o Zhk:@sTddlmZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZddlZddlmZddlmZddlmZddlmZdd lmZmZdd lmZdd lmZdd lmZdd l m!Z!ddl"m#Z#erxddl$m%Z%ddl&m'Z'd>ddZ(d?ddZ)d@d#d$Z*dAd&d'Z+Gd(d)d)eZ,ed*d+d,d-     .  dBdCd3sz_format_url..r") refindalllstriprstrip isinstancelistr/r(itemsformat) r%r'Zexpected_path_param new_paramsparamZ clean_paramvalsepnew_valZkv_strsr"r3r# _format_url"s@            rCparamsList[Parameter]specrcCsi}g}|D]:}|jr||j}nt|jdj}||}|jr+|js+|j|_t|jdd||j <|j r@| |j qd||dS)NrTZ exclude_noneobject)type propertiesrequired) Z param_schema get_schemar;contentvaluesmedia_type_schemar jsonloadsnamerKappend)rDrFrJrKpschemarOr"r"r#_openapi_params_to_json_schemaIs    rV%Tuple[List[Dict[str, Any]], Callable]cs"zddlm}Wn tytdw|jsgddfSg}i|jD]}dd||D}||D]}i}|||}|}||D] } | || j | j f<qHt t } | D] \} } | | d| q[d d d d d } | D]\}}| |rt| ||||<qt||}|r|jri}|j D]\}}|jr||j}t|jdd||<qt|dkrt | d\}}|dkrdnd}|||<nt|dkrdt |i|d<||||}|j|jd|dd}||||j|jd|d<q5q$  d#d$fd!d" }||fS)%a4Convert a valid OpenAPI spec to the JSON Schema format expected for OpenAI functions. 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The benefits of this implementation are: - Uses LLM tool calling features to encourage properly-formatted API requests; - Includes async support. .. code-block:: python 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 .. code-block:: python response = chain.invoke({"query": "Get me top two posts."}) Args: spec: OpenAPISpec or url/file/text string corresponding to one. llm: language model, should be an OpenAI function-calling model, e.g. `ChatOpenAI(model="gpt-3.5-turbo-0613")`. prompt: Main prompt template to use. request_chain: Chain for taking the functions output and executing the request. rrzqCould not import langchain_community.utilities.openapi. 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