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https://github.com/trustgraph-ai/trustgraph.git
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1346cbebb4
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
38 lines
901 B
Python
38 lines
901 B
Python
from dataclasses import dataclass, field
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from ..core.primitives import Error
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############################################################################
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# Prompt services, abstract the prompt generation
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@dataclass
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class PromptRequest:
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id: str = ""
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# JSON encoded values
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terms: dict[str, str] = field(default_factory=dict)
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# Streaming support (default false for backward compatibility)
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streaming: bool = False
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@dataclass
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class PromptResponse:
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# Error case
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error: Error | None = None
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# Just plain text
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text: str = ""
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# JSON encoded
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object: str = ""
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# Indicates final message in stream
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end_of_stream: bool = False
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# Token usage from the underlying text completion
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in_token: int | None = None
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out_token: int | None = None
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model: str | None = None
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############################################################################
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