How to handle queries that Semantic Search can’t?
Retrieval Augmented Generation (RAG) is an advanced AI technique that combines the power of external data retrieval with language generation to provide more accurate and comprehensive responses. But what happens when the user’s query is vague, complex, or doesn’t directly match the available documents?
In this article, we’ll explore how to manage challenging queries using a hybrid approach of semantic search and full-text search.
The Power of Semantic Search
The core challenge in RAG systems is retrieving relevant data for a user’s query. One popular solution is semantic search — a technique that searches through documents for content with the same meaning as the query.
Technically speaking, semantic search compares the vectors (or embeddings) of text and retrieves documents whose vectors are closest to the query. Since embedding models group similar text by meaning, this typically results in relevant matches.
The Limitation: Too Broad vs. Too Narrow
While semantic search is powerful, it often struggles in Q&A scenarios. User queries are typically short and specific, while the underlying documents are longer and may cover a variety of topics. This mismatch can lead to zero results, even when relevant information is available.
Full-Text Search: The Overlooked Alternative
Full-text search (FTS), though older, is still useful. It relies on keyword matching, where documents are retrieved based on the presence of exact words or phrases. However, it lacks the ability to understand the query’s context or intent — something semantic search does well.
Supercharging Full-Text Search with LLMs
The gap in full-text search’s contextual understanding can be bridged using Large Language Models (LLMs). Here’s how:
- Role assignment: Treat the LLM as an expert, such as an SQL specialist.
- Schema introduction: Provide the LLM with the schema of the document, including data types and column information.
- Query generation: Input the user’s question and ask the LLM to create an optimized full-text search query (e.g., an SQL query) that retrieves the most relevant data.
This approach helps retrieve useful results, even if the original query and document content don’t perfectly align.
The Best of Both Worlds: The Hybrid Approach
While full-text search is useful, semantic search remains a key tool. To maximize the effectiveness of retrieval, consider combining the two:
- Start with semantic search to identify documents based on meaning.
- If no results are found, switch to full-text search powered by an LLM for precision.
By employing this hybrid approach, you leverage the strengths of both methods, ensuring more comprehensive and accurate retrievals.
Final Thoughts
Handling complex or unclear queries in RAG systems requires more than just one tool. By blending semantic search and full-text search, you create a flexible, robust retrieval system that covers both meaning-based and keyword-based queries. This hybrid model ensures you extract the most relevant data, no matter the complexity of the user query.
