How does hybrid search works

Apr 12, 2025

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How does hybrid search works
Introduction
Keyword-Based vs. Vector-Based Retrieval
Hybrid Search Methodologies
Document Chunking Strategies for LLMs
Indexing and Embeddings for Hybrid Search
Comparison Hybrid vs. Pure Retrieval Methods
Conclusion

Introduction

Document digitization efforts have enabled large volumes of text (e.g. scanned documents converted via OCR) to be processed by Large Language Models (LLMs). However, due to context length limits, entire documents cannot be fed to an LLM at once. Instead, documents are split into chunks and relevant pieces are retrieved to ground the LLM’s responses. Effective retrieval in this setting benefits from hybrid search, which combines traditional keyword (sparse) search with semantic vector-based search. Recent research in 2024-2025 highlights that hybrid retrieval can significantly improve the accuracy of LLM-based question answering systems compared to using only one method (Domain-specific Question Answering with Hybrid Search). This review provides a structured analysis of hybrid search mechanisms for document chunking and LLM retrieval, covering algorithms, practical implementations, and comparisons to pure vector or keyword search.

Keyword-Based vs. Vector-Based Retrieval

Keyword (Sparse) Search: Traditional information retrieval relies on lexical matching. Methods like TF-IDF and BM25 rank documents based on term overlap with the query. They excel at precision when queries contain specific keywords or rare terms, ensuring those exact terms appear in results. For example, BM25 can precisely match domain-specific jargon or proper nouns that a semantic approach might overlook. However, lexical methods struggle with synonyms or contextual meaning – if a query uses different wording than the document, purely lexical retrieval may miss relevant content.

Vector (Dense) Search: Vector retrieval uses embeddings to capture semantic meaning. Each document chunk is encoded as a high-dimensional vector, and queries are encoded similarly. Relevant chunks are found via nearest-neighbor search in the embedding space based on cosine similarity or dot product. This approach finds conceptually related text even when exact keywords differ (e.g. a query “CEO” might retrieve a chunk mentioning “chief executive”). Vector search handles synonyms and paraphrasing robustly, but it may overlook exact keyword matches (especially for out-of-vocabulary terms or factual strings like codes/numbers). Dense models can also introduce false positives if two texts are semantically similar but contextually unrelated for the query.

Both approaches have complementary strengths. Recent studies note that combining lexical and semantic search leverages “better keyword matching and contextual understanding” simultaneously (HERE). In other words, keyword search contributes precision on exact matches, while vector search broadens recall to semantically related content. These observations have led to growing interest in hybrid search methods that integrate both.

Hybrid Search Methodologies

Hybrid retrieval systems aim to harness the advantages of both sparse and dense retrieval. A typical hybrid strategy will issue a query to both a keyword index (e.g. BM25 on an inverted index) and a vector index (ANN search on embeddings), then integrate the results. Key methodologies include:

Score or Rank Fusion: Merging results by combining relevance scores from each method. For example, Sultania et al. (2024) use a linear combination of BM25 score and embedding cosine similarity (along with other signals) with tunable weights (Domain-specific Question Answering with Hybrid Search). One simple approach is to normalize both scores and sum them, possibly with a weighting parameter α to favor one method. Setting α close to 0 emphasizes BM25 (exact term matching), while α near 1 leans toward vector retrieval (semantic matching) (A Proposed Large Language Model-Based Smart Search for Archive System). Tuning α allows balancing precision vs. semantic generalization for different query types. In one experiment, a hybrid retriever achieved peak precision (~83%) at α=0.8, outperforming both pure BM25 and pure vector settings . This indicates that an ~80/20 blend of semantic and lexical scoring yielded the best accuracy in that scenario. A special case of score fusion is Reciprocal Rank Fusion (RRF), which merges ranked lists from dense and sparse retrieval by their rank positions (HERE). RRF is effective for combining signals without needing complex model training.
Ensemble Retrieval Pipelines: Some systems treat hybrid search as an ensemble process. For example, a “dual retrieval strategy” can run BM25 and a learned LLM-based retriever in parallel, then ensemble their results with equal weighting (HERE). This equal combination (50/50) ensures neither lexical nor semantic matches dominate, yielding a balanced set of candidate chunks. The top-kk results from both methods can be concatenated and de-duplicated. In practice, an LLM-based semantic retriever might be a fine-tuned transformer that scores query-passage relevance. Combining it with BM25 helps catch cases where the LLM retriever might miss an exact keyword hit. Some pipelines further employ a cross-attention re-ranker on the merged candidates to refine the order. For instance, re-ranking with a trained model (like Cohere’s reranker) can address the “lost in the middle” issue by ensuring that middle portions of documents (not just the first sentences) are considered if they are relevant .
Query Expansion and Filtering: A variant of hybrid search is to use one method to augment the query for the other. An LLM can generate expanded queries or synonyms which are then used in BM25 search (bridging lexical gap), as explored by Zhu et al. (2023) and others (Domain-specific Question Answering with Hybrid Search). Conversely, keyword filters can be applied to vector results – e.g. ensure that a vital keyword from the query appears in the retrieved chunk to improve precision. These techniques integrate lexical cues into semantic retrieval indirectly. They are especially useful in domain-specific corpora where certain key terms must be present.

Overall, hybrid techniques in recent literature consistently show improved retrieval performance over single-method systems. By integrating multiple relevance signals with appropriate weighting, hybrid systems achieved higher recall and better answer accuracy in domain-specific QA settings . Prior research has reported “promising performance improvements” when incorporating keywords into semantic search (HERE), validating the hybrid approach.

Document Chunking Strategies for LLMs

Before retrieval, documents must be chunked into pieces that an LLM can effectively work with. Choosing the right chunking strategy is crucial for hybrid search effectiveness. If chunks are too large, irrelevant text may dilute the relevance scoring (for both BM25 and embeddings); if too small, context needed to understand an answer might be split up.

Common chunking techniques include:

Fixed-Size Chunking: Splitting documents into uniform chunks of a certain token or character length, often with overlaps. Overlapping (e.g. sliding windows) ensures important content near boundaries isn’t lost. A 2024 study found that ~1000-character chunks with a ~100-character overlap struck a good balance between retrieval granularity and efficiency (Domain-specific Question Answering with Hybrid Search). Increasing chunk size beyond that reduced retrieval performance (lower NDCG), likely because chunks became too coarse and started including irrelevant context . This suggests that moderately sized chunks (e.g. a few hundred words) are optimal for many LLM retrieval tasks.
Semantic Chunking: Rather than a fixed size, this approach splits text into semantically coherent units. One method is to group consecutive sentences as long as their embeddings are very similar; when similarity drops below a threshold (e.g. cosine similarity < 0.7), a new chunk is started (HERE). This creates self-contained chunks focused on a subtopic. Constraints like a maximum length (e.g. 500 characters) can be applied to keep chunks manageable . Semantic chunking avoids cutting in the middle of a topic, which can help a retriever treat each chunk as a meaningful standalone piece. It may, however, be more computationally intensive to determine the boundaries (requires computing similarity between sentences or paragraphs).

In practice, a combination of strategies is used: documents might be initially segmented by structure (e.g. by section or paragraph), then further split or merged based on length or semantic cohesion. The digitization step (if starting from scanned documents) will convert each document to text which can then be chunked. It’s important to preserve logical unit boundaries (e.g. end at sentence or paragraph breaks) when chunking for better retrieval results (Domain-specific Question Answering with Hybrid Search). Each chunk is then assigned an identifier (like a document ID and chunk index) so its source can be traced when an LLM provides an answer.

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Indexing and Embeddings for Hybrid Search

After chunking, two parallel indices are typically built to support hybrid queries: a sparse index and a vector index. The sparse index is usually an inverted index mapping terms to chunk IDs, enabling BM25 or similar algorithms to quickly retrieve chunks containing the query terms. The vector index stores each chunk’s embedding in a structure that supports fast nearest-neighbor search (e.g. HNSW graphs or IVF indices, often accessible via vector databases). Recent implementations use scalable vector stores like Pinecone or FAISS to handle large embedding collections (A Proposed Large Language Model-Based Smart Search for Archive System). Each chunk’s text is converted to a vector using a chosen embedding model (such as OpenAI’s text embeddings or domain-specific models), and these are indexed for similarity search . Meanwhile, the raw text and metadata of chunks are indexed for keyword search, which can be done with open-source search engines (Elasticsearch, Whoosh, etc.) or built-in BM25 in frameworks like LlamaIndex.

Embedding Model Selection: The choice of embedding model greatly impacts vector search quality. General-purpose sentence embeddings (e.g. BGE or Sentence-BERT) work for many tasks, but domain-specific retrieval benefits from fine-tuned models (Domain-specific Question Answering with Hybrid Search). For example, Sultania et al. fine-tuned a dense retriever on their enterprise QA data, which improved semantic matching of domain terminology . If fine-tuning data is scarce, one can still select a model known to perform well on similar domains (like using a legal-specialized embedding model for law documents). Some advanced hybrid systems even combine multiple embeddings; e.g. indexing each chunk by both its content embedding and a knowledge graph embedding (HERE), then treating those as separate “views” in the hybrid retrieval. However, a single good embedding per chunk is often sufficient when combined with lexical search.

Indexing Considerations: Hybrid search requires maintaining two indices, which has storage and update implications. The vector index may consume significant memory (each chunk’s embedding, typically hundreds of dimensions of float or int8). The sparse index size depends on vocabulary and total text, but can be optimized by standard IR compression techniques. At query time, using both indices doubles the retrieval work, but each can be highly optimized: BM25 lookups are sub-second even for millions of documents, and ANN search can also be optimized to under ~100 ms for large corpora by using approximate methods. In many systems, the benefit in accuracy outweighs the slight increase in query latency . Additionally, the two searches can be done in parallel to minimize added latency.

Once candidate chunks are retrieved, the system may apply a retrieval augmentation step: e.g. filtering out low-relevance chunks or merging overlapping answers. Some pipelines use the LLM itself to vet chunks (by asking the model to score relevance given the query). The final set of top-ranked chunks (typically a handful) are then passed to the LLM (either via the prompt or a fine-tuned retrieval-augmented model) to generate the answer. This retrieval-augmented generation (RAG) approach has been shown to reduce hallucinations and improve factual accuracy by grounding the LLM in retrieved text (HERE). The hybrid retrieval component thus directly impacts the quality of the LLM’s responses.

Comparison Hybrid vs. Pure Retrieval Methods

Hybrid search often provides a sweet spot between accuracy and efficiency. Pure lexical search and pure vector search each have cases where they excel, but in many evaluations, hybrid methods have yielded the best overall results. For example, a domain-specific QA system reported that a hybrid dense+BM25 retriever “outperforms our single-retriever system” in accuracy (Domain-specific Question Answering with Hybrid Search). By capturing both exact matches and semantic matches, hybrid retrieval generally improves recall – the set of relevant chunks found is more comprehensive. This is especially important in long document collections where a relevant answer sentence might use different wording than the query (dense retrieval finds it), or contain a crucial keyword (sparse retrieval ensures it’s caught).

In terms of accuracy, hybrid systems consistently show higher recall and often better precision. Prior studies note “performance improvements” when adding a sparse component to dense search (HERE). Empirical results in 2024 works show significant gains in ranking metrics (e.g. NDCG, accuracy of the final answer) for hybrid over either method alone . The advantage is pronounced for complex queries in specialized domains . That said, if queries are very simple or exact, a well-tuned BM25 might perform nearly as well as hybrid, and if queries are purely conceptual with no important keywords, a strong dense retriever can suffice. Hybrid ensures robustness across query types.

In terms of efficiency, a hybrid approach incurs additional computational overhead by performing two lookups instead of one. However, modern indices are efficient enough that this is rarely a bottleneck for moderate-scale corpora. Vector search can be accelerated with approximate algorithms, and keyword search is extremely fast for most queries. Some research has explored adjusting how much each component is used depending on query complexity (e.g. using more of BM25 for precise queries and more of vector search for open-ended queries) (A Proposed Large Language Model-Based Smart Search for Archive System) to optimize speed/accuracy trade-offs. Maintaining dual indexes does use more memory, but this cost is often acceptable given the accuracy benefits. Moreover, hybrid retrieval can be configured to fall back to a single method when appropriate (for instance, if BM25 finds an exact match with very high score, the system might skip vector search).

In summary, hybrid search in LLM-based document retrieval combines the precision of keyword matching and the recall of semantic embeddings (HERE). It has become a best-practice in 2024/2025 for building high-accuracy retrieval-augmented generation systems, outperforming pure vector or pure keyword approaches in many scenarios. While slightly more complex, hybrid retrieval remains practical to implement with today’s indexing technologies and provides a notable boost in the quality of LLM responses.

Conclusion

Hybrid search has emerged as a powerful technique for document-focused LLM applications. By integrating sparse and dense retrieval, systems can handle digitized documents of varying formats and vocabularies, retrieving the most relevant chunks to feed into an LLM. We reviewed how recent research incorporates hybrid mechanisms – from weighted score fusion to advanced ensemble and reranking strategies – to improve retrieval performance. We also discussed the importance of chunking strategies and indexing choices in enabling efficient hybrid search. Compared to relying solely on vector embeddings or keywords, the hybrid approach offers superior accuracy and robustness, which is crucial for reliable LLM-based question answering and assistance on large document collections. As LLM deployments grow, hybrid retrieval is likely to remain central to bridging the gap between unstructured text and accurate, context-aware AI responses.