feat(serenedb): add SereneDB doc-store engine (Go + Python connectors) (#17375)

## What

Adds [**SereneDB**](https://serenedb.com) as a selectable doc-store
engine on **both** RAGFlow paths:
- the **Go** `DocEngine` (`internal/engine/serenedb`), alongside
Elasticsearch and Infinity;
- the **Python** `DocStoreConnection` (`rag/utils/serenedb_conn.py`) +
`DOC_ENGINE=serenedb` registration.

SereneDB is a PostgreSQL-wire engine (DuckDB execution) whose single
inverted index carries **both** a scored text column (`@@`, BM25) and an
IVF vector column (`<#>`, inner product), so hybrid search is one SQL
statement. The Go engine connects with `database/sql` + `lib/pq`
(already a dependency, no new module); the Python connector uses
psycopg2 (already a dependency).

## Storage model

One table per tenant with `kb_id` as a filter column - the
**Elasticsearch / OceanBase** model, not Infinity's per-dataset tables.
This keeps BM25 statistics (IDF, avgdl) computed over the whole tenant
corpus (global IDF). Both connectors use this identical layout, so they
are storage- and retrieval-compatible: `hybrid` proxy routing and
Python↔Go switching are safe. On the Python side the connector is wired
as OceanBase's plain-SQL sibling (chunk_data JSON metadata, inline chunk
vectors, verbatim ES field names); the ES tokenizer path is unchanged.
Metadata stays one table per tenant (`ragflow_doc_meta_<tenant>`).

The query shapes mirror the Python connector, including the five
empirically-found landmines: the scored dictionary needs `frequency +
norm` (else `BM25()` silently returns 0.0), the `@@` query is the
tokenized query, the scored lexical branch matches one column, vectors
use an L2-normalized shadow column with `ip`/`sq8`, and the similarity
threshold goes directly in the ANN scan's `WHERE`. **Minimum engine
version: SereneDB 26.07.4.**

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>

api/db/services/dialog_service.py

@@ -71,7 +71,7 @@ async def _hydrate_chunk_vectors(retriever, chunks, tenant_ids, kb_ids):
     search results) keep whatever placeholder they were given. Other
     backends still carry vectors in the chunk, so we skip the round-trip.
     """
-    if settings.DOC_ENGINE_INFINITY or settings.DOC_ENGINE_OCEANBASE:
+    if settings.DOC_ENGINE_INFINITY or settings.DOC_ENGINE_OCEANBASE or settings.DOC_ENGINE_SERENEDB:
         return
     if not chunks:
         return

api/db/services/doc_metadata_service.py

@@ -470,7 +470,7 @@ class DocMetadataService:
             logging.debug(f"[update_document_metadata] Updating doc_id: {doc_id}, kb_id: {kb_id}, meta_fields: {processed_meta}")
 
             # For Elasticsearch, use efficient partial update
-            if not settings.DOC_ENGINE_INFINITY and not settings.DOC_ENGINE_OCEANBASE:
+            if not settings.DOC_ENGINE_INFINITY and not settings.DOC_ENGINE_OCEANBASE and not settings.DOC_ENGINE_SERENEDB:
                 # Check if index exists first
                 index_exists = settings.docStoreConn.index_exist(index_name, "")
                 if not index_exists: