Feat/dify rag (#2528)

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

api/core/features/annotation_reply.py

@@ -1,13 +1,8 @@
 import logging
 from typing import Optional
 
-from flask import current_app
-
-from core.embedding.cached_embedding import CacheEmbedding
 from core.entities.application_entities import InvokeFrom
-from core.index.vector_index.vector_index import VectorIndex
-from core.model_manager import ModelManager
-from core.model_runtime.entities.model_entities import ModelType
+from core.rag.datasource.vdb.vector_factory import Vector
 from extensions.ext_database import db
 from models.dataset import Dataset
 from models.model import App, AppAnnotationSetting, Message, MessageAnnotation
@@ -45,17 +40,6 @@ class AnnotationReplyFeature:
             embedding_provider_name = collection_binding_detail.provider_name
             embedding_model_name = collection_binding_detail.model_name
 
-            model_manager = ModelManager()
-            model_instance = model_manager.get_model_instance(
-                tenant_id=app_record.tenant_id,
-                provider=embedding_provider_name,
-                model_type=ModelType.TEXT_EMBEDDING,
-                model=embedding_model_name
-            )
-
-            # get embedding model
-            embeddings = CacheEmbedding(model_instance)
-
             dataset_collection_binding = DatasetCollectionBindingService.get_dataset_collection_binding(
                 embedding_provider_name,
                 embedding_model_name,
@@ -71,22 +55,14 @@ class AnnotationReplyFeature:
                 collection_binding_id=dataset_collection_binding.id
             )
 
-            vector_index = VectorIndex(
-                dataset=dataset,
-                config=current_app.config,
-                embeddings=embeddings,
-                attributes=['doc_id', 'annotation_id', 'app_id']
-            )
+            vector = Vector(dataset, attributes=['doc_id', 'annotation_id', 'app_id'])
 
-            documents = vector_index.search(
+            documents = vector.search_by_vector(
                 query=query,
-                search_type='similarity_score_threshold',
-                search_kwargs={
-                    'k': 1,
-                    'score_threshold': score_threshold,
-                    'filter': {
-                        'group_id': [dataset.id]
-                    }
+                k=1,
+                score_threshold=score_threshold,
+                filter={
+                    'group_id': [dataset.id]
                 }
             )