get_openapi_chain#
- langchain.chains.openai_functions.openapi.get_openapi_chain(spec: OpenAPISpec | str, llm: BaseLanguageModel | None = None, prompt: BasePromptTemplate | None = None, request_chain: Chain | None = None, llm_chain_kwargs: Dict | None = None, verbose: bool = False, headers: Dict | None = None, params: Dict | None = None, **kwargs: Any) SequentialChain [source]#
Deprecated since version 0.2.13: This function 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.openai_functions.openapi.get_openapi_chain.html
Create a chain for querying an API from a OpenAPI spec.
- Note: this class is deprecated. See below for a replacement implementation.
The benefits of this implementation are:
Uses LLM tool calling features to encourage properly-formatted API requests;
Includes async support.
from typing import Any from langchain.chains.openai_functions.openapi import openapi_spec_to_openai_fn from langchain_community.utilities.openapi import OpenAPISpec from langchain_core.prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI # Define API spec. Can be JSON or YAML api_spec = """ { "openapi": "3.1.0", "info": { "title": "JSONPlaceholder API", "version": "1.0.0" }, "servers": [ { "url": "https://jsonplaceholder.typicode.com" } ], "paths": { "/posts": { "get": { "summary": "Get posts", "parameters": [ { "name": "_limit", "in": "query", "required": false, "schema": { "type": "integer", "example": 2 }, "description": "Limit the number of results" } ] } } } } """ parsed_spec = OpenAPISpec.from_text(api_spec) openai_fns, call_api_fn = openapi_spec_to_openai_fn(parsed_spec) tools = [ {"type": "function", "function": fn} for fn in openai_fns ] prompt = ChatPromptTemplate.from_template( "Use the provided APIs to respond to this user query:\n\n{query}" ) llm = ChatOpenAI(model="gpt-4o-mini", temperature=0).bind_tools(tools) def _execute_tool(message) -> Any: if tool_calls := message.tool_calls: tool_call = message.tool_calls[0] response = call_api_fn(name=tool_call["name"], fn_args=tool_call["args"]) response.raise_for_status() return response.json() else: return message.content chain = prompt | llm | _execute_tool
response = chain.invoke({"query": "Get me top two posts."})
- Parameters:
spec (Union[OpenAPISpec, str]) – OpenAPISpec or url/file/text string corresponding to one.
llm (Optional[BaseLanguageModel]) – language model, should be an OpenAI function-calling model, e.g. ChatOpenAI(model=”gpt-3.5-turbo-0613”).
prompt (Optional[BasePromptTemplate]) – Main prompt template to use.
request_chain (Optional[Chain]) – Chain for taking the functions output and executing the request.
llm_chain_kwargs (Optional[Dict]) –
verbose (bool) –
headers (Optional[Dict]) –
params (Optional[Dict]) –
kwargs (Any) –
- Return type: