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

trustgraph-flow/pyproject.toml

@@ -19,6 +19,7 @@ dependencies = [
     "faiss-cpu",
     "falkordb",
     "fastembed",
+    "flashrank",
     "ibis",
     "jsonschema",
     "langchain",
@@ -83,6 +84,7 @@ graph-embeddings-write-pinecone = "trustgraph.storage.graph_embeddings.pinecone:
 graph-embeddings-write-qdrant = "trustgraph.storage.graph_embeddings.qdrant:run"
 graph-embeddings = "trustgraph.embeddings.graph_embeddings:run"
 graph-rag = "trustgraph.retrieval.graph_rag:run"
+reranker-flashrank = "trustgraph.reranker.flashrank:run"
 kg-extract-agent = "trustgraph.extract.kg.agent:run"
 kg-extract-definitions = "trustgraph.extract.kg.definitions:run"
 kg-extract-rows = "trustgraph.extract.kg.rows:run"