← Back to Projects

Hybrid Search RAG App

advancedPhase 07 · RAGPythonLangChainPineconeOpenAI

Build a Retrieval-Augmented Generation app using LangChain, Pinecone, hybrid search (keyword + semantic), and a cross-encoder reranker.

What you'll learn

  • Document loading and semantic chunking
  • Creating vector embeddings
  • Upserting vectors to a managed database
  • Performing Hybrid Search (Sparse + Dense)
  • Reranking retrieved context before LLM generation

Architecture

Documents (PDFs/Markdown)
  → Semantic Chunking
  → Embeddings Model
  → Vector Database (Pinecone)
  
User Query
  → Hybrid Search (Dense Embeddings + Sparse BM25)
  → Cross-Encoder Reranker
  → LLM Context Injection
  → Synthesized Answer

Steps

  1. Gather a corpus of domain-specific documents (e.g., 10 highly technical blog posts or papers).
  2. Write an ingestion script using LangChain or LlamaIndex to chunk the text and generate embeddings.
  3. Push the embeddings to a Pinecone serverless index configured for hybrid search (dense vectors + sparse BM25 vectors).
  4. Write a retrieval script that accepts a user query, embeds it, and fetches the top 20 hybrid search results.
  5. Pass the 20 results through a small Cross-Encoder model (like bge-reranker-base) to score their true relevance, keeping only the top 5.
  6. Inject the top 5 chunks into a prompt template and call an LLM to generate a final answer with citations.
  7. Wrap this in a simple Streamlit or Gradio UI.

← Back to the roadmap