feat: replace LLM edge scoring with cross-encoder reranker in GraphRAG (#1005)

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_rag_streaming_protocol.py

@@ -46,7 +46,7 @@ class TestGraphRagStreamingProtocol:
         client = AsyncMock()
 
         async def prompt_side_effect(prompt_name, variables=None, streaming=False, chunk_callback=None):
-            if prompt_name == "kg-edge-selection":
+            if prompt_name == "extract-concepts":
                 return PromptResult(response_type="text", text="")
             elif prompt_name == "kg-synthesis":
                 if streaming and chunk_callback:
@@ -63,14 +63,23 @@ class TestGraphRagStreamingProtocol:
         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(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 mocked dependencies"""
         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=False
         )
@@ -327,7 +336,7 @@ class TestStreamingProtocolEdgeCases:
         client = AsyncMock()
 
         async def prompt_with_empties(prompt_name, variables=None, streaming=False, chunk_callback=None):
-            if prompt_name == "kg-edge-selection":
+            if prompt_name == "extract-concepts":
                 return PromptResult(response_type="text", text="")
             elif prompt_name == "kg-synthesis":
                 if streaming and chunk_callback:
@@ -342,10 +351,14 @@ class TestStreamingProtocolEdgeCases:
 
         client.prompt.side_effect = prompt_with_empties
 
+        mock_reranker = AsyncMock()
+        mock_reranker.rerank.return_value = []
+
         rag = GraphRag(
             embeddings_client=AsyncMock(embed=AsyncMock(return_value=[[[0.1]]])),
             graph_embeddings_client=AsyncMock(query=AsyncMock(return_value=[])),
             triples_client=AsyncMock(query=AsyncMock(return_value=[])),
+            reranker_client=mock_reranker,
             prompt_client=client,
             verbose=False
         )