58 lines
2.7 KiB
Markdown
58 lines
2.7 KiB
Markdown
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No, HNSW (Hierarchical Navigable Small World) does **not** natively support field-level search. Here's why:
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## How HNSW Actually Works
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HNSW is a **vector similarity search algorithm** that operates purely on high-dimensional vectors. It:
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1. **Stores only vectors**: Each node in the HNSW graph contains just an ID and a vector (as shown in the `HNSWNoun`
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interface)
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2. **Performs proximity search**: Finds vectors that are closest in vector space using distance functions like cosine
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similarity
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3. **Has no concept of fields**: The algorithm doesn't understand document structure, field names, or metadata
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## How Brainy Implements "Field-Level Search"
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The field-level search functionality in Brainy is implemented **above** the HNSW layer, not within HNSW itself:
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### 1. **Pre-Processing Approach**
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- JSON documents are processed by `prepareJsonForVectorization()` before being converted to vectors
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- Field names and values are combined into a text representation
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- Priority fields get more weight in the text representation
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- The entire processed text is then vectorized into a single 512-dimensional vector
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### 2. **Query-Time Processing**
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- When you search for a specific field like `searchField: "company"`, the system:
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- Extracts text from that field using `extractFieldFromJson()`
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- Creates a vector from just that field's content
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- Searches the HNSW index using standard vector similarity
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### 3. **Storage Layer Enhancement**
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- Field names and mappings are tracked in the **storage layer**, not in HNSW
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- The storage system maintains metadata about available fields
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- Standard field mappings are handled outside of the vector index
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## The Fundamental Limitation
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This approach has inherent limitations because:
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1. **Single Vector Per Document**: HNSW stores one vector per document, which is a "flattened" representation of all the
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document's content
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2. **No Structural Awareness**: The vector space doesn't preserve field boundaries or hierarchical structure
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3. **Approximation**: Field-specific searches are approximations based on how well the original vectorization captured
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field-specific information
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## Alternative Approaches for True Field-Level Search
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For genuine field-level search, you would typically use:
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- **Hybrid search systems** that combine vector search with traditional indexing
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- **Multi-vector approaches** where each field gets its own vector
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- **Specialized vector databases** that support structured data natively
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- **Traditional search engines** like Elasticsearch for structured queries combined with vector search
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The current implementation is a clever workaround that provides field-aware functionality on top of a pure vector
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similarity engine, but it's not true field-level search in the traditional database sense.
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