Building Multi-Agent RAG with LangChain, pgvector, and FastAPI in Python (2026 Guide)

Beyond Simple Retrieval-Augmented Generation

Naive RAG pipelines (chunking text, creating embeddings, and querying top-k vectors) frequently return irrelevant context when user questions are ambiguous or require synthesis across multiple document domains. Multi-Agent RAG introduces specialized query-routing agents, document-retrieval agents, and answer-grading agents that verify facts before presenting answers to users.

Documented by Saizul Amin for AI engineers and machine learning practitioners.

FastAPI Service Integration

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from langchain_community.vectorstores import PGVector
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

app = FastAPI(title="Multi-Agent RAG API")

CONNECTION_STRING = "postgresql+psycopg2://user:secret@localhost:5432/rag_db"
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = PGVector(connection_string=CONNECTION_STRING, embedding_function=embeddings, collection_name="enterprise_docs")
llm = ChatOpenAI(model="gpt-4o", temperature=0.1)

class QueryPayload(BaseModel):
    question: str

@app.post("/api/v1/query")
async def handle_rag_query(payload: QueryPayload):
    docs = vectorstore.similarity_search(payload.question, k=4)
    context_str = "

".join([doc.page_content for doc in docs])
    
    prompt = ChatPromptTemplate.from_template(
        "Answer the question based strictly on this context:
{context}

Question: {question}"
    )
    chain = prompt | llm
    res = chain.invoke({"context": context_str, "question": payload.question})
    
    return {"answer": res.content, "sources_count": len(docs)}