Added OceanBase as an option for the vector store in Dify (#10010)

.gitignore

@@ -174,6 +174,7 @@ docker/volumes/unstructured/*
 docker/volumes/pgvector/data/*
 docker/volumes/pgvecto_rs/data/*
 docker/volumes/couchbase/*
+docker/volumes/oceanbase/*
 
 docker/nginx/conf.d/default.conf
 docker/nginx/ssl/*

api/.env.example

@@ -249,6 +249,14 @@ VIKINGDB_SCHEMA=http
 VIKINGDB_CONNECTION_TIMEOUT=30
 VIKINGDB_SOCKET_TIMEOUT=30
 
+# OceanBase Vector configuration
+OCEANBASE_VECTOR_HOST=127.0.0.1
+OCEANBASE_VECTOR_PORT=2881
+OCEANBASE_VECTOR_USER=root@test
+OCEANBASE_VECTOR_PASSWORD=
+OCEANBASE_VECTOR_DATABASE=test
+OCEANBASE_MEMORY_LIMIT=6G
+
 # Upload configuration
 UPLOAD_FILE_SIZE_LIMIT=15
 UPLOAD_FILE_BATCH_LIMIT=5

api/commands.py

@@ -279,6 +279,7 @@ def migrate_knowledge_vector_database():
         VectorType.VIKINGDB,
         VectorType.UPSTASH,
         VectorType.COUCHBASE,
+        VectorType.OCEANBASE,
     }
     page = 1
     while True:

api/configs/middleware/__init__.py

@@ -21,6 +21,7 @@ from configs.middleware.vdb.couchbase_config import CouchbaseConfig
 from configs.middleware.vdb.elasticsearch_config import ElasticsearchConfig
 from configs.middleware.vdb.milvus_config import MilvusConfig
 from configs.middleware.vdb.myscale_config import MyScaleConfig
+from configs.middleware.vdb.oceanbase_config import OceanBaseVectorConfig
 from configs.middleware.vdb.opensearch_config import OpenSearchConfig
 from configs.middleware.vdb.oracle_config import OracleConfig
 from configs.middleware.vdb.pgvector_config import PGVectorConfig
@@ -257,5 +258,6 @@ class MiddlewareConfig(
     VikingDBConfig,
     UpstashConfig,
     TidbOnQdrantConfig,
+    OceanBaseVectorConfig,
 ):
     pass

api/configs/middleware/vdb/oceanbase_config.py

@@ -0,0 +1,35 @@
+from typing import Optional
+
+from pydantic import Field, PositiveInt
+from pydantic_settings import BaseSettings
+
+
+class OceanBaseVectorConfig(BaseSettings):
+    """
+    Configuration settings for OceanBase Vector database
+    """
+
+    OCEANBASE_VECTOR_HOST: Optional[str] = Field(
+        description="Hostname or IP address of the OceanBase Vector server (e.g. 'localhost')",
+        default=None,
+    )
+
+    OCEANBASE_VECTOR_PORT: Optional[PositiveInt] = Field(
+        description="Port number on which the OceanBase Vector server is listening (default is 2881)",
+        default=2881,
+    )
+
+    OCEANBASE_VECTOR_USER: Optional[str] = Field(
+        description="Username for authenticating with the OceanBase Vector database",
+        default=None,
+    )
+
+    OCEANBASE_VECTOR_PASSWORD: Optional[str] = Field(
+        description="Password for authenticating with the OceanBase Vector database",
+        default=None,
+    )
+
+    OCEANBASE_VECTOR_DATABASE: Optional[str] = Field(
+        description="Name of the OceanBase Vector database to connect to",
+        default=None,
+    )

api/controllers/console/datasets/datasets.py

@@ -628,6 +628,7 @@ class DatasetRetrievalSettingApi(Resource):
                 | VectorType.BAIDU
                 | VectorType.VIKINGDB
                 | VectorType.UPSTASH
+                | VectorType.OCEANBASE
             ):
                 return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
             case (
@@ -669,6 +670,7 @@ class DatasetRetrievalSettingMockApi(Resource):
                 | VectorType.BAIDU
                 | VectorType.VIKINGDB
                 | VectorType.UPSTASH
+                | VectorType.OCEANBASE
             ):
                 return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]}
             case (

api/core/rag/datasource/vdb/oceanbase/oceanbase_vector.py

@@ -0,0 +1,209 @@
+import json
+import logging
+import math
+from typing import Any
+
+from pydantic import BaseModel, model_validator
+from pyobvector import VECTOR, ObVecClient
+from sqlalchemy import JSON, Column, String, func
+from sqlalchemy.dialects.mysql import LONGTEXT
+
+from configs import dify_config
+from core.rag.datasource.vdb.vector_base import BaseVector
+from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
+from core.rag.datasource.vdb.vector_type import VectorType
+from core.rag.embedding.embedding_base import Embeddings
+from core.rag.models.document import Document
+from extensions.ext_redis import redis_client
+from models.dataset import Dataset
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_OCEANBASE_HNSW_BUILD_PARAM = {"M": 16, "efConstruction": 256}
+DEFAULT_OCEANBASE_HNSW_SEARCH_PARAM = {"efSearch": 64}
+OCEANBASE_SUPPORTED_VECTOR_INDEX_TYPE = "HNSW"
+DEFAULT_OCEANBASE_VECTOR_METRIC_TYPE = "l2"
+
+
+class OceanBaseVectorConfig(BaseModel):
+    host: str
+    port: int
+    user: str
+    password: str
+    database: str
+
+    @model_validator(mode="before")
+    @classmethod
+    def validate_config(cls, values: dict) -> dict:
+        if not values["host"]:
+            raise ValueError("config OCEANBASE_VECTOR_HOST is required")
+        if not values["port"]:
+            raise ValueError("config OCEANBASE_VECTOR_PORT is required")
+        if not values["user"]:
+            raise ValueError("config OCEANBASE_VECTOR_USER is required")
+        if not values["database"]:
+            raise ValueError("config OCEANBASE_VECTOR_DATABASE is required")
+        return values
+
+
+class OceanBaseVector(BaseVector):
+    def __init__(self, collection_name: str, config: OceanBaseVectorConfig):
+        super().__init__(collection_name)
+        self._config = config
+        self._hnsw_ef_search = -1
+        self._client = ObVecClient(
+            uri=f"{self._config.host}:{self._config.port}",
+            user=self._config.user,
+            password=self._config.password,
+            db_name=self._config.database,
+        )
+
+    def get_type(self) -> str:
+        return VectorType.OCEANBASE
+
+    def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
+        self._vec_dim = len(embeddings[0])
+        self._create_collection()
+        self.add_texts(texts, embeddings)
+
+    def _create_collection(self) -> None:
+        lock_name = "vector_indexing_lock_" + self._collection_name
+        with redis_client.lock(lock_name, timeout=20):
+            collection_exist_cache_key = "vector_indexing_" + self._collection_name
+            if redis_client.get(collection_exist_cache_key):
+                return
+
+            if self._client.check_table_exists(self._collection_name):
+                return
+
+            self.delete()
+
+            cols = [
+                Column("id", String(36), primary_key=True, autoincrement=False),
+                Column("vector", VECTOR(self._vec_dim)),
+                Column("text", LONGTEXT),
+                Column("metadata", JSON),
+            ]
+            vidx_params = self._client.prepare_index_params()
+            vidx_params.add_index(
+                field_name="vector",
+                index_type=OCEANBASE_SUPPORTED_VECTOR_INDEX_TYPE,
+                index_name="vector_index",
+                metric_type=DEFAULT_OCEANBASE_VECTOR_METRIC_TYPE,
+                params=DEFAULT_OCEANBASE_HNSW_BUILD_PARAM,
+            )
+
+            self._client.create_table_with_index_params(
+                table_name=self._collection_name,
+                columns=cols,
+                vidxs=vidx_params,
+            )
+            vals = []
+            params = self._client.perform_raw_text_sql("SHOW PARAMETERS LIKE '%ob_vector_memory_limit_percentage%'")
+            for row in params:
+                val = int(row[6])
+                vals.append(val)
+            if len(vals) == 0:
+                print("ob_vector_memory_limit_percentage not found in parameters.")
+                exit(1)
+            if any(val == 0 for val in vals):
+                try:
+                    self._client.perform_raw_text_sql("ALTER SYSTEM SET ob_vector_memory_limit_percentage = 30")
+                except Exception as e:
+                    raise Exception(
+                        "Failed to set ob_vector_memory_limit_percentage. "
+                        + "Maybe the database user has insufficient privilege.",
+                        e,
+                    )
+            redis_client.set(collection_exist_cache_key, 1, ex=3600)
+
+    def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
+        ids = self._get_uuids(documents)
+        for id, doc, emb in zip(ids, documents, embeddings):
+            self._client.insert(
+                table_name=self._collection_name,
+                data={
+                    "id": id,
+                    "vector": emb,
+                    "text": doc.page_content,
+                    "metadata": doc.metadata,
+                },
+            )
+
+    def text_exists(self, id: str) -> bool:
+        cur = self._client.get(table_name=self._collection_name, id=id)
+        return cur.rowcount != 0
+
+    def delete_by_ids(self, ids: list[str]) -> None:
+        self._client.delete(table_name=self._collection_name, ids=ids)
+
+    def get_ids_by_metadata_field(self, key: str, value: str) -> list[str]:
+        cur = self._client.get(
+            table_name=self._collection_name,
+            where_clause=f"metadata->>'$.{key}' = '{value}'",
+            output_column_name=["id"],
+        )
+        return [row[0] for row in cur]
+
+    def delete_by_metadata_field(self, key: str, value: str) -> None:
+        ids = self.get_ids_by_metadata_field(key, value)
+        self.delete_by_ids(ids)
+
+    def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
+        return []
+
+    def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
+        ef_search = kwargs.get("ef_search", self._hnsw_ef_search)
+        if ef_search != self._hnsw_ef_search:
+            self._client.set_ob_hnsw_ef_search(ef_search)
+            self._hnsw_ef_search = ef_search
+        topk = kwargs.get("top_k", 10)
+        cur = self._client.ann_search(
+            table_name=self._collection_name,
+            vec_column_name="vector",
+            vec_data=query_vector,
+            topk=topk,
+            distance_func=func.l2_distance,
+            output_column_names=["text", "metadata"],
+            with_dist=True,
+        )
+        docs = []
+        for text, metadata, distance in cur:
+            metadata = json.loads(metadata)
+            metadata["score"] = 1 - distance / math.sqrt(2)
+            docs.append(
+                Document(
+                    page_content=text,
+                    metadata=metadata,
+                )
+            )
+        return docs
+
+    def delete(self) -> None:
+        self._client.drop_table_if_exist(self._collection_name)
+
+
+class OceanBaseVectorFactory(AbstractVectorFactory):
+    def init_vector(
+        self,
+        dataset: Dataset,
+        attributes: list,
+        embeddings: Embeddings,
+    ) -> BaseVector:
+        if dataset.index_struct_dict:
+            class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
+            collection_name = class_prefix.lower()
+        else:
+            dataset_id = dataset.id
+            collection_name = Dataset.gen_collection_name_by_id(dataset_id).lower()
+            dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.OCEANBASE, collection_name))
+        return OceanBaseVector(
+            collection_name,
+            OceanBaseVectorConfig(
+                host=dify_config.OCEANBASE_VECTOR_HOST,
+                port=dify_config.OCEANBASE_VECTOR_PORT,
+                user=dify_config.OCEANBASE_VECTOR_USER,
+                password=(dify_config.OCEANBASE_VECTOR_PASSWORD or ""),
+                database=dify_config.OCEANBASE_VECTOR_DATABASE,
+            ),
+        )

api/core/rag/datasource/vdb/vector_factory.py

@@ -134,6 +134,10 @@ class Vector:
                 from core.rag.datasource.vdb.tidb_on_qdrant.tidb_on_qdrant_vector import TidbOnQdrantVectorFactory
 
                 return TidbOnQdrantVectorFactory
+            case VectorType.OCEANBASE:
+                from core.rag.datasource.vdb.oceanbase.oceanbase_vector import OceanBaseVectorFactory
+
+                return OceanBaseVectorFactory
             case _:
                 raise ValueError(f"Vector store {vector_type} is not supported.")
 

api/core/rag/datasource/vdb/vector_type.py

@@ -21,3 +21,4 @@ class VectorType(str, Enum):
     VIKINGDB = "vikingdb"
     UPSTASH = "upstash"
     TIDB_ON_QDRANT = "tidb_on_qdrant"
+    OCEANBASE = "oceanbase"

api/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
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+    {file = "Brotli-1.1.0-cp313-cp313-win_amd64.whl", hash = "sha256:9011560a466d2eb3f5a6e4929cf4a09be405c64154e12df0dd72713f6500e32b"},
     {file = "Brotli-1.1.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:a090ca607cbb6a34b0391776f0cb48062081f5f60ddcce5d11838e67a01928d1"},
     {file = "Brotli-1.1.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2de9d02f5bda03d27ede52e8cfe7b865b066fa49258cbab568720aa5be80a47d"},
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@@ -967,6 +993,10 @@ files = [
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+    {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_aarch64.whl", hash = "sha256:aea440a510e14e818e67bfc4027880e2fb500c2ccb20ab21c7a7c8b5b4703d75"},
+    {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_i686.whl", hash = "sha256:6974f52a02321b36847cd19d1b8e381bf39939c21efd6ee2fc13a28b0d99348c"},
+    {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_ppc64le.whl", hash = "sha256:a7e53012d2853a07a4a79c00643832161a910674a893d296c9f1259859a289d2"},
+    {file = "Brotli-1.1.0-cp36-cp36m-musllinux_1_2_x86_64.whl", hash = "sha256:d7702622a8b40c49bffb46e1e3ba2e81268d5c04a34f460978c6b5517a34dd52"},
     {file = "Brotli-1.1.0-cp36-cp36m-win32.whl", hash = "sha256:a599669fd7c47233438a56936988a2478685e74854088ef5293802123b5b2460"},
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@@ -978,6 +1008,10 @@ files = [
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+    {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_aarch64.whl", hash = "sha256:cb1dac1770878ade83f2ccdf7d25e494f05c9165f5246b46a621cc849341dc01"},
+    {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_i686.whl", hash = "sha256:3ee8a80d67a4334482d9712b8e83ca6b1d9bc7e351931252ebef5d8f7335a547"},
+    {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_ppc64le.whl", hash = "sha256:5e55da2c8724191e5b557f8e18943b1b4839b8efc3ef60d65985bcf6f587dd38"},
+    {file = "Brotli-1.1.0-cp37-cp37m-musllinux_1_2_x86_64.whl", hash = "sha256:d342778ef319e1026af243ed0a07c97acf3bad33b9f29e7ae6a1f68fd083e90c"},
     {file = "Brotli-1.1.0-cp37-cp37m-win32.whl", hash = "sha256:587ca6d3cef6e4e868102672d3bd9dc9698c309ba56d41c2b9c85bbb903cdb95"},
     {file = "Brotli-1.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:2954c1c23f81c2eaf0b0717d9380bd348578a94161a65b3a2afc62c86467dd68"},
     {file = "Brotli-1.1.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:efa8b278894b14d6da122a72fefcebc28445f2d3f880ac59d46c90f4c13be9a3"},
@@ -990,6 +1024,10 @@ files = [
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     {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fce1473f3ccc4187f75b4690cfc922628aed4d3dd013d047f95a9b3919a86596"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:d2b35ca2c7f81d173d2fadc2f4f31e88cc5f7a39ae5b6db5513cf3383b0e0ec7"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:af6fa6817889314555aede9a919612b23739395ce767fe7fcbea9a80bf140fe5"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:2feb1d960f760a575dbc5ab3b1c00504b24caaf6986e2dc2b01c09c87866a943"},
+    {file = "Brotli-1.1.0-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:4410f84b33374409552ac9b6903507cdb31cd30d2501fc5ca13d18f73548444a"},
     {file = "Brotli-1.1.0-cp38-cp38-win32.whl", hash = "sha256:db85ecf4e609a48f4b29055f1e144231b90edc90af7481aa731ba2d059226b1b"},
     {file = "Brotli-1.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:3d7954194c36e304e1523f55d7042c59dc53ec20dd4e9ea9d151f1b62b4415c0"},
     {file = "Brotli-1.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5fb2ce4b8045c78ebbc7b8f3c15062e435d47e7393cc57c25115cfd49883747a"},
@@ -1002,6 +1040,10 @@ files = [
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+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:0737ddb3068957cf1b054899b0883830bb1fec522ec76b1098f9b6e0f02d9419"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:4f3607b129417e111e30637af1b56f24f7a49e64763253bbc275c75fa887d4b2"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:6c6e0c425f22c1c719c42670d561ad682f7bfeeef918edea971a79ac5252437f"},
+    {file = "Brotli-1.1.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:494994f807ba0b92092a163a0a283961369a65f6cbe01e8891132b7a320e61eb"},
     {file = "Brotli-1.1.0-cp39-cp39-win32.whl", hash = "sha256:f0d8a7a6b5983c2496e364b969f0e526647a06b075d034f3297dc66f3b360c64"},
     {file = "Brotli-1.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:cdad5b9014d83ca68c25d2e9444e28e967ef16e80f6b436918c700c117a85467"},
     {file = "Brotli-1.1.0.tar.gz", hash = "sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724"},
@@ -7269,6 +7311,22 @@ files = [
 ed25519 = ["PyNaCl (>=1.4.0)"]
 rsa = ["cryptography"]
 
+[[package]]
+name = "pyobvector"
+version = "0.1.6"
+description = "A python SDK for OceanBase Vector Store, based on SQLAlchemy, compatible with Milvus API."
+optional = false
+python-versions = "<4.0,>=3.9"
+files = [
+    {file = "pyobvector-0.1.6-py3-none-any.whl", hash = "sha256:0d700e865a85b4716b9a03384189e49288cd9d5f3cef88aed4740bc82d5fd136"},
+    {file = "pyobvector-0.1.6.tar.gz", hash = "sha256:05551addcac8c596992d5e38b480c83ca3481c6cfc6f56a1a1bddfb2e6ae037e"},
+]
+
+[package.dependencies]
+numpy = ">=1.26.0,<2.0.0"
+pymysql = ">=1.1.1,<2.0.0"
+sqlalchemy = ">=2.0.32,<3.0.0"
+
 [[package]]
 name = "pyopenssl"
 version = "24.2.1"
@@ -8677,6 +8735,11 @@ files = [
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f60021ec1574e56632be2a36b946f8143bf4e5e6af4a06d85281adc22938e0dd"},
     {file = "scikit_learn-1.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:394397841449853c2290a32050382edaec3da89e35b3e03d6cc966aebc6a8ae6"},
     {file = "scikit_learn-1.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:57cc1786cfd6bd118220a92ede80270132aa353647684efa385a74244a41e3b1"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:e9a702e2de732bbb20d3bad29ebd77fc05a6b427dc49964300340e4c9328b3f5"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-macosx_12_0_arm64.whl", hash = "sha256:b0768ad641981f5d3a198430a1d31c3e044ed2e8a6f22166b4d546a5116d7908"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:178ddd0a5cb0044464fc1bfc4cca5b1833bfc7bb022d70b05db8530da4bb3dd3"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f7284ade780084d94505632241bf78c44ab3b6f1e8ccab3d2af58e0e950f9c12"},
+    {file = "scikit_learn-1.5.2-cp313-cp313-win_amd64.whl", hash = "sha256:b7b0f9a0b1040830d38c39b91b3a44e1b643f4b36e36567b80b7c6bd2202a27f"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:757c7d514ddb00ae249832fe87100d9c73c6ea91423802872d9e74970a0e40b9"},
     {file = "scikit_learn-1.5.2-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:52788f48b5d8bca5c0736c175fa6bdaab2ef00a8f536cda698db61bd89c551c1"},
     {file = "scikit_learn-1.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:643964678f4b5fbdc95cbf8aec638acc7aa70f5f79ee2cdad1eec3df4ba6ead8"},
@@ -10919,4 +10982,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "52552faf5f4823056eb48afe05349ab2f0e9a5bc42105211ccbbb54b59e27b59"
+content-hash = "ef927b98c33d704d680e08db0e5c7d9a4e05454c66fcd6a5f656a65eb08e886b"

api/pyproject.toml

@@ -247,6 +247,7 @@ pgvecto-rs = { version = "~0.2.1", extras = ['sqlalchemy'] }
 pgvector = "0.2.5"
 pymilvus = "~2.4.4"
 pymochow = "1.3.1"
+pyobvector = "~0.1.6"
 qdrant-client = "1.7.3"
 tcvectordb = "1.3.2"
 tidb-vector = "0.0.9"

api/tests/integration_tests/vdb/oceanbase/test_oceanbase.py

@@ -0,0 +1,71 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from core.rag.datasource.vdb.oceanbase.oceanbase_vector import (
+    OceanBaseVector,
+    OceanBaseVectorConfig,
+)
+from tests.integration_tests.vdb.__mock.tcvectordb import setup_tcvectordb_mock
+from tests.integration_tests.vdb.test_vector_store import (
+    AbstractVectorTest,
+    get_example_text,
+    setup_mock_redis,
+)
+
+
+@pytest.fixture
+def oceanbase_vector():
+    return OceanBaseVector(
+        "dify_test_collection",
+        config=OceanBaseVectorConfig(
+            host="127.0.0.1",
+            port="2881",
+            user="root@test",
+            database="test",
+            password="test",
+        ),
+    )
+
+
+class OceanBaseVectorTest(AbstractVectorTest):
+    def __init__(self, vector: OceanBaseVector):
+        super().__init__()
+        self.vector = vector
+
+    def search_by_vector(self):
+        hits_by_vector = self.vector.search_by_vector(query_vector=self.example_embedding)
+        assert len(hits_by_vector) == 0
+
+    def search_by_full_text(self):
+        hits_by_full_text = self.vector.search_by_full_text(query=get_example_text())
+        assert len(hits_by_full_text) == 0
+
+    def text_exists(self):
+        exist = self.vector.text_exists(self.example_doc_id)
+        assert exist == True
+
+    def get_ids_by_metadata_field(self):
+        ids = self.vector.get_ids_by_metadata_field(key="document_id", value=self.example_doc_id)
+        assert len(ids) == 0
+
+
+@pytest.fixture
+def setup_mock_oceanbase_client():
+    with patch("core.rag.datasource.vdb.oceanbase.oceanbase_vector.ObVecClient", new_callable=MagicMock) as mock_client:
+        yield mock_client
+
+
+@pytest.fixture
+def setup_mock_oceanbase_vector(oceanbase_vector):
+    with patch.object(oceanbase_vector, "_client"):
+        yield oceanbase_vector
+
+
+def test_oceanbase_vector(
+    setup_mock_redis,
+    setup_mock_oceanbase_client,
+    setup_mock_oceanbase_vector,
+    oceanbase_vector,
+):
+    OceanBaseVectorTest(oceanbase_vector).run_all_tests()

dev/pytest/pytest_vdb.sh

@@ -13,3 +13,4 @@ pytest api/tests/integration_tests/vdb/chroma \
   api/tests/integration_tests/vdb/tcvectordb \
   api/tests/integration_tests/vdb/upstash \
   api/tests/integration_tests/vdb/couchbase \
+  api/tests/integration_tests/vdb/oceanbase \

docker/.env.example

@@ -517,6 +517,14 @@ VIKINGDB_SCHEMA=http
 VIKINGDB_CONNECTION_TIMEOUT=30
 VIKINGDB_SOCKET_TIMEOUT=30
 
+# OceanBase Vector configuration, only available when VECTOR_STORE is `oceanbase`
+OCEANBASE_VECTOR_HOST=oceanbase-vector
+OCEANBASE_VECTOR_PORT=2881
+OCEANBASE_VECTOR_USER=root@test
+OCEANBASE_VECTOR_PASSWORD=
+OCEANBASE_VECTOR_DATABASE=test
+OCEANBASE_MEMORY_LIMIT=6G
+
 # ------------------------------
 # Knowledge Configuration
 # ------------------------------

docker/docker-compose.yaml

@@ -243,6 +243,12 @@ x-shared-env: &shared-api-worker-env
   POSITION_PROVIDER_INCLUDES: ${POSITION_PROVIDER_INCLUDES:-}
   POSITION_PROVIDER_EXCLUDES: ${POSITION_PROVIDER_EXCLUDES:-}
   MAX_VARIABLE_SIZE: ${MAX_VARIABLE_SIZE:-204800}
+  OCEANBASE_VECTOR_HOST: ${OCEANBASE_VECTOR_HOST:-http://oceanbase-vector}
+  OCEANBASE_VECTOR_PORT: ${OCEANBASE_VECTOR_PORT:-2881}
+  OCEANBASE_VECTOR_USER: ${OCEANBASE_VECTOR_USER:-root@test}
+  OCEANBASE_VECTOR_PASSWORD: ${OCEANBASE_VECTOR_PASSWORD:-""}
+  OCEANBASE_VECTOR_DATABASE: ${OCEANBASE_VECTOR_DATABASE:-test}
+  OCEANBASE_MEMORY_LIMIT: ${OCEANBASE_MEMORY_LIMIT:-6G}
 
 services:
   # API service
@@ -570,6 +576,18 @@ services:
       CHROMA_SERVER_AUTHN_PROVIDER: ${CHROMA_SERVER_AUTHN_PROVIDER:-chromadb.auth.token_authn.TokenAuthenticationServerProvider}
       IS_PERSISTENT: ${CHROMA_IS_PERSISTENT:-TRUE}
 
+  # OceanBase vector database
+  oceanbase-vector:
+    image: quay.io/oceanbase/oceanbase-ce:4.3.3.0-100000142024101215
+    profiles:
+      - oceanbase-vector
+    restart: always
+    volumes:
+      - ./volumes/oceanbase/data:/root/ob
+      - ./volumes/oceanbase/conf:/root/.obd/cluster
+    environment:
+      OB_MEMORY_LIMIT: ${OCEANBASE_MEMORY_LIMIT:-6G}
+
   # Oracle vector database
   oracle:
     image: container-registry.oracle.com/database/free:latest