In the modern era of enterprise search and AI-driven applications, users don’t think in SQL queries, embeddings, or filters. They think in natural language. Yet, many systems still rely on basic keyword search or single vector retrieval, often returning irrelevant or incomplete results.
To address this, developers now combine multiple retrieval techniques such as Hybrid Search, Multi-Query Retrieval, Self-Query Retrieval, Reranking, and Query Expansion to deliver fast, precise, and context-aware results.
This blog provides a comprehensive guide to these techniques and shows how to build production-grade search pipelines using LangChain.
The Problem with Traditional Search
Traditional search methods, such as keyword matching or single-vector retrieval, face three major issues:
Limited recall: Keyword searches miss synonyms, abbreviations, and paraphrased content.
Poor handling of complex queries: Users often include filters, ranges, or multiple facets in a single query (e.g., "Sony headphones under $100 with high ratings").
Precision trade-offs: Vector searches capture semantics but may surface loosely related documents, lowering precision.
Modern search systems address these challenges by combining multiple techniques for better recall, precision, and user experience.
1️⃣ Hybrid Search Semantic + Keyword Fusion
Hybrid Search combines semantic search (vector embeddings) with keyword search (BM25, TF-IDF) to deliver both meaning and exact term matches.
How it works:
Vector Search (Semantic): Finds conceptually similar documents.
Keyword Search (BM25): Finds documents with exact term matches.
Score Fusion: Combines results using strategies like Reciprocal Rank Fusion (RRF).
Example:
Searching "ML models" returns:
"Machine learning algorithms" → Semantic match
"ML model deployment guide" → Exact keyword match
Why it’s useful:
Captures both synonyms and exact matches
Provides a strong baseline for relevance
Fast and cost-effective (no LLM calls required)
Best use cases: E-commerce catalogs, technical documentation, enterprise knowledge bases.
2️⃣ Multi-Query Retriever LLM-Powered Query Variants
The Multi-Query Retriever (LangChain) addresses queries that can be interpreted in multiple ways. Instead of sending one query to the vector store, it uses an LLM to generate multiple query variations.
How it works:
User submits a natural language query.
LLM generates semantically distinct variants.
Each variant runs through the retriever.
Results are deduplicated and combined into a rich candidate set.
Example:
User query: "impact of renewable energy in Europe"
Generated variants:
"solar energy benefits in Europe"
"wind power usage in European countries"
"renewable energy policy in Europe"
Benefits:
Captures different aspects of the user’s intent
Improves recall without manual query expansion
Complements Hybrid Search for broad coverage
When to use: Complex, multi-faceted queries or queries with ambiguous phrasing.
3️⃣ Self-Query Retrieval Extract Structured Filters
Self-Query Retrieval enables an LLM to parse natural language queries and extract structured filters (e.g., price ranges, ratings, categories).
Example:
Query: "Headphones under $100 with high ratings"
LLM extracts:
Text query → "headphones"
Filters → price < 100 AND rating > 4
Why it’s powerful:
Eliminates manual parsing
Handles complex logical constraints (AND, OR, ranges)
Provides a natural language interface for structured data
Best for: E-commerce product search, metadata-rich enterprise document search.
4️⃣ Reranking Precision Optimization
After retrieving candidates (Hybrid or Multi-Query), Reranking uses cross-encoder models to assign precise relevance scores for each [query, document] pair.
How it works:
Retrieve a set of candidates (e.g., top 50-100 documents).
Cross-encoder evaluates query-document relevance.
Reorder candidates for maximum precision.
Benefits:
Corrects retrieval errors from vector or hybrid search
Captures fine-grained relevance nuances
Improves top-k accuracy
Popular models: cross-encoder/ms-marco-MiniLM-L-6-v2, BAAI/bge-reranker-base
5️⃣ Query Expansion Optional Recall Booster
Query Expansion reformulates queries by adding related terms to improve recall.
Example:
Original: "green energy"
Expanded: "renewable energy, sustainable power, clean energy alternatives"
Techniques:
Synonym expansion
Multi-query expansion via LLM (overlaps with Multi-Query Retriever)
Pseudo-Relevance Feedback (PRF)
Trade-off: Expanding too broadly can reduce precision, so it should complement rather than replace Multi-Query or Hybrid Search.
Putting It All Together — Modern Search Pipeline
Here’s a recommended production-ready architecture:
Benefits:
Multi-Query Retriever → captures diverse intent
Hybrid Search → strong baseline relevance
Self-Query → structured filtering
Reranking → precise ordering
Use case: E-commerce product search, enterprise knowledge bases, technical documentation search.
Performance & Cost Overview
Implementation Tips
Start simple: Begin with Hybrid Search for baseline relevance.
Add complexity gradually: Introduce Multi-Query Retriever for broader coverage, then Self-Query and Reranking for precision.
Optimize caching: Cache expanded queries and parsed filters.
Monitor metrics: Track latency, precision, recall, and cost.
Use fallbacks: If the full pipeline fails, fallback to Hybrid or vector search.
Conclusion
Building a modern search system is no longer just about vector similarity. By combining:
Hybrid Search → semantic + keyword
Multi-Query Retriever → multiple semantic perspectives
Self-Query Retrieval → structured filters
Reranking → precise ordering
Query Expansion → optional recall improvement
…developers can build highly relevant, context-aware, and user-friendly search experiences.
LangChain makes it easy to integrate these techniques in production pipelines enabling enterprise-grade search that scales with complex queries, diverse content, and metadata rich datasets.
Thanks
Sreeni Ramadorai
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