o {q i:@s>ddlmZddlZddlZddlmZddlmZmZm Z m Z m Z ddl Z ddl mZddlmZddlmZddlmZdd lmZmZdd lmZdd l mZdd lmZdd lmZddlm Z errddl!m"Z"ddl#m$Z$d9ddZ%d:ddZ&d;dd Z'Gd!d"d"eZ(ed#d$d%d&     '  d) annotationsN) defaultdict) TYPE_CHECKINGAnyCallableOptionalUnion) deprecated)CallbackManagerForChainRun)BaseLanguageModel)JsonOutputFunctionsParser)BasePromptTemplateChatPromptTemplate)get_colored_text)Response)Chain)LLMChain)SequentialChain OpenAPISpec) Parameterurlstr path_paramsdictreturnc s~td|}i}|D]}|dd}||}t|trY|ddkr5|ddkr+dnd}d||}n}|ddkrS|ddkrF|d nd}|d ||}n_d|}nYt|tr|ddkrfd ndfd d |D} |ddkr~d}d}n|ddkrd}d}nd}d }||| 7}n|ddkrd|}n|ddkrd|d |}n|}|||<q |j d i|S)Nz{(.*?)}z.;*r.,;=csg|] \}}||fqS)join).0kvZkv_sepr"p/home/app/PaddleOCR-VL/.venv_paddleocr/lib/python3.10/site-packages/langchain/chains/openai_functions/openapi.py +sz_format_url..r") refindalllstriprstrip isinstancelistr#ritemsformat) rrZexpected_path_param new_paramsparamZ clean_paramvalsepnew_valZkv_strsr"r'r( _format_urls@            r8paramslist[Parameter]specrcCsi}g}|D]:}|jr||j}ntt|jj}||}|jr+|js+|j|_t |jdd||j <|j r@| |j qd||dS)NTZ exclude_noneobject)type propertiesrequired) Z param_schema get_schemanextitercontentvaluesmedia_type_schema descriptionjsonloadsnamer@append)r9r;r?r@pschemarFr"r"r(_openapi_params_to_json_schema@s    rN%tuple[list[dict[str, Any]], Callable]c s4zddlm}Wnty}zd}t||d}~ww|js%gddfSg}i|jD]}dd||D}||D]}i}|||} |} || D] } | | | j | j f<qPt t } | D] \} }| | d |qcd d d d d}| D]\}}| |rt| ||||<q||| }|r|jri}|j D]\}}|jr||j}t|jdd||<qt|d krtt| \}}|dkrdnd}|||<nt|d krdt |i|d<||||}|j|jd|dd}||||j|jd|d<q=q,   d&d'fd$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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