langchain-agent-api/
├── agent_app/
│   ├── main.py
│   └── agent.py
├── requirements.txt
├── Dockerfile
└── .env

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.utilities import SerpAPIWrapper
import os

def create_agent():
    llm = OpenAI(temperature=0, openai_api_key=os.getenv("OPENAI_API_KEY"))
    search = SerpAPIWrapper()
    tools = [Tool(name="Search", func=search.run, description="Useful for answering general questions.")]
    agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
    return agent

from fastapi import FastAPI
from pydantic import BaseModel
from agent import create_agent

app = FastAPI()
agent = create_agent()

class Query(BaseModel):
    question: str

@app.post("/ask")
async def ask_question(query: Query):
    response = agent.run(query.question)
    return {"response": response}

fastapi
uvicorn
langchain
openai
python-dotenv

pip install -r requirements.txt

FROM python:3.10-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY agent_app ./agent_app
COPY .env .
CMD ["uvicorn", "agent_app.main:app", "--host", "0.0.0.0", "--port", "8000"]

OPENAI_API_KEY=your_openai_key_here
SERPAPI_API_KEY=your_serpapi_key_here

docker build -t langchain-agent-api .

docker run --env-file .env -p 8000:8000 langchain-agent-api

curl -X POST http://localhost:8000/ask \
-H "Content-Type: application/json" \
-d '{"question": "Who is the CEO of OpenAI?"}'

version: "3.8"
services:
  langchain:
    build: .
    ports:
      - "8000:8000"
    env_file:
      - .env

docker-compose up --build