Structure

• Add your openAI key on .env file:

PROJECT_NAME="langchain-api"
OPENAI_API_KEY="sk-xxx"

• It was created two main files:
  • …/main.py → Fast API endpoints
  • …/src,/langchain_functions.py → Langchain model do generate docstring

• Also was used tested notebooks for development research
  • …/notebooks/langchainAPI.ipynb → With langchain test code
  • …/notebooks/request_test → With request API tests

Virtual Environment

• To manage the virtual environment was used Poetry, the installation can be done following the documentation on Poetry Doc.

poetry add fastapi
poetry add langchain
poetry add openai

• After download poetry, just navigate to project folder and use poetry install command

> CD PROJECT_FOLDER
> poetry install

Execution

• Run the API present in main.py in a terminal or directly on your IDE.

from fastapi import FastAPI, HTTPException
import uvicorn
from typing import Dict

import os
import sys
from dotenv import load_dotenv
load_dotenv('.env')

sys.path.append(os.getcwd().split(os.environ['PROJECT_NAME'])[0] + os.environ['PROJECT_NAME'] + '/src')
import langchain_functions

app = FastAPI()

@app.post("/generate_docstring")
def generate_docstring(request: Dict):
    try:
        # Run the LangChain function to generate the docstring
        output_code = langchain_functions.generate_docstring(request['file_contents'])
        
        # Return the code with docstring
        return {"output_code": output_code}
    
    except Exception as e:
        # Return an error if something goes wrong
        raise HTTPException(status_code=500, detail=str(e))
    
@app.get("/")
async def root():
    return {"message": "API Running"}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)