Why Does a Large Concatenated Document Yield Poor Semantic Search Results?

Below is an explanation that may address your issue:

Granularity of Context

When you embed a large, concatenated document, you create a single embedding that “blends” the topics together. This can dilute the distinct semantic signals that individual parts of the document contain. In contrast, when you embed a smaller document (or a focused chunk), the resulting embedding is more precise for that specific topic.

Loss of Specificity

By creating one embedding for many different sections/topics, you may lose the granularity required for semantic search. Queries that target specific information may poorly align with an embedding that was generated from a jumble of topics, even if those topics overlap or supplement one another.

Best Practices

For effective semantic search, it’s typically advisable to:

• Chunk your documents: Instead of creating one giant embedding, break the document into coherent segments. Each segment should capture a single idea or topic.
• Embed individual chunks: This approach lets the similarity search pick up on the most relevant segment(s) for your query.
• Combine results if necessary: Depending on your application, you can then merge or rank the results from the different chunks to provide a comprehensive answer.

Using many smaller embedding vectors keeps the context boundaries intact, whereas one large embedding can obfuscate the structure and make your query matching less effective.

Final Note

While you might think that one large vector would work just as well, the loss in context specificity is likely why you’re seeing the degradation in performance. Adjusting your strategy to embed smaller, more focused chunks should lead to more satisfying semantic search results.