Handling context window limitations in semantic search with LLMs requires strategies to prioritize relevant information while maintaining system performance. LLMs can only process a fixed amount of text at once (e.g., 4,000 or 8,000 tokens), so when working with large datasets or long documents, you need to selectively include the most pertinent content. The core approach involves preprocessing data to extract key information, using retrieval techniques to filter inputs, and designing systems that dynamically adjust context based on the task.
First, break down large datasets into manageable chunks that fit within the model’s context window. For example, split documents into paragraphs or sections and index them using embeddings. When a query arrives, use semantic similarity (e.g., cosine similarity) to retrieve the top-k chunks most relevant to the query. This reduces the amount of text fed to the LLM while preserving context. Tools like FAISS or vector databases can efficiently search embeddings for matches. However, avoid arbitrary chunking: use logical boundaries (e.g., section headers) or sliding windows with overlap to prevent splitting coherent ideas. For instance, a 512-token chunk with 64-token overlap ensures continuity between adjacent sections.
Second, implement a two-step retrieval process. Use a fast, lightweight model (like BM25 or a small transformer) for initial broad filtering, then apply the LLM for finer-grained reranking. For example, if a user searches for “machine learning optimization techniques,” first retrieve 100 candidate paragraphs using keyword matching, then use the LLM to score and select the top 5 paragraphs that best align with the query’s intent. This balances speed and accuracy. Additionally, cache frequent queries or common results to reduce redundant processing. If your application handles repeated questions (e.g., FAQ-style queries), store precomputed responses or embeddings to bypass full retrieval steps.
Finally, optimize how context is structured within the window. Place the most critical information (e.g., query, top results) at the beginning or end of the context, as some models handle these positions better. Use summarization for lengthy sources: generate concise summaries of retrieved chunks before feeding them to the LLM. For example, summarize a 1,000-token article section into a 200-token abstract. If the LLM supports it, leverage techniques like “context compression” to discard less relevant tokens mid-process. Tools like LangChain’s map-reduce pipelines can split tasks into subtasks (e.g., summarizing individual chunks) and combine results. Always test different chunk sizes and retrieval thresholds to find the right balance between completeness and performance for your specific use case.