feat: replace LLM edge scoring with cross-encoder reranker in GraphRAG

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

tests/unit/test_provenance/test_dag_structure.py

@@ -107,6 +107,7 @@ class TestGraphRagDagStructure:
         embeddings_client = AsyncMock()
         graph_embeddings_client = AsyncMock()
         triples_client = AsyncMock()
+        reranker_client = AsyncMock()
 
         embeddings_client.embed.return_value = [[0.1, 0.2]]
         graph_embeddings_client.query.return_value = [
@@ -121,27 +122,22 @@ class TestGraphRagDagStructure:
         ]
         triples_client.query.return_value = []
 
+        result = MagicMock()
+        result.document_id = "0"
+        result.query_id = "0"
+        result.score = 0.95
+        reranker_client.rerank.return_value = [result]
+
         async def mock_prompt(template_id, variables=None, **kwargs):
             if template_id == "extract-concepts":
                 return PromptResult(response_type="text", text="concept")
-            elif template_id == "kg-edge-scoring":
-                edges = variables.get("knowledge", [])
-                return PromptResult(
-                    response_type="jsonl",
-                    objects=[{"id": e["id"], "score": 10} for e in edges],
-                )
-            elif template_id == "kg-edge-reasoning":
-                edges = variables.get("knowledge", [])
-                return PromptResult(
-                    response_type="jsonl",
-                    objects=[{"id": e["id"], "reasoning": "relevant"} for e in edges],
-                )
             elif template_id == "kg-synthesis":
                 return PromptResult(response_type="text", text="Answer.")
             return PromptResult(response_type="text", text="")
 
         prompt_client.prompt.side_effect = mock_prompt
-        return prompt_client, embeddings_client, graph_embeddings_client, triples_client
+        return (prompt_client, embeddings_client, graph_embeddings_client,
+                triples_client, reranker_client)
 
     @pytest.mark.asyncio
     async def test_dag_chain(self, mock_clients):
@@ -152,7 +148,7 @@ class TestGraphRagDagStructure:
             events.append({"explain_id": explain_id, "triples": triples})
 
         await rag.query(
-            query="test", explain_callback=explain_cb, edge_score_limit=0,
+            query="test", explain_callback=explain_cb,
         )
 
         dag = _collect_events(events)