Query Expansion and Transformation in Vector Databases & RAG
In the realm of vector databases and Retrieval Augmented Generation (RAG), the effectiveness of retrieving relevant information hinges significantly on the quality of the queries. Raw user queries often lack the precision or context needed to elicit the best results from a vector index. This is where Query Expansion and Transformation techniques come into play, acting as crucial intermediaries to refine and enrich queries before they are executed.
The Challenge of Raw Queries
User queries can be ambiguous, too short, or use terminology that doesn't perfectly align with the embeddings stored in the vector database. For instance, a query like 'best coffee' might not directly match documents that discuss 'artisanal espresso' or 'pour-over brewing techniques,' even if those documents are highly relevant. This mismatch can lead to poor retrieval performance and, consequently, suboptimal RAG outputs.
What is Query Expansion?
Query expansion enriches a user's original query with additional terms or concepts to improve retrieval accuracy.
By adding synonyms, related terms, or reformulations, query expansion broadens the search scope, increasing the chances of finding relevant documents that might not have been captured by the original, narrow query.
Query expansion is a set of techniques designed to augment an initial user query with additional relevant terms. This can involve adding synonyms, hyponyms (more specific terms), hypernyms (broader terms), related words, or even reformulating the query entirely. The goal is to create a more comprehensive query that better represents the user's intent and covers a wider semantic space, thereby increasing the recall of the retrieval system without significantly sacrificing precision.
Common Query Expansion Techniques
| Technique | Description | Example |
|---|---|---|
| Synonym Expansion | Adds synonyms of query terms. | 'car' -> 'automobile', 'vehicle' |
| Thesaurus-based Expansion | Uses a thesaurus or wordnet to find related terms. | 'happy' -> 'joyful', 'content', 'pleased' |
| Query Rewriting/Reformulation | Rephrases the query to be more explicit or to match common phrasing. | 'how to fix leaky faucet' -> 'faucet leak repair guide' |
| Query Segmentation | Breaks down complex queries into simpler, more focused sub-queries. | 'best laptops for students under 1000' |
| LLM-based Expansion | Leverages Large Language Models to generate relevant expansions. | 'AI in healthcare' -> 'artificial intelligence medical applications', 'machine learning diagnostics', 'AI for patient care' |
What is Query Transformation?
Query transformation, while often used interchangeably with expansion, can also encompass broader modifications to the query's structure or representation. This might involve changing the query's format, adding metadata, or even converting it into a different type of representation that is more amenable to the retrieval system.
Query transformation modifies the query's form or representation to optimize retrieval.
This can include changing query syntax, adding contextual metadata, or converting the query into a format that better aligns with the vector database's indexing and search mechanisms.
Query transformation involves altering the query itself to improve its suitability for the retrieval system. This can range from simple syntactic changes to more complex semantic or structural modifications. For instance, transforming a natural language question into a keyword-based query, or adding contextual information (like user preferences or session history) to the query vector, are forms of query transformation. The aim is to make the query more 'searchable' and aligned with how the data is indexed.
Query Transformation Techniques
Transformations can be as simple as standardizing casing or removing stop words, or as complex as generating multiple query variations or embedding the query into a different vector space.
Consider a user asking: 'What are the latest advancements in renewable energy?' A query transformation might involve: 1. Keyword Extraction: 'latest advancements', 'renewable energy'. 2. Synonym Addition: 'renewable energy' -> 'sustainable energy', 'green energy'. 3. Temporal Context: Adding a filter for recent publications or news. 4. Vectorization: Converting the expanded query into a dense vector representation. This multi-step process transforms the initial natural language query into a more effective vector for searching a database of research papers or news articles.
Text-based content
Library pages focus on text content
Integration in RAG Systems
In a RAG pipeline, query expansion and transformation typically occur between the user input and the vector database retrieval step. An LLM can be used to perform these operations, analyzing the user's query and generating an enhanced version or multiple variations. These transformed queries are then used to fetch relevant documents, which are subsequently fed to the LLM for generating the final answer.
The effectiveness of query expansion and transformation directly impacts the 'Retrieval' part of RAG, which is foundational for generating accurate and contextually relevant responses.
Advanced Considerations
Advanced techniques might involve learning query expansion strategies from user feedback, using knowledge graphs to find semantically related terms, or employing hybrid search methods that combine vector search with keyword search. The choice of techniques depends on the specific domain, the nature of the data, and the expected user queries.
To enrich a user's original query with additional relevant terms to improve retrieval accuracy and recall.
Transforming a natural language question into a keyword-based query or adding contextual metadata to a query vector.
Learning Resources
Explains the components of a RAG pipeline, including the role of query processing and retrieval.
A foundational academic paper discussing various methods for expanding queries in information retrieval systems.
Discusses the importance of vector databases and how they handle complex data, touching upon query optimization.
Provides a concise definition and overview of query expansion within the context of information retrieval.
A practical guide demonstrating how to build a RAG system, often involving query processing steps.
Explains semantic search, which relies heavily on effective query understanding and transformation.
A survey paper exploring the applications of Large Language Models in enhancing information retrieval, including query processing.
Details advanced strategies for improving RAG performance, which often include sophisticated query handling.
A foundational tutorial on word embeddings, crucial for understanding how queries are represented and transformed into vectors.
A video explaining how search engines process and transform user queries to deliver relevant results.