Building RAG Pipelines with LangChain
Retrieval Augmented Generation is the standard for enterprise AI. Here is how to build a robust pipeline with vector databases and semantic reranking.
Why RAG?
LLMs don't know your private data. Fine-tuning is expensive and slow to update. RAG allows us to inject relevant context into the prompt at runtime. It's the difference between a generic answer and a business-specific insight.
The Architecture
I use Pinecone for vector storage and OpenAI's `text-embedding-3-small` for embeddings. The trick isn't the retrieval; it's the chunking strategy. RecursiveCharacterTextSplitter with meaningful overlap ensures context isn't severed mid-sentence.
The Reranking Step
Vector similarity isn't always semantic relevance. I add a Cross-Encoder reranker step (using Cohere) to sort the retrieved chunks before feeding them to the LLM. This dramatically reduces hallucinations.
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