## Summary
- allow ISO 8601 normalization callers to provide an explicit fallback
while preserving the existing default behavior
- use the extraction conversation time when `valid_at` is missing or
invalid
- clear an invalid optional `invalid_at` instead of writing an
unparseable value
- include the rejected timestamp value in the error log
This addresses the timestamp write-through portion of #18415. It is
intentionally separate from #18462, which fixes the semantic output
prompt.
Co-authored-by: Loong <184861530+yzl0ng@users.noreply.github.com>
### Summary
- Forward each memory's stored system_prompt and user_prompt to
extract_by_llm.
- Cover both immediate save and queued extraction paths with focused
regression tests.
- Preserve the existing default-prompt fallback when stored prompts are
empty.
Fixes#18413.
### Summary
RAGFlow's "Create empty document" flow accepts names without a file
extension. The `POST /datasets/<dataset_id>/documents?type=empty` route
calls `_upload_empty_document()`, where `Path(name).suffix.lstrip(".")`
returns `""`.
In GaussDB's A/ORA compatibility mode, that empty string is persisted as
SQL `NULL`. Because `document.suffix` was defined as `NOT NULL`, the
insert failed with a constraint violation.
Follow-up to #17526 ("Refactor: merge dataset scope graph"), which introduced two code paths that touch Infinity columns the deployed schema does not declare. This PR makes the runtime robust against the old schema while also adding the new column to the new schema so freshly created tables are correct.
Ports the dataset knowledge compilation (wiki/graph/tree/mindmap) to the
Go scheduler with a status contract, aligns wiki storage/retrieval with
Python, and sizes prompts by content_length.
## 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>
