vector_graph_search
Performs semantic vector search, then expands each result's graph neighborhood to reveal contextual relationships.
Instructions
Hybrid search: find similar nodes by vector, then expand their graph neighborhood.
This is the most powerful search tool — it combines semantic similarity (vector search) with graph structure (neighbor expansion). Use it when you want to find relevant nodes AND understand their context.
Step 1: Vector search finds the top-k most similar nodes. Step 2: For each result, expands outward by expand_depth hops.
Use this tool when: you need both semantic relevance AND graph context. Use vector_search when: you only need the similar nodes themselves. Use get_neighbors when: you already have a node and want to explore around it.
Args: label: Node label to search (must have a vector index). property: Property holding the embedding vector. query_vector: The query embedding as a list of floats. k: Number of nearest seed nodes to return (default 5). expand_depth: How many hops to expand from each seed (default 1). Use 0 to skip expansion (equivalent to plain vector_search). expand_edge_type: Optional edge type filter. If provided, only edges of this type are followed during expansion.
Returns: JSON object with "seeds" (vector results) and "neighbors" (expanded context), truncated if the output is large.
Example call: vector_graph_search("Article", "embedding", [0.1, ...], k=3, expand_depth=2, expand_edge_type="CITES")
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| label | Yes | ||
| property | Yes | ||
| query_vector | Yes | ||
| k | No | ||
| expand_depth | No | ||
| expand_edge_type | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |