GraphRAG Query-Time Explainability (#677)

Implements full explainability pipeline for GraphRAG queries, enabling
traceability from answers back to source documents.

Renamed throughout for clarity:
- provenance_callback → explain_callback
- provenance_id → explain_id
- provenance_collection → explain_collection
- message_type "provenance" → "explain"
- Queue name "provenance" → "explainability"

GraphRAG queries now emit explainability events as they execute:
1. Session - query text and timestamp
2. Retrieval - edges retrieved from subgraph
3. Selection - selected edges with LLM reasoning (JSONL with id +
   reasoning)
4. Answer - reference to synthesized response

Events stream via explain_callback during query(), enabling
real-time UX.

- Answers stored in librarian service (not inline in graph - too large)
- Document ID as URN: urn:trustgraph:answer:{session_id}
- Graph stores tg:document reference (IRI) to librarian document
- Added librarian producer/consumer to graph-rag service

- get_labelgraph() now returns (labeled_edges, uri_map)
- uri_map maps edge_id(label_s, label_p, label_o) →
  (uri_s, uri_p, uri_o)
- Explainability data stores original URIs, not labels
- Enables tracing edges back to reifying statements via tg:reifies

- Added serialize_triple() to query service (matches storage format)
- get_term_value() now handles TRIPLE type terms
- Enables querying by quoted triple in object position:
  ?stmt tg:reifies <<s p o>>

- Displays real-time explainability events during query
- Resolves rdfs:label for edge components (s, p, o)
- Traces source chain via prov:wasDerivedFrom to root document
- Output: "Source: Chunk 1 → Page 2 → Document Title"
- Label caching to avoid repeated queries

GraphRagResponse:
- explain_id: str | None
- explain_collection: str | None
- message_type: str ("chunk" or "explain")
- end_of_session: bool

trustgraph-base/trustgraph/provenance/:
- namespaces.py - Added TG_DOCUMENT predicate
- triples.py - answer_triples() supports document_id reference
- uris.py - Added edge_selection_uri()

trustgraph-base/trustgraph/schema/services/retrieval.py:
- GraphRagResponse with explain_id, explain_collection, end_of_session

trustgraph-flow/trustgraph/retrieval/graph_rag/:
- graph_rag.py - URI preservation, streaming answer accumulation
- rag.py - Librarian integration, real-time explain emission

trustgraph-flow/trustgraph/query/triples/cassandra/service.py:
- Quoted triple serialization for query matching

trustgraph-cli/trustgraph/cli/invoke_graph_rag.py:
- Full explainability display with label resolution and source tracing

tests/integration/test_rag_streaming_protocol.py

@@ -44,18 +44,23 @@ class TestGraphRagStreamingProtocol:
         """Mock prompt client that simulates realistic streaming with end_of_stream flags"""
         client = AsyncMock()
 
-        async def kg_prompt_side_effect(query, kg, timeout=600, streaming=False, chunk_callback=None):
-            if streaming and chunk_callback:
-                # Simulate realistic streaming: chunks with end_of_stream=False, then final with end_of_stream=True
-                await chunk_callback("The", False)
-                await chunk_callback(" answer", False)
-                await chunk_callback(" is here.", False)
-                await chunk_callback("", True)  # Empty final chunk with end_of_stream=True
-                return ""  # Return value not used since callback handles everything
-            else:
-                return "The answer is here."
+        async def prompt_side_effect(prompt_name, variables=None, streaming=False, chunk_callback=None):
+            if prompt_name == "kg-edge-selection":
+                # Edge selection returns empty (no edges selected)
+                return ""
+            elif prompt_name == "kg-synthesis":
+                if streaming and chunk_callback:
+                    # Simulate realistic streaming: chunks with end_of_stream=False, then final with end_of_stream=True
+                    await chunk_callback("The", False)
+                    await chunk_callback(" answer", False)
+                    await chunk_callback(" is here.", False)
+                    await chunk_callback("", True)  # Empty final chunk with end_of_stream=True
+                    return ""  # Return value not used since callback handles everything
+                else:
+                    return "The answer is here."
+            return ""
 
-        client.kg_prompt.side_effect = kg_prompt_side_effect
+        client.prompt.side_effect = prompt_side_effect
         return client
 
     @pytest.fixture
@@ -327,20 +332,24 @@ class TestStreamingProtocolEdgeCases:
         # Arrange
         client = AsyncMock()
 
-        async def kg_prompt_with_empties(query, kg, timeout=600, streaming=False, chunk_callback=None):
-            if streaming and chunk_callback:
-                await chunk_callback("text", False)
-                await chunk_callback("", False)  # Empty but not final
-                await chunk_callback("more", False)
-                await chunk_callback("", True)  # Empty and final
+        async def prompt_with_empties(prompt_name, variables=None, streaming=False, chunk_callback=None):
+            if prompt_name == "kg-edge-selection":
                 return ""
-            else:
-                return "textmore"
+            elif prompt_name == "kg-synthesis":
+                if streaming and chunk_callback:
+                    await chunk_callback("text", False)
+                    await chunk_callback("", False)  # Empty but not final
+                    await chunk_callback("more", False)
+                    await chunk_callback("", True)  # Empty and final
+                    return ""
+                else:
+                    return "textmore"
+            return ""
 
-        client.kg_prompt.side_effect = kg_prompt_with_empties
+        client.prompt.side_effect = prompt_with_empties
 
         rag = GraphRag(
-            embeddings_client=AsyncMock(embed=AsyncMock(return_value=[[0.1]])),
+            embeddings_client=AsyncMock(embed=AsyncMock(return_value=[[[0.1]]])),
             graph_embeddings_client=AsyncMock(query=AsyncMock(return_value=[])),
             triples_client=AsyncMock(query=AsyncMock(return_value=[])),
             prompt_client=client,