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https://github.com/trustgraph-ai/trustgraph.git
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01cc8dbc64
Replace the three-prompt LLM scoring pipeline (kg-edge-scoring, kg-edge-reasoning, kg-edge-selection) with a cross-encoder reranker service backed by FlashRank. The new hop_and_filter() method performs iterative graph traversal with semantic scoring at each hop, replacing the previous follow_edges/get_subgraph approach. - Add reranker service (trustgraph-base client/service, FlashRank processor) - Add gateway dispatch for reranker via API and WebSocket - Rewrite GraphRAG pipeline: hop_and_filter() with per-hop cross-encoder scoring - Remove kg_prompt() and edge_score_limit from prompt client - Update provenance: add tg:EdgeSelection type, tg:concept, tg:score predicates - Update CLIs (tg-invoke-graph-rag, tg-show-explain-trace) for new metadata - Add tg-invoke-reranker CLI tool - Add tech spec and UX developer guidance - Update all unit and integration tests
35 lines
777 B
Python
35 lines
777 B
Python
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from dataclasses import dataclass, field
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from ..core.primitives import Error
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############################################################################
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# Cross-encoder reranker
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@dataclass
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class RerankerQuery:
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query_id: str = ""
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query_text: str = ""
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@dataclass
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class RerankerDocument:
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document_id: str = ""
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document_text: str = ""
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@dataclass
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class RerankerRequest:
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queries: list[RerankerQuery] = field(default_factory=list)
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documents: list[RerankerDocument] = field(default_factory=list)
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limit: int = 10
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@dataclass
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class RerankerResult:
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document_id: str = ""
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query_id: str = ""
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score: float = 0.0
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@dataclass
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class RerankerResponse:
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error: Error | None = None
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results: list[RerankerResult] = field(default_factory=list)
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