feat: hybrid retrieval (BM25 + vector RRF fusion) for document-RAG (#875) (#1030)

Adds a sparse keyword retrieval path beside the existing vector path in
document-RAG, fused by weighted Reciprocal Rank Fusion on chunk_id, behind
--retrieval-mode (vector | keyword | hybrid, default vector).

The keyword index is a new pluggable service (KeywordIndexService /
KeywordIndexClientSpec); the first backend is SQLite FTS5, consuming Chunk
messages off the ingestion stream and answering BM25 queries from one
process, since the index is a single local file. Query text is sanitized
into per-term quoted phrases (raw text is not valid FTS5 syntax), which
also makes dotted clause numbers and error codes exact-match without a
trigram index. Indexes are scoped per (workspace, collection) and dropped
on collection deletion.

The keyword-index client spec is only registered when the sparse path is
enabled, so existing flow definitions without keyword-index queues are
untouched; with retrieval_mode=vector the retrieval path is unchanged. In
hybrid mode a keyword-path failure degrades to vector-only.

dev-tools/proc-group/groups/embeddings-store.yaml

@@ -32,6 +32,14 @@ processors:
       id: graph-embeddings-write
       store_uri: http://localhost:6333
 
+  # Keyword (BM25) index: ingest-write and query in one processor, since
+  # the FTS5 index is a single local file.
+  - class: trustgraph.storage.kw_index.fts5.Processor
+    params:
+      <<: *defaults
+      id: kw-index
+      index_path: /tmp/tg-kw-index.db
+
   - class: trustgraph.query.row_embeddings.qdrant.Processor
     params:
       <<: *defaults