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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
43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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from . request_response_spec import RequestResponse, RequestResponseSpec
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from .. schema import (
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RerankerRequest, RerankerResponse,
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RerankerQuery, RerankerDocument,
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)
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class RerankerClient(RequestResponse):
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async def rerank(self, queries, documents, limit=10, timeout=300):
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resp = await self.request(
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RerankerRequest(
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queries=[
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RerankerQuery(query_id=q["id"], query_text=q["text"])
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for q in queries
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],
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documents=[
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RerankerDocument(
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document_id=d["id"], document_text=d["text"]
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)
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for d in documents
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],
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limit=limit,
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),
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timeout=timeout
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)
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if resp.error:
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raise RuntimeError(resp.error.message)
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return resp.results
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class RerankerClientSpec(RequestResponseSpec):
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def __init__(
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self, request_name, response_name,
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):
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super(RerankerClientSpec, self).__init__(
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request_name = request_name,
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request_schema = RerankerRequest,
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response_name = response_name,
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response_schema = RerankerResponse,
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impl = RerankerClient,
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)
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