mirror of
https://github.com/langgenius/dify.git
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delete vdb tablestore and dependencies
This commit is contained in:
Byron Wang
parent
6bb6a08769
commit
32d6747ff2
@ -1,413 +0,0 @@
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import json
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import logging
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import math
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from collections.abc import Iterable
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from typing import Any
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import tablestore # type: ignore
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from pydantic import BaseModel, model_validator
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from tablestore import BatchGetRowRequest, TableInBatchGetRowItem
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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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logger = logging.getLogger(__name__)
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class TableStoreConfig(BaseModel):
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access_key_id: str | None = None
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access_key_secret: str | None = None
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instance_name: str | None = None
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endpoint: str | None = None
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normalize_full_text_bm25_score: bool | None = False
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@model_validator(mode="before")
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@classmethod
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def validate_config(cls, values: 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._normalize_full_text_bm25_score = config.normalize_full_text_bm25_score
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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}_tags"
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def create_collection(self, 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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def get_by_ids(self, ids: list[str]) -> list[Document]:
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docs = []
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request = BatchGetRowRequest()
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columns_to_get = [Field.METADATA_KEY, Field.CONTENT_KEY]
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rows_to_get = [[("id", _id)] for _id in ids]
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request.add(TableInBatchGetRowItem(self._table_name, rows_to_get, columns_to_get, None, 1))
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result = self._tablestore_client.batch_get_row(request)
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table_result = result.get_result_by_table(self._table_name)
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for item in table_result:
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if item.is_ok and item.row:
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kv = {k: v for k, v, _ in item.row.attribute_columns}
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docs.append(Document(page_content=kv[Field.CONTENT_KEY], metadata=json.loads(kv[Field.METADATA_KEY])))
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return docs
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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: documents[i].page_content,
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Field.VECTOR: embeddings[i],
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Field.METADATA_KEY: 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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result = 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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assert isinstance(result, tuple | list)
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# Unpack the tuple result
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_, return_row, _ = result
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return return_row is not None
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def delete_by_ids(self, ids: list[str]):
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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):
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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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document_ids_filter = kwargs.get("document_ids_filter")
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filtered_list = None
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if document_ids_filter:
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filtered_list = ["document_id=" + item for item in document_ids_filter]
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score_threshold = float(kwargs.get("score_threshold") or 0.0)
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return self._search_by_vector(query_vector, filtered_list, top_k, score_threshold)
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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", 4)
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document_ids_filter = kwargs.get("document_ids_filter")
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filtered_list = None
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if document_ids_filter:
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filtered_list = ["document_id=" + item for item in document_ids_filter]
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score_threshold = float(kwargs.get("score_threshold") or 0.0)
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return self._search_by_full_text(query, filtered_list, top_k, score_threshold)
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def delete(self):
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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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logger.info("Collection %s already exists.", self._collection_name)
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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):
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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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logger.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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logger.info("Tablestore create table[%s] successfully.", self._table_name)
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def _create_search_index_if_not_exist(self, dimension: int):
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search_index_list = self._tablestore_client.list_search_index(table_name=self._table_name)
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assert isinstance(search_index_list, Iterable)
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if self._index_name in [t[1] for t in search_index_list]:
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logger.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,
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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,
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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,
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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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logger.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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assert isinstance(search_index_list, Iterable)
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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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logger.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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logger.info("Tablestore delete system table[%s] successfully.", self._index_name)
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def _delete_search_index(self):
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self._tablestore_client.delete_search_index(self._table_name, self._index_name)
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logger.info("Tablestore delete index[%s] successfully.", self._index_name)
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def _write_row(self, primary_key: str, attributes: dict[str, Any]):
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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].items():
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tags.append(str(key) + "=" + str(value))
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attribute_columns = [
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(Field.CONTENT_KEY, attributes[Field.CONTENT_KEY]),
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(Field.VECTOR, json.dumps(attributes[Field.VECTOR])),
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(
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Field.METADATA_KEY,
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json.dumps(attributes[Field.METADATA_KEY]),
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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):
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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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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=1000,
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get_total_count=False,
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)
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rows: list[str] = []
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next_token = None
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while True:
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if next_token is not None:
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query.next_token = next_token
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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(
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column_names=[Field.PRIMARY_KEY], return_type=tablestore.ColumnReturnType.SPECIFIED
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),
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)
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if search_response is not None:
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rows.extend([row[0][0][1] for row in list(search_response.rows)])
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if search_response is None or search_response.next_token == b"":
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break
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else:
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next_token = search_response.next_token
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return rows
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def _search_by_vector(
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self, query_vector: list[float], document_ids_filter: list[str] | None, top_k: int, score_threshold: float
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) -> list[Document]:
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knn_vector_query = tablestore.KnnVectorQuery(
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field_name=Field.VECTOR,
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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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if document_ids_filter:
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knn_vector_query.filter = tablestore.TermsQuery(self._tags_field, document_ids_filter)
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sort = tablestore.Sort(sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)])
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search_query = tablestore.SearchQuery(knn_vector_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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documents = []
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for search_hit in search_response.search_hits:
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if search_hit.score >= score_threshold:
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ots_column_map = {}
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for col in search_hit.row[1]:
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ots_column_map[col[0]] = col[1]
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vector_str = ots_column_map.get(Field.VECTOR)
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metadata_str = ots_column_map.get(Field.METADATA_KEY)
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vector = json.loads(vector_str) if vector_str else None
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metadata = json.loads(metadata_str) if metadata_str else {}
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metadata["score"] = search_hit.score
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documents.append(
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Document(
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page_content=ots_column_map.get(Field.CONTENT_KEY) or "",
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vector=vector,
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metadata=metadata,
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)
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)
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documents = sorted(documents, key=lambda x: x.metadata["score"] if x.metadata else 0, reverse=True)
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return documents
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@staticmethod
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def _normalize_score_exp_decay(score: float, k: float = 0.15) -> float:
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"""
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Args:
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score: BM25 search score.
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k: decay factor, the larger the k, the steeper the low score end
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"""
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normalized_score = 1 - math.exp(-k * score)
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return max(0.0, min(1.0, normalized_score))
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def _search_by_full_text(
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self, query: str, document_ids_filter: list[str] | None, top_k: int, score_threshold: float
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) -> list[Document]:
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bool_query = tablestore.BoolQuery(must_queries=[], filter_queries=[], should_queries=[], must_not_queries=[])
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bool_query.must_queries.append(tablestore.MatchQuery(text=query, field_name=Field.CONTENT_KEY))
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if document_ids_filter:
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bool_query.filter_queries.append(tablestore.TermsQuery(self._tags_field, document_ids_filter))
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search_query = tablestore.SearchQuery(
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query=bool_query,
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sort=tablestore.Sort(sorters=[tablestore.ScoreSort(sort_order=tablestore.SortOrder.DESC)]),
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limit=top_k,
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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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documents = []
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for search_hit in search_response.search_hits:
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score = None
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if self._normalize_full_text_bm25_score:
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score = self._normalize_score_exp_decay(search_hit.score)
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# skip when score is below threshold and use normalize score
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if score and score <= score_threshold:
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continue
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ots_column_map = {}
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for col in search_hit.row[1]:
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ots_column_map[col[0]] = col[1]
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metadata_str = ots_column_map.get(Field.METADATA_KEY)
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metadata = json.loads(metadata_str) if metadata_str else {}
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vector_str = ots_column_map.get(Field.VECTOR)
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vector = json.loads(vector_str) if vector_str else None
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if score:
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metadata["score"] = score
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documents.append(
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Document(
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page_content=ots_column_map.get(Field.CONTENT_KEY) or "",
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vector=vector,
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metadata=metadata,
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)
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)
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if self._normalize_full_text_bm25_score:
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documents = sorted(documents, key=lambda x: x.metadata["score"] if x.metadata else 0, reverse=True)
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return documents
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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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normalize_full_text_bm25_score=dify_config.TABLESTORE_NORMALIZE_FULLTEXT_BM25_SCORE,
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),
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)
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@ -171,10 +171,6 @@ 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
|
||||
case VectorType.HUAWEI_CLOUD:
|
||||
from core.rag.datasource.vdb.huawei.huawei_cloud_vector import HuaweiCloudVectorFactory
|
||||
|
||||
|
||||
@ -4,7 +4,6 @@ version = "1.11.4"
|
||||
requires-python = ">=3.11,<3.13"
|
||||
|
||||
dependencies = [
|
||||
"aliyun-log-python-sdk~=0.9.37",
|
||||
"arize-phoenix-otel~=0.9.2",
|
||||
"azure-identity==1.16.1",
|
||||
"beautifulsoup4==4.12.2",
|
||||
@ -22,14 +21,14 @@ dependencies = [
|
||||
"flask-sqlalchemy~=3.1.1",
|
||||
"gevent~=25.9.1",
|
||||
"gmpy2~=2.2.1",
|
||||
"google-api-core==2.18.0",
|
||||
"google-api-core==2.29.0",
|
||||
"google-api-python-client==2.90.0",
|
||||
"google-auth==2.48.0",
|
||||
"google-auth-httplib2==0.2.0",
|
||||
"google-cloud-aiplatform==1.49.0",
|
||||
"googleapis-common-protos==1.63.0",
|
||||
"google-cloud-aiplatform==1.136.0",
|
||||
"googleapis-common-protos==1.72.0",
|
||||
"gunicorn~=23.0.0",
|
||||
"httpx[socks]~=0.27.0",
|
||||
"httpx[socks]~=0.28.0",
|
||||
"jieba==0.42.1",
|
||||
"json-repair>=0.55.1",
|
||||
"jsonschema>=4.25.1",
|
||||
@ -41,26 +40,27 @@ dependencies = [
|
||||
"openpyxl~=3.1.5",
|
||||
"opik~=1.8.72",
|
||||
"litellm==1.77.1", # Pinned to avoid madoka dependency issue
|
||||
"opentelemetry-api==1.27.0",
|
||||
"opentelemetry-distro==0.48b0",
|
||||
"opentelemetry-exporter-otlp==1.27.0",
|
||||
"opentelemetry-exporter-otlp-proto-common==1.27.0",
|
||||
"opentelemetry-exporter-otlp-proto-grpc==1.27.0",
|
||||
"opentelemetry-exporter-otlp-proto-http==1.27.0",
|
||||
"opentelemetry-instrumentation==0.48b0",
|
||||
"opentelemetry-instrumentation-celery==0.48b0",
|
||||
"opentelemetry-instrumentation-flask==0.48b0",
|
||||
"opentelemetry-instrumentation-httpx==0.48b0",
|
||||
"opentelemetry-instrumentation-redis==0.48b0",
|
||||
"opentelemetry-instrumentation-httpx==0.48b0",
|
||||
"opentelemetry-instrumentation-sqlalchemy==0.48b0",
|
||||
"opentelemetry-propagator-b3==1.27.0",
|
||||
"opentelemetry-api==1.39.1",
|
||||
"opentelemetry-distro==0.60b1",
|
||||
"opentelemetry-exporter-otlp==1.39.1",
|
||||
"opentelemetry-exporter-otlp-proto-common==1.39.1",
|
||||
"opentelemetry-exporter-otlp-proto-grpc==1.39.1",
|
||||
"opentelemetry-exporter-otlp-proto-http==1.39.1",
|
||||
"opentelemetry-instrumentation==0.60b1",
|
||||
"opentelemetry-instrumentation-celery==0.60b1",
|
||||
"opentelemetry-instrumentation-flask==0.60b1",
|
||||
"opentelemetry-instrumentation-httpx==0.60b1",
|
||||
"opentelemetry-instrumentation-redis==0.60b1",
|
||||
"opentelemetry-instrumentation-httpx==0.60b1",
|
||||
"opentelemetry-instrumentation-sqlalchemy==0.60b1",
|
||||
"opentelemetry-propagator-b3==1.39.1",
|
||||
# opentelemetry-proto1.28.0 depends on protobuf (>=5.0,<6.0),
|
||||
# which is conflict with googleapis-common-protos (1.63.0)
|
||||
"opentelemetry-proto==1.27.0",
|
||||
"opentelemetry-sdk==1.27.0",
|
||||
"opentelemetry-semantic-conventions==0.48b0",
|
||||
"opentelemetry-util-http==0.48b0",
|
||||
"protobuf~=6.33.5",
|
||||
"opentelemetry-proto==1.39.1",
|
||||
"opentelemetry-sdk==1.39.1",
|
||||
"opentelemetry-semantic-conventions==0.60b1",
|
||||
"opentelemetry-util-http==0.60b1",
|
||||
"pandas[excel,output-formatting,performance]~=2.2.2",
|
||||
"psycogreen~=1.0.2",
|
||||
"psycopg2-binary~=2.9.6",
|
||||
@ -218,7 +218,6 @@ vdb = [
|
||||
"pyobvector~=0.2.17",
|
||||
"qdrant-client==1.9.0",
|
||||
"intersystems-irispython>=5.1.0",
|
||||
"tablestore==6.3.7",
|
||||
"tcvectordb~=1.6.4",
|
||||
"tidb-vector==0.0.9",
|
||||
"upstash-vector==0.6.0",
|
||||
|
||||
403
api/uv.lock
generated
403
api/uv.lock
generated
@ -291,22 +291,6 @@ dependencies = [
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/32/eb/5e82e419c3061823f3feae9b5681588762929dc4da0176667297c2784c1a/alibabacloud_tea_xml-0.0.3.tar.gz", hash = "sha256:979cb51fadf43de77f41c69fc69c12529728919f849723eb0cd24eb7b048a90c", size = 3466, upload-time = "2025-07-01T08:04:55.144Z" }
|
||||
|
||||
[[package]]
|
||||
name = "aliyun-log-python-sdk"
|
||||
version = "0.9.37"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "dateparser" },
|
||||
{ name = "elasticsearch" },
|
||||
{ name = "jmespath" },
|
||||
{ name = "lz4" },
|
||||
{ name = "protobuf" },
|
||||
{ name = "python-dateutil" },
|
||||
{ name = "requests" },
|
||||
{ name = "six" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/90/70/291d494619bb7b0cbcc00689ad995945737c2c9e0bff2733e0aa7dbaee14/aliyun_log_python_sdk-0.9.37.tar.gz", hash = "sha256:ea65c9cca3a7377cef87d568e897820338328a53a7acb1b02f1383910e103f68", size = 152549, upload-time = "2025-11-27T07:56:06.098Z" }
|
||||
|
||||
[[package]]
|
||||
name = "aliyun-python-sdk-core"
|
||||
version = "2.16.0"
|
||||
@ -1185,37 +1169,6 @@ toml = [
|
||||
{ name = "tomli", marker = "python_full_version <= '3.11'" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "crc32c"
|
||||
version = "2.8"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e3/66/7e97aa77af7cf6afbff26e3651b564fe41932599bc2d3dce0b2f73d4829a/crc32c-2.8.tar.gz", hash = "sha256:578728964e59c47c356aeeedee6220e021e124b9d3e8631d95d9a5e5f06e261c", size = 48179, upload-time = "2025-10-17T06:20:13.61Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/dc/0b/5e03b22d913698e9cc563f39b9f6bbd508606bf6b8e9122cd6bf196b87ea/crc32c-2.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:e560a97fbb96c9897cb1d9b5076ef12fc12e2e25622530a1afd0de4240f17e1f", size = 66329, upload-time = "2025-10-17T06:19:01.771Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/6b/38/2fe0051ffe8c6a650c8b1ac0da31b8802d1dbe5fa40a84e4b6b6f5583db5/crc32c-2.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6762d276d90331a490ef7e71ffee53b9c0eb053bd75a272d786f3b08d3fe3671", size = 62988, upload-time = "2025-10-17T06:19:02.953Z" },
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{ url = "https://files.pythonhosted.org/packages/ca/29/63972fc1452778e2092ae998c50cbfc2fc93e3fa9798a0278650cd6169c5/crc32c-2.8-cp311-cp311-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:711743da6ccc70b3c6718c328947b0b6f34a1fe6a6c27cc6c1d69cc226bf70e9", size = 80200, upload-time = "2025-10-17T06:19:04.617Z" },
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{ url = "https://files.pythonhosted.org/packages/cb/3a/60eb49d7bdada4122b3ffd45b0df54bdc1b8dd092cda4b069a287bdfcff4/crc32c-2.8-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:5eb4094a2054774f13b26f21bf56792bb44fa1fcee6c6ad099387a43ffbfb4fa", size = 81757, upload-time = "2025-10-17T06:19:05.496Z" },
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{ url = "https://files.pythonhosted.org/packages/f5/63/6efc1b64429ef7d23bd58b75b7ac24d15df327e3ebbe9c247a0f7b1c2ed1/crc32c-2.8-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:fff15bf2bd3e95780516baae935ed12be88deaa5ebe6143c53eb0d26a7bdc7b7", size = 80830, upload-time = "2025-10-17T06:19:06.621Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/e1/eb/0ae9f436f8004f1c88f7429e659a7218a3879bd11a6b18ed1257aad7e98b/crc32c-2.8-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:4c0e11e3826668121fa53e0745635baf5e4f0ded437e8ff63ea56f38fc4f970a", size = 80095, upload-time = "2025-10-17T06:19:07.381Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/9e/81/4afc9d468977a4cd94a2eb62908553345009a7c0d30e74463a15d4b48ec3/crc32c-2.8-cp311-cp311-win32.whl", hash = "sha256:38f915336715d1f1353ab07d7d786f8a789b119e273aea106ba55355dfc9101d", size = 64886, upload-time = "2025-10-17T06:19:08.497Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d6/e8/94e839c9f7e767bf8479046a207afd440a08f5c59b52586e1af5e64fa4a0/crc32c-2.8-cp311-cp311-win_amd64.whl", hash = "sha256:60e0a765b1caab8d31b2ea80840639253906a9351d4b861551c8c8625ea20f86", size = 66639, upload-time = "2025-10-17T06:19:09.338Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/b6/36/fd18ef23c42926b79c7003e16cb0f79043b5b179c633521343d3b499e996/crc32c-2.8-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:572ffb1b78cce3d88e8d4143e154d31044a44be42cb3f6fbbf77f1e7a941c5ab", size = 66379, upload-time = "2025-10-17T06:19:10.115Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/7f/b8/c584958e53f7798dd358f5bdb1bbfc97483134f053ee399d3eeb26cca075/crc32c-2.8-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:cf827b3758ee0c4aacd21ceca0e2da83681f10295c38a10bfeb105f7d98f7a68", size = 63042, upload-time = "2025-10-17T06:19:10.946Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/62/e6/6f2af0ec64a668a46c861e5bc778ea3ee42171fedfc5440f791f470fd783/crc32c-2.8-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:106fbd79013e06fa92bc3b51031694fcc1249811ed4364ef1554ee3dd2c7f5a2", size = 61528, upload-time = "2025-10-17T06:19:11.768Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/17/8b/4a04bd80a024f1a23978f19ae99407783e06549e361ab56e9c08bba3c1d3/crc32c-2.8-cp312-cp312-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:6dde035f91ffbfe23163e68605ee5a4bb8ceebd71ed54bb1fb1d0526cdd125a2", size = 80028, upload-time = "2025-10-17T06:19:12.554Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/21/8f/01c7afdc76ac2007d0e6a98e7300b4470b170480f8188475b597d1f4b4c6/crc32c-2.8-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e41ebe7c2f0fdcd9f3a3fd206989a36b460b4d3f24816d53e5be6c7dba72c5e1", size = 81531, upload-time = "2025-10-17T06:19:13.406Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/32/2b/8f78c5a8cc66486be5f51b6f038fc347c3ba748d3ea68be17a014283c331/crc32c-2.8-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:ecf66cf90266d9c15cea597d5cc86c01917cd1a238dc3c51420c7886fa750d7e", size = 80608, upload-time = "2025-10-17T06:19:14.223Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/db/86/fad1a94cdeeeb6b6e2323c87f970186e74bfd6fbfbc247bf5c88ad0873d5/crc32c-2.8-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:59eee5f3a69ad0793d5fa9cdc9b9d743b0cd50edf7fccc0a3988a821fef0208c", size = 79886, upload-time = "2025-10-17T06:19:15.345Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d5/db/1a7cb6757a1e32376fa2dfce00c815ea4ee614a94f9bff8228e37420c183/crc32c-2.8-cp312-cp312-win32.whl", hash = "sha256:a73d03ce3604aa5d7a2698e9057a0eef69f529c46497b27ee1c38158e90ceb76", size = 64896, upload-time = "2025-10-17T06:19:16.457Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/bf/8e/2024de34399b2e401a37dcb54b224b56c747b0dc46de4966886827b4d370/crc32c-2.8-cp312-cp312-win_amd64.whl", hash = "sha256:56b3b7d015247962cf58186e06d18c3d75a1a63d709d3233509e1c50a2d36aa2", size = 66645, upload-time = "2025-10-17T06:19:17.235Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a7/1d/dd926c68eb8aac8b142a1a10b8eb62d95212c1cf81775644373fe7cceac2/crc32c-2.8-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:5833f4071da7ea182c514ba17d1eee8aec3c5be927d798222fbfbbd0f5eea02c", size = 62345, upload-time = "2025-10-17T06:20:09.39Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/51/be/803404e5abea2ef2c15042edca04bbb7f625044cca879e47f186b43887c2/crc32c-2.8-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:1dc4da036126ac07b39dd9d03e93e585ec615a2ad28ff12757aef7de175295a8", size = 61229, upload-time = "2025-10-17T06:20:10.236Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/fc/3a/00cc578cd27ed0b22c9be25cef2c24539d92df9fa80ebd67a3fc5419724c/crc32c-2.8-pp311-pypy311_pp73-manylinux1_x86_64.manylinux_2_28_x86_64.manylinux_2_5_x86_64.whl", hash = "sha256:15905fa78344654e241371c47e6ed2411f9eeb2b8095311c68c88eccf541e8b4", size = 64108, upload-time = "2025-10-17T06:20:11.072Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/6b/bc/0587ef99a1c7629f95dd0c9d4f3d894de383a0df85831eb16c48a6afdae4/crc32c-2.8-pp311-pypy311_pp73-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:c596f918688821f796434e89b431b1698396c38bf0b56de873621528fe3ecb1e", size = 64815, upload-time = "2025-10-17T06:20:11.919Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/73/42/94f2b8b92eae9064fcfb8deef2b971514065bd606231f8857ff8ae02bebd/crc32c-2.8-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:8d23c4fe01b3844cb6e091044bc1cebdef7d16472e058ce12d9fadf10d2614af", size = 66659, upload-time = "2025-10-17T06:20:12.766Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "crcmod"
|
||||
version = "1.7"
|
||||
@ -1309,21 +1262,6 @@ wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/c3/be/d0d44e092656fe7a06b55e6103cbce807cdbdee17884a5367c68c9860853/dataclasses_json-0.6.7-py3-none-any.whl", hash = "sha256:0dbf33f26c8d5305befd61b39d2b3414e8a407bedc2834dea9b8d642666fb40a", size = 28686, upload-time = "2024-06-09T16:20:16.715Z" },
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]
|
||||
|
||||
[[package]]
|
||||
name = "dateparser"
|
||||
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|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "python-dateutil" },
|
||||
{ name = "pytz" },
|
||||
{ name = "regex" },
|
||||
{ name = "tzlocal" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a9/30/064144f0df1749e7bb5faaa7f52b007d7c2d08ec08fed8411aba87207f68/dateparser-1.2.2.tar.gz", hash = "sha256:986316f17cb8cdc23ea8ce563027c5ef12fc725b6fb1d137c14ca08777c5ecf7", size = 329840, upload-time = "2025-06-26T09:29:23.211Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/87/22/f020c047ae1346613db9322638186468238bcfa8849b4668a22b97faad65/dateparser-1.2.2-py3-none-any.whl", hash = "sha256:5a5d7211a09013499867547023a2a0c91d5a27d15dd4dbcea676ea9fe66f2482", size = 315453, upload-time = "2025-06-26T09:29:21.412Z" },
|
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]
|
||||
|
||||
[[package]]
|
||||
name = "decorator"
|
||||
version = "5.2.1"
|
||||
@ -1371,7 +1309,6 @@ name = "dify-api"
|
||||
version = "1.11.4"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "aliyun-log-python-sdk" },
|
||||
{ name = "apscheduler" },
|
||||
{ name = "arize-phoenix-otel" },
|
||||
{ name = "azure-identity" },
|
||||
@ -1431,6 +1368,7 @@ dependencies = [
|
||||
{ name = "opik" },
|
||||
{ name = "packaging" },
|
||||
{ name = "pandas", extra = ["excel", "output-formatting", "performance"] },
|
||||
{ name = "protobuf" },
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Loading…
Reference in New Issue
Block a user