Feat/vector db manage (#997)

Co-authored-by: jyong <jyong@dify.ai>

api/core/index/vector_index/base.py

@@ -173,3 +173,49 @@ class BaseVectorIndex(BaseIndex):
 
         self.dataset = dataset
         logging.info(f"Dataset {dataset.id} recreate successfully.")
+
+    def create_qdrant_dataset(self, dataset: Dataset):
+        logging.info(f"create_qdrant_dataset {dataset.id}")
+
+        try:
+            self.delete()
+        except UnexpectedStatusCodeException as e:
+            if e.status_code != 400:
+                # 400 means index not exists
+                raise e
+
+        dataset_documents = db.session.query(DatasetDocument).filter(
+            DatasetDocument.dataset_id == dataset.id,
+            DatasetDocument.indexing_status == 'completed',
+            DatasetDocument.enabled == True,
+            DatasetDocument.archived == False,
+        ).all()
+
+        documents = []
+        for dataset_document in dataset_documents:
+            segments = db.session.query(DocumentSegment).filter(
+                DocumentSegment.document_id == dataset_document.id,
+                DocumentSegment.status == 'completed',
+                DocumentSegment.enabled == True
+            ).all()
+
+            for segment in segments:
+                document = Document(
+                    page_content=segment.content,
+                    metadata={
+                        "doc_id": segment.index_node_id,
+                        "doc_hash": segment.index_node_hash,
+                        "document_id": segment.document_id,
+                        "dataset_id": segment.dataset_id,
+                    }
+                )
+
+                documents.append(document)
+
+        if documents:
+            try:
+                self.create(documents)
+            except Exception as e:
+                raise e
+
+        logging.info(f"Dataset {dataset.id} recreate successfully.")

api/core/index/vector_index/milvus_vector_index.py

@@ -0,0 +1,114 @@
+from typing import Optional, cast
+
+from langchain.embeddings.base import Embeddings
+from langchain.schema import Document, BaseRetriever
+from langchain.vectorstores import VectorStore, milvus
+from pydantic import BaseModel, root_validator
+
+from core.index.base import BaseIndex
+from core.index.vector_index.base import BaseVectorIndex
+from core.vector_store.milvus_vector_store import MilvusVectorStore
+from core.vector_store.weaviate_vector_store import WeaviateVectorStore
+from models.dataset import Dataset
+
+
+class MilvusConfig(BaseModel):
+    endpoint: str
+    user: str
+    password: str
+    batch_size: int = 100
+
+    @root_validator()
+    def validate_config(cls, values: dict) -> dict:
+        if not values['endpoint']:
+            raise ValueError("config MILVUS_ENDPOINT is required")
+        if not values['user']:
+            raise ValueError("config MILVUS_USER is required")
+        if not values['password']:
+            raise ValueError("config MILVUS_PASSWORD is required")
+        return values
+
+
+class MilvusVectorIndex(BaseVectorIndex):
+    def __init__(self, dataset: Dataset, config: MilvusConfig, embeddings: Embeddings):
+        super().__init__(dataset, embeddings)
+        self._client = self._init_client(config)
+
+    def get_type(self) -> str:
+        return 'milvus'
+
+    def get_index_name(self, dataset: Dataset) -> str:
+        if self.dataset.index_struct_dict:
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
+                # original class_prefix
+                class_prefix += '_Node'
+
+            return class_prefix
+
+        dataset_id = dataset.id
+        return "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
+
+
+    def to_index_struct(self) -> dict:
+        return {
+            "type": self.get_type(),
+            "vector_store": {"class_prefix": self.get_index_name(self.dataset)}
+        }
+
+    def create(self, texts: list[Document], **kwargs) -> BaseIndex:
+        uuids = self._get_uuids(texts)
+        self._vector_store = WeaviateVectorStore.from_documents(
+            texts,
+            self._embeddings,
+            client=self._client,
+            index_name=self.get_index_name(self.dataset),
+            uuids=uuids,
+            by_text=False
+        )
+
+        return self
+
+    def _get_vector_store(self) -> VectorStore:
+        """Only for created index."""
+        if self._vector_store:
+            return self._vector_store
+
+        attributes = ['doc_id', 'dataset_id', 'document_id']
+        if self._is_origin():
+            attributes = ['doc_id']
+
+        return WeaviateVectorStore(
+            client=self._client,
+            index_name=self.get_index_name(self.dataset),
+            text_key='text',
+            embedding=self._embeddings,
+            attributes=attributes,
+            by_text=False
+        )
+
+    def _get_vector_store_class(self) -> type:
+        return MilvusVectorStore
+
+    def delete_by_document_id(self, document_id: str):
+        if self._is_origin():
+            self.recreate_dataset(self.dataset)
+            return
+
+        vector_store = self._get_vector_store()
+        vector_store = cast(self._get_vector_store_class(), vector_store)
+
+        vector_store.del_texts({
+            "operator": "Equal",
+            "path": ["document_id"],
+            "valueText": document_id
+        })
+
+    def _is_origin(self):
+        if self.dataset.index_struct_dict:
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
+                # original class_prefix
+                return True
+
+        return False

api/core/index/vector_index/qdrant_vector_index.py

@@ -44,15 +44,20 @@ class QdrantVectorIndex(BaseVectorIndex):
 
     def get_index_name(self, dataset: Dataset) -> str:
         if self.dataset.index_struct_dict:
-            return self.dataset.index_struct_dict['vector_store']['collection_name']
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
+                # original class_prefix
+                class_prefix += '_Node'
+
+            return class_prefix
 
         dataset_id = dataset.id
-        return "Index_" + dataset_id.replace("-", "_")
+        return "Vector_index_" + dataset_id.replace("-", "_") + '_Node'
 
     def to_index_struct(self) -> dict:
         return {
             "type": self.get_type(),
-            "vector_store": {"collection_name": self.get_index_name(self.dataset)}
+            "vector_store": {"class_prefix": self.get_index_name(self.dataset)}
         }
 
     def create(self, texts: list[Document], **kwargs) -> BaseIndex:
@@ -62,7 +67,7 @@ class QdrantVectorIndex(BaseVectorIndex):
             self._embeddings,
             collection_name=self.get_index_name(self.dataset),
             ids=uuids,
-            content_payload_key='text',
+            content_payload_key='page_content',
             **self._client_config.to_qdrant_params()
         )
 
@@ -72,7 +77,9 @@ class QdrantVectorIndex(BaseVectorIndex):
         """Only for created index."""
         if self._vector_store:
             return self._vector_store
-        
+        attributes = ['doc_id', 'dataset_id', 'document_id']
+        if self._is_origin():
+            attributes = ['doc_id']
         client = qdrant_client.QdrantClient(
             **self._client_config.to_qdrant_params()
         )
@@ -81,7 +88,7 @@ class QdrantVectorIndex(BaseVectorIndex):
             client=client,
             collection_name=self.get_index_name(self.dataset),
             embeddings=self._embeddings,
-            content_payload_key='text'
+            content_payload_key='page_content'
         )
 
     def _get_vector_store_class(self) -> type:
@@ -108,8 +115,8 @@ class QdrantVectorIndex(BaseVectorIndex):
 
     def _is_origin(self):
         if self.dataset.index_struct_dict:
-            class_prefix: str = self.dataset.index_struct_dict['vector_store']['collection_name']
-            if class_prefix.startswith('Vector_'):
+            class_prefix: str = self.dataset.index_struct_dict['vector_store']['class_prefix']
+            if not class_prefix.endswith('_Node'):
                 # original class_prefix
                 return True