id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
dc70e738e6bd-29 | Raises:
ValueError: If any metadata value is not one of the known
types (string, int, float, or list of strings).
"""
def raise_error(key: str, value: Any) -> None:
raise ValueError(
f"Metadata value for key '{key}' must be a string, int, "
+ f"float, or list ... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
dc70e738e6bd-30 | "distance_threshold": None,
}
"""Default search kwargs."""
allowed_search_types = [
"similarity",
"similarity_distance_threshold",
"similarity_score_threshold",
"mmr",
]
"""Allowed search types."""
class Config:
"""Configuration for this pydantic object.""... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
dc70e738e6bd-31 | return self.vectorstore.add_documents(documents, **kwargs)
[docs] async def aadd_documents(
self, documents: List[Document], **kwargs: Any
) -> List[str]:
"""Add documents to vectorstore."""
return await self.vectorstore.aadd_documents(documents, **kwargs) | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/base.html |
55ba1c957158-0 | Source code for langchain.vectorstores.redis.filters
from enum import Enum
from functools import wraps
from numbers import Number
from typing import Any, Callable, Dict, List, Optional, Union
from langchain.utilities.redis import TokenEscaper
# disable mypy error for dunder method overrides
# mypy: disable-error-code="... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-1 | return self._field == other._field and self._value == other._value
def _set_value(
self, val: Any, val_type: type, operator: RedisFilterOperator
) -> None:
# check that the operator is supported by this class
if operator not in self.OPERATORS:
raise ValueError(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-2 | return wrapper
[docs]class RedisTag(RedisFilterField):
"""A RedisFilterField representing a tag in a Redis index."""
OPERATORS: Dict[RedisFilterOperator, str] = {
RedisFilterOperator.EQ: "==",
RedisFilterOperator.NE: "!=",
RedisFilterOperator.IN: "==",
}
OPERATOR_MAP: Dict[RedisF... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-3 | >>> filter = RedisTag("brand") == "nike"
"""
self._set_tag_value(other, RedisFilterOperator.EQ)
return RedisFilterExpression(str(self))
@check_operator_misuse
def __ne__(self, other: Union[List[str], str]) -> "RedisFilterExpression":
"""Create a RedisTag inequality filter express... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-4 | RedisFilterOperator.GT: ">",
RedisFilterOperator.LE: "<=",
RedisFilterOperator.GE: ">=",
}
OPERATOR_MAP: Dict[RedisFilterOperator, str] = {
RedisFilterOperator.EQ: "@%s:[%f %f]",
RedisFilterOperator.NE: "(-@%s:[%f %f])",
RedisFilterOperator.GT: "@%s:[(%f +inf]",
R... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-5 | >>> filter = RedisNum("zipcode") == 90210
"""
self._set_value(other, Number, RedisFilterOperator.EQ)
return RedisFilterExpression(str(self))
@check_operator_misuse
def __ne__(self, other: Union[int, float]) -> "RedisFilterExpression":
"""Create a Numeric inequality filter express... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-6 | """Create a Numeric greater than or equal to filter expression
Args:
other (Number): The value to filter on.
Example:
>>> from langchain.vectorstores.redis import RedisNum
>>> filter = RedisNum("age") >= 18
"""
self._set_value(other, Number, RedisFilte... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-7 | >>> filter = RedisText("job") == "engineer"
"""
self._set_value(other, str, RedisFilterOperator.EQ)
return RedisFilterExpression(str(self))
@check_operator_misuse
def __ne__(self, other: str) -> "RedisFilterExpression":
"""Create a RedisText inequality filter expression
A... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-8 | RedisFilterExpressions can be combined using the & and | operators to create
complex logical expressions that evaluate to the Redis Query language.
This presents an interface by which users can create complex queries
without having to know the Redis Query language.
Filter expressions are not initialized... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
55ba1c957158-9 | # top level check that allows recursive calls to __str__
if not self._filter and not self._operator:
raise ValueError("Improperly initialized RedisFilterExpression")
# allow for single filter expression without operators as last
# expression in the chain might not have an operator
... | lang/api.python.langchain.com/en/latest/_modules/langchain/vectorstores/redis/filters.html |
2be7e00a2b88-0 | Source code for langchain.agents.agent_types
"""Module definitions of agent types together with corresponding agents."""
from enum import Enum
[docs]class AgentType(str, Enum):
"""An enum for agent types.
See documentation: https://python.langchain.com/docs/modules/agents/agent_types/
"""
ZERO_SHOT_REAC... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
bed605727000-0 | 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,
Li... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-1 | """Return values of the agent."""
return ["output"]
[docs] def get_allowed_tools(self) -> Optional[List[str]]:
return None
[docs] @abstractmethod
def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
)... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-2 | """Return response when agent has been stopped due to max iterations."""
if early_stopping_method == "force":
# `force` just returns a constant string
return AgentFinish(
{"output": "Agent stopped due to iteration limit or time limit."}, ""
)
else:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-3 | # Convert file to Path object.
if isinstance(file_path, str):
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
agent_dict = self.d... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-4 | callbacks: Callbacks to run.
**kwargs: User inputs.
Returns:
Actions specifying what tool to use.
"""
[docs] @abstractmethod
async def aplan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-5 | """Return dictionary representation of agent."""
_dict = super().dict()
try:
_dict["_type"] = str(self._agent_type)
except NotImplementedError:
pass
return _dict
[docs] def save(self, file_path: Union[Path, str]) -> None:
"""Save the agent.
Args... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-6 | [docs] @abstractmethod
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
"""Parse text into agent action/finish."""
[docs]class MultiActionAgentOutputParser(
BaseOutputParser[Union[List[AgentAction], AgentFinish]]
):
"""Base class for parsing agent output into agent actions/finish.""... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-7 | Returns:
Action specifying what tool to use.
"""
inputs = {**kwargs, **{"intermediate_steps": intermediate_steps}}
output = self.runnable.invoke(inputs, config={"callbacks": callbacks})
return output
[docs] async def aplan(
self,
intermediate_steps: List[Tu... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-8 | return self._input_keys
[docs] def plan(
self,
intermediate_steps: List[Tuple[AgentAction, str]],
callbacks: Callbacks = None,
**kwargs: Any,
) -> Union[
List[AgentAction],
AgentFinish,
]:
"""Given input, decided what to do.
Args:
in... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-9 | llm_chain: LLMChain
"""LLMChain to use for agent."""
output_parser: AgentOutputParser
"""Output parser to use for agent."""
stop: List[str]
"""List of strings to stop on."""
@property
def input_keys(self) -> List[str]:
"""Return the input keys.
Returns:
List of in... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-10 | """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:
Action specifying what tool to use.
"""
outp... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-11 | raise ValueError("fix_text not implemented for this agent.")
@property
def _stop(self) -> List[str]:
return [
f"\n{self.observation_prefix.rstrip()}",
f"\n\t{self.observation_prefix.rstrip()}",
]
def _construct_scratchpad(
self, intermediate_steps: List[Tuple[... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-12 | """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:
Action specifying what tool to use.
"""
full... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-13 | if isinstance(prompt, PromptTemplate):
prompt.template += "\n{agent_scratchpad}"
elif isinstance(prompt, FewShotPromptTemplate):
prompt.suffix += "\n{agent_scratchpad}"
else:
raise ValueError(f"Got unexpected prompt type {type(prompt)}")
re... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-14 | )
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser()
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
[docs] def return_stoppe... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-15 | # If we can extract, we send the correct stuff
return parsed_output
else:
# If we can extract, but the tool is not the final tool,
# we just return the full output
return AgentFinish({"output": full_output}, full_output)
else:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-16 | """Whether to return the agent's trajectory of intermediate steps
at the end in addition to the final output."""
max_iterations: Optional[int] = 15
"""The maximum number of steps to take before ending the execution
loop.
Setting to 'None' could lead to an infinite loop."""
max_execution_tim... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-17 | [docs] @classmethod
def from_agent_and_tools(
cls,
agent: Union[BaseSingleActionAgent, BaseMultiActionAgent],
tools: Sequence[BaseTool],
callbacks: Callbacks = None,
**kwargs: Any,
) -> AgentExecutor:
"""Create from agent and tools."""
return cls(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-18 | agent = values["agent"]
if isinstance(agent, Runnable):
try:
output_type = agent.OutputType
except Exception as _:
multi_action = False
else:
multi_action = output_type == Union[List[AgentAction], AgentFinish]
if mul... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-19 | def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if self.return_intermediate_steps:
return self.agent.return_values + ["intermediate_steps"]
else:
return self.agent.return_values
[docs] def lookup_tool(self, name... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-20 | final_output["intermediate_steps"] = intermediate_steps
return final_output
def _take_next_step(
self,
name_to_tool_map: Dict[str, BaseTool],
color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager:... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-21 | else:
observation = "Invalid or incomplete response"
elif isinstance(self.handle_parsing_errors, str):
observation = self.handle_parsing_errors
elif callable(self.handle_parsing_errors):
observation = self.handle_parsing_errors(e)
e... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-22 | observation = tool.run(
agent_action.tool_input,
verbose=self.verbose,
color=color,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
else:
tool_... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-23 | else:
raise_error = False
if raise_error:
raise ValueError(
"An output parsing error occurred. "
"In order to pass this error back to the agent and have it try "
"again, pass `handle_parsing_errors=True` to the Agent... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-24 | ) -> Tuple[AgentAction, str]:
if run_manager:
await run_manager.on_agent_action(
agent_action, verbose=self.verbose, color="green"
)
# Otherwise we lookup the tool
if agent_action.tool in name_to_tool_map:
tool = nam... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-25 | ) -> Dict[str, Any]:
"""Run text through and get agent response."""
# Construct a mapping of tool name to tool for easy lookup
name_to_tool_map = {tool.name: tool for tool in self.tools}
# We construct a mapping from each tool to a color, used for logging.
color_mapping = get_col... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-26 | self.early_stopping_method, intermediate_steps, **inputs
)
return self._return(output, intermediate_steps, run_manager=run_manager)
async def _acall(
self,
inputs: Dict[str, str],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Dict[str, str]:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-27 | # See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return await self._areturn(
tool_return, intermediate_steps, run_manager=run_manager
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
bed605727000-28 | self, intermediate_steps: List[Tuple[AgentAction, str]]
) -> List[Tuple[AgentAction, str]]:
if (
isinstance(self.trim_intermediate_steps, int)
and self.trim_intermediate_steps > 0
):
return intermediate_steps[-self.trim_intermediate_steps :]
elif callable(... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
1cdc3a081cec-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Tools provide access to various resources and services.
LangChain has a large ecosystem of integrations with various external resources
like local and remote file systems, APIs and databases.
These integrations allow developers to create versatile applicatio... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-1 | from langchain.tools.google_search.tool import GoogleSearchResults, GoogleSearchRun
from langchain.tools.google_scholar.tool import GoogleScholarQueryRun
from langchain.tools.metaphor_search.tool import MetaphorSearchResults
from langchain.tools.google_serper.tool import GoogleSerperResults, GoogleSerperRun
from langch... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-2 | from langchain.utilities.google_search import GoogleSearchAPIWrapper
from langchain.utilities.google_serper import GoogleSerperAPIWrapper
from langchain.utilities.google_scholar import GoogleScholarAPIWrapper
from langchain.utilities.metaphor_search import MetaphorSearchAPIWrapper
from langchain.utilities.awslambda imp... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-3 | def _get_tools_requests_put() -> BaseTool:
return RequestsPutTool(requests_wrapper=TextRequestsWrapper())
def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
retu... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-4 | )
return Tool(
name="Open-Meteo-API",
description="Useful for when you want to get weather information from the OpenMeteo API. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
_LLM_TOOLS: Dict[str, Callable[[BaseLanguageModel], BaseTool]] =... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-5 | )
return Tool(
name="TMDB-API",
description="Useful for when you want to get information from The Movie Database. The input should be a question in natural language that this API can answer.",
func=chain.run,
)
def _get_podcast_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-6 | return ArxivQueryRun(api_wrapper=ArxivAPIWrapper(**kwargs))
def _get_golden_query(**kwargs: Any) -> BaseTool:
return GoldenQueryRun(api_wrapper=GoldenQueryAPIWrapper(**kwargs))
def _get_pubmed(**kwargs: Any) -> BaseTool:
return PubmedQueryRun(api_wrapper=PubMedAPIWrapper(**kwargs))
def _get_google_serper(**kwar... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-7 | return Tool(
"Dall-E-Image-Generator",
DallEAPIWrapper(**kwargs).run,
"A wrapper around OpenAI DALL-E API. Useful for when you need to generate images from a text description. Input should be an image description.",
)
def _get_twilio(**kwargs: Any) -> BaseTool:
return Tool(
name=... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-8 | def _get_graphql_tool(**kwargs: Any) -> BaseTool:
graphql_endpoint = kwargs["graphql_endpoint"]
wrapper = GraphQLAPIWrapper(graphql_endpoint=graphql_endpoint)
return BaseGraphQLTool(graphql_wrapper=wrapper)
def _get_openweathermap(**kwargs: Any) -> BaseTool:
return OpenWeatherMapQueryRun(api_wrapper=Ope... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-9 | "memorize": (_get_memorize, []),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha_appid"]),
"google-search": (_get_google_search, ["google_api_key", "google_cse_id"]),
"google-search-results-json": (
_get... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-10 | ["searchapi_api_key", "aiosession"],
),
"serpapi": (_get_serpapi, ["serpapi_api_key", "aiosession"]),
"dalle-image-generator": (_get_dalle_image_generator, ["openai_api_key"]),
"twilio": (_get_twilio, ["account_sid", "auth_token", "from_number"]),
"searx-search": (_get_searx_search, ["searx_host", "... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-11 | ["api_login", "api_password", "aiosession"],
),
"eleven_labs_text2speech": (_get_eleven_labs_text2speech, ["eleven_api_key"]),
"google_cloud_texttospeech": (_get_google_cloud_texttospeech, []),
}
def _handle_callbacks(
callback_manager: Optional[BaseCallbackManager], callbacks: Callbacks
) -> Callbacks:... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-12 | " `pip install --upgrade transformers huggingface_hub`."
)
hf_tool = load_tool(
task_or_repo_id,
model_repo_id=model_repo_id,
token=token,
remote=remote,
**kwargs,
)
outputs = hf_tool.outputs
if set(outputs) != {"text"}:
raise NotImplementedError("... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-13 | callbacks: Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns:
List of tools.
"""
tools = []
callbacks = _handle_callbacks(
callback_manager=kwargs.get("callback_manager"), callbacks=callbacks
)
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
1cdc3a081cec-14 | )
sub_kwargs = {k: kwargs[k] for k in extra_keys}
tool = _get_llm_tool_func(llm=llm, **sub_kwargs)
tools.append(tool)
elif name in _EXTRA_OPTIONAL_TOOLS:
_get_tool_func, extra_keys = _EXTRA_OPTIONAL_TOOLS[name]
sub_kwargs = {k: kwargs[k] for k in extra... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
d9e0d3e4936a-0 | Source code for langchain.agents.schema
from typing import Any, Dict, List, Tuple
from langchain.prompts.chat import ChatPromptTemplate
from langchain.schema import AgentAction
[docs]class AgentScratchPadChatPromptTemplate(ChatPromptTemplate):
"""Chat prompt template for the agent scratchpad."""
def _construct_... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/schema.html |
15864a0009ca-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.callbacks.base import BaseCallbackMa... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
15864a0009ca-1 | agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION
if agent is not None and agent_path is not None:
raise ValueError(
"Both `agent` and `agent_path` are specified, "
"but at most only one should be."
)
if agent is not None:
if agent not in AGENT_TO_CLASS:
r... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
a47cfa8c9416-0 | Source code for langchain.agents.utils
from typing import Sequence
from langchain.tools.base import BaseTool
[docs]def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input."""
for tool in tools:
if not tool.is_single_input:
raise... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/utils.html |
2c3dd313aa3c-0 | Source code for langchain.agents.loading
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, List, Optional, Union
import yaml
from langchain.agents.agent import BaseMultiActionAgent, BaseSingleActionAgent
from langchain.agents.tools import Tool
from langc... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
2c3dd313aa3c-1 | tools: List of tools this agent has access to.
**kwargs: Additional keyword arguments passed to the agent executor.
Returns:
An agent executor.
"""
if "_type" not in config:
raise ValueError("Must specify an agent Type in config")
load_from_tools = config.pop("load_from_llm_and_t... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
2c3dd313aa3c-2 | del config["output_parser"]
combined_config = {**config, **kwargs}
return agent_cls(**combined_config) # type: ignore
[docs]def load_agent(
path: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
"""Unified method for loading an agent from LangChainHub or local fs... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
2c3dd313aa3c-3 | # Load the agent from the config now.
return load_agent_from_config(config, **kwargs) | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
56a059692dee-0 | Source code for langchain.agents.agent_iterator
from __future__ import annotations
import logging
import time
from abc import ABC, abstractmethod
from asyncio import CancelledError
from functools import wraps
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
NoReturn,
Optional,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-1 | self,
agent_executor: AgentExecutor,
inputs: Any,
callbacks: Callbacks = None,
*,
tags: Optional[list[str]] = None,
include_run_info: bool = False,
async_: bool = False,
):
"""
Initialize the AgentExecutorIterator with the given AgentExecutor,
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-2 | return self._tags
@tags.setter
@rebuild_callback_manager_on_set
def tags(self, tags: Optional[List[str]]) -> None:
"""When tags are changed after __init__, rebuild callback mgr"""
self._tags = tags
@property
def agent_executor(self) -> AgentExecutor:
return self._agent_execut... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-3 | )
[docs] def reset(self) -> None:
"""
Reset the iterator to its initial state, clearing intermediate steps,
iterations, and time elapsed.
"""
logger.debug("(Re)setting AgentExecutorIterator to fresh state")
self.intermediate_steps: list[tuple[AgentAction, str]] = []
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-4 | return self._final_outputs
@final_outputs.setter
def final_outputs(self, outputs: Optional[Dict[str, Any]]) -> None:
# have access to intermediate steps by design in iterator,
# so return only outputs may as well always be true.
self._final_outputs = None
if outputs:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-5 | """
pass
async def _on_first_async_step(self) -> None:
"""
Perform any necessary setup for the first step of the asynchronous iterator.
"""
# on first step, need to await callback manager and start async timeout ctxmgr
if self.iterations == 0:
assert isins... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-6 | return await self._acall_next()
except StopAsyncIteration:
raise
except (TimeoutError, CancelledError):
await self.timeout_manager.__aexit__(None, None, None)
self.timeout_manager = None
return await self._astop()
except BaseException as e:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-7 | run_manager: Optional[CallbackManagerForChainRun],
) -> Dict[str, Union[str, List[Tuple[AgentAction, str]]]]:
"""
Process the output of the next step,
handling AgentFinish and tool return cases.
"""
logger.debug("Processing output of Agent loop step")
if isinstance(ne... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-8 | """
Process the output of the next async step,
handling AgentFinish and tool return cases.
"""
logger.debug("Processing output of async Agent loop step")
if isinstance(next_step_output, AgentFinish):
logger.debug(
"Hit AgentFinish: _areturn -> on_chain... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-9 | self.intermediate_steps,
**self.inputs,
)
assert (
isinstance(self.run_manager, CallbackManagerForChainRun)
or self.run_manager is None
)
returned_output = self.agent_executor._return(
output, self.intermediate_steps, run_manager=self.run_m... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
56a059692dee-10 | )
next_step_output = self._execute_next_step(self.run_manager)
output = self._process_next_step_output(next_step_output, self.run_manager)
self.update_iterations()
return output
async def _acall_next(self) -> dict[str, Any]:
"""
Perform a single iteration of the async... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/agent_iterator.html |
859cebb5958a-0 | Source code for langchain.agents.tools
"""Interface for tools."""
from typing import List, Optional
from langchain.callbacks.manager import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain.tools.base import BaseTool, Tool, tool
[docs]class InvalidTool(BaseTool):
"""Tool that is ... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/tools.html |
1631908ee054-0 | Source code for langchain.agents.conversational.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.agent_types import AgentTy... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
1631908ee054-1 | """Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
hum... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
1631908ee054-2 | def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Option... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
9f5b8a241d0b-0 | Source code for langchain.agents.conversational.output_parser
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class ConvoOutpu... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/output_parser.html |
cbedca31a27f-0 | Source code for langchain.agents.self_ask_with_search.base
"""Chain that does self-ask with search."""
from typing import Any, Sequence, Union
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types import AgentType
from langchain.agents.self_ask_with_search.output_p... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
cbedca31a27f-1 | super()._validate_tools(tools)
if len(tools) != 1:
raise ValueError(f"Exactly one tool must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Intermediate Answer"}:
raise ValueError(
f"Tool name should be Interme... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/self_ask_with_search/base.html |
6317ad2ee89a-0 | Source code for langchain.agents.output_parsers.react_single_input
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FINAL_ANSWER_ACTION = "Fina... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_single_input.html |
6317ad2ee89a-1 | includes_answer = FINAL_ANSWER_ACTION in text
regex = (
r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
)
action_match = re.search(regex, text, re.DOTALL)
if action_match:
if includes_answer:
raise OutputParserException(
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_single_input.html |
6317ad2ee89a-2 | send_to_llm=True,
)
else:
raise OutputParserException(f"Could not parse LLM output: `{text}`")
@property
def _type(self) -> str:
return "react-single-input" | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_single_input.html |
90559fe37185-0 | Source code for langchain.agents.output_parsers.json
from __future__ import annotations
import logging
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.output_parsers.json import parse_json_markdown
from langchain.schema import AgentAction, AgentFinish, OutputParserException
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/json.html |
90559fe37185-1 | )
except Exception as e:
raise OutputParserException(f"Could not parse LLM output: {text}") from e
@property
def _type(self) -> str:
return "json-agent" | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/json.html |
9069d8de205f-0 | Source code for langchain.agents.output_parsers.openai_tools
import asyncio
import json
from json import JSONDecodeError
from typing import List, Union
from langchain.agents.agent import MultiActionAgentOutputParser
from langchain.schema import (
AgentAction,
AgentFinish,
OutputParserException,
)
from langc... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_tools.html |
9069d8de205f-1 | # name called `__arg1` to handle old style tools that do not expose a
# schema and expect a single string argument as an input.
# We unpack the argument here if it exists.
# Open AI does not support passing in a JSON array as an argument.
if "__arg1" in _tool_input:
tool_inpu... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_tools.html |
9069d8de205f-2 | if not isinstance(result[0], ChatGeneration):
raise ValueError("This output parser only works on ChatGeneration output")
message = result[0].message
return parse_ai_message_to_openai_tool_action(message)
[docs] async def aparse_result(
self, result: List[Generation], *, partial: b... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_tools.html |
56fc65b1a72c-0 | Source code for langchain.agents.output_parsers.xml
from typing import Union
from langchain.agents import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish
[docs]class XMLAgentOutputParser(AgentOutputParser):
"""Parses tool invocations and final answers in XML format.
Expects output to be ... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/xml.html |
56fc65b1a72c-1 | else:
raise ValueError
[docs] def get_format_instructions(self) -> str:
raise NotImplementedError
@property
def _type(self) -> str:
return "xml-agent" | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/xml.html |
f0b100dd319a-0 | Source code for langchain.agents.output_parsers.openai_functions
import asyncio
import json
from json import JSONDecodeError
from typing import List, Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import (
AgentAction,
AgentFinish,
OutputParserException,
)
from langchain.sc... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_functions.html |
f0b100dd319a-1 | _tool_input = {}
else:
# otherwise it returns a json object
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
raise OutputParserException(
f"Could not parse tool input: {function_call} beca... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_functions.html |
f0b100dd319a-2 | message = result[0].message
return self._parse_ai_message(message)
[docs] async def aparse_result(
self, result: List[Generation], *, partial: bool = False
) -> Union[AgentAction, AgentFinish]:
return await asyncio.get_running_loop().run_in_executor(
None, self.parse_result, r... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/openai_functions.html |
7d2b5364915f-0 | Source code for langchain.agents.output_parsers.self_ask
from typing import Sequence, Union
from langchain.agents.agent import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
[docs]class SelfAskOutputParser(AgentOutputParser):
"""Parses self-ask style LLM calls.
Ex... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/self_ask.html |
20a51e53e700-0 | Source code for langchain.agents.output_parsers.react_json_single_input
import json
import re
from typing import Union
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FINAL_ANSW... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_json_single_input.html |
20a51e53e700-1 | if not found:
# Fast fail to parse Final Answer.
raise ValueError("action not found")
action = found.group(1)
response = json.loads(action.strip())
includes_action = "action" in response
if includes_answer and includes_action:
... | lang/api.python.langchain.com/en/latest/_modules/langchain/agents/output_parsers/react_json_single_input.html |
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