No, HNSW (Hierarchical Navigable Small World) does **not** natively support field-level search. Here's why:

## How HNSW Actually Works

HNSW is a **vector similarity search algorithm** that operates purely on high-dimensional vectors. It:

1. **Stores only vectors**: Each node in the HNSW graph contains just an ID and a vector (as shown in the `HNSWNoun`
   interface)
2. **Performs proximity search**: Finds vectors that are closest in vector space using distance functions like cosine
   similarity
3. **Has no concept of fields**: The algorithm doesn't understand document structure, field names, or metadata

## How Brainy Implements "Field-Level Search"

The field-level search functionality in Brainy is implemented **above** the HNSW layer, not within HNSW itself:

### 1. **Pre-Processing Approach**

- JSON documents are processed by `prepareJsonForVectorization()` before being converted to vectors
- Field names and values are combined into a text representation
- Priority fields get more weight in the text representation
- The entire processed text is then vectorized into a single 512-dimensional vector

### 2. **Query-Time Processing**

- When you search for a specific field like `searchField: "company"`, the system:
    - Extracts text from that field using `extractFieldFromJson()`
    - Creates a vector from just that field's content
    - Searches the HNSW index using standard vector similarity

### 3. **Storage Layer Enhancement**

- Field names and mappings are tracked in the **storage layer**, not in HNSW
- The storage system maintains metadata about available fields
- Standard field mappings are handled outside of the vector index

## The Fundamental Limitation

This approach has inherent limitations because:

1. **Single Vector Per Document**: HNSW stores one vector per document, which is a "flattened" representation of all the
   document's content
2. **No Structural Awareness**: The vector space doesn't preserve field boundaries or hierarchical structure
3. **Approximation**: Field-specific searches are approximations based on how well the original vectorization captured
   field-specific information

## Alternative Approaches for True Field-Level Search

For genuine field-level search, you would typically use:

- **Hybrid search systems** that combine vector search with traditional indexing
- **Multi-vector approaches** where each field gets its own vector
- **Specialized vector databases** that support structured data natively
- **Traditional search engines** like Elasticsearch for structured queries combined with vector search

The current implementation is a clever workaround that provides field-aware functionality on top of a pure vector
similarity engine, but it's not true field-level search in the traditional database sense.
