Combining vector search with large language models (LLMs) effectively requires careful attention to data preparation, search optimization, and context handling. The goal is to use vector search to retrieve relevant information and then let the LLM process that data to generate accurate, context-aware responses. Key practices include structuring data for efficient retrieval, optimizing search performance, and managing the interaction between retrieved results and the LLM’s input constraints. Below are three best practices to achieve this.
First, focus on data chunking and preprocessing. Vector search works best when data is divided into meaningful, manageable chunks. For example, splitting long documents into paragraphs or sections ensures that each chunk represents a coherent idea, making retrieval more precise. Use embeddings (vector representations) generated by models like BERT or OpenAI’s text-embedding models to convert these chunks into vectors. Metadata, such as document titles or timestamps, should be included to add context. For instance, in a customer support chatbot, you might chunk FAQs into individual questions and answers, embed them, and store metadata like product categories. This setup allows the vector search to retrieve the most relevant FAQ entries, which the LLM can then refine into a final answer.
Second, optimize the vector indexing and query process. Use efficient indexing algorithms like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File Index) to speed up retrieval. Tools like FAISS, Pinecone, or Elasticsearch simplify this step. When querying, balance speed and accuracy: limit the number of results to reduce noise, and apply filters using metadata to narrow the scope. For example, if a user asks about a specific software version, filter results by that version before passing them to the LLM. Also, experiment with similarity metrics—cosine similarity often works well, but in some cases, Euclidean distance or dot product might be better. Testing with real-world queries helps identify the optimal setup.
Finally, manage the interaction between search results and the LLM’s context window. LLMs have token limits, so prioritize the most relevant retrieved chunks. If the search returns 10 results but the LLM can only process 5, use a scoring system (e.g., combining similarity scores and metadata relevance) to pick the top 5. Structure the prompt to clearly separate retrieved context from the user’s query. For example: “Based on the following information: [chunk1], [chunk2], … Answer: [question].” If chunks are too long, summarize them using the LLM itself before inclusion. For instance, you could prompt the LLM to condense a 300-word chunk into a 50-word summary, ensuring critical details fit within the context window without overwhelming it.
By focusing on these areas—data preparation, search optimization, and context management—you can build systems that leverage the strengths of both vector search and LLMs efficiently.