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# Ragex.VectorStore (Ragex v0.4.0)

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Vector similarity search for code embeddings.

Provides efficient cosine similarity search over code entity embeddings stored in the graph store. Supports filtering by entity type, similarity thresholds, and result limits.

## Summary

### Functions

child_spec(init_arg)

Returns a specification to start this module under a supervisor.

cosine_similarity(vec1, vec2)

Calculates cosine similarity between two embedding vectors.

nearest_neighbors(query_embedding, k, opts \\ [])

Finds the k nearest neighbors to a query embedding.

search(query_embedding, opts \\ [])

Searches for similar code entities based on a query embedding.

start_link(opts \\ [])
stats()

Returns statistics about the vector store.

## Functions

# child_spec(init_arg)

Returns a specification to start this module under a supervisor.

See `Supervisor`.

# cosine_similarity(vec1, vec2)

Calculates cosine similarity between two embedding vectors.

Returns a float between -1.0 and 1.0, where 1.0 means identical direction. For normalized embeddings (like ours), this is equivalent to dot product.

# nearest_neighbors(query_embedding, k, opts \\ [])

Finds the k nearest neighbors to a query embedding.

Similar to `search/2` but always returns exactly k results (or fewer if not enough embeddings exist).

# search(query_embedding, opts \\ [])

Searches for similar code entities based on a query embedding.

## Parameters

- `query_embedding`: List of floats representing the query vector
- `opts`: Keyword list of options:
  - `:limit` - Maximum results to return (default: 10)
  - `:threshold` - Minimum similarity score 0.0-1.0 (default: 0.0)
  - `:node_type` - Filter by node type (:module, :function, etc.)

## Returns

List of results sorted by similarity (highest first), each containing:

- `:node_type` - Type of the entity
- `:node_id` - ID of the entity
- `:score` - Similarity score (0.0 to 1.0)
- `:text` - Original text description
- `:embedding` - The embedding vector

## Example

    {:ok, query_emb} = Bumblebee.embed("function to calculate sum")
    results = VectorStore.search(query_emb, limit: 5, threshold: 0.7)

# start_link(opts \\ [])

# stats()

Returns statistics about the vector store.