get_openapi_chain

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_classic.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}"
)
model = 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 | model | _execute_tool

response = chain.invoke({"query": "Get me top two posts."})