RAG Pipeline Template
A production-ready retrieval-augmented generation pipeline — document ingestion, chunking, embedding, vector storage in Pinecone, hybrid search, and a streaming chat API. Python + LangChain.
A production-ready retrieval-augmented generation pipeline — document ingestion, chunking, embedding, vector storage in Pinecone, hybrid search, and a streaming chat API. Python + LangChain.
What is RAG Pipeline Template?
RAG (retrieval-augmented generation) is the architecture behind document Q&A products, customer support bots trained on your knowledge base, and any AI product that needs to answer questions from a private corpus of documents. Building a RAG pipeline from scratch involves solving a dozen non-trivial sub-problems: document loading and cleaning, semantic chunking strategies, embedding model selection, vector store indexing, metadata filtering, hybrid search combining vector and keyword retrieval, context window assembly, and streaming the response back to the client. This template solves all of them. It's a complete Python backend — document ingestion pipeline, a FastAPI server with a streaming chat endpoint, and Pinecone vector storage. Not a Jupyter notebook tutorial — a deployable production service with proper error handling, logging, and rate limiting.
Everything in the package.
Document Ingestion Pipeline
Loaders for PDF, Word, Markdown, HTML, and plain text. Automatic chunking with configurable overlap. Metadata extraction and storage alongside embeddings.
Embedding & Indexing
OpenAI text-embedding-3-small for embeddings (configurable to any model). Batch indexing to Pinecone with upsert idempotency — re-index without duplicating vectors.
Hybrid Search
Combines semantic vector search with BM25 keyword search using Reciprocal Rank Fusion. More accurate than pure vector search on technical documents and short queries.
Streaming Chat API
FastAPI endpoint with Server-Sent Events streaming. Returns source citations alongside the answer. CORS configured for browser clients.
Conversation Memory
Per-session conversation history management with configurable context window. Supports both stateless (pass history) and stateful (server-side session) modes.
Metadata Filtering
Filter retrieval by document source, date range, tags, or any custom metadata. Namespace documents by tenant or topic for multi-document deployments.
Reranking
Optional Cohere reranking step that reorders retrieved chunks by relevance before assembly into context. Measurably improves answer quality on complex queries.
Deployment Config
Dockerfile, Railway deployment config, AWS Lambda handler, and environment variable template. Deploy to your infrastructure in under an hour.
Built for production, not demos.
LangChain LCEL Architecture
Built on LangChain Expression Language — composable, debuggable, and easy to extend. Swap any component (embeddings, LLM, vector store) with a one-line change.
Pinecone Serverless
Uses Pinecone's serverless tier — no provisioned pods, no fixed monthly cost. Pay per query. Scales from a 10-document prototype to a million-document production index.
Source Citations
Every answer includes source citations — the document name, page number, and the exact chunk that was used to generate the answer. Builds user trust and aids verification.
Async Throughout
FastAPI with async handlers and LangChain async chains. Handles concurrent requests efficiently without blocking on IO operations.
Evaluation Suite
RAGAS evaluation scripts to measure retrieval quality (context precision, recall) and answer quality (faithfulness, relevance) against a test question set.
Observability
LangSmith tracing integration — every retrieval and generation step is logged to LangSmith for debugging, latency analysis, and prompt iteration.
Who is this for?
Developers, indie hackers, and product teams who want to skip the boilerplate and ship faster.
Document Q&A Products
The classic RAG use case: upload your documents, ask questions, get answers with citations. This pipeline is the backend for that product — add any chat UI on top.
Customer Support Bots on Your Knowledge Base
Index your help docs, FAQs, and product documentation. Your support bot answers from your actual content — no hallucination, no off-topic responses.
Internal Knowledge Retrieval
Index your company's internal docs, Notion pages, Confluence, or Slack exports. Let employees ask questions and get answers from company knowledge.
AI SaaS Products That Need a Knowledge Layer
If your AI product needs to answer questions from domain-specific content, this pipeline is your retrieval layer. Connect it to any LLM and any frontend.
Technologies used.
Python Layer
AI / Embeddings
Vector DB
Deployment
Common questions.
Can I use this with Claude instead of GPT-4?
Yes — the LLM is a swappable component via LangChain. Replace ChatOpenAI with ChatAnthropic and pass your Anthropic API key. The rest of the pipeline is model-agnostic.
Can I use a different vector store instead of Pinecone?
Yes — LangChain supports Weaviate, Qdrant, pgvector, and Chroma as drop-in replacements. The README includes swap instructions for each.
What's the cost to run this in production?
Depends on your query volume and document count. Pinecone serverless charges per query (~$0.00001/query). OpenAI embeddings are ~$0.02 per million tokens. A small knowledge base with moderate traffic costs a few dollars a month.
Does this handle large documents (100+ page PDFs)?
Yes — the ingestion pipeline chunks documents with configurable size and overlap. Large PDFs are split into retrievable chunks. The README includes guidance on chunk size tuning for different document types.
Is this a production API or a prototype?
It's a production API. Error handling, request validation, rate limiting, structured logging, and async processing are all included. 390+ developers are using it as the backend of live AI products.
Need something custom-built?
Need a RAG pipeline built for a specific domain — legal documents, medical records, financial reports, codebases? Or need it integrated into an existing product with custom ingestion sources, multi-tenant architecture, or fine-tuned embeddings? Our AI team has built production RAG systems at scale.
Ready to ship faster?
Buy RAG Pipeline Template today and go from zero to production in hours — not weeks.
Setup and implementation
Review prerequisites, installation steps, and configuration before integrating this product.
Read the setup documentation