To integrate semantic search with Retrieval-Augmented Generation (RAG), you need to combine a retrieval system that understands context with a language model that generates answers. Semantic search improves RAG by retrieving documents based on meaning rather than keywords, ensuring the generator receives relevant context. This involves embedding models for semantic understanding, a vector database for efficient retrieval, and a language model to synthesize the final output. The process typically includes encoding data into vectors, querying the database, and feeding results to the generator.
First, set up the semantic search component. Use a pre-trained embedding model like BERT or sentence-transformers to convert text into high-dimensional vectors. These models capture semantic relationships, allowing similar phrases (e.g., “canine” and “dog”) to have close vector representations. Store these embeddings in a vector database such as FAISS, Pinecone, or Milvus, which enables fast similarity searches. For example, if your RAG system answers customer support questions, encode your FAQ articles into vectors. When a user asks, “How do I reset my password?” the system converts the query to a vector, finds the closest matches in the database, and retrieves the top-k relevant articles.
Next, connect the retrieval system to the generator. Most RAG implementations use a framework like LangChain or LlamaIndex to streamline this. After retrieving documents, pass them as context to a language model (e.g., GPT-4 or Llama 2) alongside the user’s query. For instance, you might structure the prompt as: “Answer the user’s question: [query] using this context: [retrieved documents].” Ensure the generator is configured to prioritize the provided context. If the retrieved documents mention password reset steps via email, the model should generate instructions aligned with that method, even if the base model’s training data includes alternative approaches.
Finally, optimize the pipeline. Experiment with chunking strategies for your source documents—smaller chunks (e.g., 256 tokens) may improve retrieval precision, while larger chunks provide broader context. Implement re-ranking to refine results: after retrieving top-k candidates, use a cross-encoder model to score their relevance more accurately. Monitor performance with metrics like recall@k (how often the correct document is in the top results) and check if the generator’s outputs align with the context. For example, if users report inaccuracies about password reset methods, verify whether the retrieval step is fetching outdated articles or the generator is ignoring the context. Update the vector database regularly to reflect new data, and fine-tune the embedding model on domain-specific text if generic embeddings underperform.