Semantic Search

The embedding model is the core AI component that transforms text (or other data) into high-dimensional vector representations. These dense vectors capture the semantic meaning of the input, positioning similar concepts close together in the vector space. Common models include sentence transformers like all-MiniLM-L6-v2 and BGE (BAAI General Embedding). The choice of model directly impacts retrieval quality, with factors like dimensionality (e.g., 384, 768, 1024 dimensions), training data, and domain specificity being critical considerations.

A vector index is a specialized data structure that enables fast Approximate Nearest Neighbor (ANN) search across millions of embeddings. Instead of an exhaustive—and prohibitively slow—comparison, ANN algorithms trade perfect accuracy for massive speed gains. Key algorithms include:

• HNSW (Hierarchical Navigable Small World): A graph-based method known for high recall and speed.
• IVF (Inverted File Index): Clusters vectors into Voronoi cells for coarse-grained filtering.
• IVF-PQ: Combines IVF with Product Quantization to compress vectors, reducing memory usage. Libraries like FAISS, Weaviate, and Qdrant implement these indices.

Before creating embeddings, raw documents must be intelligently segmented into chunks. Effective chunking balances context preservation with manageable chunk size for the embedding model. Strategies include:

• Fixed-size chunking: Simple but can split coherent ideas.
• Recursive chunking: Splits by separators (e.g., paragraphs, sentences) recursively.
• Semantic chunking: Uses models to identify topical boundaries. The pipeline also handles text normalization (lowercasing, punctuation removal), cleaning, and often metadata extraction (source, author, timestamp) to enrich retrieved results.

This component processes the user's raw query to optimize it for semantic retrieval. It goes beyond the query's literal terms to understand its intent. Techniques include:

• Query Expansion: Adding synonyms or related terms (e.g., "car" might expand to "automobile, vehicle").
• Query Rewriting: Using a lightweight LLM to rephrase the query for clarity or to match document style.
• Hybrid Query Formulation: Creating both a sparse vector (for traditional keyword matching via BM25) and a dense vector (for semantic matching) to support hybrid search.
• Filter Generation: Extracting explicit filters from the query (e.g., "documents from 2023") to apply during retrieval.

The initial ANN search returns a candidate set. A reranker model then performs a more computationally expensive, precise comparison between the query and each candidate to produce a final, high-quality ranking. Models like Cohere Rerank, BGE Reranker, or cross-encoders are used. Fusion strategies combine results from multiple retrieval pathways:

• Reciprocal Rank Fusion (RRF): Merges rankings from semantic and keyword searches without scores.
• Weighted Score Fusion: Combines similarity scores from different vector spaces or models. This stage is critical for achieving high precision in the top results.

While semantic search finds conceptually similar content, practical applications require filtering by hard metadata constraints. This engine allows queries like "find concepts related to neural networks, but only from PDF documents published after 2022." It operates alongside the vector index, using inverted indexes for fast metadata lookups (e.g., doc_type = PDF, date > 2022-01-01). Systems perform the ANN search and apply metadata filters concurrently or sequentially, ensuring retrieved results are both semantically relevant and conform to business logic.

A specialized database designed to store, index, and query high-dimensional vector embeddings. It is the foundational storage layer for semantic search, enabling efficient similarity lookups.

• Core Function: Stores numerical representations (embeddings) of text, images, or other data.
• Key Operations: Insert, update, and perform nearest neighbor searches on millions of vectors.
• Examples: Pinecone, Weaviate, Qdrant, and Milvus are dedicated vector databases. PostgreSQL with the pgvector extension adds vector capabilities to a relational database.

A machine learning model that converts discrete data (like text or images) into dense, continuous vector representations (embeddings) where semantic similarity is reflected by geometric proximity.

• Purpose: Creates the numerical "fingerprints" used for semantic comparison.
• Characteristics: Models like text-embedding-ada-002 or BGE-M3 are trained to place semantically similar phrases close together in vector space.
• Integration: The choice of embedding model directly impacts retrieval quality. Models can be general-purpose or fine-tuned on domain-specific corpora.

A class of algorithms that efficiently finds the closest vectors in high-dimensional spaces, trading perfect accuracy for massive gains in speed and reduced memory usage. This is the computational engine of semantic search at scale.

• The Problem: Exact nearest neighbor search in high dimensions is computationally prohibitive for large datasets.
• The Trade-off: ANN algorithms accept a small margin of error in exchange for sub-linear query times.
• Common Algorithms: HNSW (Hierarchical Navigable Small World), IVF (Inverted File Index), and PQ (Product Quantization) are frequently used, often in combination.

A retrieval paradigm that uses dense vector representations (embeddings) of both queries and documents to find relevant information through similarity comparison. It contrasts with traditional sparse retrieval (e.g., BM25) which relies on keyword overlap.

• Mechanism: The query is converted into an embedding via the same model used for the documents. Relevance is scored by vector similarity (e.g., cosine similarity).
• Advantage: Captures semantic meaning and synonyms, enabling retrieval based on conceptual intent rather than lexical match.
• Use Case: Forms the first-stage retriever in modern Retrieval-Augmented Generation (RAG) architectures.

A structured semantic network that represents real-world entities (nodes) and their interrelationships (edges) with defined properties. It provides deterministic, logical grounding that complements the statistical similarity of vector search.

• Structure: Based on ontologies that formally define entity types and relationship types (predicates).
• Query Mechanism: Uses graph traversal and logical query languages like SPARQL or Cypher.
• Synergy with Semantic Search: Often used in hybrid retrieval systems, where vector search finds conceptually related content and the knowledge graph provides explicit, verifiable facts and relationships.

The process of intelligently segmenting a large document into smaller, coherent units (chunks) optimized for semantic retrieval, as opposed to simple fixed-size splitting.

• Goal: Preserve logical and semantic boundaries (e.g., by paragraph, section, or topic) to ensure each chunk is a self-contained unit of meaning.
• Importance: Poor chunking can sever context, leading to irrelevant or incomplete retrievals. Effective chunking is critical for high recall and precision.
• Techniques: Can use rule-based methods (markdown headers), model-based sentence boundary detection, or even recursive semantic splitting using LLMs.