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/integration/test_graph_rag_streaming_integration.py

@@ -63,11 +63,6 @@ class TestGraphRagStreaming:
         async def prompt_side_effect(prompt_id, variables, streaming=False, chunk_callback=None, **kwargs):
             if prompt_id == "extract-concepts":
                 return PromptResult(response_type="text", text="")
-            elif prompt_id == "kg-edge-scoring":
-                # Edge scoring returns JSONL with IDs and scores
-                return PromptResult(response_type="text", text='{"id": "abc12345", "score": 0.9}\n')
-            elif prompt_id == "kg-edge-reasoning":
-                return PromptResult(response_type="text", text='{"id": "abc12345", "reasoning": "Relevant to query"}\n')
             elif prompt_id == "kg-synthesis":
                 if streaming and chunk_callback:
                     # Simulate streaming chunks with end_of_stream flags
@@ -88,14 +83,23 @@ class TestGraphRagStreaming:
         client.prompt.side_effect = prompt_side_effect
         return client
 
+    @pytest.fixture
+    def mock_reranker_client(self):
+        """Mock reranker client for cross-encoder edge filtering"""
+        client = AsyncMock()
+        client.rerank.return_value = []
+        return client
+
     @pytest.fixture
     def graph_rag_streaming(self, mock_embeddings_client, mock_graph_embeddings_client,
-                            mock_triples_client, mock_streaming_prompt_client):
+                            mock_triples_client, mock_reranker_client,
+                            mock_streaming_prompt_client):
         """Create GraphRag instance with streaming support"""
         return GraphRag(
             embeddings_client=mock_embeddings_client,
             graph_embeddings_client=mock_graph_embeddings_client,
             triples_client=mock_triples_client,
+            reranker_client=mock_reranker_client,
             prompt_client=mock_streaming_prompt_client,
             verbose=True
         )