"""Chain that makes API calls and summarizes the responses to answer a question.""" from __future__ import annotations from collections.abc import Sequence from typing import Any from urllib.parse import urlparse from langchain_core._api import deprecated from langchain_core.callbacks import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from pydantic import Field, model_validator from typing_extensions import Self from langchain_classic.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT from langchain_classic.chains.base import Chain from langchain_classic.chains.llm import LLMChain def _extract_scheme_and_domain(url: str) -> tuple[str, str]: """Extract the scheme + domain from a given URL. Args: url: The input URL. Returns: A 2-tuple of scheme and domain """ parsed_uri = urlparse(url) return parsed_uri.scheme, parsed_uri.netloc def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool: """Check if a URL is in the allowed domains. Args: url: The input URL. limit_to_domains: The allowed domains. Returns: `True` if the URL is in the allowed domains, `False` otherwise. """ scheme, domain = _extract_scheme_and_domain(url) for allowed_domain in limit_to_domains: allowed_scheme, allowed_domain_ = _extract_scheme_and_domain(allowed_domain) if scheme == allowed_scheme and domain == allowed_domain_: return True return False try: from langchain_community.utilities.requests import TextRequestsWrapper @deprecated( since="0.2.13", message=( "This class is deprecated and will be removed in langchain 1.0. " "See API reference for replacement: " "https://api.python.langchain.com/en/latest/chains/langchain.chains.api.base.APIChain.html" ), removal="1.0", ) class APIChain(Chain): """Chain that makes API calls and summarizes the responses to answer a question. *Security Note*: This API chain uses the requests toolkit to make GET, POST, PATCH, PUT, and DELETE requests to an API. Exercise care in who is allowed to use this chain. If exposing to end users, consider that users will be able to make arbitrary requests on behalf of the server hosting the code. For example, users could ask the server to make a request to a private API that is only accessible from the server. Control access to who can submit issue requests using this toolkit and what network access it has. See https://python.langchain.com/docs/security for more information. !!! note This class is deprecated. See below for a replacement implementation using LangGraph. The benefits of this implementation are: - Uses LLM tool calling features to encourage properly-formatted API requests; - Support for both token-by-token and step-by-step streaming; - Support for checkpointing and memory of chat history; - Easier to modify or extend (e.g., with additional tools, structured responses, etc.) Install LangGraph with: ```bash pip install -U langgraph ``` ```python from typing import Annotated, Sequence from typing_extensions import TypedDict from langchain_classic.chains.api.prompt import API_URL_PROMPT from langchain_community.agent_toolkits.openapi.toolkit import RequestsToolkit from langchain_community.utilities.requests import TextRequestsWrapper from langchain_core.messages import BaseMessage from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI from langchain_core.runnables import RunnableConfig from langgraph.graph import END, StateGraph from langgraph.graph.message import add_messages from langgraph.prebuilt.tool_node import ToolNode # NOTE: There are inherent risks in giving models discretion # to execute real-world actions. We must "opt-in" to these # risks by setting allow_dangerous_request=True to use these tools. # This can be dangerous for calling unwanted requests. Please make # sure your custom OpenAPI spec (yaml) is safe and that permissions # associated with the tools are narrowly-scoped. ALLOW_DANGEROUS_REQUESTS = True # Subset of spec for https://jsonplaceholder.typicode.com api_spec = \"\"\" openapi: 3.0.0 info: title: JSONPlaceholder API version: 1.0.0 servers: - url: https://jsonplaceholder.typicode.com paths: /posts: get: summary: Get posts parameters: &id001 - name: _limit in: query required: false schema: type: integer example: 2 description: Limit the number of results \"\"\" model = ChatOpenAI(model="gpt-4o-mini", temperature=0) toolkit = RequestsToolkit( requests_wrapper=TextRequestsWrapper(headers={}), # no auth required allow_dangerous_requests=ALLOW_DANGEROUS_REQUESTS, ) tools = toolkit.get_tools() api_request_chain = ( API_URL_PROMPT.partial(api_docs=api_spec) | model.bind_tools(tools, tool_choice="any") ) class ChainState(TypedDict): \"\"\"LangGraph state.\"\"\" messages: Annotated[Sequence[BaseMessage], add_messages] async def acall_request_chain(state: ChainState, config: RunnableConfig): last_message = state["messages"][-1] response = await api_request_chain.ainvoke( {"question": last_message.content}, config ) return {"messages": [response]} async def acall_model(state: ChainState, config: RunnableConfig): response = await model.ainvoke(state["messages"], config) return {"messages": [response]} graph_builder = StateGraph(ChainState) graph_builder.add_node("call_tool", acall_request_chain) graph_builder.add_node("execute_tool", ToolNode(tools)) graph_builder.add_node("call_model", acall_model) graph_builder.set_entry_point("call_tool") graph_builder.add_edge("call_tool", "execute_tool") graph_builder.add_edge("execute_tool", "call_model") graph_builder.add_edge("call_model", END) chain = graph_builder.compile() ``` ```python example_query = "Fetch the top two posts. What are their titles?" events = chain.astream( {"messages": [("user", example_query)]}, stream_mode="values", ) async for event in events: event["messages"][-1].pretty_print() ``` """ api_request_chain: LLMChain api_answer_chain: LLMChain requests_wrapper: TextRequestsWrapper = Field(exclude=True) api_docs: str question_key: str = "question" #: :meta private: output_key: str = "output" #: :meta private: limit_to_domains: Sequence[str] | None = Field(default_factory=list) """Use to limit the domains that can be accessed by the API chain. * For example, to limit to just the domain `https://www.example.com`, set `limit_to_domains=["https://www.example.com"]`. * The default value is an empty tuple, which means that no domains are allowed by default. By design this will raise an error on instantiation. * Use a None if you want to allow all domains by default -- this is not recommended for security reasons, as it would allow malicious users to make requests to arbitrary URLS including internal APIs accessible from the server. """ @property def input_keys(self) -> list[str]: """Expect input key. :meta private: """ return [self.question_key] @property def output_keys(self) -> list[str]: """Expect output key. :meta private: """ return [self.output_key] @model_validator(mode="after") def validate_api_request_prompt(self) -> Self: """Check that api request prompt expects the right variables.""" input_vars = self.api_request_chain.prompt.input_variables expected_vars = {"question", "api_docs"} if set(input_vars) != expected_vars: msg = f"Input variables should be {expected_vars}, got {input_vars}" raise ValueError(msg) return self @model_validator(mode="before") @classmethod def validate_limit_to_domains(cls, values: dict) -> Any: """Check that allowed domains are valid.""" # This check must be a pre=True check, so that a default of None # won't be set to limit_to_domains if it's not provided. if "limit_to_domains" not in values: msg = ( "You must specify a list of domains to limit access using " "`limit_to_domains`" ) raise ValueError(msg) if ( not values["limit_to_domains"] and values["limit_to_domains"] is not None ): msg = ( "Please provide a list of domains to limit access using " "`limit_to_domains`." ) raise ValueError(msg) return values @model_validator(mode="after") def validate_api_answer_prompt(self) -> Self: """Check that api answer prompt expects the right variables.""" input_vars = self.api_answer_chain.prompt.input_variables expected_vars = {"question", "api_docs", "api_url", "api_response"} if set(input_vars) != expected_vars: msg = f"Input variables should be {expected_vars}, got {input_vars}" raise ValueError(msg) return self def _call( self, inputs: dict[str, Any], run_manager: CallbackManagerForChainRun | None = None, ) -> dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() question = inputs[self.question_key] api_url = self.api_request_chain.predict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) _run_manager.on_text(api_url, color="green", end="\n", verbose=self.verbose) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains, ): msg = ( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) raise ValueError(msg) api_response = self.requests_wrapper.get(api_url) _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose, ) answer = self.api_answer_chain.predict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} async def _acall( self, inputs: dict[str, Any], run_manager: AsyncCallbackManagerForChainRun | None = None, ) -> dict[str, str]: _run_manager = ( run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() ) question = inputs[self.question_key] api_url = await self.api_request_chain.apredict( question=question, api_docs=self.api_docs, callbacks=_run_manager.get_child(), ) await _run_manager.on_text( api_url, color="green", end="\n", verbose=self.verbose, ) api_url = api_url.strip() if self.limit_to_domains and not _check_in_allowed_domain( api_url, self.limit_to_domains, ): msg = ( f"{api_url} is not in the allowed domains: {self.limit_to_domains}" ) raise ValueError(msg) api_response = await self.requests_wrapper.aget(api_url) await _run_manager.on_text( str(api_response), color="yellow", end="\n", verbose=self.verbose, ) answer = await self.api_answer_chain.apredict( question=question, api_docs=self.api_docs, api_url=api_url, api_response=api_response, callbacks=_run_manager.get_child(), ) return {self.output_key: answer} @classmethod def from_llm_and_api_docs( cls, llm: BaseLanguageModel, api_docs: str, headers: dict | None = None, api_url_prompt: BasePromptTemplate = API_URL_PROMPT, api_response_prompt: BasePromptTemplate = API_RESPONSE_PROMPT, limit_to_domains: Sequence[str] | None = (), **kwargs: Any, ) -> APIChain: """Load chain from just an LLM and the api docs.""" get_request_chain = LLMChain(llm=llm, prompt=api_url_prompt) requests_wrapper = TextRequestsWrapper(headers=headers) get_answer_chain = LLMChain(llm=llm, prompt=api_response_prompt) return cls( api_request_chain=get_request_chain, api_answer_chain=get_answer_chain, requests_wrapper=requests_wrapper, api_docs=api_docs, limit_to_domains=limit_to_domains, **kwargs, ) @property def _chain_type(self) -> str: return "api_chain" except ImportError: class APIChain: # type: ignore[no-redef] """Raise an ImportError if APIChain is used without langchain_community.""" def __init__(self, *_: Any, **__: Any) -> None: """Raise an ImportError if APIChain is used without langchain_community.""" msg = ( "To use the APIChain, you must install the langchain_community package." "pip install langchain_community" ) raise ImportError(msg)