graph-retrieval

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Exposes graph-based retrieval as a tool capability via `query_graph`. Reads normalized graph store files, builds a query-relevant subgraph, and returns LLM-friendly semantic triples with replayable evidence metadata.

AI & Automation 1,325 stars 265 forks Updated 2 days ago MIT

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Skill Content

# Graph Retrieval Skill ## Purpose Expose graph-based retrieval as a tool capability without breaking the existing RepoBrain execution chain. ## Tool - `query_graph(query, max_hops=2, workspace='.')` ## Behavior - Reads normalized graph store files under `.repobrain/graph/`. - Builds a query-relevant subgraph. - Returns LLM-friendly semantic triples plus replayable evidence metadata. ## Output Contract ```json { "summary": "...", "triples": [["subject", "predicate", "object"]], "evidence": [{"retrieval_id": "...", "tool_name": "..."}], "nodes": [...], "edges": [...] } ``` ## Design Notes - Keeps tool-driven and replayable architecture. - Does not bypass pipeline. - Intended for structure/dependency questions and context enrichment.

Details

Author
study8677
Repository
study8677/repobrain
Created
9 months ago
Last Updated
2 days ago
Language
Python
License
MIT

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