## 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>
### What problem does this PR solve?
- Clear stale pipeline IDs and generated data when updating documents
without `pipeline_id`.
- Support tree compilation results in pipeline workflows.
- Update compilation templates in place while preserving existing
template IDs.
- Improve duplicate-template validation messages.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] New Feature (non-breaking change which adds functionality)
Co-authored-by: Jin Hai <haijin.chn@gmail.com>
### Summary
Handle searching dataset without embedding model
In this PR, Searching datasets with different embedding models or
searching dataset with/without embedding models are not allowed. We will
improve the behavior later.
### What problem does this PR solve?
Closes#12962
MCPToolCallSessions created during agent execution (in `Agent.__init__`)
are never explicitly closed. Each session starts its own event loop
thread and opens an SSE/HTTP connection to the MCP server. When the
canvas goes out of scope, these threads and connections remain alive
indefinitely, accumulating over time and causing resource exhaustion
after prolonged use.
### Solution
1. Add a `Graph.close()` method that iterates all components, finds
MCPToolCallSessions held by Agent tools, and calls `close_sync()` on
each to properly shut down the event loop, thread, and connection.
2. Call `canvas.close()` in `finally` blocks after `canvas.run()`
completes in `canvas_service.py` and `canvas_app.py`.
3. Move MCP session cleanup to `finally` blocks in `test_tool` endpoint
(`mcp_server_app.py`) and `get_mcp_tools` (`api_utils.py`) to ensure
sessions are closed even on exceptions.
### Type of change
- [x] Bug Fix (non-breaking change which fixes an issue)
---------
Co-authored-by: conflict-resolver <conflict-resolver@local>
Co-authored-by: Zhichang Yu <yuzhichang@gmail.com>
