id stringlengths 14 16 | text stringlengths 31 2.41k | source stringlengths 53 121 |
|---|---|---|
94e2996c717b-18 | "\n",
" ",
"",
]
elif language == Language.LATEX:
return [
# First, try to split along Latex sections
"\n\\\chapter{",
"\n\\\section{",
"\n\\\subsection{",
"\n\\\subsubsection{... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
94e2996c717b-19 | return [
# Split along compiler informations definitions
"\npragma ",
"\nusing ",
# Split along contract definitions
"\ncontract ",
"\ninterface ",
"\nlibrary ",
# Split along method definitio... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
94e2996c717b-20 | splits = self._tokenizer(text)
return self._merge_splits(splits, self._separator)
[docs]class SpacyTextSplitter(TextSplitter):
"""Implementation of splitting text that looks at sentences using Spacy."""
def __init__(
self, separator: str = "\n\n", pipeline: str = "en_core_web_sm", **kwargs: Any
... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
94e2996c717b-21 | separators = self.get_separators_for_language(Language.MARKDOWN)
super().__init__(separators=separators, **kwargs)
[docs]class LatexTextSplitter(RecursiveCharacterTextSplitter):
"""Attempts to split the text along Latex-formatted layout elements."""
def __init__(self, **kwargs: Any) -> None:
"""... | https://api.python.langchain.com/en/latest/_modules/langchain/text_splitter.html |
9c176e0cb7c3-0 | 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 |
9c176e0cb7c3-1 | def delete(self, url: str, **kwargs: Any) -> requests.Response:
"""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.Clien... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
9c176e0cb7c3-2 | """PATCH the URL and return the text asynchronously."""
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]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
9c176e0cb7c3-3 | """POST to the URL and return the text."""
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, ur... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
9c176e0cb7c3-4 | """PUT the URL and return the text asynchronously."""
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.req... | https://api.python.langchain.com/en/latest/_modules/langchain/requests.html |
d04e2872c89e-0 | Source code for langchain.schema
"""Common schema objects."""
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import (
Any,
Dict,
Generic,
List,
NamedTuple,
Optional,
Sequence,
TypeVar,
Union,
)
from uuid import UUI... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-1 | """Agent's return value."""
return_values: dict
log: str
[docs]class Generation(Serializable):
"""Output of a single generation."""
text: str
"""Generated text output."""
generation_info: Optional[Dict[str, Any]] = None
"""Raw generation info response from the provider"""
"""May include ... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-2 | """Type of the message, used for serialization."""
return "system"
[docs]class FunctionMessage(BaseMessage):
name: str
@property
def type(self) -> str:
"""Type of the message, used for serialization."""
return "function"
[docs]class ChatMessage(BaseMessage):
"""Type of message wi... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-3 | Returns:
List of messages (BaseMessages).
"""
return [_message_from_dict(m) for m in messages]
[docs]class ChatGeneration(Generation):
"""Output of a single generation."""
text = ""
message: BaseMessage
@root_validator
def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]:
... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-4 | llm_output=self.llm_output,
)
)
else:
if self.llm_output is not None:
llm_output = self.llm_output.copy()
llm_output["token_usage"] = dict()
else:
llm_output = None
... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-5 | """Save the context of this model run to memory."""
[docs] @abstractmethod
def clear(self) -> None:
"""Clear memory contents."""
[docs]class BaseChatMessageHistory(ABC):
"""Base interface for chat message history
See `ChatMessageHistory` for default implementation.
"""
"""
Example:
... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-6 | raise NotImplementedError
[docs] @abstractmethod
def clear(self) -> None:
"""Remove all messages from the store"""
[docs]class Document(Serializable):
"""Interface for interacting with a document."""
page_content: str
metadata: dict = Field(default_factory=dict)
[docs]class BaseRetriever(ABC)... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-7 | """Parse the output of an LLM call.
A method which takes in a string (assumed output of a language model )
and parses it into some structure.
Args:
text: output of language model
Returns:
structured output
"""
[docs] def parse_with_prompt(self, completi... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
d04e2872c89e-8 | @property
def _type(self) -> str:
return "default"
[docs] def parse(self, text: str) -> str:
return text
[docs]class OutputParserException(ValueError):
"""Exception that output parsers should raise to signify a parsing error.
This exists to differentiate parsing errors from other code or ... | https://api.python.langchain.com/en/latest/_modules/langchain/schema.html |
1b552510fc88-0 | Source code for langchain.document_transformers
"""Transform documents"""
from typing import Any, Callable, List, Sequence
import numpy as np
from pydantic import BaseModel, Field
from langchain.embeddings.base import Embeddings
from langchain.math_utils import cosine_similarity
from langchain.schema import BaseDocumen... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
1b552510fc88-1 | redundant_stacked = np.column_stack(redundant)
redundant_sorted = np.argsort(similarity[redundant])[::-1]
included_idxs = set(range(len(embedded_documents)))
for first_idx, second_idx in redundant_stacked[redundant_sorted]:
if first_idx in included_idxs and second_idx in included_idxs:
#... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
1b552510fc88-2 | arbitrary_types_allowed = True
[docs] def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Filter down documents."""
stateful_documents = get_stateful_documents(documents)
embedded_documents = _get_embeddings_from_stateful_docs(
... | https://api.python.langchain.com/en/latest/_modules/langchain/document_transformers.html |
8cfc73f54b33-0 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
8cfc73f54b33-1 | if load_from_tools:
if llm is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
"then LLM must be provided"
)
if tools is None:
raise ValueError(
"If `load_from_llm_and_tools` is set to True, "
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
8cfc73f54b33-2 | if hub_result := try_load_from_hub(
path, _load_agent_from_file, "agents", {"json", "yaml"}
):
return hub_result
else:
return _load_agent_from_file(path, **kwargs)
def _load_agent_from_file(
file: Union[str, Path], **kwargs: Any
) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/loading.html |
cacf3f41db8b-0 | Source code for langchain.agents.agent_types
from enum import Enum
[docs]class AgentType(str, Enum):
"""Enumerator with the Agent types."""
ZERO_SHOT_REACT_DESCRIPTION = "zero-shot-react-description"
REACT_DOCSTORE = "react-docstore"
SELF_ASK_WITH_SEARCH = "self-ask-with-search"
CONVERSATIONAL_REACT... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_types.html |
10c3327965d4-0 | 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://api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
10c3327965d4-1 | 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... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/initialize.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-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://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-13 | `"generate"` calls the agent's LLM Chain one final time to generate
a final answer based on the previous steps.
"""
handle_parsing_errors: Union[
bool, str, Callable[[OutputParserException], str]
] = False
"""How to handle errors raised by the agent's output parser.
Defaults to `Fals... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-14 | f"provided tools ({[tool.name for tool in tools]})"
)
return values
@root_validator()
def validate_return_direct_tool(cls, values: Dict) -> Dict:
"""Validate that tools are compatible with agent."""
agent = values["agent"]
tools = values["tools"]
if isinst... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-15 | return {tool.name: tool for tool in self.tools}[name]
def _should_continue(self, iterations: int, time_elapsed: float) -> bool:
if self.max_iterations is not None and iterations >= self.max_iterations:
return False
if (
self.max_execution_time is not None
and time... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-16 | run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loop.
Override this to take control of how the agent makes and acts on choices.
"""
try:
# Call the LL... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-17 | **tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and return.
if isinstance(output, AgentFinish):
return output
actions: List[AgentAction]
if isinstance(output, AgentAction):
actions... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-18 | color_mapping: Dict[str, str],
inputs: Dict[str, str],
intermediate_steps: List[Tuple[AgentAction, str]],
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
) -> Union[AgentFinish, List[Tuple[AgentAction, str]]]:
"""Take a single step in the thought-action-observation loo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-19 | output.tool_input,
verbose=self.verbose,
color=None,
callbacks=run_manager.get_child() if run_manager else None,
**tool_run_kwargs,
)
return [(output, observation)]
# If the tool chosen is the finishing tool, then we end and... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-20 | **tool_run_kwargs,
)
return agent_action, observation
# Use asyncio.gather to run multiple tool.arun() calls concurrently
result = await asyncio.gather(
*[_aperform_agent_action(agent_action) for agent_action in actions]
)
return list(result)
d... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-21 | next_step_action = next_step_output[0]
# See if tool should return directly
tool_return = self._get_tool_return(next_step_action)
if tool_return is not None:
return self._return(
tool_return, intermediate_steps, run_manager=run_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-22 | color_mapping,
inputs,
intermediate_steps,
run_manager=run_manager,
)
if isinstance(next_step_output, AgentFinish):
return await self._areturn(
next_step_ou... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
45339159c89f-23 | if agent_action.tool in name_to_tool_map:
if name_to_tool_map[agent_action.tool].return_direct:
return AgentFinish(
{self.agent.return_values[0]: observation},
"",
)
return None | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent.html |
96bcfe0241f4-0 | Source code for langchain.agents.load_tools
# flake8: noqa
"""Load tools."""
import warnings
from typing import Any, Dict, List, Optional, Callable, Tuple
from mypy_extensions import Arg, KwArg
from langchain.agents.tools import Tool
from langchain.base_language import BaseLanguageModel
from langchain.callbacks.base im... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-1 | from langchain.tools.shell.tool import ShellTool
from langchain.tools.sleep.tool import SleepTool
from langchain.tools.wikipedia.tool import WikipediaQueryRun
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.tools.openweathermap.tool import OpenWeatherMapQueryRun
from langchain.utiliti... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-2 | def _get_tools_requests_delete() -> BaseTool:
return RequestsDeleteTool(requests_wrapper=TextRequestsWrapper())
def _get_terminal() -> BaseTool:
return ShellTool()
def _get_sleep() -> BaseTool:
return SleepTool()
_BASE_TOOLS: Dict[str, Callable[[], BaseTool]] = {
"python_repl": _get_python_repl,
"re... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-3 | return Tool(
name="Calculator",
description="Useful for when you need to answer questions about math.",
func=LLMMathChain.from_llm(llm=llm).run,
coroutine=LLMMathChain.from_llm(llm=llm).arun,
)
def _get_open_meteo_api(llm: BaseLanguageModel) -> BaseTool:
chain = APIChain.from_llm... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-4 | func=chain.run,
)
def _get_tmdb_api(llm: BaseLanguageModel, **kwargs: Any) -> BaseTool:
tmdb_bearer_token = kwargs["tmdb_bearer_token"]
chain = APIChain.from_llm_and_api_docs(
llm,
tmdb_docs.TMDB_DOCS,
headers={"Authorization": f"Bearer {tmdb_bearer_token}"},
)
return Tool(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-5 | def _get_google_search(**kwargs: Any) -> BaseTool:
return GoogleSearchRun(api_wrapper=GoogleSearchAPIWrapper(**kwargs))
def _get_wikipedia(**kwargs: Any) -> BaseTool:
return WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper(**kwargs))
def _get_arxiv(**kwargs: Any) -> BaseTool:
return ArxivQueryRun(api_wrapp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-6 | )
def _get_searx_search(**kwargs: Any) -> BaseTool:
return SearxSearchRun(wrapper=SearxSearchWrapper(**kwargs))
def _get_searx_search_results_json(**kwargs: Any) -> BaseTool:
wrapper_kwargs = {k: v for k, v in kwargs.items() if k != "num_results"}
return SearxSearchResults(wrapper=SearxSearchWrapper(**wrapp... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-7 | ] = {
"news-api": (_get_news_api, ["news_api_key"]),
"tmdb-api": (_get_tmdb_api, ["tmdb_bearer_token"]),
"podcast-api": (_get_podcast_api, ["listen_api_key"]),
}
_EXTRA_OPTIONAL_TOOLS: Dict[str, Tuple[Callable[[KwArg(Any)], BaseTool], List[str]]] = {
"wolfram-alpha": (_get_wolfram_alpha, ["wolfram_alpha... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-8 | "searx-search": (_get_searx_search, ["searx_host", "engines", "aiosession"]),
"wikipedia": (_get_wikipedia, ["top_k_results", "lang"]),
"arxiv": (
_get_arxiv,
["top_k_results", "load_max_docs", "load_all_available_meta"],
),
"pupmed": (
_get_pupmed,
["top_k_results", "loa... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-9 | **kwargs: Any,
) -> BaseTool:
"""Loads a tool from the HuggingFace Hub.
Args:
task_or_repo_id: Task or model repo id.
model_repo_id: Optional model repo id.
token: Optional token.
remote: Optional remote. Defaults to False.
**kwargs:
Returns:
A tool.
"""
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-10 | Args:
tool_names: name of tools to load.
llm: Optional language model, may be needed to initialize certain tools.
callbacks: Optional callback manager or list of callback handlers.
If not provided, default global callback manager will be used.
Returns:
List of tools.
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
96bcfe0241f4-11 | f"provided: {missing_keys}"
)
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]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/load_tools.html |
28c34f5ff74a-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 pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
28c34f5ff74a-1 | [docs] @classmethod
def create_prompt(
cls,
tools: Sequence[BaseTool],
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
ai_prefix: str = "AI",
human_prefix: str = "Human",
input_variables: Optional[List[str... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
28c34f5ff74a-2 | validate_tools_single_input(cls.__name__, tools)
[docs] @classmethod
def from_llm_and_tools(
cls,
llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: s... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational/base.html |
e34d7b3012a3-0 | Source code for langchain.agents.conversational_chat.base
"""An agent designed to hold a conversation in addition to using tools."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from lang... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
e34d7b3012a3-1 | return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
@classmethod
def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
super()._validate_tools(tools)
validate_tools_single_input(cls.__name__, too... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
e34d7b3012a3-2 | ) -> List[BaseMessage]:
"""Construct the scratchpad that lets the agent continue its thought process."""
thoughts: List[BaseMessage] = []
for action, observation in intermediate_steps:
thoughts.append(AIMessage(content=action.log))
human_message = HumanMessage(
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/conversational_chat/base.html |
cba691317bf1-0 | Source code for langchain.agents.structured_chat.base
import re
from typing import Any, List, Optional, Sequence, Tuple
from pydantic import Field
from langchain.agents.agent import Agent, AgentOutputParser
from langchain.agents.structured_chat.output_parser import (
StructuredChatOutputParserWithRetries,
)
from la... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
cba691317bf1-1 | return (
f"This was your previous work "
f"(but I haven't seen any of it! I only see what "
f"you return as final answer):\n{agent_scratchpad}"
)
else:
return agent_scratchpad
@classmethod
def _validate_tools(cls, tools: Sequence[Ba... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
cba691317bf1-2 | template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
if input_variables is None:
input_variables = ["input", "agent_scratchpad"]
_memory_prompts = memory_prompts or []
messages = [
SystemMessagePromptTemplate.from_template(template),
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
cba691317bf1-3 | )
tool_names = [tool.name for tool in tools]
_output_parser = output_parser or cls._get_default_output_parser(llm=llm)
return cls(
llm_chain=llm_chain,
allowed_tools=tool_names,
output_parser=_output_parser,
**kwargs,
)
@property
de... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/structured_chat/base.html |
c07438d62305-0 | Source code for langchain.agents.openai_functions_agent.base
"""Module implements an agent that uses OpenAI's APIs function enabled API."""
import json
from dataclasses import dataclass
from json import JSONDecodeError
from typing import Any, List, Optional, Sequence, Tuple, Union
from pydantic import root_validator
fr... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
c07438d62305-1 | ]
else:
return [AIMessage(content=agent_action.log)]
def _create_function_message(
agent_action: AgentAction, observation: str
) -> FunctionMessage:
"""Convert agent action and observation into a function message.
Args:
agent_action: the tool invocation request from the agent
obs... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
c07438d62305-2 | function_name = function_call["name"]
try:
_tool_input = json.loads(function_call["arguments"])
except JSONDecodeError:
raise OutputParserException(
f"Could not parse tool input: {function_call} because "
f"the `arguments` is not valid JSON."
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
c07438d62305-3 | of the variables. For an easy way to construct this prompt, use
`OpenAIFunctionsAgent.create_prompt(...)`
"""
llm: BaseLanguageModel
tools: Sequence[BaseTool]
prompt: BasePromptTemplate
[docs] def get_allowed_tools(self) -> List[str]:
"""Get allowed tools."""
return list([... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
c07438d62305-4 | **kwargs: User inputs.
Returns:
Action specifying what tool to use.
"""
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
selected_inputs = {
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
}
full_inputs... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
c07438d62305-5 | )
agent_decision = _parse_ai_message(predicted_message)
return agent_decision
[docs] @classmethod
def create_prompt(
cls,
system_message: Optional[SystemMessage] = SystemMessage(
content="You are a helpful AI assistant."
),
extra_prompt_messages: Option... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
c07438d62305-6 | """Construct an agent from an LLM and tools."""
if not isinstance(llm, ChatOpenAI):
raise ValueError("Only supported with ChatOpenAI models.")
prompt = cls.create_prompt(
extra_prompt_messages=extra_prompt_messages,
system_message=system_message,
)
ret... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/openai_functions_agent/base.html |
e17f399bf20c-0 | Source code for langchain.agents.react.base
"""Chain that implements the ReAct paper from https://arxiv.org/pdf/2210.03629.pdf."""
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, AgentOutputParser
from langchain.agents.agent_types impo... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
e17f399bf20c-1 | super()._validate_tools(tools)
if len(tools) != 2:
raise ValueError(f"Exactly two tools must be specified, but got {tools}")
tool_names = {tool.name for tool in tools}
if tool_names != {"Lookup", "Search"}:
raise ValueError(
f"Tool names should be Lookup a... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
e17f399bf20c-2 | if term.lower() != self.lookup_str:
self.lookup_str = term.lower()
self.lookup_index = 0
else:
self.lookup_index += 1
lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
if len(lookups) == 0:
return "No Results"
elif sel... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
e17f399bf20c-3 | raise ValueError(f"Tool name should be Play, got {tool_names}")
[docs]class ReActChain(AgentExecutor):
"""Chain that implements the ReAct paper.
Example:
.. code-block:: python
from langchain import ReActChain, OpenAI
react = ReAct(llm=OpenAI())
"""
def __init__(self, llm... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/react/base.html |
1848f4c9ddcc-0 | Source code for langchain.agents.mrkl.base
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from pydantic import Field
from langchain.agents.agent import Agent, AgentExecutor, A... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
1848f4c9ddcc-1 | @property
def observation_prefix(self) -> str:
"""Prefix to append the observation with."""
return "Observation: "
@property
def llm_prefix(self) -> str:
"""Prefix to append the llm call with."""
return "Thought:"
[docs] @classmethod
def create_prompt(
cls,
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
1848f4c9ddcc-2 | llm: BaseLanguageModel,
tools: Sequence[BaseTool],
callback_manager: Optional[BaseCallbackManager] = None,
output_parser: Optional[AgentOutputParser] = None,
prefix: str = PREFIX,
suffix: str = SUFFIX,
format_instructions: str = FORMAT_INSTRUCTIONS,
input_variable... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
1848f4c9ddcc-3 | f"a description must always be provided."
)
super()._validate_tools(tools)
[docs]class MRKLChain(AgentExecutor):
"""Chain that implements the MRKL system.
Example:
.. code-block:: python
from langchain import OpenAI, MRKLChain
from langchain.chains.mrkl.ba... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
1848f4c9ddcc-4 | action_description="useful for searching"
),
ChainConfig(
action_name="Calculator",
action=llm_math_chain.run,
action_description="useful for doing math"
)
]
... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/mrkl/base.html |
385ea07de428-0 | Source code for langchain.agents.agent_toolkits.playwright.toolkit
"""Playwright web browser toolkit."""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Type, cast
from pydantic import Extra, root_validator
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
385ea07de428-1 | """Check that the arguments are valid."""
lazy_import_playwright_browsers()
if values.get("async_browser") is None and values.get("sync_browser") is None:
raise ValueError("Either async_browser or sync_browser must be specified.")
return values
[docs] def get_tools(self) -> List[B... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/playwright/toolkit.html |
4935b06df2fc-0 | Source code for langchain.agents.agent_toolkits.openapi.toolkit
"""Requests toolkit."""
from __future__ import annotations
from typing import Any, List
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.agent_toolkits.json.base import crea... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
4935b06df2fc-1 | func=self.json_agent.run,
description=DESCRIPTION,
)
request_toolkit = RequestsToolkit(requests_wrapper=self.requests_wrapper)
return [*request_toolkit.get_tools(), json_agent_tool]
[docs] @classmethod
def from_llm(
cls,
llm: BaseLanguageModel,
json_spe... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/toolkit.html |
a0c7db5c4fbc-0 | Source code for langchain.agents.agent_toolkits.openapi.base
"""OpenAPI spec agent."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.openapi.prompt import (
OPENAPI_PREFIX,
OPENAPI_SUFFIX,
)
from langchain.agents.agent_toolkits... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
a0c7db5c4fbc-1 | input_variables=input_variables,
)
llm_chain = LLMChain(
llm=llm,
prompt=prompt,
callback_manager=callback_manager,
)
tool_names = [tool.name for tool in tools]
agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names, **kwargs)
return AgentExecutor.from_agent_... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/openapi/base.html |
46b195e8550c-0 | Source code for langchain.agents.agent_toolkits.python.base
"""Python agent."""
from typing import Any, Dict, Optional
from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent
from langchain.agents.agent_toolkits.python.prompt import PREFIX
from langchain.agents.mrkl.base import ZeroShotAgent
from langch... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
46b195e8550c-1 | elif agent_type == AgentType.OPENAI_FUNCTIONS:
system_message = SystemMessage(content=prefix)
_prompt = OpenAIFunctionsAgent.create_prompt(system_message=system_message)
agent = OpenAIFunctionsAgent(
llm=llm,
prompt=_prompt,
tools=tools,
callback_m... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/python/base.html |
b0822cf912d4-0 | Source code for langchain.agents.agent_toolkits.spark.base
"""Agent for working with pandas objects."""
from typing import Any, Dict, List, Optional
from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.spark.prompt import PREFIX, SUFFIX
from langchain.agents.mrkl.base import ZeroShotAge... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
b0822cf912d4-1 | ) -> AgentExecutor:
"""Construct a spark agent from an LLM and dataframe."""
if not _validate_spark_df(df) and not _validate_spark_connect_df(df):
raise ValueError("Spark is not installed. run `pip install pyspark`.")
if input_variables is None:
input_variables = ["df", "input", "agent_scrat... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/spark/base.html |
6f6dbf4a4161-0 | Source code for langchain.agents.agent_toolkits.nla.toolkit
"""Toolkit for interacting with API's using natural language."""
from __future__ import annotations
from typing import Any, List, Optional, Sequence
from pydantic import Field
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.a... | https://api.python.langchain.com/en/latest/_modules/langchain/agents/agent_toolkits/nla/toolkit.html |
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