Feat/add retriever rerank

api/commands.py

@@ -8,6 +8,8 @@
 import uuid
 
 import click
+import qdrant_client
+from qdrant_client.http.models import TextIndexParams, TextIndexType, TokenizerType
 from tqdm import tqdm
 from flask import current_app, Flask
 from langchain.embeddings import OpenAIEmbeddings
@@ -484,6 +486,38 @@ def normalization_collections():
     click.echo(click.style('Congratulations! restore {} dataset indexes.'.format(len(normalization_count)), fg='green'))
 
 
+@click.command('add-qdrant-full-text-index', help='add qdrant full text index')
+def add_qdrant_full_text_index():
+    click.echo(click.style('Start add full text index.', fg='green'))
+    binds = db.session.query(DatasetCollectionBinding).all()
+    if binds and current_app.config['VECTOR_STORE'] == 'qdrant':
+        qdrant_url = current_app.config['QDRANT_URL']
+        qdrant_api_key = current_app.config['QDRANT_API_KEY']
+        client = qdrant_client.QdrantClient(
+            qdrant_url,
+            api_key=qdrant_api_key,  # For Qdrant Cloud, None for local instance
+        )
+        for bind in binds:
+            try:
+                text_index_params = TextIndexParams(
+                    type=TextIndexType.TEXT,
+                    tokenizer=TokenizerType.MULTILINGUAL,
+                    min_token_len=2,
+                    max_token_len=20,
+                    lowercase=True
+                )
+                client.create_payload_index(bind.collection_name, 'page_content',
+                                            field_schema=text_index_params)
+            except Exception as e:
+                click.echo(
+                    click.style('Create full text index error: {} {}'.format(e.__class__.__name__, str(e)),
+                                fg='red'))
+            click.echo(
+                click.style(
+                    'Congratulations! add collection {} full text index successful.'.format(bind.collection_name),
+                    fg='green'))
+
+
 def deal_dataset_vector(flask_app: Flask, dataset: Dataset, normalization_count: list):
     with flask_app.app_context():
         try:
@@ -647,24 +681,24 @@ def update_app_model_configs(batch_size):
 
             pbar.update(len(data_batch))
 
+
 @click.command('migrate_default_input_to_dataset_query_variable')
 @click.option("--batch-size", default=500, help="Number of records to migrate in each batch.")
 def migrate_default_input_to_dataset_query_variable(batch_size):
-
     click.secho("Starting...", fg='green')
 
     total_records = db.session.query(AppModelConfig) \
         .join(App, App.app_model_config_id == AppModelConfig.id) \
         .filter(App.mode == 'completion') \
         .filter(AppModelConfig.dataset_query_variable == None) \
         .count()
-    
+
     if total_records == 0:
         click.secho("No data to migrate.", fg='green')
         return
 
     num_batches = (total_records + batch_size - 1) // batch_size
-    
+
     with tqdm(total=total_records, desc="Migrating Data") as pbar:
         for i in range(num_batches):
             offset = i * batch_size
@@ -697,22 +731,22 @@ def migrate_default_input_to_dataset_query_variable(batch_size):
                     for form in user_input_form:
                         paragraph = form.get('paragraph')
                         if paragraph \
-                            and paragraph.get('variable') == 'query':
-                                data.dataset_query_variable = 'query'
-                                break
-                        
+                                and paragraph.get('variable') == 'query':
+                            data.dataset_query_variable = 'query'
+                            break
+
                         if paragraph \
-                            and paragraph.get('variable') == 'default_input':
-                                data.dataset_query_variable = 'default_input'
-                                break
+                                and paragraph.get('variable') == 'default_input':
+                            data.dataset_query_variable = 'default_input'
+                            break
 
                 db.session.commit()
 
             except Exception as e:
                 click.secho(f"Error while migrating data: {e}, app_id: {data.app_id}, app_model_config_id: {data.id}",
                             fg='red')
                 continue
-            
+
             click.secho(f"Successfully migrated batch {i + 1}/{num_batches}.", fg='green')
 
             pbar.update(len(data_batch))
@@ -731,3 +765,4 @@ def register_commands(app):
     app.cli.add_command(update_app_model_configs)
     app.cli.add_command(normalization_collections)
     app.cli.add_command(migrate_default_input_to_dataset_query_variable)
+    app.cli.add_command(add_qdrant_full_text_index)

api/controllers/console/datasets/datasets.py

@@ -170,6 +170,7 @@ def patch(self, dataset_id):
                             help='Invalid indexing technique.')
         parser.add_argument('permission', type=str, location='json', choices=(
             'only_me', 'all_team_members'), help='Invalid permission.')
+        parser.add_argument('retrieval_model', type=dict, location='json', help='Invalid retrieval model.')
         args = parser.parse_args()
 
         # The role of the current user in the ta table must be admin or owner
@@ -401,6 +402,7 @@ def post(self):
 
 class DatasetApiDeleteApi(Resource):
     resource_type = 'dataset'
+
     @setup_required
     @login_required
     @account_initialization_required
@@ -436,6 +438,50 @@ def get(self):
         }
 
 
+class DatasetRetrievalSettingApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        vector_type = current_app.config['VECTOR_STORE']
+        if vector_type == 'milvus':
+            return {
+                'retrieval_method': [
+                    'semantic_search'
+                ]
+            }
+        elif vector_type == 'qdrant' or vector_type == 'weaviate':
+            return {
+                'retrieval_method': [
+                    'semantic_search', 'full_text_search', 'hybrid_search'
+                ]
+            }
+        else:
+            raise ValueError("Unsupported vector db type.")
+
+
+class DatasetRetrievalSettingMockApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, vector_type):
+
+        if vector_type == 'milvus':
+            return {
+                'retrieval_method': [
+                    'semantic_search'
+                ]
+            }
+        elif vector_type == 'qdrant' or vector_type == 'weaviate':
+            return {
+                'retrieval_method': [
+                    'semantic_search', 'full_text_search', 'hybrid_search'
+                ]
+            }
+        else:
+            raise ValueError("Unsupported vector db type.")
+
+
 api.add_resource(DatasetListApi, '/datasets')
 api.add_resource(DatasetApi, '/datasets/<uuid:dataset_id>')
 api.add_resource(DatasetQueryApi, '/datasets/<uuid:dataset_id>/queries')
@@ -445,3 +491,5 @@ def get(self):
 api.add_resource(DatasetApiKeyApi, '/datasets/api-keys')
 api.add_resource(DatasetApiDeleteApi, '/datasets/api-keys/<uuid:api_key_id>')
 api.add_resource(DatasetApiBaseUrlApi, '/datasets/api-base-info')
+api.add_resource(DatasetRetrievalSettingApi, '/datasets/retrieval-setting')
+api.add_resource(DatasetRetrievalSettingMockApi, '/datasets/retrieval-setting/<string:vector_type>')

api/controllers/console/datasets/datasets_document.py

@@ -221,6 +221,8 @@ def post(self, dataset_id):
         parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False,
                             location='json')
+        parser.add_argument('retrieval_model', type=dict, required=False, nullable=False,
+                            location='json')
         args = parser.parse_args()
 
         if not dataset.indexing_technique and not args['indexing_technique']:
@@ -263,6 +265,8 @@ def post(self):
         parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False,
                             location='json')
+        parser.add_argument('retrieval_model', type=dict, required=False, nullable=False,
+                            location='json')
         args = parser.parse_args()
         if args['indexing_technique'] == 'high_quality':
             try:

api/controllers/console/datasets/hit_testing.py

@@ -42,19 +42,18 @@ def post(self, dataset_id):
 
         parser = reqparse.RequestParser()
         parser.add_argument('query', type=str, location='json')
+        parser.add_argument('retrieval_model', type=dict, required=False, location='json')
         args = parser.parse_args()
 
-        query = args['query']
-
-        if not query or len(query) > 250:
-            raise ValueError('Query is required and cannot exceed 250 characters')
+        HitTestingService.hit_testing_args_check(args)
 
         try:
             response = HitTestingService.retrieve(
                 dataset=dataset,
-                query=query,
+                query=args['query'],
                 account=current_user,
-                limit=10,
+                retrieval_model=args['retrieval_model'],
+                limit=10
             )
 
             return {"query": response['query'], 'records': marshal(response['records'], hit_testing_record_fields)}

api/controllers/console/workspace/models.py

@@ -19,7 +19,7 @@ class DefaultModelApi(Resource):
     def get(self):
         parser = reqparse.RequestParser()
         parser.add_argument('model_type', type=str, required=True, nullable=False,
-                            choices=['text-generation', 'embeddings', 'speech2text'], location='args')
+                            choices=['text-generation', 'embeddings', 'speech2text', 'reranking'], location='args')
         args = parser.parse_args()
 
         tenant_id = current_user.current_tenant_id
@@ -71,19 +71,18 @@ def get(self):
     @account_initialization_required
     def post(self):
         parser = reqparse.RequestParser()
-        parser.add_argument('model_name', type=str, required=True, nullable=False, location='json')
-        parser.add_argument('model_type', type=str, required=True, nullable=False,
-                            choices=['text-generation', 'embeddings', 'speech2text'], location='json')
-        parser.add_argument('provider_name', type=str, required=True, nullable=False, location='json')
+        parser.add_argument('model_settings', type=list, required=True, nullable=False, location='json')
         args = parser.parse_args()
 
         provider_service = ProviderService()
-        provider_service.update_default_model_of_model_type(
-            tenant_id=current_user.current_tenant_id,
-            model_type=args['model_type'],
-            provider_name=args['provider_name'],
-            model_name=args['model_name']
-        )
+        model_settings = args['model_settings']
+        for model_setting in model_settings:
+            provider_service.update_default_model_of_model_type(
+                tenant_id=current_user.current_tenant_id,
+                model_type=model_setting['model_type'],
+                provider_name=model_setting['provider_name'],
+                model_name=model_setting['model_name']
+            )
 
         return {'result': 'success'}
 

api/controllers/service_api/dataset/document.py

@@ -36,6 +36,8 @@ def post(self, tenant_id, dataset_id):
                             location='json')
         parser.add_argument('indexing_technique', type=str, choices=Dataset.INDEXING_TECHNIQUE_LIST, nullable=False,
                             location='json')
+        parser.add_argument('retrieval_model', type=dict, required=False, nullable=False,
+                            location='json')
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)
@@ -95,6 +97,8 @@ def post(self, tenant_id, dataset_id, document_id):
         parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
         parser.add_argument('doc_language', type=str, default='English', required=False, nullable=False,
                             location='json')
+        parser.add_argument('retrieval_model', type=dict, required=False, nullable=False,
+                            location='json')
         args = parser.parse_args()
         dataset_id = str(dataset_id)
         tenant_id = str(tenant_id)

api/core/agent/agent/multi_dataset_router_agent.py

@@ -14,7 +14,6 @@
 from core.model_providers.models.entity.message import to_prompt_messages
 from core.model_providers.models.llm.base import BaseLLM
 from core.third_party.langchain.llms.fake import FakeLLM
-from core.tool.dataset_retriever_tool import DatasetRetrieverTool
 
 
 class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
@@ -60,7 +59,6 @@ def plan(
             return AgentFinish(return_values={"output": ''}, log='')
         elif len(self.tools) == 1:
             tool = next(iter(self.tools))
-            tool = cast(DatasetRetrieverTool, tool)
             rst = tool.run(tool_input={'query': kwargs['input']})
             # output = ''
             # rst_json = json.loads(rst)