How to Build a Production-Ready RAG System with LangChain and Pinecone

What RAG Actually Solves

Large language models hallucinate when they do not know something. RAG (Retrieval Augmented Generation) fixes this by giving the model relevant context from your own data at query time. Instead of relying on training data, the model reads the actual documents before answering. The result is accurate, grounded responses that cite real information.

The Five Components of a RAG System

Every RAG system has the same five parts: a document loader, a text splitter, an embedding model, a vector store, and a retrieval chain. Understanding each one is essential before writing any code.

1. Document Loading

LangChain has loaders for PDFs, Word documents, web pages, Notion databases, Google Drive, and dozens of other sources. Load your documents first, then process them. Keep the source metadata — you will need it to cite sources in responses.

2. Text Splitting

Split documents into chunks of 500-1000 tokens with 100-token overlap. The overlap ensures context is not lost at chunk boundaries. Use RecursiveCharacterTextSplitter — it respects sentence and paragraph boundaries rather than cutting mid-sentence.

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 800,
  chunkOverlap: 100,
});
const chunks = await splitter.splitDocuments(docs);

3. Embeddings

Convert each chunk into a vector using OpenAI's text-embedding-3-small model. This 1536-dimension vector captures the semantic meaning of the text. Similar concepts will have vectors close together in the embedding space — this is what makes semantic search possible.

4. Vector Store

Store the vectors in Pinecone for production or pgvector for PostgreSQL-native deployments. Pinecone is fully managed and scales automatically. pgvector keeps everything in your existing database, which simplifies the stack and reduces cost. For most projects under 1 million documents, pgvector is the right choice.

5. Retrieval Chain

At query time, embed the user's question, find the top-k most similar chunks in the vector store, and pass them as context to the LLM. LangChain's RetrievalQAChain handles this end-to-end, including conversation history management.

Production Considerations

Cache embeddings — recomputing them is expensive. Implement a re-ranking step for better retrieval quality: retrieve 20 candidates, use a cross-encoder to re-rank them, pass the top 4 to the LLM. Add a confidence threshold — if the retrieved chunks are not relevant enough, return "I don't know" rather than hallucinating. Log every retrieval so you can debug and improve the system over time.

I build RAG systems for enterprise clients who need accurate AI over their own data. Book a consultation to discuss your use case.