a agk: @szddlmZddlZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZddlZddlmZddlmZddlmZddlmZdd lmZmZdd lmZdd lmZdd lmZdd l m!Z!ddl"m#Z#erddl$m%Z%ddl&m'Z'ddddddZ(ddddddZ)dddddd Z*dd!d"d#d$Z+Gd%d&d&eZ,ed'd(d)d*d5d,d-d.d/d0dd0d0dd1d2 d3d4Z-dS)6) annotationsN) defaultdict) TYPE_CHECKINGAnyCallableDictListOptionalTupleUnion) deprecated)CallbackManagerForChainRun)BaseLanguageModel)JsonOutputFunctionsParser)BasePromptTemplateChatPromptTemplate)get_colored_text)Response)Chain)LLMChain)SequentialChain OpenAPISpec) Parameterrboolz Optional[str])o prefer_shortreturncCs,t|dd}t|dd}|r$|p"|S|p*|S)Nsummary description)getattr)rrrrr!w/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/openai_functions/openapi.py_get_descriptions   r#strdict)url path_paramsrc std|}i}|D]b}|dd}||}t|tr|ddkrl|ddkrXdnd}d||}nF|ddkr|ddkr|d nd}|d ||}n d|}nt|tr2|ddkrd ndfd d |D} |ddkrd}d}n |ddkrd}d}nd}d }||| 7}n>|ddkrLd|}n$|ddkrld|d |}n|}|||<q|j fi|S) Nz{(.*?)}z.;*r.,;=csg|]\}}||fqSr!)join).0kvZkv_sepr!r" 3z_format_url..) refindalllstriprstrip isinstancelistr.r%itemsformat) r&r'Zexpected_path_param new_paramsparamZ clean_paramvalsepnew_valZkv_strsr!r2r" _format_url"s@         rCzList[Parameter]r)paramsspecrcCsi}g}|D]t}|jr$||j}nt|jdj}||}|jrV|jsV|j|_t|jdd||j <|j r | |j q d||dS)NrTZ exclude_noneobject)type propertiesrequired) Z param_schema get_schemar;contentvaluesmedia_type_schemarjsonloadsnamerJappend)rDrErIrJpschemarNr!r!r"_openapi_params_to_json_schemaIs  rUz%Tuple[List[Dict[str, Any]], Callable])rErcsBzddlm}Wnty*tdYn0|js>gddfSg}i|jD]}dd||D}||D]}i}|||}|}||D]} | || j | j f<qt t } | D]\} } | | d| qd d d d d } | D]"\}}| |rt| ||||<q||}|r|jri}|j D]4\}}|jr:||j}t|jdd||<q:t|dkrt | d\}}|dkrdnd}|||<n"t|dkrdt |i|d<||||}|j|jd|dd}||||j|jd|d<qpqLd!dddddddfdd }||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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