Build a RAG chatbot with LangChain, LanceDB, and n4n.ai

Building a langchain lancedb n4n.ai rag chatbot is straightforward if you treat retrieval and generation as separate, testable components. This guide walks through a working pipeline from document ingestion to chat endpoint, using LangChain for orchestration, LanceDB for vector storage, and n4n.ai as the OpenAI-compatible model gateway.

Step 1: Install dependencies and configure keys

Create a virtual environment and install the packages you actually need—no bloat:

pip install langchain langchain-community langchain-openai lancedb fastapi uvicorn openai

Set your API keys. You need an OpenAI key for embeddings (or swap in a local model) and an n4n.ai key for chat completion:

export OPENAI_API_KEY=sk-...
export N4N_API_KEY=nt-...

Keep secrets out of source. Read them from env in code.

Step 2: Ingest documents and index in LanceDB

LanceDB stores vectors column‑wise on disk, which makes filtered retrieval cheap. Load a text file, split it, embed, and write the table.

import lancedb
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import LanceDB

db = lancedb.connect("/tmp/lancedb")
loader = TextLoader("product_docs.txt")
raw_docs = loader.load()

splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(raw_docs)

embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vector_store = LanceDB.from_documents(
    chunks,
    embeddings,
    connection=db,
    table_name="rag_product",
)

Why chunk size 500?

Anything under 200 tokens loses context; over 1000 makes retrieval imprecise. 500 with 50 overlap is a sane default for policy docs. Tune after you measure recall.

The retrieval layer of our langchain lancedb n4n.ai rag chatbot relies on LanceDB’s as_retriever interface, so we avoid writing raw SQL vectors queries.

Step 3: Build the retrieval chain

Use LangChain Expression Language (LCEL) to compose retriever, prompt, and model. This keeps each stage unit‑testable.

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

retriever = vector_store.as_retriever(search_kwargs={"k": 4})

prompt = ChatPromptTemplate.from_template(
    "Answer using only the context.\n\nContext:\n{context}\n\nQuestion: {question}"
)

def format_docs(docs):
    return "\n\n".join(d.page_content for d in docs)

# `chat` is defined in Step 4
chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | chat
    | StrOutputParser()
)

k=4 returns four chunks. More chunks increase latency and prompt cost without guaranteed quality gains. Start low.

Step 4: Configure the generation model via n4n.ai

Point LangChain’s ChatOpenAI at the n4n.ai OpenAI‑compatible endpoint. n4n.ai exposes a single endpoint covering 240+ models and handles provider fallback automatically, so the base URL works without per‑provider branching.

from langchain_openai import ChatOpenAI

chat = ChatOpenAI(
    model="gpt-4o-mini",
    api_key="nt-...",  # N4N_API_KEY
    base_url="https://api.n4n.ai/v1",
    temperature=0.2,
)

Temperature 0.2 keeps answers grounded. If the underlying provider is rate‑limited, the gateway routes to a healthy equivalent model without code changes.

Step 5: Expose the chatbot as an HTTP service

Wrap the chain in FastAPI. Keep the request shape minimal.

from fastapi import FastAPI

app = FastAPI()

@app.post("/chat")
async def chat_endpoint(payload: dict):
    answer = chain.invoke(payload["question"])
    return {"answer": answer}

# Run with: uvicorn main:app --port 8000

This is the deployment surface for the langchain lancedb n4n.ai rag chatbot. Put it behind auth and request validation before production.

Step 6: Verify the pipeline end to end

Start the server and send a question that exists in product_docs.txt:

uvicorn main:app --port 8000 &
curl -X POST http://localhost:8000/chat \
  -H "content-type: application/json" \
  -d '{"question":"What is the refund window for annual plans?"}'

Success criteria:

• Response JSON contains an answer field.
• The answer cites details present in the ingested text, not generic LLM knowledge.
• Latency is dominated by embedding lookup + model generation, typically sub‑second locally.

If you get hallucinated answers, increase k or tighten the prompt with “If unknown, say ‘not in docs’.” If latency spikes, reduce chunk overlap.

When you deploy the langchain lancedb n4n.ai rag chatbot, watch token metering on the gateway to catch prompt bloat early. The separation above makes it trivial to swap LanceDB for another vector store or n4n.ai for a different OpenAI‑compatible endpoint without touching chain logic.