]class Agent(BaseSingleActionAgent):
"""Class responsible for calling the language model and deciding the action.
This is driven by an LLMChain. The prompt in the LLMChain MUST include
a variable called "agent_scratchpad" where the agent can put its
intermediary work.
"""
llm_chain: LLMCh... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-8 | return thoughts
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[AgentAction, AgentFinish]:
"""Given input, decided what to do.
Args:
intermediate_steps: Steps the LLM has t... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-9 | """Create the full inputs for the LLMChain from intermediate steps."""
thoughts = self._construct_scratchpad(intermediate_steps)
new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
full_inputs = {**kwargs, **new_inputs}
return full_inputs
@property
def input_keys(self... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-10 | """Create a prompt for this class."""
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
"""Validate that appropriate tools are passed in."""
pass
@classmethod
@abstractmethod
def _get_default_output_parser(cls, **kwargs: Any) -> AgentOutputParser:
"""G... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-11 | # `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
elif early_stopping_method == "generate":
# Generate does one final forward pass
thoughts = ""
for acti... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
c9638ff90a75-12 | }
class ExceptionTool(BaseTool):
name = "_Exception"
description = "Exception tool"
def _run(
self,
query: str,
run_manager: Optional[CallbackManagerForToolRun] = None,
) -> str:
return query
async def _arun(
self,
query: str,
run_manager: Opti... | https://python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
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