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feat: support vastbase vector database (#16308)
This commit is contained in:
parent
cd9e6609ad
commit
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@ -271,6 +271,7 @@ def migrate_knowledge_vector_database():
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upper_collection_vector_types = {
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upper_collection_vector_types = {
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VectorType.MILVUS,
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VectorType.MILVUS,
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VectorType.PGVECTOR,
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VectorType.PGVECTOR,
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VectorType.VASTBASE,
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VectorType.RELYT,
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VectorType.RELYT,
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VectorType.WEAVIATE,
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VectorType.WEAVIATE,
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VectorType.ORACLE,
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VectorType.ORACLE,
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@ -39,6 +39,7 @@ from .vdb.tencent_vector_config import TencentVectorDBConfig
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from .vdb.tidb_on_qdrant_config import TidbOnQdrantConfig
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from .vdb.tidb_on_qdrant_config import TidbOnQdrantConfig
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from .vdb.tidb_vector_config import TiDBVectorConfig
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from .vdb.tidb_vector_config import TiDBVectorConfig
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from .vdb.upstash_config import UpstashConfig
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from .vdb.upstash_config import UpstashConfig
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from .vdb.vastbase_vector_config import VastbaseVectorConfig
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from .vdb.vikingdb_config import VikingDBConfig
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from .vdb.vikingdb_config import VikingDBConfig
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from .vdb.weaviate_config import WeaviateConfig
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from .vdb.weaviate_config import WeaviateConfig
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@ -270,6 +271,7 @@ class MiddlewareConfig(
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OpenSearchConfig,
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OpenSearchConfig,
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OracleConfig,
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OracleConfig,
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PGVectorConfig,
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PGVectorConfig,
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VastbaseVectorConfig,
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PGVectoRSConfig,
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PGVectoRSConfig,
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QdrantConfig,
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QdrantConfig,
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RelytConfig,
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RelytConfig,
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45
api/configs/middleware/vdb/vastbase_vector_config.py
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45
api/configs/middleware/vdb/vastbase_vector_config.py
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@ -0,0 +1,45 @@
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from typing import Optional
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from pydantic import Field, PositiveInt
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from pydantic_settings import BaseSettings
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class VastbaseVectorConfig(BaseSettings):
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"""
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Configuration settings for Vector (Vastbase with vector extension)
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"""
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VASTBASE_HOST: Optional[str] = Field(
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description="Hostname or IP address of the Vastbase server with Vector extension (e.g., 'localhost')",
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default=None,
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)
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VASTBASE_PORT: PositiveInt = Field(
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description="Port number on which the Vastbase server is listening (default is 5432)",
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default=5432,
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)
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VASTBASE_USER: Optional[str] = Field(
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description="Username for authenticating with the Vastbase database",
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default=None,
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)
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VASTBASE_PASSWORD: Optional[str] = Field(
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description="Password for authenticating with the Vastbase database",
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default=None,
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)
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VASTBASE_DATABASE: Optional[str] = Field(
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description="Name of the Vastbase database to connect to",
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default=None,
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)
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VASTBASE_MIN_CONNECTION: PositiveInt = Field(
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description="Min connection of the Vastbase database",
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default=1,
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)
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VASTBASE_MAX_CONNECTION: PositiveInt = Field(
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description="Max connection of the Vastbase database",
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default=5,
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)
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@ -657,6 +657,7 @@ class DatasetRetrievalSettingApi(Resource):
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| VectorType.ELASTICSEARCH
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| VectorType.ELASTICSEARCH
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| VectorType.ELASTICSEARCH_JA
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| VectorType.ELASTICSEARCH_JA
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| VectorType.PGVECTOR
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| VectorType.PGVECTOR
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| VectorType.VASTBASE
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| VectorType.TIDB_ON_QDRANT
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| VectorType.TIDB_ON_QDRANT
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| VectorType.LINDORM
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| VectorType.LINDORM
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| VectorType.COUCHBASE
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| VectorType.COUCHBASE
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@ -706,6 +707,7 @@ class DatasetRetrievalSettingMockApi(Resource):
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| VectorType.ELASTICSEARCH_JA
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| VectorType.ELASTICSEARCH_JA
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| VectorType.COUCHBASE
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| VectorType.COUCHBASE
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| VectorType.PGVECTOR
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| VectorType.PGVECTOR
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| VectorType.VASTBASE
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| VectorType.LINDORM
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| VectorType.LINDORM
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| VectorType.OPENGAUSS
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| VectorType.OPENGAUSS
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| VectorType.OCEANBASE
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| VectorType.OCEANBASE
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0
api/core/rag/datasource/vdb/pyvastbase/__init__.py
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0
api/core/rag/datasource/vdb/pyvastbase/__init__.py
Normal file
243
api/core/rag/datasource/vdb/pyvastbase/vastbase_vector.py
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243
api/core/rag/datasource/vdb/pyvastbase/vastbase_vector.py
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@ -0,0 +1,243 @@
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import json
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import uuid
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from contextlib import contextmanager
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from typing import Any
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import psycopg2.extras # type: ignore
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import psycopg2.pool # type: ignore
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from pydantic import BaseModel, model_validator
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from configs import dify_config
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from core.rag.datasource.vdb.vector_base import BaseVector
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from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
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from core.rag.datasource.vdb.vector_type import VectorType
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from core.rag.embedding.embedding_base import Embeddings
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from core.rag.models.document import Document
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from extensions.ext_redis import redis_client
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from models.dataset import Dataset
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class VastbaseVectorConfig(BaseModel):
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host: str
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port: int
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user: str
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password: str
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database: str
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min_connection: int
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max_connection: int
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@model_validator(mode="before")
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@classmethod
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def validate_config(cls, values: dict) -> dict:
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if not values["host"]:
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raise ValueError("config VASTBASE_HOST is required")
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if not values["port"]:
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raise ValueError("config VASTBASE_PORT is required")
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if not values["user"]:
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raise ValueError("config VASTBASE_USER is required")
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if not values["password"]:
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raise ValueError("config VASTBASE_PASSWORD is required")
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if not values["database"]:
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raise ValueError("config VASTBASE_DATABASE is required")
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if not values["min_connection"]:
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raise ValueError("config VASTBASE_MIN_CONNECTION is required")
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if not values["max_connection"]:
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raise ValueError("config VASTBASE_MAX_CONNECTION is required")
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if values["min_connection"] > values["max_connection"]:
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raise ValueError("config VASTBASE_MIN_CONNECTION should less than VASTBASE_MAX_CONNECTION")
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return values
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SQL_CREATE_TABLE = """
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CREATE TABLE IF NOT EXISTS {table_name} (
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id UUID PRIMARY KEY,
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text TEXT NOT NULL,
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meta JSONB NOT NULL,
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embedding floatvector({dimension}) NOT NULL
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);
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"""
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SQL_CREATE_INDEX = """
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CREATE INDEX IF NOT EXISTS embedding_cosine_v1_idx ON {table_name}
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USING hnsw (embedding floatvector_cosine_ops) WITH (m = 16, ef_construction = 64);
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"""
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class VastbaseVector(BaseVector):
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def __init__(self, collection_name: str, config: VastbaseVectorConfig):
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super().__init__(collection_name)
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self.pool = self._create_connection_pool(config)
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self.table_name = f"embedding_{collection_name}"
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def get_type(self) -> str:
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return VectorType.VASTBASE
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def _create_connection_pool(self, config: VastbaseVectorConfig):
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return psycopg2.pool.SimpleConnectionPool(
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config.min_connection,
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config.max_connection,
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host=config.host,
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port=config.port,
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user=config.user,
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password=config.password,
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database=config.database,
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)
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@contextmanager
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def _get_cursor(self):
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conn = self.pool.getconn()
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cur = conn.cursor()
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try:
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yield cur
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finally:
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cur.close()
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conn.commit()
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self.pool.putconn(conn)
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def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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dimension = len(embeddings[0])
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self._create_collection(dimension)
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return self.add_texts(texts, embeddings)
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def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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values = []
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pks = []
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for i, doc in enumerate(documents):
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if doc.metadata is not None:
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doc_id = doc.metadata.get("doc_id", str(uuid.uuid4()))
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pks.append(doc_id)
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values.append(
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(
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doc_id,
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doc.page_content,
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json.dumps(doc.metadata),
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embeddings[i],
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)
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)
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with self._get_cursor() as cur:
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psycopg2.extras.execute_values(
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cur, f"INSERT INTO {self.table_name} (id, text, meta, embedding) VALUES %s", values
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)
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return pks
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def text_exists(self, id: str) -> bool:
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with self._get_cursor() as cur:
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cur.execute(f"SELECT id FROM {self.table_name} WHERE id = %s", (id,))
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return cur.fetchone() is not None
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def get_by_ids(self, ids: list[str]) -> list[Document]:
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with self._get_cursor() as cur:
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cur.execute(f"SELECT meta, text FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
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docs = []
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for record in cur:
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docs.append(Document(page_content=record[1], metadata=record[0]))
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return docs
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def delete_by_ids(self, ids: list[str]) -> None:
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# Avoiding crashes caused by performing delete operations on empty lists in certain scenarios
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# Scenario 1: extract a document fails, resulting in a table not being created.
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# Then clicking the retry button triggers a delete operation on an empty list.
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if not ids:
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return
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with self._get_cursor() as cur:
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cur.execute(f"DELETE FROM {self.table_name} WHERE id IN %s", (tuple(ids),))
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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with self._get_cursor() as cur:
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cur.execute(f"DELETE FROM {self.table_name} WHERE meta->>%s = %s", (key, value))
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def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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"""
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Search the nearest neighbors to a vector.
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:param query_vector: The input vector to search for similar items.
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:param top_k: The number of nearest neighbors to return, default is 5.
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:return: List of Documents that are nearest to the query vector.
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"""
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top_k = kwargs.get("top_k", 4)
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if not isinstance(top_k, int) or top_k <= 0:
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raise ValueError("top_k must be a positive integer")
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with self._get_cursor() as cur:
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cur.execute(
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f"SELECT meta, text, embedding <=> %s AS distance FROM {self.table_name}"
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f" ORDER BY distance LIMIT {top_k}",
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(json.dumps(query_vector),),
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)
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docs = []
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score_threshold = float(kwargs.get("score_threshold") or 0.0)
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for record in cur:
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metadata, text, distance = record
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score = 1 - distance
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metadata["score"] = score
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if score > score_threshold:
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docs.append(Document(page_content=text, metadata=metadata))
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return docs
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def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
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top_k = kwargs.get("top_k", 5)
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if not isinstance(top_k, int) or top_k <= 0:
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raise ValueError("top_k must be a positive integer")
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with self._get_cursor() as cur:
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cur.execute(
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f"""SELECT meta, text, ts_rank(to_tsvector(coalesce(text, '')), plainto_tsquery(%s)) AS score
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FROM {self.table_name}
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WHERE to_tsvector(text) @@ plainto_tsquery(%s)
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ORDER BY score DESC
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LIMIT {top_k}""",
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# f"'{query}'" is required in order to account for whitespace in query
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(f"'{query}'", f"'{query}'"),
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)
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docs = []
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for record in cur:
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metadata, text, score = record
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metadata["score"] = score
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docs.append(Document(page_content=text, metadata=metadata))
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return docs
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def delete(self) -> None:
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with self._get_cursor() as cur:
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cur.execute(f"DROP TABLE IF EXISTS {self.table_name}")
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def _create_collection(self, dimension: int):
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cache_key = f"vector_indexing_{self._collection_name}"
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lock_name = f"{cache_key}_lock"
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with redis_client.lock(lock_name, timeout=20):
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collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
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if redis_client.get(collection_exist_cache_key):
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return
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with self._get_cursor() as cur:
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cur.execute(SQL_CREATE_TABLE.format(table_name=self.table_name, dimension=dimension))
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# Vastbase 支持的向量维度取值范围为 [1,16000]
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if dimension <= 16000:
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cur.execute(SQL_CREATE_INDEX.format(table_name=self.table_name))
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redis_client.set(collection_exist_cache_key, 1, ex=3600)
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class VastbaseVectorFactory(AbstractVectorFactory):
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> VastbaseVector:
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if dataset.index_struct_dict:
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class_prefix: str = dataset.index_struct_dict["vector_store"]["class_prefix"]
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collection_name = class_prefix
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else:
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dataset_id = dataset.id
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collection_name = Dataset.gen_collection_name_by_id(dataset_id)
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dataset.index_struct = json.dumps(self.gen_index_struct_dict(VectorType.VASTBASE, collection_name))
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return VastbaseVector(
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collection_name=collection_name,
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config=VastbaseVectorConfig(
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host=dify_config.VASTBASE_HOST or "localhost",
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port=dify_config.VASTBASE_PORT,
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user=dify_config.VASTBASE_USER or "dify",
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password=dify_config.VASTBASE_PASSWORD or "",
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database=dify_config.VASTBASE_DATABASE or "dify",
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min_connection=dify_config.VASTBASE_MIN_CONNECTION,
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max_connection=dify_config.VASTBASE_MAX_CONNECTION,
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),
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)
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@ -74,6 +74,10 @@ class Vector:
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from core.rag.datasource.vdb.pgvector.pgvector import PGVectorFactory
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from core.rag.datasource.vdb.pgvector.pgvector import PGVectorFactory
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return PGVectorFactory
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return PGVectorFactory
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case VectorType.VASTBASE:
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from core.rag.datasource.vdb.pyvastbase.vastbase_vector import VastbaseVectorFactory
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return VastbaseVectorFactory
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case VectorType.PGVECTO_RS:
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case VectorType.PGVECTO_RS:
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from core.rag.datasource.vdb.pgvecto_rs.pgvecto_rs import PGVectoRSFactory
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from core.rag.datasource.vdb.pgvecto_rs.pgvecto_rs import PGVectoRSFactory
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@ -7,7 +7,9 @@ class VectorType(StrEnum):
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MILVUS = "milvus"
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MILVUS = "milvus"
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MYSCALE = "myscale"
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MYSCALE = "myscale"
|
||||||
PGVECTOR = "pgvector"
|
PGVECTOR = "pgvector"
|
||||||
|
VASTBASE = "vastbase"
|
||||||
PGVECTO_RS = "pgvecto-rs"
|
PGVECTO_RS = "pgvecto-rs"
|
||||||
|
|
||||||
QDRANT = "qdrant"
|
QDRANT = "qdrant"
|
||||||
RELYT = "relyt"
|
RELYT = "relyt"
|
||||||
TIDB_VECTOR = "tidb_vector"
|
TIDB_VECTOR = "tidb_vector"
|
||||||
|
@ -0,0 +1,27 @@
|
|||||||
|
from core.rag.datasource.vdb.pyvastbase.vastbase_vector import VastbaseVector, VastbaseVectorConfig
|
||||||
|
from tests.integration_tests.vdb.test_vector_store import (
|
||||||
|
AbstractVectorTest,
|
||||||
|
get_example_text,
|
||||||
|
setup_mock_redis,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
class VastbaseVectorTest(AbstractVectorTest):
|
||||||
|
def __init__(self):
|
||||||
|
super().__init__()
|
||||||
|
self.vector = VastbaseVector(
|
||||||
|
collection_name=self.collection_name,
|
||||||
|
config=VastbaseVectorConfig(
|
||||||
|
host="localhost",
|
||||||
|
port=5434,
|
||||||
|
user="dify",
|
||||||
|
password="Difyai123456",
|
||||||
|
database="dify",
|
||||||
|
min_connection=1,
|
||||||
|
max_connection=5,
|
||||||
|
),
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def test_vastbase_vector(setup_mock_redis):
|
||||||
|
VastbaseVectorTest().run_all_tests()
|
@ -441,6 +441,15 @@ PGVECTOR_MAX_CONNECTION=5
|
|||||||
PGVECTOR_PG_BIGM=false
|
PGVECTOR_PG_BIGM=false
|
||||||
PGVECTOR_PG_BIGM_VERSION=1.2-20240606
|
PGVECTOR_PG_BIGM_VERSION=1.2-20240606
|
||||||
|
|
||||||
|
# vastbase configurations, only available when VECTOR_STORE is `vastbase`
|
||||||
|
VASTBASE_HOST=vastbase
|
||||||
|
VASTBASE_PORT=5432
|
||||||
|
VASTBASE_USER=dify
|
||||||
|
VASTBASE_PASSWORD=Difyai123456
|
||||||
|
VASTBASE_DATABASE=dify
|
||||||
|
VASTBASE_MIN_CONNECTION=1
|
||||||
|
VASTBASE_MAX_CONNECTION=5
|
||||||
|
|
||||||
# pgvecto-rs configurations, only available when VECTOR_STORE is `pgvecto-rs`
|
# pgvecto-rs configurations, only available when VECTOR_STORE is `pgvecto-rs`
|
||||||
PGVECTO_RS_HOST=pgvecto-rs
|
PGVECTO_RS_HOST=pgvecto-rs
|
||||||
PGVECTO_RS_PORT=5432
|
PGVECTO_RS_PORT=5432
|
||||||
|
@ -363,6 +363,30 @@ services:
|
|||||||
timeout: 3s
|
timeout: 3s
|
||||||
retries: 30
|
retries: 30
|
||||||
|
|
||||||
|
# get image from https://www.vastdata.com.cn/
|
||||||
|
vastbase:
|
||||||
|
image: vastdata/vastbase-vector
|
||||||
|
profiles:
|
||||||
|
- vastbase
|
||||||
|
restart: always
|
||||||
|
environment:
|
||||||
|
- VB_DBCOMPATIBILITY=PG
|
||||||
|
- VB_DB=dify
|
||||||
|
- VB_USERNAME=dify
|
||||||
|
- VB_PASSWORD=Difyai123456
|
||||||
|
ports:
|
||||||
|
- '5434:5432'
|
||||||
|
volumes:
|
||||||
|
- ./vastbase/lic:/home/vastbase/vastbase/lic
|
||||||
|
- ./vastbase/data:/home/vastbase/data
|
||||||
|
- ./vastbase/backup:/home/vastbase/backup
|
||||||
|
- ./vastbase/backup_log:/home/vastbase/backup_log
|
||||||
|
healthcheck:
|
||||||
|
test: [ 'CMD', 'pg_isready' ]
|
||||||
|
interval: 1s
|
||||||
|
timeout: 3s
|
||||||
|
retries: 30
|
||||||
|
|
||||||
# pgvecto-rs vector store
|
# pgvecto-rs vector store
|
||||||
pgvecto-rs:
|
pgvecto-rs:
|
||||||
image: tensorchord/pgvecto-rs:pg16-v0.3.0
|
image: tensorchord/pgvecto-rs:pg16-v0.3.0
|
||||||
|
@ -163,6 +163,13 @@ x-shared-env: &shared-api-worker-env
|
|||||||
PGVECTOR_MAX_CONNECTION: ${PGVECTOR_MAX_CONNECTION:-5}
|
PGVECTOR_MAX_CONNECTION: ${PGVECTOR_MAX_CONNECTION:-5}
|
||||||
PGVECTOR_PG_BIGM: ${PGVECTOR_PG_BIGM:-false}
|
PGVECTOR_PG_BIGM: ${PGVECTOR_PG_BIGM:-false}
|
||||||
PGVECTOR_PG_BIGM_VERSION: ${PGVECTOR_PG_BIGM_VERSION:-1.2-20240606}
|
PGVECTOR_PG_BIGM_VERSION: ${PGVECTOR_PG_BIGM_VERSION:-1.2-20240606}
|
||||||
|
VASTBASE_HOST: ${VASTBASE_HOST:-vastbase}
|
||||||
|
VASTBASE_PORT: ${VASTBASE_PORT:-5432}
|
||||||
|
VASTBASE_USER: ${VASTBASE_USER:-dify}
|
||||||
|
VASTBASE_PASSWORD: ${VASTBASE_PASSWORD:-Difyai123456}
|
||||||
|
VASTBASE_DATABASE: ${VASTBASE_DATABASE:-dify}
|
||||||
|
VASTBASE_MIN_CONNECTION: ${VASTBASE_MIN_CONNECTION:-1}
|
||||||
|
VASTBASE_MAX_CONNECTION: ${VASTBASE_MAX_CONNECTION:-5}
|
||||||
PGVECTO_RS_HOST: ${PGVECTO_RS_HOST:-pgvecto-rs}
|
PGVECTO_RS_HOST: ${PGVECTO_RS_HOST:-pgvecto-rs}
|
||||||
PGVECTO_RS_PORT: ${PGVECTO_RS_PORT:-5432}
|
PGVECTO_RS_PORT: ${PGVECTO_RS_PORT:-5432}
|
||||||
PGVECTO_RS_USER: ${PGVECTO_RS_USER:-postgres}
|
PGVECTO_RS_USER: ${PGVECTO_RS_USER:-postgres}
|
||||||
@ -840,6 +847,30 @@ services:
|
|||||||
timeout: 3s
|
timeout: 3s
|
||||||
retries: 30
|
retries: 30
|
||||||
|
|
||||||
|
# get image from https://www.vastdata.com.cn/
|
||||||
|
vastbase:
|
||||||
|
image: vastdata/vastbase-vector
|
||||||
|
profiles:
|
||||||
|
- vastbase
|
||||||
|
restart: always
|
||||||
|
environment:
|
||||||
|
- VB_DBCOMPATIBILITY=PG
|
||||||
|
- VB_DB=dify
|
||||||
|
- VB_USERNAME=dify
|
||||||
|
- VB_PASSWORD=Difyai123456
|
||||||
|
ports:
|
||||||
|
- '5434:5432'
|
||||||
|
volumes:
|
||||||
|
- ./vastbase/lic:/home/vastbase/vastbase/lic
|
||||||
|
- ./vastbase/data:/home/vastbase/data
|
||||||
|
- ./vastbase/backup:/home/vastbase/backup
|
||||||
|
- ./vastbase/backup_log:/home/vastbase/backup_log
|
||||||
|
healthcheck:
|
||||||
|
test: [ 'CMD', 'pg_isready' ]
|
||||||
|
interval: 1s
|
||||||
|
timeout: 3s
|
||||||
|
retries: 30
|
||||||
|
|
||||||
# pgvecto-rs vector store
|
# pgvecto-rs vector store
|
||||||
pgvecto-rs:
|
pgvecto-rs:
|
||||||
image: tensorchord/pgvecto-rs:pg16-v0.3.0
|
image: tensorchord/pgvecto-rs:pg16-v0.3.0
|
||||||
|
Loading…
x
Reference in New Issue
Block a user