id stringlengths 14 16 | text stringlengths 4 1.28k | source stringlengths 54 121 |
|---|---|---|
e10fb3e7f63f-42 | langchain.prompts.base.BasePromptTemplate
classmethod from_llm_and_tools(llm, tools, callback_manager=None, extra_prompt_messages=None, system_message=SystemMessage(content='You are a helpful AI assistant.', additional_kwargs={}), **kwargs)[source]ο
Construct an agent from an LLM and tools.
Parameters
llm (langchain.ba... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-43 | Return type
List[str]
plan(intermediate_steps, callbacks=None, **kwargs)[source]ο
Given input, decided what to do.
Parameters
intermediate_steps (List[Tuple[langchain.schema.AgentAction, str]]) β Steps the LLM has taken to date, along with observations
**kwargs β User inputs.
callbacks (Optional[Union[List[langchain.ca... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-44 | class langchain.agents.ReActChain(llm, docstore, *, memory=None, callbacks=None, callback_manager=None, verbose=None, tags=None, agent, tools, return_intermediate_steps=False, max_iterations=15, max_execution_time=None, early_stopping_method='force', handle_parsing_errors=False)[source]ο
Bases: langchain.agents.agent.A... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-45 | verbose (bool) β
tags (Optional[List[str]]) β
agent (Union[langchain.agents.agent.BaseSingleActionAgent, langchain.agents.agent.BaseMultiActionAgent]) β
tools (Sequence[langchain.tools.base.BaseTool]) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
ear... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-46 | output_parser (langchain.agents.agent.AgentOutputParser) β
allowed_tools (Optional[List[str]]) β
Return type
None
classmethod create_prompt(tools)[source]ο
Return default prompt.
Parameters
tools (Sequence[langchain.tools.base.BaseTool]) β
Return type
langchain.prompts.base.BasePromptTemplate
class langchain.agents.... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-47 | search_chain = GoogleSerperAPIWrapper()
self_ask = SelfAskWithSearchChain(llm=OpenAI(), search_chain=search_chain)
Parameters
llm (langchain.base_language.BaseLanguageModel) β
search_chain (Union[langchain.utilities.google_serper.GoogleSerperAPIWrapper, langchain.utilities.serpapi.SerpAPIWrapper]) β
memory (Optional[... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-48 | return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
handle_parsing_errors (Union[bool, str, Callable[[langchain.schema.OutputParserException], str]]) β
Return type
None
class langchain.agents.StructuredChatAgent(*, llm_chain, outp... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-49 | classmethod create_prompt(tools, prefix='Respond to the human as helpfully and accurately as possible. You have access to the following tools:', suffix='Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-50 | input question to answer\nThought: consider previous and subsequent steps\nAction:\n```\n$JSON_BLOB\n```\nObservation: action result\n... (repeat Thought/Action/Observation N times)\nThought: I know what to respond\nAction:\n```\n{{{{\nΒ "action": "Final Answer",\nΒ "action_input": "Final response to human"\n}}}}\n```'... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-51 | Create a prompt for this class.
Parameters
tools (Sequence[langchain.tools.base.BaseTool]) β
prefix (str) β
suffix (str) β
human_message_template (str) β
format_instructions (str) β
input_variables (Optional[List[str]]) β
memory_prompts (Optional[List[langchain.prompts.base.BasePromptTemplate]]) β
Return type
la... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-52 | classmethod from_llm_and_tools(llm, tools, callback_manager=None, output_parser=None, prefix='Respond to the human as helpfully and accurately as possible. You have access to the following tools:', suffix='Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond direc... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-53 | this format:\n\nQuestion: input question to answer\nThought: consider previous and subsequent steps\nAction:\n```\n$JSON_BLOB\n```\nObservation: action result\n... (repeat Thought/Action/Observation N times)\nThought: I know what to respond\nAction:\n```\n{{{{\nΒ "action": "Final Answer",\nΒ "action_input": "Final resp... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-54 | Construct an agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
tools (Sequence[langchain.tools.base.BaseTool]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
output_parser (Optional[langchain.agents.agent.AgentOutputParser]) β
prefix (str) β
su... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-55 | Prefix to append the observation with.
class langchain.agents.Tool(name, func, description, *, args_schema=None, return_direct=False, verbose=False, callbacks=None, callback_manager=None, handle_tool_error=False, coroutine=None)[source]ο
Bases: langchain.tools.base.BaseTool
Tool that takes in function or coroutine dire... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-56 | coroutine (Optional[Callable[[...], Awaitable[str]]]) β
Return type
None
attribute coroutine: Optional[Callable[[...], Awaitable[str]]] = Noneο
The asynchronous version of the function.
attribute description: str = ''ο
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-57 | kwargs (Any) β
Return type
langchain.tools.base.Tool
property args: dictο
The toolβs input arguments.
class langchain.agents.ZeroShotAgent(*, llm_chain, output_parser=None, allowed_tools=None)[source]ο
Bases: langchain.agents.agent.Agent
Agent for the MRKL chain.
Parameters
llm_chain (langchain.chains.llm.LLMChain) β ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-58 | classmethod create_prompt(tools, prefix='Answer the following questions as best you can. You have access to the following tools:', suffix='Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions='Use the following format:\n\nQuestion: the input question you must answer\nThought: you should always ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-59 | prompt.
prefix (str) β String to put before the list of tools.
suffix (str) β String to put after the list of tools.
input_variables (Optional[List[str]]) β List of input variables the final prompt will expect.
format_instructions (str) β
Returns
A PromptTemplate with the template assembled from the pieces here.
Retur... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-60 | langchain.prompts.prompt.PromptTemplate
classmethod from_llm_and_tools(llm, tools, callback_manager=None, output_parser=None, prefix='Answer the following questions as best you can. You have access to the following tools:', suffix='Begin!\n\nQuestion: {input}\nThought:{agent_scratchpad}', format_instructions='Use the f... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-61 | Parameters
llm (langchain.base_language.BaseLanguageModel) β
tools (Sequence[langchain.tools.base.BaseTool]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
output_parser (Optional[langchain.agents.agent.AgentOutputParser]) β
prefix (str) β
suffix (str) β
format_instructions (str) β ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-62 | Parameters
llm (langchain.base_language.BaseLanguageModel) β
path (Union[str, List[str]]) β
pandas_kwargs (Optional[dict]) β
kwargs (Any) β
Return type
langchain.agents.agent.AgentExecutor | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-63 | langchain.agents.create_json_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to interact with JSON.\nYour goal is to return a final answer by interacting with the JSON.\nYou have access to the following tools which help you learn more about the JSON you are interacting with.\nOnly use the b... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-64 | you cannot use it.\nYou should only add one key at a time to the path. You cannot add multiple keys at once.\nIf you encounter a "KeyError", go back to the previous key, look at the available keys, and try again.\n\nIf the question does not seem to be related to the JSON, just return "I don\'t know" as the answer.\nAlw... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-65 | JSON, as this is not a valid answer. Keep digging until you find the answer and explicitly return it.\n', suffix='Begin!"\n\nQuestion: {input}\nThought: I should look at the keys that exist in data to see what I have access to\n{agent_scratchpad}', format_instructions='Use the following format:\n\nQuestion: the input q... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-66 | Construct a json agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.json.toolkit.JsonToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str) β
inpu... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-67 | langchain.agents.create_openapi_agent(llm, toolkit, callback_manager=None, prefix="You are an agent designed to answer questions by making web requests to an API given the openapi spec.\n\nIf the question does not seem related to the API, return I don't know. Do not make up an answer.\nOnly use information provided by ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-68 | request by checking which parameters are required. For parameters with a fixed set of values, please use the spec to look at which values are allowed.\n\nUse the exact parameter names as listed in the spec, do not make up any names or abbreviate the names of parameters.\nIf you get a not found error, ensure that you ar... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-69 | Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question', input_variables=None, max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, return_intermediate_steps=False, agent_executor_kwargs=None, **kwar... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-70 | Construct a json agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.openapi.toolkit.OpenAPIToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str) β... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-71 | langchain.agents.agent.AgentExecutor
langchain.agents.create_pandas_dataframe_agent(llm, df, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager=None, prefix=None, suffix=None, input_variables=None, verbose=False, return_intermediate_steps=False, max_iterations=15, max_execution_time=None, early_stopping... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-72 | verbose (bool) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Optional[float]) β
early_stopping_method (str) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
include_df_in_prompt (Optional[bool]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentE... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-73 | langchain.agents.create_pbi_agent(llm, toolkit, powerbi=None, callback_manager=None, prefix='You are an agent designed to help users interact with a PowerBI Dataset.\n\nAgent has access to a tool that can write a query based on the question and then run those against PowerBI, Microsofts business intelligence tool. The ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-74 | in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix='Begin!\n\nQuestion: {input}\nThought: I can first ask which tables I have, then how each table is defined and then ask the query tool... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-75 | the final answer\nFinal Answer: the final answer to the original input question', examples=None, input_variables=None, top_k=10, verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-76 | Construct a pbi agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit]) β
powerbi (Optional[langchain.utilities.powerbi.PowerBIDataset]) β
callback_manager (Optional[langchain.callbacks.base.BaseCallb... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-77 | langchain.agents.create_pbi_chat_agent(llm, toolkit, powerbi=None, callback_manager=None, output_parser=None, prefix='Assistant is a large language model built to help users interact with a PowerBI Dataset.\n\nAssistant has access to a tool that can write a query based on the question and then run those against PowerBI... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-78 | make sure to represent numbers in readable ways, like 1M instead of 1000000. Unless the user specifies a specific number of examples they wish to obtain, always limit your query to at most {top_k} results.\n', suffix="TOOLS\n------\nAssistant can ask the user to use tools to look up information that may be helpful in a... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-79 | Construct a pbi agent from an Chat LLM and tools.
If you supply only a toolkit and no powerbi dataset, the same LLM is used for both.
Parameters
llm (langchain.chat_models.base.BaseChatModel) β
toolkit (Optional[langchain.agents.agent_toolkits.powerbi.toolkit.PowerBIToolkit]) β
powerbi (Optional[langchain.utilities.p... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-80 | verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
langchain.agents.create_spark_dataframe_agent(llm, df, callback_manager=None, prefix='\nYou are working with a spark dataframe in Python. The name of the dataframe is `df`.\n... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-81 | Parameters
llm (langchain.llms.base.BaseLLM) β
df (Any) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
input_variables (Optional[List[str]]) β
verbose (bool) β
return_intermediate_steps (bool) β
max_iterations (Optional[int]) β
max_execution_time (Op... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-82 | langchain.agents.create_spark_sql_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to interact with Spark SQL.\nGiven an input question, create a syntactically correct Spark SQL query to run, then look at the results of the query and return the answer.\nUnless the user specifies a specific n... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-83 | query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix='Begin!\n\nQuestion: {input}\nThought: I should look at the tables in the database to see what I can... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-84 | the final answer to the original input question', input_variables=None, top_k=10, max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-85 | Construct a sql agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.spark_sql.toolkit.SparkSQLToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
suffix (str) β
format_instructions (str)... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-86 | langchain.agents.create_sql_agent(llm, toolkit, agent_type=AgentType.ZERO_SHOT_REACT_DESCRIPTION, callback_manager=None, prefix='You are an agent designed to interact with a SQL database.\nGiven an input question, create a syntactically correct {dialect} query to run, then look at the results of the query and return th... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-87 | it. If you get an error while executing a query, rewrite the query and try again.\n\nDO NOT make any DML statements (INSERT, UPDATE, DELETE, DROP etc.) to the database.\n\nIf the question does not seem related to the database, just return "I don\'t know" as the answer.\n', suffix=None, format_instructions='Use the foll... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-88 | max_iterations=15, max_execution_time=None, early_stopping_method='force', verbose=False, agent_executor_kwargs=None, **kwargs)[source]ο | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-89 | Construct a sql agent from an LLM and tools.
Parameters
llm (langchain.base_language.BaseLanguageModel) β
toolkit (langchain.agents.agent_toolkits.sql.toolkit.SQLDatabaseToolkit) β
agent_type (langchain.agents.agent_types.AgentType) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefi... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-90 | langchain.agents.agent.AgentExecutor
langchain.agents.create_vectorstore_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to answer questions about sets of documents.\nYou have access to tools for interacting with the documents, and the inputs to the tools are questions.\nSometimes, you will... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-91 | prefix (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.AgentExecutor
langchain.agents.create_vectorstore_router_agent(llm, toolkit, callback_manager=None, prefix='You are an agent designed to answer questions.\nYou have access ... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-92 | toolkit (langchain.agents.agent_toolkits.vectorstore.toolkit.VectorStoreRouterToolkit) β
callback_manager (Optional[langchain.callbacks.base.BaseCallbackManager]) β
prefix (str) β
verbose (bool) β
agent_executor_kwargs (Optional[Dict[str, Any]]) β
kwargs (Dict[str, Any]) β
Return type
langchain.agents.agent.Agent... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-93 | llm (langchain.base_language.BaseLanguageModel) β Language model to use as the agent.
agent (Optional[langchain.agents.agent_types.AgentType]) β Agent type to use. If None and agent_path is also None, will default to
AgentType.ZERO_SHOT_REACT_DESCRIPTION.
callback_manager (Optional[langchain.callbacks.base.BaseCallback... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-94 | langchain.agents.agent.AgentExecutor
langchain.agents.load_agent(path, **kwargs)[source]ο
Unified method for loading a agent from LangChainHub or local fs.
Parameters
path (Union[str, pathlib.Path]) β
kwargs (Any) β
Return type
Union[langchain.agents.agent.BaseSingleActionAgent, langchain.agents.agent.BaseMultiAction... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-95 | kwargs (Any) β
Returns
A tool.
Return type
langchain.tools.base.BaseTool
langchain.agents.load_tools(tool_names, llm=None, callbacks=None, **kwargs)[source]ο
Load tools based on their name.
Parameters
tool_names (List[str]) β name of tools to load.
llm (Optional[langchain.base_language.BaseLanguageModel]) β Optional l... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-96 | Make tools out of functions, can be used with or without arguments.
Parameters
*args β The arguments to the tool.
return_direct (bool) β Whether to return directly from the tool rather
than continuing the agent loop.
args_schema (Optional[Type[pydantic.main.BaseModel]]) β optional argument schema for user to specify
in... | https://api.python.langchain.com/en/latest/modules/agents.html |
e10fb3e7f63f-97 | # Searches the API for the query.
return | https://api.python.langchain.com/en/latest/modules/agents.html |
6c85200782ee-0 | Source code for langchain.requests
"""Lightweight wrapper around requests library, with async support."""
from contextlib import asynccontextmanager
from typing import Any, AsyncGenerator, Dict, Optional
import aiohttp
import requests
from pydantic import BaseModel, Extra
class Requests(BaseModel):
"""Wrapper aroun... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-1 | return requests.get(url, headers=self.headers, **kwargs)
def post(self, url: str, data: Dict[str, Any], **kwargs: Any) -> requests.Response:
"""POST to the URL and return the text."""
return requests.post(url, json=data, headers=self.headers, **kwargs)
def patch(self, url: str, data: Dict[str, A... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-2 | """DELETE the URL and return the text."""
return requests.delete(url, headers=self.headers, **kwargs)
@asynccontextmanager
async def _arequest(
self, method: str, url: str, **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""Make an async request."""
if not se... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-3 | ) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""GET the URL and return the text asynchronously."""
async with self._arequest("GET", url, **kwargs) as response:
yield response
@asynccontextmanager
async def apost(
self, url: str, data: Dict[str, Any], **kwargs: Any
)... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-4 | async with self._arequest("PATCH", url, **kwargs) as response:
yield response
@asynccontextmanager
async def aput(
self, url: str, data: Dict[str, Any], **kwargs: Any
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
"""PUT the URL and return the text asynchronously."""
... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-5 | """Lightweight wrapper around requests library.
The main purpose of this wrapper is to always return a text output.
"""
headers: Optional[Dict[str, str]] = None
aiosession: Optional[aiohttp.ClientSession] = None
class Config:
"""Configuration for this pydantic object."""
extra = Extr... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-6 | return self.requests.post(url, data, **kwargs).text
[docs] def patch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""PATCH the URL and return the text."""
return self.requests.patch(url, data, **kwargs).text
[docs] def put(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-7 | async with self.requests.aget(url, **kwargs) as response:
return await response.text()
[docs] async def apost(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
"""POST to the URL and return the text asynchronously."""
async with self.requests.apost(url, **kwargs) as response:
... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6c85200782ee-8 | async with self.requests.aput(url, **kwargs) as response:
return await response.text()
[docs] async def adelete(self, url: str, **kwargs: Any) -> str:
"""DELETE the URL and return the text asynchronously."""
async with self.requests.adelete(url, **kwargs) as response:
return a... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
6adb2cb75201-0 | Source code for langchain.text_splitter
"""Functionality for splitting text."""
from __future__ import annotations
import copy
import logging
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import Enum
from typing import (
AbstractSet,
Any,
Callable,
Collection,... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-1 | ) -> List[str]:
# Now that we have the separator, split the text
if separator:
if keep_separator:
# The parentheses in the pattern keep the delimiters in the result.
_splits = re.split(f"({separator})", text)
splits = [_splits[i] + _splits[i + 1] for i in range(1, len... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-2 | def __init__(
self,
chunk_size: int = 4000,
chunk_overlap: int = 200,
length_function: Callable[[str], int] = len,
keep_separator: bool = False,
add_start_index: bool = False,
) -> None:
"""Create a new TextSplitter.
Args:
chunk_size: Maxim... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-3 | )
self._chunk_size = chunk_size
self._chunk_overlap = chunk_overlap
self._length_function = length_function
self._keep_separator = keep_separator
self._add_start_index = add_start_index
[docs] @abstractmethod
def split_text(self, text: str) -> List[str]:
"""Split t... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-4 | if self._add_start_index:
index = text.find(chunk, index + 1)
metadata["start_index"] = index
new_doc = Document(page_content=chunk, metadata=metadata)
documents.append(new_doc)
return documents
[docs] def split_documents(self, documents... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-5 | else:
return text
def _merge_splits(self, splits: Iterable[str], separator: str) -> List[str]:
# We now want to combine these smaller pieces into medium size
# chunks to send to the LLM.
separator_len = self._length_function(separator)
docs = []
current_doc: List[... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-6 | doc = self._join_docs(current_doc, separator)
if doc is not None:
docs.append(doc)
# Keep on popping if:
# - we have a larger chunk than in the chunk overlap
# - or if we still have any chunks and the length is long
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-7 | if doc is not None:
docs.append(doc)
return docs
[docs] @classmethod
def from_huggingface_tokenizer(cls, tokenizer: Any, **kwargs: Any) -> TextSplitter:
"""Text splitter that uses HuggingFace tokenizer to count length."""
try:
from transformers import PreTrainedTok... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-8 | [docs] @classmethod
def from_tiktoken_encoder(
cls: Type[TS],
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwar... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-9 | else:
enc = tiktoken.get_encoding(encoding_name)
def _tiktoken_encoder(text: str) -> int:
return len(
enc.encode(
text,
allowed_special=allowed_special,
disallowed_special=disallowed_special,
)
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-10 | return self.split_documents(list(documents))
[docs] async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Asynchronously transform a sequence of documents by splitting them."""
raise NotImplementedError
[docs]class CharacterTextSpli... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-11 | splits = _split_text_with_regex(text, self._separator, self._keep_separator)
_separator = "" if self._keep_separator else self._separator
return self._merge_splits(splits, _separator)
[docs]class LineType(TypedDict):
"""Line type as typed dict."""
metadata: Dict[str, str]
content: str
[docs]... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-12 | return_each_line: Return each line w/ associated headers
"""
# Output line-by-line or aggregated into chunks w/ common headers
self.return_each_line = return_each_line
# Given the headers we want to split on,
# (e.g., "#, ##, etc") order by length
self.headers_to_split_on... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-13 | ):
# If the last line in the aggregated list
# has the same metadata as the current line,
# append the current content to the last lines's content
aggregated_chunks[-1]["content"] += " \n" + line["content"]
else:
# Otherwise, a... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-14 | # Content and metadata of the chunk currently being processed
current_content: List[str] = []
current_metadata: Dict[str, str] = {}
# Keep track of the nested header structure
# header_stack: List[Dict[str, Union[int, str]]] = []
header_stack: List[HeaderType] = []
initia... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-15 | len(stripped_line) == len(sep)
or stripped_line[len(sep)] == " "
):
# Ensure we are tracking the header as metadata
if name is not None:
# Get the current header level
current_header_level = sep.c... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-16 | "level": current_header_level,
"name": name,
"data": stripped_line[len(sep) :].strip(),
}
header_stack.append(header)
# Update initial_metadata with the current header
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-17 | }
)
current_content.clear()
current_metadata = initial_metadata.copy()
if current_content:
lines_with_metadata.append(
{"content": "\n".join(current_content), "metadata": current_metadata}
)
# lines_with_metadata... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-18 | tokens_per_chunk: int
decode: Callable[[list[int]], str]
encode: Callable[[str], List[int]]
[docs]def split_text_on_tokens(*, text: str, tokenizer: Tokenizer) -> List[str]:
"""Split incoming text and return chunks."""
splits: List[str] = []
input_ids = tokenizer.encode(text)
start_idx = 0
cu... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-19 | """Implementation of splitting text that looks at tokens."""
def __init__(
self,
encoding_name: str = "gpt2",
model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all"... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-20 | else:
enc = tiktoken.get_encoding(encoding_name)
self._tokenizer = enc
self._allowed_special = allowed_special
self._disallowed_special = disallowed_special
[docs] def split_text(self, text: str) -> List[str]:
def _encode(_text: str) -> List[int]:
return self._... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-21 | def __init__(
self,
chunk_overlap: int = 50,
model_name: str = "sentence-transformers/all-mpnet-base-v2",
tokens_per_chunk: Optional[int] = None,
**kwargs: Any,
) -> None:
"""Create a new TextSplitter."""
super().__init__(**kwargs, chunk_overlap=chunk_overlap)... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-22 | self._initialize_chunk_configuration(tokens_per_chunk=tokens_per_chunk)
def _initialize_chunk_configuration(
self, *, tokens_per_chunk: Optional[int]
) -> None:
self.maximum_tokens_per_chunk = cast(int, self._model.max_seq_length)
if tokens_per_chunk is None:
self.tokens_per_... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-23 | def encode_strip_start_and_stop_token_ids(text: str) -> List[int]:
return self._encode(text)[1:-1]
tokenizer = Tokenizer(
chunk_overlap=self._chunk_overlap,
tokens_per_chunk=self.tokens_per_chunk,
decode=self.tokenizer.decode,
encode=encode_strip_start... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-24 | )
return token_ids_with_start_and_end_token_ids
[docs]class Language(str, Enum):
CPP = "cpp"
GO = "go"
JAVA = "java"
JS = "js"
PHP = "php"
PROTO = "proto"
PYTHON = "python"
RST = "rst"
RUBY = "ruby"
RUST = "rust"
SCALA = "scala"
SWIFT = "swift"
MARKDOWN = "mar... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-25 | """
def __init__(
self,
separators: Optional[List[str]] = None,
keep_separator: bool = True,
**kwargs: Any,
) -> None:
"""Create a new TextSplitter."""
super().__init__(keep_separator=keep_separator, **kwargs)
self._separators = separators or ["\n\n", "\n"... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-26 | separator = _s
new_separators = separators[i + 1 :]
break
splits = _split_text_with_regex(text, separator, self._keep_separator)
# Now go merging things, recursively splitting longer texts.
_good_splits = []
_separator = "" if self._keep_separator else sep... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-27 | if _good_splits:
merged_text = self._merge_splits(_good_splits, _separator)
final_chunks.extend(merged_text)
return final_chunks
[docs] def split_text(self, text: str) -> List[str]:
return self._split_text(text, self._separators)
[docs] @classmethod
def from_language(
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-28 | "\nvoid ",
"\nint ",
"\nfloat ",
"\ndouble ",
# Split along control flow statements
"\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
# Split by the normal... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-29 | " ",
"",
]
elif language == Language.JAVA:
return [
# Split along class definitions
"\nclass ",
# Split along method definitions
"\npublic ",
"\nprotected ",
"\nprivate ",
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-30 | "\nif ",
"\nfor ",
"\nwhile ",
"\nswitch ",
"\ncase ",
"\ndefault ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif langu... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-31 | # Split along message definitions
"\nmessage ",
# Split along service definitions
"\nservice ",
# Split along enum definitions
"\nenum ",
# Split along option definitions
"\noption ",
# Split ... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-32 | return [
# Split along section titles
"\n=+\n",
"\n-+\n",
"\n\*+\n",
# Split along directive markers
"\n\n.. *\n\n",
# Split by the normal type of lines
"\n\n",
"\n",
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-33 | " ",
"",
]
elif language == Language.RUST:
return [
# Split along function definitions
"\nfn ",
"\nconst ",
"\nlet ",
# Split along control flow statements
"\nif ",
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
6adb2cb75201-34 | "\nfor ",
"\nwhile ",
"\nmatch ",
"\ncase ",
# Split by the normal type of lines
"\n\n",
"\n",
" ",
"",
]
elif language == Language.SWIFT:
return [
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
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