mirror of
https://github.com/infiniflow/ragflow.git
synced 2026-08-04 23:00:30 +08:00
## 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>
114 lines
3.3 KiB
Go
114 lines
3.3 KiB
Go
//
|
|
// Copyright 2026 The InfiniFlow Authors. All Rights Reserved.
|
|
//
|
|
// Licensed under the Apache License, Version 2.0 (the "License");
|
|
// you may not use this file except in compliance with the License.
|
|
// You may obtain a copy of the License at
|
|
//
|
|
// http://www.apache.org/licenses/LICENSE-2.0
|
|
//
|
|
// Unless required by applicable law or agreed to in writing, software
|
|
// distributed under the License is distributed on an "AS IS" BASIS,
|
|
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
// See the License for the specific language governing permissions and
|
|
// limitations under the License.
|
|
//
|
|
|
|
package serenedb
|
|
|
|
import (
|
|
"context"
|
|
"fmt"
|
|
"strconv"
|
|
)
|
|
|
|
// asChunkMap coerces a skill document to a chunk map.
|
|
func asChunkMap(doc interface{}) (map[string]interface{}, error) {
|
|
m, ok := doc.(map[string]interface{})
|
|
if !ok {
|
|
return nil, fmt.Errorf("serenedb: document must be a map, got %T", doc)
|
|
}
|
|
return m, nil
|
|
}
|
|
|
|
// ensureSkillTable creates the skill table (named by indexName) if absent,
|
|
// inferring the vector size from the documents.
|
|
func (e *serenedbEngine) ensureSkillTable(ctx context.Context, indexName string, docs []map[string]interface{}) error {
|
|
exists, err := e.tableExists(ctx, indexName)
|
|
if err != nil {
|
|
return err
|
|
}
|
|
if exists {
|
|
return nil
|
|
}
|
|
size := 0
|
|
for _, doc := range docs {
|
|
for k := range doc {
|
|
if m := vectorColumnPattern.FindStringSubmatch(k); m != nil {
|
|
size, _ = strconv.Atoi(m[1])
|
|
}
|
|
}
|
|
}
|
|
for _, stmt := range chunkTableDDL(indexName, size) {
|
|
if err := e.exec(ctx, stmt); err != nil {
|
|
return fmt.Errorf("serenedb: create skill table %s: %w", indexName, err)
|
|
}
|
|
}
|
|
return nil
|
|
}
|
|
|
|
// IndexDocument upserts a single skill document.
|
|
func (e *serenedbEngine) IndexDocument(ctx context.Context, indexName, docID string, doc interface{}) error {
|
|
m, err := asChunkMap(doc)
|
|
if err != nil {
|
|
return err
|
|
}
|
|
if _, ok := m["id"]; !ok {
|
|
m["id"] = docID
|
|
}
|
|
if err := e.ensureSkillTable(ctx, indexName, []map[string]interface{}{m}); err != nil {
|
|
return err
|
|
}
|
|
cols, vals := prepareChunkRow(m, "")
|
|
query, args := buildUpsert(indexName, cols, [][]interface{}{vals})
|
|
return e.exec(ctx, query, args...)
|
|
}
|
|
|
|
// BulkIndex upserts a batch of skill documents.
|
|
func (e *serenedbEngine) BulkIndex(ctx context.Context, indexName string, docs []interface{}) (interface{}, error) {
|
|
maps := make([]map[string]interface{}, 0, len(docs))
|
|
for _, d := range docs {
|
|
m, err := asChunkMap(d)
|
|
if err != nil {
|
|
return nil, err
|
|
}
|
|
maps = append(maps, m)
|
|
}
|
|
if len(maps) == 0 {
|
|
return nil, nil
|
|
}
|
|
if err := e.ensureSkillTable(ctx, indexName, maps); err != nil {
|
|
return nil, err
|
|
}
|
|
for _, m := range maps {
|
|
cols, vals := prepareChunkRow(m, "")
|
|
query, args := buildUpsert(indexName, cols, [][]interface{}{vals})
|
|
if err := e.exec(ctx, query, args...); err != nil {
|
|
return nil, fmt.Errorf("serenedb: bulk index %s: %w", indexName, err)
|
|
}
|
|
}
|
|
return len(maps), nil
|
|
}
|
|
|
|
// DeleteDocument removes a skill document by id. A missing table is not an error.
|
|
func (e *serenedbEngine) DeleteDocument(ctx context.Context, indexName, docID string) error {
|
|
exists, err := e.tableExists(ctx, indexName)
|
|
if err != nil {
|
|
return err
|
|
}
|
|
if !exists {
|
|
return nil
|
|
}
|
|
return e.exec(ctx, fmt.Sprintf("DELETE FROM %s WHERE id = $1", indexName), docID)
|
|
}
|