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Hybrid Search – Combining Semantic and Keyword Search

Fundamentals of AI Engineering

Foundational Models

Transformers

Fine Tuning

Vector Databases

RAG

LangChain

In modern search systems, especially those that rely on AI and machine learning, Hybrid Search combines traditional keyword-based search with more advanced semantic search.

  • Keyword search matches exact terms or phrases in the search query with those in the documents.
  • Semantic search understands the context and meaning behind words, allowing for more relevant and nuanced search results.
  • Hybrid search improves the accuracy and relevance of search results by combining these two approaches, offering the benefits of both systems.

Limitations of Keyword Search:

  • Exact Matches Only: Fails when synonyms or different phrases refer to the same thing.
  • Lack of Context Understanding: Cannot distinguish between multiple meanings (e.g., “apple” as a fruit vs. tech company).
  • Ambiguity: Keywords can have multiple meanings, leading to irrelevant search results (e.g., “bank” could refer to a financial institution or a riverbank).

Limitations of Semantic Search:

  • Lack of Precision: May return results that are too broad or loosely related.
  • Computational Complexity: Requires more resources, potentially slowing down the search process.
  • Data Requirements: Needs well-trained models and embeddings, requiring substantial training data.

Hybrid search addresses these challenges by combining keyword search for precision and semantic search for contextual relevance.

Keyword Search:

  • Exact Match: Matches words in the query to words in documents.
  • Boolean Operators: Uses operators like AND, OR, NOT for more precise queries.
  • TF-IDF (Term Frequency-Inverse Document Frequency): Ranks documents based on word frequency.

Semantic Search:

  • Embeddings: Represents words, sentences, or documents as high-dimensional vectors.
  • Similarity Metrics: Uses cosine similarity, Euclidean distance, etc., to find the most relevant documents.
  • Pretrained Models: Uses models like BERT, GPT, or T5 to capture contextual meanings.

How Hybrid Search Works

Hybrid search combines keyword search and semantic search in the following workflow:

  1. Query Processing: The system receives a query and processes it through both keyword and semantic pipelines.
  2. Keyword Search Phase: Filters data using exact term matches.
  3. Semantic Search Phase: Ranks or refines results based on contextual meaning.
  4. Merging Results:
    • Combines keyword-based and semantic-based results.
    • Uses a weighting scheme to balance precision and contextual relevance.
    • Applies re-ranking techniques for improved accuracy.
  1. Query Processing: Both keyword and semantic search are applied.
  2. Keyword Search Phase: Filters data using exact matches.
  3. Semantic Search Phase: Applies contextual ranking to refine results.
  4. Merging the Results: Combines and re-ranks results for improved accuracy.

Hybrid Search – Use Cases and Applications

Hybrid Search Application Overview.
Hybrid Search Application Overview.

E-Commerce:

  • Keyword Search: Finds products by exact terms (e.g., “running shoes”).
  • Semantic Search: Recommends products based on user intent (e.g., “best shoes for running on trails”).

Search Engines:

  • Keyword Search: Retrieves documents based on exact matches.
  • Semantic Search: Ranks results based on context and synonyms.

Document Retrieval in Knowledge Systems:

  • Keyword Search: Finds documents by specific terms.
  • Semantic Search: Ranks documents based on relevance even if query terms don’t match exactly.

Customer Support and Chatbots:

  • Keyword Search: Matches queries to FAQs.
  • Semantic Search: Returns the most relevant answers based on intent.

Healthcare:

  • Keyword Search: Finds clinical papers and patient records.
  • Semantic Search: Identifies relevant documents using medical terminology.

Balancing Keyword and Semantic Search:

  • Weighting: Adjust the importance of each phase depending on the use case.
  • Filtering vs. Ranking: Keyword search for filtering, semantic search for ranking.

Leveraging Pre-trained Models:

  • Uses BERT, RoBERTa, T5, etc., for improved contextual understanding.

Efficient Indexing:

  • Keyword Search: Uses inverted indexes.
  • Semantic Search: Uses vector indexes (e.g., FAISS, Pinecone, Weaviate).

Real-time Search Optimization:

  • Uses pre-filtering with keyword search to improve speed.
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