mirror of
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
154 lines
3.6 KiB
Python
154 lines
3.6 KiB
Python
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import json
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import dataclasses
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from .. schema import PromptRequest, PromptResponse
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from .. schema import prompt_request_queue
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from .. schema import prompt_response_queue
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from . base import BaseClient
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# Ugly
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@dataclasses.dataclass
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class Definition:
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name: str
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definition: str
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@dataclasses.dataclass
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class Relationship:
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s: str
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p: str
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o: str
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o_entity: str
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@dataclasses.dataclass
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class Topic:
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name: str
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definition: str
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class PromptClient(BaseClient):
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def __init__(
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self,
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subscriber=None,
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input_queue=None,
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output_queue=None,
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pulsar_host="pulsar://pulsar:6650",
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pulsar_api_key=None,
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):
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if input_queue == None:
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input_queue = prompt_request_queue
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if output_queue == None:
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output_queue = prompt_response_queue
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super(PromptClient, self).__init__(
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subscriber=subscriber,
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input_queue=input_queue,
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output_queue=output_queue,
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pulsar_host=pulsar_host,
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pulsar_api_key=pulsar_api_key,
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input_schema=PromptRequest,
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output_schema=PromptResponse,
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)
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def request(self, id, variables, timeout=300):
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resp = self.call(
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id=id,
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terms={
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k: json.dumps(v)
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for k, v in variables.items()
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},
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timeout=timeout
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)
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if resp.text: return resp.text
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return json.loads(resp.object)
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def request_definitions(self, chunk, timeout=300):
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defs = self.request(
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id="extract-definitions",
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variables={
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"text": chunk
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},
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timeout=timeout
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)
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return [
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Definition(name=d["entity"], definition=d["definition"])
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for d in defs
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]
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def request_relationships(self, chunk, timeout=300):
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rels = self.request(
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id="extract-relationships",
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variables={
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"text": chunk
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},
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timeout=timeout
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)
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return [
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Relationship(
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s=d["subject"],
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p=d["predicate"],
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o=d["object"],
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o_entity=d["object-entity"]
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)
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for d in rels
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]
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def request_topics(self, chunk, timeout=300):
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topics = self.request(
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id="extract-topics",
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variables={
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"text": chunk
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},
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timeout=timeout
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)
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return [
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Topic(name=d["topic"], definition=d["definition"])
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for d in topics
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]
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def request_rows(self, schema, chunk, timeout=300):
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return self.request(
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id="extract-rows",
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variables={
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"chunk": chunk,
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"row-schema": {
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"name": schema.name,
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"description": schema.description,
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"fields": [
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{
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"name": f.name, "type": str(f.type),
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"size": f.size, "primary": f.primary,
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"description": f.description,
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}
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for f in schema.fields
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]
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}
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},
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timeout=timeout
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)
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def request_document_prompt(self, query, documents, timeout=300):
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return self.request(
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id="document-prompt",
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variables={
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"query": query,
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"documents": documents,
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},
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timeout=timeout
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)
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