Building RAG Pipelines with LangChain

Retrieval-Augmented Generation (RAG) is transforming how we build AI applications. Here's what I learned about creating efficient document retrieval pipelines.

What is RAG?

RAG combines the power of large language models with external knowledge retrieval. Instead of relying solely on the model's training data, we can augment responses with up-to-date, domain-specific information.

The Pipeline

1. 1.Document Loading - Ingest documents from various sources
2. 2.Text Splitting - Break documents into meaningful chunks
3. 3.Embedding - Convert text into vector representations
4. 4.Vector Store - Index embeddings for efficient retrieval
5. 5.Retrieval - Find relevant chunks based on user query
6. 6.Generation - Use LLM to synthesize answer from retrieved context

Key Learnings

• ▹Chunk size matters: Too small loses context, too large dilutes relevance
• ▹Overlap between chunks helps maintain continuity
• ▹Hybrid search (semantic + keyword) often outperforms pure semantic search
• ▹Re-ranking retrieved documents significantly improves quality

Next up: Exploring different embedding models and their trade-offs.