mirror of
https://git.mirrors.martin98.com/https://github.com/langgenius/dify.git
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feat: support Tablestore vector database (#16601)
Co-authored-by: xiaozhiqing.xzq <xiaozhiqing.xzq@alibaba-inc.com>
This commit is contained in:
wanttobeamaster
GitHub
parent
98f2e2c729
commit
7f70cadacb
@ -137,7 +137,7 @@ WEB_API_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
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CONSOLE_CORS_ALLOW_ORIGINS=http://127.0.0.1:3000,*
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# Vector database configuration
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# support: weaviate, qdrant, milvus, myscale, relyt, pgvecto_rs, pgvector, pgvector, chroma, opensearch, tidb_vector, couchbase, vikingdb, upstash, lindorm, oceanbase, opengauss
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# support: weaviate, qdrant, milvus, myscale, relyt, pgvecto_rs, pgvector, pgvector, chroma, opensearch, tidb_vector, couchbase, vikingdb, upstash, lindorm, oceanbase, opengauss, tablestore
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VECTOR_STORE=weaviate
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# Weaviate configuration
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@ -212,6 +212,12 @@ PGVECTOR_DATABASE=postgres
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PGVECTOR_MIN_CONNECTION=1
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PGVECTOR_MAX_CONNECTION=5
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# TableStore Vector configuration
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TABLESTORE_ENDPOINT=https://instance-name.cn-hangzhou.ots.aliyuncs.com
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TABLESTORE_INSTANCE_NAME=instance-name
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TABLESTORE_ACCESS_KEY_ID=xxx
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TABLESTORE_ACCESS_KEY_SECRET=xxx
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# Tidb Vector configuration
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TIDB_VECTOR_HOST=xxx.eu-central-1.xxx.aws.tidbcloud.com
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TIDB_VECTOR_PORT=4000
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@ -276,6 +276,7 @@ def migrate_knowledge_vector_database():
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VectorType.ORACLE,
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VectorType.ELASTICSEARCH,
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VectorType.OPENGAUSS,
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VectorType.TABLESTORE,
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}
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lower_collection_vector_types = {
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VectorType.ANALYTICDB,
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@ -33,6 +33,7 @@ from .vdb.pgvector_config import PGVectorConfig
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from .vdb.pgvectors_config import PGVectoRSConfig
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from .vdb.qdrant_config import QdrantConfig
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from .vdb.relyt_config import RelytConfig
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from .vdb.tablestore_config import TableStoreConfig
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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_vector_config import TiDBVectorConfig
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@ -283,5 +284,6 @@ class MiddlewareConfig(
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OceanBaseVectorConfig,
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BaiduVectorDBConfig,
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OpenGaussConfig,
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TableStoreConfig,
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):
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pass
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30
api/configs/middleware/vdb/tablestore_config.py
Normal file
30
api/configs/middleware/vdb/tablestore_config.py
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@ -0,0 +1,30 @@
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from typing import Optional
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from pydantic import Field
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from pydantic_settings import BaseSettings
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class TableStoreConfig(BaseSettings):
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"""
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Configuration settings for TableStore.
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"""
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TABLESTORE_ENDPOINT: Optional[str] = Field(
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description="Endpoint address of the TableStore server (e.g. 'https://instance-name.cn-hangzhou.ots.aliyuncs.com')",
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default=None,
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)
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TABLESTORE_INSTANCE_NAME: Optional[str] = Field(
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description="Instance name to access TableStore server (eg. 'instance-name')",
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default=None,
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)
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TABLESTORE_ACCESS_KEY_ID: Optional[str] = Field(
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description="AccessKey id for the instance name",
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default=None,
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)
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TABLESTORE_ACCESS_KEY_SECRET: Optional[str] = Field(
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description="AccessKey secret for the instance name",
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default=None,
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)
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@ -664,6 +664,7 @@ class DatasetRetrievalSettingApi(Resource):
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| VectorType.MILVUS
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| VectorType.OPENGAUSS
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| VectorType.OCEANBASE
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| VectorType.TABLESTORE
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):
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return {
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"retrieval_method": [
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@ -708,6 +709,7 @@ class DatasetRetrievalSettingMockApi(Resource):
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| VectorType.LINDORM
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| VectorType.OPENGAUSS
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| VectorType.OCEANBASE
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| VectorType.TABLESTORE
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):
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return {
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"retrieval_method": [
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0
api/core/rag/datasource/vdb/tablestore/__init__.py
Normal file
0
api/core/rag/datasource/vdb/tablestore/__init__.py
Normal file
295
api/core/rag/datasource/vdb/tablestore/tablestore_vector.py
Normal file
295
api/core/rag/datasource/vdb/tablestore/tablestore_vector.py
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@ -0,0 +1,295 @@
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import json
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import logging
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from typing import Any, Optional
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import tablestore # 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.field import Field
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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 import Dataset
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class TableStoreConfig(BaseModel):
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access_key_id: Optional[str] = None
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access_key_secret: Optional[str] = None
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instance_name: Optional[str] = None
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endpoint: Optional[str] = None
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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["access_key_id"]:
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raise ValueError("config ACCESS_KEY_ID is required")
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if not values["access_key_secret"]:
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raise ValueError("config ACCESS_KEY_SECRET is required")
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if not values["instance_name"]:
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raise ValueError("config INSTANCE_NAME is required")
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if not values["endpoint"]:
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raise ValueError("config ENDPOINT is required")
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return values
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class TableStoreVector(BaseVector):
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def __init__(self, collection_name: str, config: TableStoreConfig):
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super().__init__(collection_name)
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self._config = config
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self._tablestore_client = tablestore.OTSClient(
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config.endpoint,
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config.access_key_id,
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config.access_key_secret,
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config.instance_name,
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)
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self._table_name = f"{collection_name}"
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self._index_name = f"{collection_name}_idx"
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self._tags_field = f"{Field.METADATA_KEY.value}_tags"
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def get_type(self) -> str:
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return VectorType.TABLESTORE
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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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self.add_texts(documents=texts, embeddings=embeddings, **kwargs)
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def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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uuids = self._get_uuids(documents)
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for i in range(len(documents)):
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self._write_row(
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primary_key=uuids[i],
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attributes={
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Field.CONTENT_KEY.value: documents[i].page_content,
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Field.VECTOR.value: embeddings[i],
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Field.METADATA_KEY.value: documents[i].metadata,
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},
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)
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return uuids
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def text_exists(self, id: str) -> bool:
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_, return_row, _ = self._tablestore_client.get_row(
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table_name=self._table_name, primary_key=[("id", id)], columns_to_get=["id"]
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)
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return return_row is not None
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def delete_by_ids(self, ids: list[str]) -> None:
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if not ids:
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return
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for id in ids:
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self._delete_row(id=id)
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def get_ids_by_metadata_field(self, key: str, value: str):
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return self._search_by_metadata(key, value)
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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ids = self.get_ids_by_metadata_field(key, value)
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self.delete_by_ids(ids)
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def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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top_k = kwargs.get("top_k", 4)
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return self._search_by_vector(query_vector, top_k)
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def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
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return self._search_by_full_text(query)
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def delete(self) -> None:
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self._delete_table_if_exist()
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def _create_collection(self, dimension: int):
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lock_name = f"vector_indexing_lock_{self._collection_name}"
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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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logging.info(f"Collection {self._collection_name} already exists.")
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return
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self._create_table_if_not_exist()
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self._create_search_index_if_not_exist(dimension)
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redis_client.set(collection_exist_cache_key, 1, ex=3600)
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def _create_table_if_not_exist(self) -> None:
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table_list = self._tablestore_client.list_table()
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if self._table_name in table_list:
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logging.info("Tablestore system table[%s] already exists", self._table_name)
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return None
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schema_of_primary_key = [("id", "STRING")]
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table_meta = tablestore.TableMeta(self._table_name, schema_of_primary_key)
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table_options = tablestore.TableOptions()
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reserved_throughput = tablestore.ReservedThroughput(tablestore.CapacityUnit(0, 0))
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self._tablestore_client.create_table(table_meta, table_options, reserved_throughput)
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logging.info("Tablestore create table[%s] successfully.", self._table_name)
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def _create_search_index_if_not_exist(self, dimension: int) -> None:
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search_index_list = self._tablestore_client.list_search_index(table_name=self._table_name)
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if self._index_name in [t[1] for t in search_index_list]:
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logging.info("Tablestore system index[%s] already exists", self._index_name)
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return None
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field_schemas = [
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tablestore.FieldSchema(
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Field.CONTENT_KEY.value,
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tablestore.FieldType.TEXT,
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analyzer=tablestore.AnalyzerType.MAXWORD,
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index=True,
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enable_sort_and_agg=False,
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store=False,
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),
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tablestore.FieldSchema(
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Field.VECTOR.value,
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tablestore.FieldType.VECTOR,
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vector_options=tablestore.VectorOptions(
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data_type=tablestore.VectorDataType.VD_FLOAT_32,
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dimension=dimension,
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metric_type=tablestore.VectorMetricType.VM_COSINE,
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),
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),
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tablestore.FieldSchema(
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Field.METADATA_KEY.value,
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tablestore.FieldType.KEYWORD,
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index=True,
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store=False,
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),
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tablestore.FieldSchema(
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self._tags_field,
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tablestore.FieldType.KEYWORD,
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index=True,
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store=False,
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is_array=True,
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),
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]
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index_meta = tablestore.SearchIndexMeta(field_schemas)
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self._tablestore_client.create_search_index(self._table_name, self._index_name, index_meta)
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logging.info("Tablestore create system index[%s] successfully.", self._index_name)
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def _delete_table_if_exist(self):
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search_index_list = self._tablestore_client.list_search_index(table_name=self._table_name)
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for resp_tuple in search_index_list:
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self._tablestore_client.delete_search_index(resp_tuple[0], resp_tuple[1])
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logging.info("Tablestore delete index[%s] successfully.", self._index_name)
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self._tablestore_client.delete_table(self._table_name)
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logging.info("Tablestore delete system table[%s] successfully.", self._index_name)
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def _delete_search_index(self) -> None:
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self._tablestore_client.delete_search_index(self._table_name, self._index_name)
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logging.info("Tablestore delete index[%s] successfully.", self._index_name)
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def _write_row(self, primary_key: str, attributes: dict[str, Any]) -> None:
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pk = [("id", primary_key)]
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tags = []
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for key, value in attributes[Field.METADATA_KEY.value].items():
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tags.append(str(key) + "=" + str(value))
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attribute_columns = [
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(Field.CONTENT_KEY.value, attributes[Field.CONTENT_KEY.value]),
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(Field.VECTOR.value, json.dumps(attributes[Field.VECTOR.value])),
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(
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Field.METADATA_KEY.value,
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json.dumps(attributes[Field.METADATA_KEY.value]),
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),
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(self._tags_field, json.dumps(tags)),
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]
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row = tablestore.Row(pk, attribute_columns)
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self._tablestore_client.put_row(self._table_name, row)
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def _delete_row(self, id: str) -> None:
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primary_key = [("id", id)]
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row = tablestore.Row(primary_key)
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self._tablestore_client.delete_row(self._table_name, row, None)
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logging.info("Tablestore delete row successfully. id:%s", id)
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def _search_by_metadata(self, key: str, value: str) -> list[str]:
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query = tablestore.SearchQuery(
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tablestore.TermQuery(self._tags_field, str(key) + "=" + str(value)),
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limit=100,
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get_total_count=False,
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)
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search_response = self._tablestore_client.search(
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table_name=self._table_name,
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index_name=self._index_name,
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search_query=query,
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columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX),
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)
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return [row[0][0][1] for row in search_response.rows]
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def _search_by_vector(self, query_vector: list[float], top_k: int) -> list[Document]:
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ots_query = tablestore.KnnVectorQuery(
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field_name=Field.VECTOR.value,
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top_k=top_k,
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float32_query_vector=query_vector,
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)
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sort = tablestore.Sort(sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)])
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search_query = tablestore.SearchQuery(ots_query, limit=top_k, get_total_count=False, sort=sort)
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search_response = self._tablestore_client.search(
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table_name=self._table_name,
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index_name=self._index_name,
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search_query=search_query,
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columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX),
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)
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logging.info(
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"Tablestore search successfully. request_id:%s",
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search_response.request_id,
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)
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return self._to_query_result(search_response)
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def _to_query_result(self, search_response: tablestore.SearchResponse) -> list[Document]:
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documents = []
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for row in search_response.rows:
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documents.append(
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Document(
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page_content=row[1][2][1],
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vector=json.loads(row[1][3][1]),
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metadata=json.loads(row[1][0][1]),
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)
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)
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return documents
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def _search_by_full_text(self, query: str) -> list[Document]:
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search_query = tablestore.SearchQuery(
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query=tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY.value),
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sort=tablestore.Sort(sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)]),
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limit=100,
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)
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search_response = self._tablestore_client.search(
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table_name=self._table_name,
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index_name=self._index_name,
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search_query=search_query,
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columns_to_get=tablestore.ColumnsToGet(return_type=tablestore.ColumnReturnType.ALL_FROM_INDEX),
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)
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return self._to_query_result(search_response)
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class TableStoreVectorFactory(AbstractVectorFactory):
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> TableStoreVector:
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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.TABLESTORE, collection_name))
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return TableStoreVector(
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collection_name=collection_name,
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config=TableStoreConfig(
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endpoint=dify_config.TABLESTORE_ENDPOINT,
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instance_name=dify_config.TABLESTORE_INSTANCE_NAME,
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access_key_id=dify_config.TABLESTORE_ACCESS_KEY_ID,
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access_key_secret=dify_config.TABLESTORE_ACCESS_KEY_SECRET,
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),
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)
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@ -152,6 +152,10 @@ class Vector:
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from core.rag.datasource.vdb.opengauss.opengauss import OpenGaussFactory
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return OpenGaussFactory
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case VectorType.TABLESTORE:
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from core.rag.datasource.vdb.tablestore.tablestore_vector import TableStoreVectorFactory
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return TableStoreVectorFactory
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case _:
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raise ValueError(f"Vector store {vector_type} is not supported.")
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@ -25,3 +25,4 @@ class VectorType(StrEnum):
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TIDB_ON_QDRANT = "tidb_on_qdrant"
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OCEANBASE = "oceanbase"
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OPENGAUSS = "opengauss"
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TABLESTORE = "tablestore"
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154
api/poetry.lock
generated
154
api/poetry.lock
generated
@ -1770,6 +1770,123 @@ files = [
|
||||
[package.extras]
|
||||
toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
|
||||
|
||||
[[package]]
|
||||
name = "crc32c"
|
||||
version = "2.7.1"
|
||||
description = "A python package implementing the crc32c algorithm in hardware and software"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
groups = ["vdb"]
|
||||
files = [
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{file = "crc32c-2.7.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:522fba1770aad8f7eb189f21fca591a51d96dcc749859088f462281324aec30b"},
|
||||
{file = "crc32c-2.7.1-cp39-cp39-win32.whl", hash = "sha256:812723e222b6a9fe0562554d72f4f072c3a95720c60ee500984e7d0e568caac3"},
|
||||
{file = "crc32c-2.7.1-cp39-cp39-win_amd64.whl", hash = "sha256:6793fcfe9d4130230d196abbe4021c01ffe8e85c92633bf3c8559f9836c227f5"},
|
||||
{file = "crc32c-2.7.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:2e83fedebcdeb80c19e76b7a0e5103528bb062521c40702bf34516a429e81df3"},
|
||||
{file = "crc32c-2.7.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:30004a7383538ef93bda9b22f7b3805bc0aa5625ab2675690e1b676b19417d4b"},
|
||||
{file = "crc32c-2.7.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a01b0983aa87f517c12418f9898ecf2083bf86f4ea04122e053357c3edb0d73f"},
|
||||
{file = "crc32c-2.7.1-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cb2b963c42128b38872e9ed63f04a73ce1ff89a1dfad7ea38add6fe6296497b8"},
|
||||
{file = "crc32c-2.7.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:cdd5e576fee5d255c1e68a4dae4420f21e57e6f05900b38d5ae47c713fc3330d"},
|
||||
{file = "crc32c-2.7.1-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:79f0ff50863aeb441fbfa87e9db6542ddfe3e941189dece832b0af2e454dbab0"},
|
||||
{file = "crc32c-2.7.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0cd27a1e400d77e9872fa1303e8f9d30bd050df35ee4858354ce0b59f8227d32"},
|
||||
{file = "crc32c-2.7.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:274739b3e1591bd4b7ec98764f2f79c6fbcc0f7d7676d5f17369832fe14ee4f0"},
|
||||
{file = "crc32c-2.7.1-pp37-pypy37_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:050f52045b4a033a245e0ee4357e1a793de5af6496c82250ef13d8cb90a21e20"},
|
||||
{file = "crc32c-2.7.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ceb4ca126f75694bda020a307221563d3c522719c0acedcc81ffb985b4867c94"},
|
||||
{file = "crc32c-2.7.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:eabefe7a6fb5dfc6318fb35f4d98893baef17ebda9b311498e870526d32168e7"},
|
||||
{file = "crc32c-2.7.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:217edd9ba8c5f0c3ad60c82a11fa78f01162fa106fd7f5d17175dac6bf1eedf9"},
|
||||
{file = "crc32c-2.7.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:15d640d9d4aa213aec6c837f602081a17d1522f8cd78b52334b62ee27b083410"},
|
||||
{file = "crc32c-2.7.1-pp38-pypy38_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:519878822bf9bdead63c25a5e4bdc26d2eae9da6056f92b9b5f3023c08f1d016"},
|
||||
{file = "crc32c-2.7.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:2bf69cfa4c3ea9f060fe06db00b7e34f771c83f73dd2c3568c2c9019479e34c2"},
|
||||
{file = "crc32c-2.7.1-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:e89d51c90f6730b67b12c97d49099ba18d0fdce18541fab94d2be95d1c939adb"},
|
||||
{file = "crc32c-2.7.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:488a0feba1bb005d0dd2f702c1da4849d083e88d82cd27b83ac2d2d93af80755"},
|
||||
{file = "crc32c-2.7.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:919262b7a12ef63f222ec19c0e092f39268802652e11669315257ae6249ec79f"},
|
||||
{file = "crc32c-2.7.1-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4181240f6080c38eec9dd1539cd23a304a12100d3f4ffe43234f32064fae5ef0"},
|
||||
{file = "crc32c-2.7.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:fedde1e53507d0ede1980e8109442edd108c04ab100abcd5145c274820dacd4f"},
|
||||
{file = "crc32c-2.7.1.tar.gz", hash = "sha256:f91b144a21eef834d64178e01982bb9179c354b3e9e5f4c803b0e5096384968c"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "crcmod"
|
||||
version = "1.7"
|
||||
@ -2022,6 +2139,19 @@ files = [
|
||||
[package.extras]
|
||||
dev = ["coverage", "pytest (>=7.4.4)"]
|
||||
|
||||
[[package]]
|
||||
name = "enum34"
|
||||
version = "1.1.10"
|
||||
description = "Python 3.4 Enum backported to 3.3, 3.2, 3.1, 2.7, 2.6, 2.5, and 2.4"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["vdb"]
|
||||
files = [
|
||||
{file = "enum34-1.1.10-py2-none-any.whl", hash = "sha256:a98a201d6de3f2ab3db284e70a33b0f896fbf35f8086594e8c9e74b909058d53"},
|
||||
{file = "enum34-1.1.10-py3-none-any.whl", hash = "sha256:c3858660960c984d6ab0ebad691265180da2b43f07e061c0f8dca9ef3cffd328"},
|
||||
{file = "enum34-1.1.10.tar.gz", hash = "sha256:cce6a7477ed816bd2542d03d53db9f0db935dd013b70f336a95c73979289f248"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "esdk-obs-python"
|
||||
version = "3.24.6.1"
|
||||
@ -8302,6 +8432,28 @@ mpmath = ">=1.1.0,<1.4"
|
||||
[package.extras]
|
||||
dev = ["hypothesis (>=6.70.0)", "pytest (>=7.1.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "tablestore"
|
||||
version = "6.1.0"
|
||||
description = "Aliyun TableStore(OTS) SDK"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
groups = ["vdb"]
|
||||
files = [
|
||||
{file = "tablestore-6.1.0.tar.gz", hash = "sha256:bfe6a3e0fe88a230729723c357f4a46b8869a06a4b936db20692ed587a721c1c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
certifi = ">=2016.2.28"
|
||||
crc32c = ">=2.7.1"
|
||||
enum34 = ">=1.1.6"
|
||||
flatbuffers = ">=22.9.24"
|
||||
future = ">=0.16.0"
|
||||
numpy = ">=1.11.0"
|
||||
protobuf = ">=3.20.0,<=5.27.4"
|
||||
six = ">=1.11.0"
|
||||
urllib3 = ">=1.14"
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
version = "0.9.0"
|
||||
@ -9996,4 +10148,4 @@ cffi = ["cffi (>=1.11)"]
|
||||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.11,<3.13"
|
||||
content-hash = "5ee6591e61f05986ab0bb50e5a540ef4eee4b086e8da8e9f01ac1176730ad051"
|
||||
content-hash = "a094082d2fd4d8ea480ac800e54029bdb604de70a7b4348778bdcddb39b06c7e"
|
||||
|
||||
@ -130,6 +130,7 @@ pymilvus = "~2.5.0"
|
||||
pymochow = "1.3.1"
|
||||
pyobvector = "~0.1.6"
|
||||
qdrant-client = "1.7.3"
|
||||
tablestore = "6.1.0"
|
||||
tcvectordb = "~1.6.4"
|
||||
tidb-vector = "0.0.9"
|
||||
upstash-vector = "0.6.0"
|
||||
|
||||
@ -0,0 +1,34 @@
|
||||
import os
|
||||
|
||||
from core.rag.datasource.vdb.tablestore.tablestore_vector import (
|
||||
TableStoreConfig,
|
||||
TableStoreVector,
|
||||
)
|
||||
from tests.integration_tests.vdb.test_vector_store import (
|
||||
AbstractVectorTest,
|
||||
setup_mock_redis,
|
||||
)
|
||||
|
||||
|
||||
class TableStoreVectorTest(AbstractVectorTest):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.vector = TableStoreVector(
|
||||
collection_name=self.collection_name,
|
||||
config=TableStoreConfig(
|
||||
endpoint=os.getenv("TABLESTORE_ENDPOINT"),
|
||||
instance_name=os.getenv("TABLESTORE_INSTANCE_NAME"),
|
||||
access_key_id=os.getenv("TABLESTORE_ACCESS_KEY_ID"),
|
||||
access_key_secret=os.getenv("TABLESTORE_ACCESS_KEY_SECRET"),
|
||||
),
|
||||
)
|
||||
|
||||
def get_ids_by_metadata_field(self):
|
||||
ids = self.vector.get_ids_by_metadata_field(key="doc_id", value=self.example_doc_id)
|
||||
assert ids is not None
|
||||
assert len(ids) == 1
|
||||
assert ids[0] == self.example_doc_id
|
||||
|
||||
|
||||
def test_tablestore_vector(setup_mock_redis):
|
||||
TableStoreVectorTest().run_all_tests()
|
||||
@ -383,7 +383,7 @@ SUPABASE_URL=your-server-url
|
||||
# ------------------------------
|
||||
|
||||
# The type of vector store to use.
|
||||
# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`, `oceanbase`, `opengauss`.
|
||||
# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `elasticsearch-ja`, `analyticdb`, `couchbase`, `vikingdb`, `oceanbase`, `opengauss`, `tablestore`.
|
||||
VECTOR_STORE=weaviate
|
||||
|
||||
# The Weaviate endpoint URL. Only available when VECTOR_STORE is `weaviate`.
|
||||
@ -570,6 +570,13 @@ OPENGAUSS_ENABLE_PQ=false
|
||||
UPSTASH_VECTOR_URL=https://xxx-vector.upstash.io
|
||||
UPSTASH_VECTOR_TOKEN=dify
|
||||
|
||||
# TableStore Vector configuration
|
||||
# (only used when VECTOR_STORE is tablestore)
|
||||
TABLESTORE_ENDPOINT=https://instance-name.cn-hangzhou.ots.aliyuncs.com
|
||||
TABLESTORE_INSTANCE_NAME=instance-name
|
||||
TABLESTORE_ACCESS_KEY_ID=xxx
|
||||
TABLESTORE_ACCESS_KEY_SECRET=xxx
|
||||
|
||||
# ------------------------------
|
||||
# Knowledge Configuration
|
||||
# ------------------------------
|
||||
|
||||
@ -263,6 +263,10 @@ x-shared-env: &shared-api-worker-env
|
||||
OPENGAUSS_ENABLE_PQ: ${OPENGAUSS_ENABLE_PQ:-false}
|
||||
UPSTASH_VECTOR_URL: ${UPSTASH_VECTOR_URL:-https://xxx-vector.upstash.io}
|
||||
UPSTASH_VECTOR_TOKEN: ${UPSTASH_VECTOR_TOKEN:-dify}
|
||||
TABLESTORE_ENDPOINT: ${TABLESTORE_ENDPOINT:-https://instance-name.cn-hangzhou.ots.aliyuncs.com}
|
||||
TABLESTORE_INSTANCE_NAME: ${TABLESTORE_INSTANCE_NAME:-instance-name}
|
||||
TABLESTORE_ACCESS_KEY_ID: ${TABLESTORE_ACCESS_KEY_ID:-xxx}
|
||||
TABLESTORE_ACCESS_KEY_SECRET: ${TABLESTORE_ACCESS_KEY_SECRET:-xxx}
|
||||
UPLOAD_FILE_SIZE_LIMIT: ${UPLOAD_FILE_SIZE_LIMIT:-15}
|
||||
UPLOAD_FILE_BATCH_LIMIT: ${UPLOAD_FILE_BATCH_LIMIT:-5}
|
||||
ETL_TYPE: ${ETL_TYPE:-dify}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user