## 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>
Ports doc-level auto-metadata extraction to Go and adds the
knowledge_compiler component with scheduler/routing. Fixes Extractor
metadata injection type assertion and enable_metadata default-on.
### What problem does this PR solve?
Implement OpenAI chat completions in GO
POST /api/v1/openai/<chat_id>/chat/completions
OpenAI chat cli: internal/development.md
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Implement Delete in GO and refactor functions
### Type of change
- [x] Refactoring
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added a remove_chunks command to delete specific or all chunks from a
document.
* Added new endpoints for chunk removal and chunk update.
* **Refactor**
* Renamed index commands to dataset/metadata table terminology and
updated REST routes accordingly.
* Updated chunk update flow to a JSON POST style and improved metadata
error messages.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
### What problem does this PR solve?
Implement UpdateDataset and UpdateMetadata in GO
Add cli:
UPDATE CHUNK <chunk_id> OF DATASET <dataset_name> SET <update_fields>
REMOVE TAGS 'tag1', 'tag2' from DATASET 'dataset_name';
SET METADATA OF DOCUMENT <doc_id> TO <meta>
### Type of change
- [ ] Refactoring
### What problem does this PR solve?
Implement InsertDataset and InsertMetadata in GO
new internal cli for go:
INSERT DATASET FROM FILE "file_name"
INSERT METADATA FROM FILE "file_name"
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Implement Create/Drop Index/Metadata index in GO
New API handling in GO:
POST/kb/index
DELETE /kb/index
POST /tenant/doc_meta_index
DELETE /tenant/doc_meta_index
CREATE INDEX FOR DATASET 'dataset_name' VECTOR_SIZE 1024;
DROP INDEX FOR DATASET 'dataset_name';
CREATE INDEX DOC_META;
DROP INDEX DOC_META;
### Type of change
- [x] Refactoring
### What problem does this PR solve?
Implement GetChunk() in Infinity in GO
Add cli:
GET CHUNK 'XXX';
LIST CHUNKS OF DOCUMENT 'XXX';
### Type of change
- [x] Refactoring
# RAGFlow Go Implementation Plan 🚀
This repository tracks the progress of porting RAGFlow to Go. We'll
implement core features and provide performance comparisons between
Python and Go versions.
## Implementation Checklist
- [x] User Management APIs
- [x] Dataset Management Operations
- [x] Retrieval Test
- [x] Chat Management Operations
- [x] Infinity Go SDK
---------
Signed-off-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: Yingfeng Zhang <yingfeng.zhang@gmail.com>
