feat: add support for Matrixone database (#20714)
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LiuBo
0
api/core/rag/datasource/vdb/matrixone/__init__.py
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api/core/rag/datasource/vdb/matrixone/__init__.py
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233
api/core/rag/datasource/vdb/matrixone/matrixone_vector.py
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api/core/rag/datasource/vdb/matrixone/matrixone_vector.py
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@@ -0,0 +1,233 @@
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import json
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import logging
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import uuid
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from functools import wraps
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from typing import Any, Optional
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from mo_vector.client import MoVectorClient # type: ignore
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from pydantic import BaseModel, model_validator
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from configs import dify_config
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from core.rag.datasource.vdb.vector_base import BaseVector
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from core.rag.datasource.vdb.vector_factory import AbstractVectorFactory
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from core.rag.datasource.vdb.vector_type import VectorType
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from core.rag.embedding.embedding_base import Embeddings
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from core.rag.models.document import Document
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from extensions.ext_redis import redis_client
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from models.dataset import Dataset
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logger = logging.getLogger(__name__)
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class MatrixoneConfig(BaseModel):
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host: str = "localhost"
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port: int = 6001
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user: str = "dump"
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password: str = "111"
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database: str = "dify"
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metric: str = "l2"
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@model_validator(mode="before")
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@classmethod
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def validate_config(cls, values: dict) -> dict:
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if not values["host"]:
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raise ValueError("config host is required")
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if not values["port"]:
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raise ValueError("config port is required")
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if not values["user"]:
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raise ValueError("config user is required")
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if not values["password"]:
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raise ValueError("config password is required")
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if not values["database"]:
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raise ValueError("config database is required")
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return values
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def ensure_client(func):
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@wraps(func)
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def wrapper(self, *args, **kwargs):
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if self.client is None:
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self.client = self._get_client(None, False)
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return func(self, *args, **kwargs)
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return wrapper
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class MatrixoneVector(BaseVector):
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"""
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Matrixone vector storage implementation.
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"""
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def __init__(self, collection_name: str, config: MatrixoneConfig):
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super().__init__(collection_name)
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self.config = config
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self.collection_name = collection_name.lower()
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self.client = None
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@property
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def collection_name(self):
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return self._collection_name
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@collection_name.setter
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def collection_name(self, value):
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self._collection_name = value
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def get_type(self) -> str:
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return VectorType.MATRIXONE
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def create(self, texts: list[Document], embeddings: list[list[float]], **kwargs):
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if self.client is None:
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self.client = self._get_client(len(embeddings[0]), True)
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return self.add_texts(texts, embeddings)
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def _get_client(self, dimension: Optional[int] = None, create_table: bool = False) -> MoVectorClient:
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"""
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Create a new client for the collection.
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The collection will be created if it doesn't exist.
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"""
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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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client = MoVectorClient(
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connection_string=f"mysql+pymysql://{self.config.user}:{self.config.password}@{self.config.host}:{self.config.port}/{self.config.database}",
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table_name=self.collection_name,
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vector_dimension=dimension,
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create_table=create_table,
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)
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collection_exist_cache_key = f"vector_indexing_{self._collection_name}"
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if redis_client.get(collection_exist_cache_key):
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return client
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try:
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client.create_full_text_index()
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except Exception as e:
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logger.exception("Failed to create full text index")
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redis_client.set(collection_exist_cache_key, 1, ex=3600)
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return client
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def add_texts(self, documents: list[Document], embeddings: list[list[float]], **kwargs):
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if self.client is None:
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self.client = self._get_client(len(embeddings[0]), True)
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assert self.client is not None
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ids = []
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for _, doc in enumerate(documents):
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if doc.metadata is not None:
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doc_id = doc.metadata.get("doc_id", str(uuid.uuid4()))
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ids.append(doc_id)
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self.client.insert(
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texts=[doc.page_content for doc in documents],
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embeddings=embeddings,
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metadatas=[doc.metadata for doc in documents],
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ids=ids,
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)
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return ids
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@ensure_client
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def text_exists(self, id: str) -> bool:
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assert self.client is not None
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result = self.client.get(ids=[id])
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return len(result) > 0
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@ensure_client
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def delete_by_ids(self, ids: list[str]) -> None:
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assert self.client is not None
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if not ids:
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return
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self.client.delete(ids=ids)
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@ensure_client
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def get_ids_by_metadata_field(self, key: str, value: str):
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assert self.client is not None
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results = self.client.query_by_metadata(filter={key: value})
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return [result.id for result in results]
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@ensure_client
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def delete_by_metadata_field(self, key: str, value: str) -> None:
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assert self.client is not None
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self.client.delete(filter={key: value})
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@ensure_client
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def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
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assert self.client is not None
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top_k = kwargs.get("top_k", 5)
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document_ids_filter = kwargs.get("document_ids_filter")
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filter = None
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if document_ids_filter:
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filter = {"document_id": {"$in": document_ids_filter}}
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results = self.client.query(
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query_vector=query_vector,
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k=top_k,
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filter=filter,
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)
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docs = []
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# TODO: add the score threshold to the query
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for result in results:
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metadata = result.metadata
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docs.append(
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Document(
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page_content=result.document,
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metadata=metadata,
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)
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)
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return docs
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@ensure_client
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def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]:
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assert self.client is not None
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top_k = kwargs.get("top_k", 5)
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document_ids_filter = kwargs.get("document_ids_filter")
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filter = None
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if document_ids_filter:
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filter = {"document_id": {"$in": document_ids_filter}}
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score_threshold = float(kwargs.get("score_threshold", 0.0))
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results = self.client.full_text_query(
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keywords=[query],
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k=top_k,
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filter=filter,
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)
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docs = []
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for result in results:
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metadata = result.metadata
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if isinstance(metadata, str):
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import json
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metadata = json.loads(metadata)
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score = 1 - result.distance
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if score >= score_threshold:
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metadata["score"] = score
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docs.append(
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Document(
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page_content=result.document,
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metadata=metadata,
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)
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)
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return docs
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@ensure_client
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def delete(self) -> None:
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assert self.client is not None
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self.client.delete()
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class MatrixoneVectorFactory(AbstractVectorFactory):
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def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings) -> MatrixoneVector:
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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.MATRIXONE, collection_name))
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config = MatrixoneConfig(
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host=dify_config.MATRIXONE_HOST or "localhost",
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port=dify_config.MATRIXONE_PORT or 6001,
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user=dify_config.MATRIXONE_USER or "dump",
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password=dify_config.MATRIXONE_PASSWORD or "111",
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database=dify_config.MATRIXONE_DATABASE or "dify",
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metric=dify_config.MATRIXONE_METRIC or "l2",
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)
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return MatrixoneVector(collection_name=collection_name, config=config)
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@@ -164,6 +164,10 @@ class Vector:
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from core.rag.datasource.vdb.huawei.huawei_cloud_vector import HuaweiCloudVectorFactory
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return HuaweiCloudVectorFactory
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case VectorType.MATRIXONE:
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from core.rag.datasource.vdb.matrixone.matrixone_vector import MatrixoneVectorFactory
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return MatrixoneVectorFactory
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case _:
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raise ValueError(f"Vector store {vector_type} is not supported.")
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@@ -29,3 +29,4 @@ class VectorType(StrEnum):
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OPENGAUSS = "opengauss"
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TABLESTORE = "tablestore"
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HUAWEI_CLOUD = "huawei_cloud"
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MATRIXONE = "matrixone"
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