Hybrid Search with Vector + Keyword

1. Introduction

Hybrid search combines vector and keyword-based searches to enhance the search experience. This approach leverages the strengths of both methods, enabling more accurate and context-aware search results.

2. Key Concepts

2.1 Vector Search

Vector search retrieves items based on their semantic meaning, represented as vectors in high-dimensional space. It excels at understanding context and relevance beyond exact keyword matches.

2.2 Keyword Search

Keyword search matches user queries against indexed terms, often relying on traditional text matching algorithms. It is effective for precise queries but may miss broader contextual relationships.

2.3 Hybrid Search

By integrating both vector and keyword searches, hybrid search provides a comprehensive solution that improves retrieval accuracy and relevance.

3. Architecture

The architecture of a hybrid search system typically includes the following components:

Data Ingestion Layer
Vector Database for storing embeddings
Keyword Index for text data
Search Interface for processing user queries
Ranking Engine for combining results


        graph TD;
            A[User Query] --> B[Search Interface];
            B --> C{Type of Search};
            C -->|Keyword| D[Keyword Index];
            C -->|Vector| E[Vector Database];
            D --> F[Ranked Results];
            E --> F;
            F --> G[Final Results Display];

4. Implementation

Implementing a hybrid search involves several steps:

Data Preparation: Preprocess and embed your data.
Indexing: Create a keyword index and a vector database.
Query Processing: Parse user queries to determine search type.
Search Execution: Execute keyword and vector searches as needed.
Result Ranking: Combine results from both searches and rank them.
Results Presentation: Display results to the user in a user-friendly manner.

Important Note: Ensure that your vector embeddings are updated regularly to reflect changes in your data set.

4.1 Code Example: Query Processing


        def process_query(user_query):
            if is_keyword_search(user_query):
                results = keyword_search(user_query)
            else:
                vector = embed_query(user_query)
                results = vector_search(vector)
            return combine_results(results)

5. Best Practices

Regularly update your embeddings to reflect the latest data.
Optimize your keyword index for faster retrieval times.
Use user feedback to refine your search algorithms.
Monitor search performance regularly and adjust parameters as needed.

6. FAQ

Q1: What is the advantage of hybrid search?

A1: Hybrid search improves retrieval accuracy by combining the strengths of vector and keyword searches, allowing for more context-aware results.

Q2: How do I decide between vector and keyword search?

A2: If the query is precise and specific, keyword search may suffice. For broader, context-driven queries, vector search is more suitable.

Q3: What technologies can I use for vector databases?

A3: Technologies such as Faiss, Milvus, or Pinecone are popular choices for handling vector embeddings and searches.