Retrieval Augmented Generation, or RAG, represents a fundamental shift in how we approach LLM knowledge enhancement. Rather than solely relying on an LLM's built-in knowledge, RAG dynamically injects relevant information into the conversation by retrieving it from an external knowledge base. Think of it as giving your LLM access to a personalized library that it can reference during conversations.
Understanding RAG requires breaking down its core components and how they work together:
The journey begins with your documents. These could be anything from technical manuals and research papers to customer support tickets and internal wikis. The processing pipeline typically involves:
Text Extraction - Converting various file formats into plain text while preserving important structural information. This seemingly simple step can become complex when dealing with PDFs, images with text, or documents with complex formatting.
Chunking - Breaking down documents into smaller, manageable pieces. This isn't as straightforward as it might seem. Chunk too small, and you lose context. Chunk too large, and you might dilute the relevance of your retrievals. The art lies in maintaining semantic coherence while optimizing for retrieval.
Once you have your chunks, you need to convert them into a format that enables efficient similarity search. This is where embeddings come in - dense vector representations of your text that capture semantic meaning.
The choice of embedding model is crucial. While OpenAI's text-embedding-ada-002 is popular, you might opt for domain-specific models or newer alternatives like BGE or INSTRUCTOR. Each comes with its own tradeoffs in terms of accuracy, speed, and cost.
Your embeddings need to be stored in a way that enables fast retrieval. Vector databases like Pinecone, Weaviate, or FAISS become essential here. The choice of database and index type impacts both retrieval speed and accuracy.
Consider factors like: