fix: margin in rerank switch

# Conflicts:
#	api/poetry.lock

.github/workflows/web-tests.yml

@@ -0,0 +1,46 @@
+name: Web Tests
+
+on:
+  pull_request:
+    branches:
+      - main
+    paths:
+      - web/**
+
+concurrency:
+  group: web-tests-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  test:
+    name: Web Tests
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ./web
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Check changed files
+        id: changed-files
+        uses: tj-actions/changed-files@v45
+        with:
+          files: web/**
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        if: steps.changed-files.outputs.any_changed == 'true'
+        with:
+          node-version: 20
+          cache: yarn
+          cache-dependency-path: ./web/package.json
+
+      - name: Install dependencies
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: yarn install --frozen-lockfile
+
+      - name: Run tests
+        if: steps.changed-files.outputs.any_changed == 'true'
+        run: yarn test

api/.env.example

@@ -162,6 +162,8 @@ PGVECTOR_PORT=5433
 PGVECTOR_USER=postgres
 PGVECTOR_PASSWORD=postgres
 PGVECTOR_DATABASE=postgres
+PGVECTOR_MIN_CONNECTION=1
+PGVECTOR_MAX_CONNECTION=5
 
 # Tidb Vector configuration
 TIDB_VECTOR_HOST=xxx.eu-central-1.xxx.aws.tidbcloud.com

api/app.py

@@ -53,11 +53,9 @@ from services.account_service import AccountService
 
 warnings.simplefilter("ignore", ResourceWarning)
 
-# fix windows platform
-if os.name == "nt":
-    os.system('tzutil /s "UTC"')
-else:
-    os.environ["TZ"] = "UTC"
+os.environ["TZ"] = "UTC"
+# windows platform not support tzset
+if hasattr(time, "tzset"):
     time.tzset()
 
 

api/commands.py

@@ -652,7 +652,7 @@ where sites.id is null limit 1000"""
                         app_was_created.send(app, account=account)
                 except Exception as e:
                     failed_app_ids.append(app_id)
-                    click.echo(click.style("FFailed to fix missing site for app {}".format(app_id), fg="red"))
+                    click.echo(click.style("Failed to fix missing site for app {}".format(app_id), fg="red"))
                     logging.exception(f"Fix app related site missing issue failed, error: {e}")
                     continue
 

api/configs/middleware/__init__.py

@@ -5,6 +5,7 @@ from pydantic import Field, NonNegativeInt, PositiveFloat, PositiveInt, computed
 from pydantic_settings import BaseSettings
 
 from configs.middleware.cache.redis_config import RedisConfig
+from configs.middleware.external.bedrock_config import BedrockConfig
 from configs.middleware.storage.aliyun_oss_storage_config import AliyunOSSStorageConfig
 from configs.middleware.storage.amazon_s3_storage_config import S3StorageConfig
 from configs.middleware.storage.azure_blob_storage_config import AzureBlobStorageConfig
@@ -222,5 +223,6 @@ class MiddlewareConfig(
     TiDBVectorConfig,
     WeaviateConfig,
     ElasticsearchConfig,
+    BedrockConfig,
 ):
     pass

api/configs/middleware/external/bedrock_config.py

@@ -0,0 +1,20 @@
+from typing import Optional
+
+from pydantic import Field
+from pydantic_settings import BaseSettings
+
+
+class BedrockConfig(BaseSettings):
+    """
+    bedrock configs
+    """
+
+    AWS_SECRET_ACCESS_KEY: Optional[str] = Field(
+        description="AWS secret access key",
+        default=None,
+    )
+
+    AWS_ACCESS_KEY_ID: Optional[str] = Field(
+        description="AWS secret access id",
+        default=None,
+    )

api/configs/middleware/vdb/pgvector_config.py

@@ -33,3 +33,13 @@ class PGVectorConfig(BaseSettings):
         description="Name of the PostgreSQL database to connect to",
         default=None,
     )
+
+    PGVECTOR_MIN_CONNECTION: PositiveInt = Field(
+        description="Min connection of the PostgreSQL database",
+        default=1,
+    )
+
+    PGVECTOR_MAX_CONNECTION: PositiveInt = Field(
+        description="Max connection of the PostgreSQL database",
+        default=5,
+    )

api/controllers/console/__init__.py

@@ -37,7 +37,17 @@ from .auth import activate, data_source_bearer_auth, data_source_oauth, forgot_p
 from .billing import billing
 
 # Import datasets controllers
-from .datasets import data_source, datasets, datasets_document, datasets_segments, file, hit_testing, website
+from .datasets import (
+    data_source,
+    datasets,
+    datasets_document,
+    datasets_segments,
+    external,
+    file,
+    hit_testing,
+    test_external,
+    website,
+)
 
 # Import explore controllers
 from .explore import (

api/controllers/console/datasets/datasets.py

@@ -49,7 +49,7 @@ class DatasetListApi(Resource):
         page = request.args.get("page", default=1, type=int)
         limit = request.args.get("limit", default=20, type=int)
         ids = request.args.getlist("ids")
-        provider = request.args.get("provider", default="vendor")
+        # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
 
@@ -57,7 +57,7 @@ class DatasetListApi(Resource):
             datasets, total = DatasetService.get_datasets_by_ids(ids, current_user.current_tenant_id)
         else:
             datasets, total = DatasetService.get_datasets(
-                page, limit, provider, current_user.current_tenant_id, current_user, search, tag_ids
+                page, limit, current_user.current_tenant_id, current_user, search, tag_ids
             )
 
         # check embedding setting
@@ -110,6 +110,26 @@ class DatasetListApi(Resource):
             nullable=True,
             help="Invalid indexing technique.",
         )
+        parser.add_argument(
+            "external_knowledge_api_id",
+            type=str,
+            nullable=True,
+            required=False,
+        )
+        parser.add_argument(
+            "provider",
+            type=str,
+            nullable=True,
+            choices=Dataset.PROVIDER_LIST,
+            required=False,
+            default="vendor",
+        )
+        parser.add_argument(
+            "external_knowledge_id",
+            type=str,
+            nullable=True,
+            required=False,
+        )
         args = parser.parse_args()
 
         # The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
@@ -123,6 +143,9 @@ class DatasetListApi(Resource):
                 indexing_technique=args["indexing_technique"],
                 account=current_user,
                 permission=DatasetPermissionEnum.ONLY_ME,
+                provider=args["provider"],
+                external_knowledge_api_id=args["external_knowledge_api_id"],
+                external_knowledge_id=args["external_knowledge_id"],
             )
         except services.errors.dataset.DatasetNameDuplicateError:
             raise DatasetNameDuplicateError()
@@ -211,6 +234,33 @@ class DatasetApi(Resource):
         )
         parser.add_argument("retrieval_model", type=dict, location="json", help="Invalid retrieval model.")
         parser.add_argument("partial_member_list", type=list, location="json", help="Invalid parent user list.")
+
+        parser.add_argument(
+            "external_retrieval_model",
+            type=dict,
+            required=False,
+            nullable=True,
+            location="json",
+            help="Invalid external retrieval model.",
+        )
+
+        parser.add_argument(
+            "external_knowledge_id",
+            type=str,
+            required=False,
+            nullable=True,
+            location="json",
+            help="Invalid external knowledge id.",
+        )
+
+        parser.add_argument(
+            "external_knowledge_api_id",
+            type=str,
+            required=False,
+            nullable=True,
+            location="json",
+            help="Invalid external knowledge api id.",
+        )
         args = parser.parse_args()
         data = request.get_json()
 

api/controllers/console/datasets/external.py

@@ -0,0 +1,282 @@
+from flask import request
+from flask_login import current_user
+from flask_restful import Resource, marshal, reqparse
+from werkzeug.exceptions import Forbidden, InternalServerError, NotFound
+
+import services
+from controllers.console import api
+from controllers.console.app.error import ProviderNotInitializeError
+from controllers.console.datasets.error import DatasetNameDuplicateError
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from fields.dataset_fields import dataset_detail_fields
+from libs.login import login_required
+from services.dataset_service import DatasetService
+from services.external_knowledge_service import ExternalDatasetService
+from services.hit_testing_service import HitTestingService
+
+
+def _validate_name(name):
+    if not name or len(name) < 1 or len(name) > 100:
+        raise ValueError("Name must be between 1 to 100 characters.")
+    return name
+
+
+def _validate_description_length(description):
+    if description and len(description) > 400:
+        raise ValueError("Description cannot exceed 400 characters.")
+    return description
+
+
+class ExternalApiTemplateListApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self):
+        page = request.args.get("page", default=1, type=int)
+        limit = request.args.get("limit", default=20, type=int)
+        search = request.args.get("keyword", default=None, type=str)
+
+        external_knowledge_apis, total = ExternalDatasetService.get_external_knowledge_apis(
+            page, limit, current_user.current_tenant_id, search
+        )
+        response = {
+            "data": [item.to_dict() for item in external_knowledge_apis],
+            "has_more": len(external_knowledge_apis) == limit,
+            "limit": limit,
+            "total": total,
+            "page": page,
+        }
+        return response, 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument(
+            "name",
+            nullable=False,
+            required=True,
+            help="Name is required. Name must be between 1 to 100 characters.",
+            type=_validate_name,
+        )
+        parser.add_argument(
+            "settings",
+            type=dict,
+            location="json",
+            nullable=False,
+            required=True,
+        )
+        args = parser.parse_args()
+
+        ExternalDatasetService.validate_api_list(args["settings"])
+
+        # The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
+        if not current_user.is_dataset_editor:
+            raise Forbidden()
+
+        try:
+            external_knowledge_api = ExternalDatasetService.create_external_knowledge_api(
+                tenant_id=current_user.current_tenant_id, user_id=current_user.id, args=args
+            )
+        except services.errors.dataset.DatasetNameDuplicateError:
+            raise DatasetNameDuplicateError()
+
+        return external_knowledge_api.to_dict(), 201
+
+
+class ExternalApiTemplateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, external_knowledge_api_id):
+        external_knowledge_api_id = str(external_knowledge_api_id)
+        external_knowledge_api = ExternalDatasetService.get_external_knowledge_api(external_knowledge_api_id)
+        if external_knowledge_api is None:
+            raise NotFound("API template not found.")
+
+        return external_knowledge_api.to_dict(), 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def patch(self, external_knowledge_api_id):
+        external_knowledge_api_id = str(external_knowledge_api_id)
+
+        parser = reqparse.RequestParser()
+        parser.add_argument(
+            "name",
+            nullable=False,
+            required=True,
+            help="type is required. Name must be between 1 to 100 characters.",
+            type=_validate_name,
+        )
+        parser.add_argument(
+            "settings",
+            type=dict,
+            location="json",
+            nullable=False,
+            required=True,
+        )
+        args = parser.parse_args()
+        ExternalDatasetService.validate_api_list(args["settings"])
+
+        external_knowledge_api = ExternalDatasetService.update_external_knowledge_api(
+            tenant_id=current_user.current_tenant_id,
+            user_id=current_user.id,
+            external_knowledge_api_id=external_knowledge_api_id,
+            args=args,
+        )
+
+        return external_knowledge_api.to_dict(), 200
+
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def delete(self, external_knowledge_api_id):
+        external_knowledge_api_id = str(external_knowledge_api_id)
+
+        # The role of the current user in the ta table must be admin, owner, or editor
+        if not current_user.is_editor or current_user.is_dataset_operator:
+            raise Forbidden()
+
+        ExternalDatasetService.delete_external_knowledge_api(current_user.current_tenant_id, external_knowledge_api_id)
+        return {"result": "success"}, 200
+
+
+class ExternalApiUseCheckApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def get(self, external_knowledge_api_id):
+        external_knowledge_api_id = str(external_knowledge_api_id)
+
+        external_knowledge_api_is_using, count = ExternalDatasetService.external_knowledge_api_use_check(
+            external_knowledge_api_id
+        )
+        return {"is_using": external_knowledge_api_is_using, "count": count}, 200
+
+
+class ExternalDatasetInitApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        # The role of the current user in the ta table must be admin, owner, or editor
+        if not current_user.is_editor:
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("external_knowledge_api_id", type=str, required=True, nullable=True, location="json")
+        # parser.add_argument('name', nullable=False, required=True,
+        #                     help='name is required. Name must be between 1 to 100 characters.',
+        #                     type=_validate_name)
+        # parser.add_argument('description', type=str, required=True, nullable=True, location='json')
+        parser.add_argument("data_source", type=dict, required=True, nullable=True, location="json")
+        parser.add_argument("process_parameter", type=dict, required=True, nullable=True, location="json")
+
+        args = parser.parse_args()
+
+        # The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
+        if not current_user.is_dataset_editor:
+            raise Forbidden()
+
+        # validate args
+        ExternalDatasetService.document_create_args_validate(
+            current_user.current_tenant_id, args["external_knowledge_api_id"], args["process_parameter"]
+        )
+
+        try:
+            dataset, documents, batch = ExternalDatasetService.init_external_dataset(
+                tenant_id=current_user.current_tenant_id,
+                user_id=current_user.id,
+                args=args,
+            )
+        except Exception as ex:
+            raise ProviderNotInitializeError(ex.description)
+        response = {"dataset": dataset, "documents": documents, "batch": batch}
+
+        return response
+
+
+class ExternalDatasetCreateApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self):
+        # The role of the current user in the ta table must be admin, owner, or editor
+        if not current_user.is_editor:
+            raise Forbidden()
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("external_knowledge_api_id", type=str, required=True, nullable=False, location="json")
+        parser.add_argument("external_knowledge_id", type=str, required=True, nullable=False, location="json")
+        parser.add_argument(
+            "name",
+            nullable=False,
+            required=True,
+            help="name is required. Name must be between 1 to 100 characters.",
+            type=_validate_name,
+        )
+        parser.add_argument("description", type=str, required=False, nullable=True, location="json")
+        parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
+
+        args = parser.parse_args()
+
+        # The role of the current user in the ta table must be admin, owner, or editor, or dataset_operator
+        if not current_user.is_dataset_editor:
+            raise Forbidden()
+
+        try:
+            dataset = ExternalDatasetService.create_external_dataset(
+                tenant_id=current_user.current_tenant_id,
+                user_id=current_user.id,
+                args=args,
+            )
+        except services.errors.dataset.DatasetNameDuplicateError:
+            raise DatasetNameDuplicateError()
+
+        return marshal(dataset, dataset_detail_fields), 201
+
+
+class ExternalKnowledgeHitTestingApi(Resource):
+    @setup_required
+    @login_required
+    @account_initialization_required
+    def post(self, dataset_id):
+        dataset_id_str = str(dataset_id)
+        dataset = DatasetService.get_dataset(dataset_id_str)
+        if dataset is None:
+            raise NotFound("Dataset not found.")
+
+        try:
+            DatasetService.check_dataset_permission(dataset, current_user)
+        except services.errors.account.NoPermissionError as e:
+            raise Forbidden(str(e))
+
+        parser = reqparse.RequestParser()
+        parser.add_argument("query", type=str, location="json")
+        parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
+        args = parser.parse_args()
+
+        HitTestingService.hit_testing_args_check(args)
+
+        try:
+            response = HitTestingService.external_retrieve(
+                dataset=dataset,
+                query=args["query"],
+                account=current_user,
+                external_retrieval_model=args["external_retrieval_model"],
+            )
+
+            return response
+        except Exception as e:
+            raise InternalServerError(str(e))
+
+
+api.add_resource(ExternalKnowledgeHitTestingApi, "/datasets/<uuid:dataset_id>/external-hit-testing")
+api.add_resource(ExternalDatasetCreateApi, "/datasets/external")
+api.add_resource(ExternalApiTemplateListApi, "/datasets/external-knowledge-api")
+api.add_resource(ExternalApiTemplateApi, "/datasets/external-knowledge-api/<uuid:external_knowledge_api_id>")
+api.add_resource(ExternalApiUseCheckApi, "/datasets/external-knowledge-api/<uuid:external_knowledge_api_id>/use-check")

api/controllers/console/datasets/hit_testing.py

@@ -47,6 +47,7 @@ class HitTestingApi(Resource):
         parser = reqparse.RequestParser()
         parser.add_argument("query", type=str, location="json")
         parser.add_argument("retrieval_model", type=dict, required=False, location="json")
+        parser.add_argument("external_retrieval_model", type=dict, required=False, location="json")
         args = parser.parse_args()
 
         HitTestingService.hit_testing_args_check(args)
@@ -57,6 +58,7 @@ class HitTestingApi(Resource):
                 query=args["query"],
                 account=current_user,
                 retrieval_model=args["retrieval_model"],
+                external_retrieval_model=args["external_retrieval_model"],
                 limit=10,
             )
 

api/controllers/console/datasets/test_external.py

@@ -0,0 +1,33 @@
+from flask_restful import Resource, reqparse
+
+from controllers.console import api
+from controllers.console.setup import setup_required
+from controllers.console.wraps import account_initialization_required
+from libs.login import login_required
+from services.external_knowledge_service import ExternalDatasetService
+
+
+class TestExternalApi(Resource):
+    def post(self):
+        parser = reqparse.RequestParser()
+        parser.add_argument("retrieval_setting", nullable=False, required=True, type=dict, location="json")
+        parser.add_argument(
+            "query",
+            nullable=False,
+            required=True,
+            type=str,
+        )
+        parser.add_argument(
+            "knowledge_id",
+            nullable=False,
+            required=True,
+            type=str,
+        )
+        args = parser.parse_args()
+        result = ExternalDatasetService.test_external_knowledge_retrieval(
+            args["retrieval_setting"], args["query"], args["knowledge_id"]
+        )
+        return result, 200
+
+
+api.add_resource(TestExternalApi, "/retrieval")

api/controllers/service_api/dataset/dataset.py

@@ -28,11 +28,11 @@ class DatasetListApi(DatasetApiResource):
 
         page = request.args.get("page", default=1, type=int)
         limit = request.args.get("limit", default=20, type=int)
-        provider = request.args.get("provider", default="vendor")
+        # provider = request.args.get("provider", default="vendor")
         search = request.args.get("keyword", default=None, type=str)
         tag_ids = request.args.getlist("tag_ids")
 
-        datasets, total = DatasetService.get_datasets(page, limit, provider, tenant_id, current_user, search, tag_ids)
+        datasets, total = DatasetService.get_datasets(page, limit, tenant_id, current_user, search, tag_ids)
         # check embedding setting
         provider_manager = ProviderManager()
         configurations = provider_manager.get_configurations(tenant_id=current_user.current_tenant_id)
@@ -82,6 +82,26 @@ class DatasetListApi(DatasetApiResource):
             required=False,
             nullable=False,
         )
+        parser.add_argument(
+            "external_knowledge_api_id",
+            type=str,
+            nullable=True,
+            required=False,
+            default="_validate_name",
+        )
+        parser.add_argument(
+            "provider",
+            type=str,
+            nullable=True,
+            required=False,
+            default="vendor",
+        )
+        parser.add_argument(
+            "external_knowledge_id",
+            type=str,
+            nullable=True,
+            required=False,
+        )
         args = parser.parse_args()
 
         try:
@@ -91,6 +111,9 @@ class DatasetListApi(DatasetApiResource):
                 indexing_technique=args["indexing_technique"],
                 account=current_user,
                 permission=args["permission"],
+                provider=args["provider"],
+                external_knowledge_api_id=args["external_knowledge_api_id"],
+                external_knowledge_id=args["external_knowledge_id"],
             )
         except services.errors.dataset.DatasetNameDuplicateError:
             raise DatasetNameDuplicateError()

api/core/app/apps/base_app_generate_response_converter.py

@@ -75,10 +75,10 @@ class AppGenerateResponseConverter(ABC):
         :return:
         """
         # show_retrieve_source
+        updated_resources = []
         if "retriever_resources" in metadata:
-            metadata["retriever_resources"] = []
             for resource in metadata["retriever_resources"]:
-                metadata["retriever_resources"].append(
+                updated_resources.append(
                     {
                         "segment_id": resource["segment_id"],
                         "position": resource["position"],
@@ -87,6 +87,7 @@ class AppGenerateResponseConverter(ABC):
                         "content": resource["content"],
                     }
                 )
+            metadata["retriever_resources"] = updated_resources
 
         # show annotation reply
         if "annotation_reply" in metadata:

api/core/app/apps/base_app_runner.py

@@ -309,7 +309,7 @@ class AppRunner:
             if not prompt_messages:
                 prompt_messages = result.prompt_messages
 
-            if not usage and result.delta.usage:
+            if result.delta.usage:
                 usage = result.delta.usage
 
         if not usage:

api/core/callback_handler/index_tool_callback_handler.py

@@ -59,7 +59,7 @@ class DatasetIndexToolCallbackHandler:
             for item in resource:
                 dataset_retriever_resource = DatasetRetrieverResource(
                     message_id=self._message_id,
-                    position=item.get("position"),
+                    position=item.get("position") or 0,
                     dataset_id=item.get("dataset_id"),
                     dataset_name=item.get("dataset_name"),
                     document_id=item.get("document_id"),

api/core/embedding/cached_embedding.py

@@ -5,6 +5,7 @@ from typing import Optional, cast
 import numpy as np
 from sqlalchemy.exc import IntegrityError
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
@@ -56,7 +57,9 @@ class CacheEmbedding(Embeddings):
                 for i in range(0, len(embedding_queue_texts), max_chunks):
                     batch_texts = embedding_queue_texts[i : i + max_chunks]
 
-                    embedding_result = self._model_instance.invoke_text_embedding(texts=batch_texts, user=self._user)
+                    embedding_result = self._model_instance.invoke_text_embedding(
+                        texts=batch_texts, user=self._user, input_type=EmbeddingInputType.DOCUMENT
+                    )
 
                     for vector in embedding_result.embeddings:
                         try:
@@ -100,7 +103,9 @@ class CacheEmbedding(Embeddings):
             redis_client.expire(embedding_cache_key, 600)
             return list(np.frombuffer(base64.b64decode(embedding), dtype="float"))
         try:
-            embedding_result = self._model_instance.invoke_text_embedding(texts=[text], user=self._user)
+            embedding_result = self._model_instance.invoke_text_embedding(
+                texts=[text], user=self._user, input_type=EmbeddingInputType.QUERY
+            )
 
             embedding_results = embedding_result.embeddings[0]
             embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()

api/core/embedding/embedding_constant.py

@@ -0,0 +1,10 @@
+from enum import Enum
+
+
+class EmbeddingInputType(Enum):
+    """
+    Enum for embedding input type.
+    """
+
+    DOCUMENT = "document"
+    QUERY = "query"

api/core/llm_generator/prompts.py

@@ -65,7 +65,6 @@ SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
     "Please help me predict the three most likely questions that human would ask, "
     "and keeping each question under 20 characters.\n"
     "MAKE SURE your output is the SAME language as the Assistant's latest response"
-    "(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n"
     "The output must be an array in JSON format following the specified schema:\n"
     '["question1","question2","question3"]\n'
 )

api/core/model_manager.py

@@ -3,6 +3,7 @@ import os
 from collections.abc import Callable, Generator, Sequence
 from typing import IO, Optional, Union, cast
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle
 from core.entities.provider_entities import ModelLoadBalancingConfiguration
 from core.errors.error import ProviderTokenNotInitError
@@ -158,12 +159,15 @@ class ModelInstance:
             tools=tools,
         )
 
-    def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) -> TextEmbeddingResult:
+    def invoke_text_embedding(
+        self, texts: list[str], user: Optional[str] = None, input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT
+    ) -> TextEmbeddingResult:
         """
         Invoke large language model
 
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         if not isinstance(self.model_type_instance, TextEmbeddingModel):
@@ -176,6 +180,7 @@ class ModelInstance:
             credentials=self.credentials,
             texts=texts,
             user=user,
+            input_type=input_type,
         )
 
     def get_text_embedding_num_tokens(self, texts: list[str]) -> int:

api/core/model_runtime/model_providers/__base/text_embedding_model.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from pydantic import ConfigDict
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
 from core.model_runtime.model_providers.__base.ai_model import AIModel
@@ -20,35 +21,47 @@ class TextEmbeddingModel(AIModel):
     model_config = ConfigDict(protected_namespaces=())
 
     def invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
-        Invoke large language model
+        Invoke text embedding model
 
         :param model: model name
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         self.started_at = time.perf_counter()
 
         try:
-            return self._invoke(model, credentials, texts, user)
+            return self._invoke(model, credentials, texts, user, input_type)
         except Exception as e:
             raise self._transform_invoke_error(e)
 
     @abstractmethod
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
-        Invoke large language model
+        Invoke text embedding model
 
         :param model: model name
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         raise NotImplementedError

api/core/model_runtime/model_providers/_position.yaml

@@ -38,3 +38,5 @@
 - perfxcloud
 - zhinao
 - fireworks
+- mixedbread
+- nomic

api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py

@@ -7,6 +7,7 @@ import numpy as np
 import tiktoken
 from openai import AzureOpenAI
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import AIModelEntity, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -17,8 +18,23 @@ from core.model_runtime.model_providers.azure_openai._constant import EMBEDDING_
 
 class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel):
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
         base_model_name = credentials["base_model_name"]
         credentials_kwargs = self._to_credential_kwargs(credentials)
         client = AzureOpenAI(**credentials_kwargs)

api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from requests import post
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -35,7 +36,12 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
     api_base: str = "http://api.baichuan-ai.com/v1/embeddings"
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -44,6 +50,7 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         api_key = credentials["api_key"]

api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py

@@ -13,6 +13,7 @@ from botocore.exceptions import (
     UnknownServiceError,
 )
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -30,7 +31,12 @@ logger = logging.getLogger(__name__)
 
 class BedrockTextEmbeddingModel(TextEmbeddingModel):
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -39,6 +45,7 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         client_config = Config(region_name=credentials["aws_region"])

api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py

@@ -5,6 +5,7 @@ import cohere
 import numpy as np
 from cohere.core import RequestOptions
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -25,7 +26,12 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -34,6 +40,7 @@ class CohereTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         # get model properties

api/core/model_runtime/model_providers/fireworks/fireworks.yaml

@@ -15,6 +15,7 @@ help:
     en_US: https://fireworks.ai/account/api-keys
 supported_model_types:
   - llm
+  - text-embedding
 configurate_methods:
   - predefined-model
 provider_credential_schema:

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-11b-vision-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-11b-vision-instruct
+label:
+  zh_Hans: Llama 3.2 11B Vision Instruct
+  en_US: Llama 3.2 11B Vision Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.2'
+  output: '0.2'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-1b-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-1b-instruct
+label:
+  zh_Hans: Llama 3.2 1B Instruct
+  en_US: Llama 3.2 1B Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.1'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-3b-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-3b-instruct
+label:
+  zh_Hans: Llama 3.2 3B Instruct
+  en_US: Llama 3.2 3B Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.1'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-90b-vision-instruct.yaml

@@ -0,0 +1,46 @@
+model: accounts/fireworks/models/llama-v3p2-90b-vision-instruct
+label:
+  zh_Hans: Llama 3.2 90B Vision Instruct
+  en_US: Llama 3.2 90B Vision Instruct
+model_type: llm
+features:
+  - agent-thought
+  - tool-call
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+  - name: max_tokens
+    use_template: max_tokens
+  - name: context_length_exceeded_behavior
+    default: None
+    label:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    help:
+      zh_Hans: 上下文长度超出行为
+      en_US: Context Length Exceeded Behavior
+    type: string
+    options:
+      - None
+      - truncate
+      - error
+  - name: response_format
+    use_template: response_format
+pricing:
+  input: '0.9'
+  output: '0.9'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/fireworks/text_embedding/UAE-Large-V1.yaml

@@ -0,0 +1,12 @@
+model: WhereIsAI/UAE-Large-V1
+label:
+  zh_Hans: UAE-Large-V1
+  en_US: UAE-Large-V1
+model_type: text-embedding
+model_properties:
+  context_size: 512
+  max_chunks: 1
+pricing:
+  input: '0.008'
+  unit: '0.000001'
+  currency: 'USD'

api/core/model_runtime/model_providers/fireworks/text_embedding/gte-base.yaml

@@ -0,0 +1,12 @@
+model: thenlper/gte-base
+label:
+  zh_Hans: GTE-base
+  en_US: GTE-base
+model_type: text-embedding
+model_properties:
+  context_size: 512
+  max_chunks: 1
+pricing:
+  input: '0.008'
+  unit: '0.000001'
+  currency: 'USD'

api/core/model_runtime/model_providers/fireworks/text_embedding/gte-large.yaml

@@ -0,0 +1,12 @@
+model: thenlper/gte-large
+label:
+  zh_Hans: GTE-large
+  en_US: GTE-large
+model_type: text-embedding
+model_properties:
+  context_size: 512
+  max_chunks: 1
+pricing:
+  input: '0.008'
+  unit: '0.000001'
+  currency: 'USD'

api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.5.yaml

@@ -0,0 +1,12 @@
+model: nomic-ai/nomic-embed-text-v1.5
+label:
+  zh_Hans: nomic-embed-text-v1.5
+  en_US: nomic-embed-text-v1.5
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+  max_chunks: 16
+pricing:
+  input: '0.008'
+  unit: '0.000001'
+  currency: 'USD'

api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.yaml

@@ -0,0 +1,12 @@
+model: nomic-ai/nomic-embed-text-v1
+label:
+  zh_Hans: nomic-embed-text-v1
+  en_US: nomic-embed-text-v1
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+  max_chunks: 16
+pricing:
+  input: '0.008'
+  unit: '0.000001'
+  currency: 'USD'

api/core/model_runtime/model_providers/fireworks/text_embedding/text_embedding.py

@@ -0,0 +1,151 @@
+import time
+from collections.abc import Mapping
+from typing import Optional, Union
+
+import numpy as np
+from openai import OpenAI
+
+from core.embedding.embedding_constant import EmbeddingInputType
+from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+from core.model_runtime.model_providers.fireworks._common import _CommonFireworks
+
+
+class FireworksTextEmbeddingModel(_CommonFireworks, TextEmbeddingModel):
+    """
+    Model class for Fireworks text embedding model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
+    ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
+
+        credentials_kwargs = self._to_credential_kwargs(credentials)
+        client = OpenAI(**credentials_kwargs)
+
+        extra_model_kwargs = {}
+        if user:
+            extra_model_kwargs["user"] = user
+
+        extra_model_kwargs["encoding_format"] = "float"
+
+        context_size = self._get_context_size(model, credentials)
+        max_chunks = self._get_max_chunks(model, credentials)
+
+        inputs = []
+        indices = []
+        used_tokens = 0
+
+        for i, text in enumerate(texts):
+            # Here token count is only an approximation based on the GPT2 tokenizer
+            # TODO: Optimize for better token estimation and chunking
+            num_tokens = self._get_num_tokens_by_gpt2(text)
+
+            if num_tokens >= context_size:
+                cutoff = int(np.floor(len(text) * (context_size / num_tokens)))
+                # if num tokens is larger than context length, only use the start
+                inputs.append(text[0:cutoff])
+            else:
+                inputs.append(text)
+            indices += [i]
+
+        batched_embeddings = []
+        _iter = range(0, len(inputs), max_chunks)
+
+        for i in _iter:
+            embeddings_batch, embedding_used_tokens = self._embedding_invoke(
+                model=model,
+                client=client,
+                texts=inputs[i : i + max_chunks],
+                extra_model_kwargs=extra_model_kwargs,
+            )
+            used_tokens += embedding_used_tokens
+            batched_embeddings += embeddings_batch
+
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens)
+        return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model)
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+
+    def validate_credentials(self, model: str, credentials: Mapping) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            # transform credentials to kwargs for model instance
+            credentials_kwargs = self._to_credential_kwargs(credentials)
+            client = OpenAI(**credentials_kwargs)
+
+            # call embedding model
+            self._embedding_invoke(model=model, client=client, texts=["ping"], extra_model_kwargs={})
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    def _embedding_invoke(
+        self, model: str, client: OpenAI, texts: Union[list[str], str], extra_model_kwargs: dict
+    ) -> tuple[list[list[float]], int]:
+        """
+        Invoke embedding model
+        :param model: model name
+        :param client: model client
+        :param texts: texts to embed
+        :param extra_model_kwargs: extra model kwargs
+        :return: embeddings and used tokens
+        """
+        response = client.embeddings.create(model=model, input=texts, **extra_model_kwargs)
+        return [data.embedding for data in response.data], response.usage.total_tokens
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: input tokens
+        :return: usage
+        """
+        input_price_info = self.get_price(
+            model=model, credentials=credentials, tokens=tokens, price_type=PriceType.INPUT
+        )
+
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at,
+        )
+
+        return usage

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash-001
+label:
+  en_US: Gemini 1.5 Flash 001
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash-002
+label:
+  en_US: Gemini 1.5 Flash 002
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash-8b-exp-0924
+label:
+  en_US: Gemini 1.5 Flash 8B 0924
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml

@@ -1,6 +1,6 @@
 model: gemini-1.5-flash-latest
 label:
-  en_US: Gemini 1.5 Flash
+  en_US: Gemini 1.5 Flash Latest
 model_type: llm
 features:
   - agent-thought
@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-flash
+label:
+  en_US: Gemini 1.5 Flash
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 1048576
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-pro-001
+label:
+  en_US: Gemini 1.5 Pro 001
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-pro-002
+label:
+  en_US: Gemini 1.5 Pro 002
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml

@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml

@@ -1,6 +1,6 @@
 model: gemini-1.5-pro-latest
 label:
-  en_US: Gemini 1.5 Pro
+  en_US: Gemini 1.5 Pro Latest
 model_type: llm
 features:
   - agent-thought
@@ -32,6 +32,15 @@ parameter_rules:
     max: 8192
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml

@@ -0,0 +1,48 @@
+model: gemini-1.5-pro
+label:
+  en_US: Gemini 1.5 Pro
+model_type: llm
+features:
+  - agent-thought
+  - vision
+  - tool-call
+  - stream-tool-call
+model_properties:
+  mode: chat
+  context_size: 2097152
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: top_k
+    label:
+      zh_Hans: 取样数量
+      en_US: Top k
+    type: int
+    help:
+      zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+      en_US: Only sample from the top K options for each subsequent token.
+    required: false
+  - name: max_tokens_to_sample
+    use_template: max_tokens
+    required: true
+    default: 8192
+    min: 1
+    max: 8192
+  - name: response_format
+    use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
+pricing:
+  input: '0.00'
+  output: '0.00'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml

@@ -27,6 +27,15 @@ parameter_rules:
     default: 4096
     min: 1
     max: 4096
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml

@@ -31,6 +31,15 @@ parameter_rules:
     max: 2048
   - name: response_format
     use_template: response_format
+  - name: stream
+    label:
+      zh_Hans: 流式输出
+      en_US: Stream
+    type: boolean
+    help:
+      zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。
+      en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once.
+    default: false
 pricing:
   input: '0.00'
   output: '0.00'

api/core/model_runtime/model_providers/google/llm/llm.py

@@ -9,8 +9,8 @@ import google.ai.generativelanguage as glm
 import google.generativeai as genai
 import requests
 from google.api_core import exceptions
-from google.generativeai import client
-from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory
+from google.generativeai.client import _ClientManager
+from google.generativeai.types import ContentType, GenerateContentResponse
 from google.generativeai.types.content_types import to_part
 from PIL import Image
 
@@ -200,24 +200,16 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
                     history.append(content)
 
         # Create a new ClientManager with tenant's API key
-        new_client_manager = client._ClientManager()
+        new_client_manager = _ClientManager()
         new_client_manager.configure(api_key=credentials["google_api_key"])
         new_custom_client = new_client_manager.make_client("generative")
 
         google_model._client = new_custom_client
 
-        safety_settings = {
-            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
-        }
-
         response = google_model.generate_content(
             contents=history,
             generation_config=genai.types.GenerationConfig(**config_kwargs),
             stream=stream,
-            safety_settings=safety_settings,
             tools=self._convert_tools_to_glm_tool(tools) if tools else None,
             request_options={"timeout": 600},
         )

api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-11b-text-preview
+label:
+  zh_Hans: Llama 3.2 11B Text (Preview)
+  en_US: Llama 3.2 11B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-1b-preview
+label:
+  zh_Hans: Llama 3.2 1B Text (Preview)
+  en_US: Llama 3.2 1B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama-3.2-3b-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-3b-preview
+label:
+  zh_Hans: Llama 3.2 3B Text (Preview)
+  en_US: Llama 3.2 3B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/groq/llm/llama-3.2-90b-text-preview.yaml

@@ -0,0 +1,25 @@
+model: llama-3.2-90b-text-preview
+label:
+  zh_Hans: Llama 3.2 90B Text (Preview)
+  en_US: Llama 3.2 90B Text (Preview)
+model_type: llm
+features:
+  - agent-thought
+model_properties:
+  mode: chat
+  context_size: 131072
+parameter_rules:
+  - name: temperature
+    use_template: temperature
+  - name: top_p
+    use_template: top_p
+  - name: max_tokens
+    use_template: max_tokens
+    default: 512
+    min: 1
+    max: 8192
+pricing:
+  input: '0.05'
+  output: '0.1'
+  unit: '0.000001'
+  currency: USD

api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py

@@ -6,6 +6,7 @@ import numpy as np
 import requests
 from huggingface_hub import HfApi, InferenceClient
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@@ -18,8 +19,23 @@ HUGGINGFACE_ENDPOINT_API = "https://api.endpoints.huggingface.cloud/v2/endpoint/
 
 class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel):
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
         client = InferenceClient(token=credentials["huggingfacehub_api_token"])
 
         execute_model = model

api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py

@@ -1,6 +1,7 @@
 import time
 from typing import Optional
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@@ -23,7 +24,12 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -38,6 +44,7 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         server_url = credentials["server_url"]

api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py

@@ -9,6 +9,7 @@ from tencentcloud.common.profile.client_profile import ClientProfile
 from tencentcloud.common.profile.http_profile import HttpProfile
 from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -26,7 +27,12 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -35,6 +41,7 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
 

api/core/model_runtime/model_providers/jina/jina.yaml

@@ -1,6 +1,6 @@
 provider: jina
 label:
-  en_US: Jina
+  en_US: Jina AI
 description:
   en_US: Embedding and Rerank Model Supported
 icon_small:
@@ -11,7 +11,7 @@ background: "#EFFDFD"
 help:
   title:
     en_US: Get your API key from Jina AI
-    zh_Hans: 从 Jina 获取 API Key
+    zh_Hans: 从 Jina AI 获取 API Key
   url:
     en_US: https://jina.ai/
 supported_model_types:

api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from requests import post
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@@ -27,8 +28,37 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
 
     api_base: str = "https://api.jina.ai/v1"
 
+    def _to_payload(self, model: str, texts: list[str], credentials: dict, input_type: EmbeddingInputType) -> dict:
+        """
+        Parse model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return: parsed credentials
+        """
+
+        def transform_jina_input_text(model, text):
+            if model == "jina-clip-v1":
+                return {"text": text}
+            return text
+
+        data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]}
+
+        # model specific parameters
+        if model == "jina-embeddings-v3":
+            # set `task` type according to input type for the best performance
+            data["task"] = "retrieval.query" if input_type == EmbeddingInputType.QUERY else "retrieval.passage"
+
+        return data
+
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -37,6 +67,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         api_key = credentials["api_key"]
@@ -49,15 +80,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
         url = base_url + "/embeddings"
         headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
 
-        def transform_jina_input_text(model, text):
-            if model == "jina-clip-v1":
-                return {"text": text}
-            return text
-
-        data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]}
-
-        if model == "jina-embeddings-v3":
-            data["task"] = "text-matching"
+        data = self._to_payload(model=model, texts=texts, credentials=credentials, input_type=input_type)
 
         try:
             response = post(url, headers=headers, data=dumps(data))

api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py

@@ -5,6 +5,7 @@ from typing import Optional
 from requests import post
 from yarl import URL
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@@ -22,11 +23,16 @@ from core.model_runtime.model_providers.__base.text_embedding_model import TextE
 
 class LocalAITextEmbeddingModel(TextEmbeddingModel):
     """
-    Model class for Jina text embedding model.
+    Model class for LocalAI text embedding model.
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -35,6 +41,7 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         if len(texts) != 1:

api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from requests import post
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -34,7 +35,12 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
     api_base: str = "https://api.minimax.chat/v1/embeddings"
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -43,6 +49,7 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         api_key = credentials["minimax_api_key"]

api/core/model_runtime/model_providers/mixedbread/mixedbread.py

@@ -0,0 +1,27 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class MixedBreadProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING)
+
+            # Use `mxbai-embed-large-v1` model for validate,
+            model_instance.validate_credentials(model="mxbai-embed-large-v1", credentials=credentials)
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex

api/core/model_runtime/model_providers/mixedbread/mixedbread.yaml

@@ -0,0 +1,31 @@
+provider: mixedbread
+label:
+  en_US: MixedBread
+description:
+  en_US: Embedding and Rerank Model Supported
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.png
+background: "#EFFDFD"
+help:
+  title:
+    en_US: Get your API key from MixedBread AI
+    zh_Hans: 从 MixedBread 获取 API Key
+  url:
+    en_US: https://www.mixedbread.ai/
+supported_model_types:
+  - text-embedding
+  - rerank
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/mixedbread/rerank/mxbai-rerank-large-v1-en.yaml

@@ -0,0 +1,4 @@
+model: mxbai-rerank-large-v1
+model_type: rerank
+model_properties:
+  context_size: 512

api/core/model_runtime/model_providers/mixedbread/rerank/rerank.py

@@ -0,0 +1,125 @@
+from typing import Optional
+
+import httpx
+
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType
+from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.rerank_model import RerankModel
+
+
+class MixedBreadRerankModel(RerankModel):
+    """
+    Model class for MixedBread rerank model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        query: str,
+        docs: list[str],
+        score_threshold: Optional[float] = None,
+        top_n: Optional[int] = None,
+        user: Optional[str] = None,
+    ) -> RerankResult:
+        """
+        Invoke rerank model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param query: search query
+        :param docs: docs for reranking
+        :param score_threshold: score threshold
+        :param top_n: top n documents to return
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=[])
+
+        base_url = credentials.get("base_url", "https://api.mixedbread.ai/v1")
+        base_url = base_url.removesuffix("/")
+
+        try:
+            response = httpx.post(
+                base_url + "/reranking",
+                json={"model": model, "query": query, "input": docs, "top_k": top_n, "return_input": True},
+                headers={"Authorization": f"Bearer {credentials.get('api_key')}", "Content-Type": "application/json"},
+            )
+            response.raise_for_status()
+            results = response.json()
+
+            rerank_documents = []
+            for result in results["data"]:
+                rerank_document = RerankDocument(
+                    index=result["index"],
+                    text=result["input"],
+                    score=result["score"],
+                )
+                if score_threshold is None or result["score"] >= score_threshold:
+                    rerank_documents.append(rerank_document)
+
+            return RerankResult(model=model, docs=rerank_documents)
+        except httpx.HTTPStatusError as e:
+            raise InvokeServerUnavailableError(str(e))
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(
+                model=model,
+                credentials=credentials,
+                query="What is the capital of the United States?",
+                docs=[
+                    "Carson City is the capital city of the American state of Nevada. At the 2010 United States "
+                    "Census, Carson City had a population of 55,274.",
+                    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that "
+                    "are a political division controlled by the United States. Its capital is Saipan.",
+                ],
+                score_threshold=0.8,
+            )
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        """
+        return {
+            InvokeConnectionError: [httpx.ConnectError],
+            InvokeServerUnavailableError: [httpx.RemoteProtocolError],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [httpx.HTTPStatusError],
+            InvokeBadRequestError: [httpx.RequestError],
+        }
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+        generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.RERANK,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "512"))},
+        )
+
+        return entity

api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-2d-large-v1-en.yaml

@@ -0,0 +1,8 @@
+model: mxbai-embed-2d-large-v1
+model_type: text-embedding
+model_properties:
+  context_size: 512
+pricing:
+  input: '0.0001'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-large-v1-en.yaml

@@ -0,0 +1,8 @@
+model: mxbai-embed-large-v1
+model_type: text-embedding
+model_properties:
+  context_size: 512
+pricing:
+  input: '0.0001'
+  unit: '0.001'
+  currency: USD

api/core/model_runtime/model_providers/mixedbread/text_embedding/text_embedding.py

@@ -0,0 +1,170 @@
+import time
+from json import JSONDecodeError, dumps
+from typing import Optional
+
+import requests
+
+from core.embedding.embedding_constant import EmbeddingInputType
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+
+
+class MixedBreadTextEmbeddingModel(TextEmbeddingModel):
+    """
+    Model class for MixedBread text embedding model.
+    """
+
+    api_base: str = "https://api.mixedbread.ai/v1"
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
+    ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
+        api_key = credentials["api_key"]
+        if not api_key:
+            raise CredentialsValidateFailedError("api_key is required")
+
+        base_url = credentials.get("base_url", self.api_base)
+        base_url = base_url.removesuffix("/")
+
+        url = base_url + "/embeddings"
+        headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"}
+
+        data = {"model": model, "input": texts}
+
+        try:
+            response = requests.post(url, headers=headers, data=dumps(data))
+        except Exception as e:
+            raise InvokeConnectionError(str(e))
+
+        if response.status_code != 200:
+            try:
+                resp = response.json()
+                msg = resp["detail"]
+                if response.status_code == 401:
+                    raise InvokeAuthorizationError(msg)
+                elif response.status_code == 429:
+                    raise InvokeRateLimitError(msg)
+                elif response.status_code == 500:
+                    raise InvokeServerUnavailableError(msg)
+                else:
+                    raise InvokeBadRequestError(msg)
+            except JSONDecodeError as e:
+                raise InvokeServerUnavailableError(
+                    f"Failed to convert response to json: {e} with text: {response.text}"
+                )
+
+        try:
+            resp = response.json()
+            embeddings = resp["data"]
+            usage = resp["usage"]
+        except Exception as e:
+            raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}")
+
+        usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"])
+
+        result = TextEmbeddingResult(
+            model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage
+        )
+
+        return result
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            self._invoke(model=model, credentials=credentials, texts=["ping"])
+        except Exception as e:
+            raise CredentialsValidateFailedError(f"Credentials validation failed: {e}")
+
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        return {
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError, InvokeBadRequestError],
+        }
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: input tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at,
+        )
+
+        return usage
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity:
+        """
+        generate custom model entities from credentials
+        """
+        entity = AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model),
+            model_type=ModelType.TEXT_EMBEDDING,
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "512"))},
+        )
+
+        return entity

api/core/model_runtime/model_providers/nomic/_assets/icon_l_en.svg

@@ -0,0 +1,13 @@
+<svg width="93" height="31" viewBox="0 0 93 31" fill="none" xmlns="http://www.w3.org/2000/svg">
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+</svg>

api/core/model_runtime/model_providers/nomic/_common.py

@@ -0,0 +1,28 @@
+from core.model_runtime.errors.invoke import (
+    InvokeAuthorizationError,
+    InvokeBadRequestError,
+    InvokeConnectionError,
+    InvokeError,
+    InvokeRateLimitError,
+    InvokeServerUnavailableError,
+)
+
+
+class _CommonNomic:
+    @property
+    def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+        """
+        Map model invoke error to unified error
+        The key is the error type thrown to the caller
+        The value is the error type thrown by the model,
+        which needs to be converted into a unified error type for the caller.
+
+        :return: Invoke error mapping
+        """
+        return {
+            InvokeConnectionError: [InvokeConnectionError],
+            InvokeServerUnavailableError: [InvokeServerUnavailableError],
+            InvokeRateLimitError: [InvokeRateLimitError],
+            InvokeAuthorizationError: [InvokeAuthorizationError],
+            InvokeBadRequestError: [KeyError, InvokeBadRequestError],
+        }

api/core/model_runtime/model_providers/nomic/nomic.py

@@ -0,0 +1,26 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class NomicAtlasProvider(ModelProvider):
+    def validate_provider_credentials(self, credentials: dict) -> None:
+        """
+        Validate provider credentials
+
+        if validate failed, raise exception
+
+        :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+        """
+        try:
+            model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING)
+            model_instance.validate_credentials(model="nomic-embed-text-v1.5", credentials=credentials)
+        except CredentialsValidateFailedError as ex:
+            raise ex
+        except Exception as ex:
+            logger.exception(f"{self.get_provider_schema().provider} credentials validate failed")
+            raise ex

api/core/model_runtime/model_providers/nomic/nomic.yaml

@@ -0,0 +1,29 @@
+provider: nomic
+label:
+  zh_Hans: Nomic Atlas
+  en_US: Nomic Atlas
+icon_small:
+  en_US: icon_s_en.png
+icon_large:
+  en_US: icon_l_en.svg
+background: "#EFF1FE"
+help:
+  title:
+    en_US: Get your API key from Nomic Atlas
+    zh_Hans: 从Nomic Atlas获取 API Key
+  url:
+    en_US: https://atlas.nomic.ai/data
+supported_model_types:
+  - text-embedding
+configurate_methods:
+  - predefined-model
+provider_credential_schema:
+  credential_form_schemas:
+    - variable: nomic_api_key
+      label:
+        en_US: API Key
+      type: secret-input
+      required: true
+      placeholder:
+        zh_Hans: 在此输入您的 API Key
+        en_US: Enter your API Key

api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.5.yaml

@@ -0,0 +1,8 @@
+model: nomic-embed-text-v1.5
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+pricing:
+  input: "0.1"
+  unit: "0.000001"
+  currency: USD

api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.yaml

@@ -0,0 +1,8 @@
+model: nomic-embed-text-v1
+model_type: text-embedding
+model_properties:
+  context_size: 8192
+pricing:
+  input: "0.1"
+  unit: "0.000001"
+  currency: USD

api/core/model_runtime/model_providers/nomic/text_embedding/text_embedding.py

@@ -0,0 +1,165 @@
+import time
+from functools import wraps
+from typing import Optional
+
+from nomic import embed
+from nomic import login as nomic_login
+
+from core.embedding.embedding_constant import EmbeddingInputType
+from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.text_embedding_entities import (
+    EmbeddingUsage,
+    TextEmbeddingResult,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import (
+    TextEmbeddingModel,
+)
+from core.model_runtime.model_providers.nomic._common import _CommonNomic
+
+
+def nomic_login_required(func):
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        try:
+            if not kwargs.get("credentials"):
+                raise ValueError("missing credentials parameters")
+            credentials = kwargs.get("credentials")
+            if "nomic_api_key" not in credentials:
+                raise ValueError("missing nomic_api_key in credentials parameters")
+            # nomic login
+            nomic_login(credentials["nomic_api_key"])
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+class NomicTextEmbeddingModel(_CommonNomic, TextEmbeddingModel):
+    """
+    Model class for nomic text embedding model.
+    """
+
+    def _invoke(
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
+    ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
+        embeddings, prompt_tokens, total_tokens = self.embed_text(
+            model=model,
+            credentials=credentials,
+            texts=texts,
+        )
+
+        # calc usage
+        usage = self._calc_response_usage(
+            model=model, credentials=credentials, tokens=prompt_tokens, total_tokens=total_tokens
+        )
+        return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model)
+
+    def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+        """
+        Get number of tokens for given prompt messages
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :return:
+        """
+        return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+
+    def validate_credentials(self, model: str, credentials: dict) -> None:
+        """
+        Validate model credentials
+
+        :param model: model name
+        :param credentials: model credentials
+        :return:
+        """
+        try:
+            # call embedding model
+            self.embed_text(model=model, credentials=credentials, texts=["ping"])
+        except Exception as ex:
+            raise CredentialsValidateFailedError(str(ex))
+
+    @nomic_login_required
+    def embed_text(self, model: str, credentials: dict, texts: list[str]) -> tuple[list[list[float]], int, int]:
+        """Call out to Nomic's embedding endpoint.
+
+        Args:
+            model: The model to use for embedding.
+            texts: The list of texts to embed.
+
+        Returns:
+            List of embeddings, one for each text, and tokens usage.
+        """
+        embeddings: list[list[float]] = []
+        prompt_tokens = 0
+        total_tokens = 0
+
+        response = embed.text(
+            model=model,
+            texts=texts,
+        )
+
+        if not (response and "embeddings" in response):
+            raise ValueError("Embedding data is missing in the response.")
+
+        if not (response and "usage" in response):
+            raise ValueError("Response usage is missing.")
+
+        if "prompt_tokens" not in response["usage"]:
+            raise ValueError("Response usage does not contain prompt tokens.")
+
+        if "total_tokens" not in response["usage"]:
+            raise ValueError("Response usage does not contain total tokens.")
+
+        embeddings = [list(map(float, e)) for e in response["embeddings"]]
+        total_tokens = response["usage"]["total_tokens"]
+        prompt_tokens = response["usage"]["prompt_tokens"]
+        return embeddings, prompt_tokens, total_tokens
+
+    def _calc_response_usage(self, model: str, credentials: dict, tokens: int, total_tokens: int) -> EmbeddingUsage:
+        """
+        Calculate response usage
+
+        :param model: model name
+        :param credentials: model credentials
+        :param tokens: prompt tokens
+        :param total_tokens: total tokens
+        :return: usage
+        """
+        # get input price info
+        input_price_info = self.get_price(
+            model=model,
+            credentials=credentials,
+            price_type=PriceType.INPUT,
+            tokens=tokens,
+        )
+
+        # transform usage
+        usage = EmbeddingUsage(
+            tokens=tokens,
+            total_tokens=total_tokens,
+            unit_price=input_price_info.unit_price,
+            price_unit=input_price_info.unit,
+            total_price=input_price_info.total_amount,
+            currency=input_price_info.currency,
+            latency=time.perf_counter() - self.started_at,
+        )
+
+        return usage

api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from requests import post
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -27,7 +28,12 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
     models: list[str] = ["NV-Embed-QA"]
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -36,6 +42,7 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         api_key = credentials["api_key"]

api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py

@@ -6,6 +6,7 @@ from typing import Optional
 import numpy as np
 import oci
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -41,7 +42,12 @@ class OCITextEmbeddingModel(TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -50,6 +56,7 @@ class OCITextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         # get model properties

api/core/model_runtime/model_providers/ollama/llm/llm.py

@@ -364,14 +364,21 @@ class OllamaLargeLanguageModel(LargeLanguageModel):
 
             if chunk_json["done"]:
                 # calculate num tokens
-                if "prompt_eval_count" in chunk_json and "eval_count" in chunk_json:
-                    # transform usage
+                if "prompt_eval_count" in chunk_json:
                     prompt_tokens = chunk_json["prompt_eval_count"]
-                    completion_tokens = chunk_json["eval_count"]
                 else:
-                    # calculate num tokens
-                    prompt_tokens = self._get_num_tokens_by_gpt2(prompt_messages[0].content)
-                    completion_tokens = self._get_num_tokens_by_gpt2(full_text)
+                    prompt_message_content = prompt_messages[0].content
+                    if isinstance(prompt_message_content, str):
+                        prompt_tokens = self._get_num_tokens_by_gpt2(prompt_message_content)
+                    else:
+                        content_text = ""
+                        for message_content in prompt_message_content:
+                            if message_content.type == PromptMessageContentType.TEXT:
+                                message_content = cast(TextPromptMessageContent, message_content)
+                                content_text += message_content.data
+                        prompt_tokens = self._get_num_tokens_by_gpt2(content_text)
+
+                completion_tokens = chunk_json.get("eval_count", self._get_num_tokens_by_gpt2(full_text))
 
                 # transform usage
                 usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)

api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py

@@ -8,6 +8,7 @@ from urllib.parse import urljoin
 import numpy as np
 import requests
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import (
     AIModelEntity,
@@ -38,7 +39,12 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -47,6 +53,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
 

api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py

@@ -6,6 +6,7 @@ import numpy as np
 import tiktoken
 from openai import OpenAI
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.validate import CredentialsValidateFailedError
@@ -19,7 +20,12 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -28,6 +34,7 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         # transform credentials to kwargs for model instance

api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py

@@ -7,6 +7,7 @@ from urllib.parse import urljoin
 import numpy as np
 import requests
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import (
     AIModelEntity,
@@ -28,7 +29,12 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -37,6 +43,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
 

api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py

@@ -5,6 +5,7 @@ from typing import Optional
 from requests import post
 from requests.exceptions import ConnectionError, InvalidSchema, MissingSchema
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.model_entities import PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
@@ -25,7 +26,12 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -34,6 +40,7 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         server_url = credentials["server_url"]

api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py

@@ -7,6 +7,7 @@ from urllib.parse import urljoin
 import numpy as np
 import requests
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import (
     AIModelEntity,
@@ -28,7 +29,12 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
     """
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -37,6 +43,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
 

api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py

@@ -4,6 +4,7 @@ from typing import Optional
 
 from replicate import Client as ReplicateClient
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@@ -14,8 +15,23 @@ from core.model_runtime.model_providers.replicate._common import _CommonReplicat
 
 class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel):
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
+        """
+        Invoke text embedding model
+
+        :param model: model name
+        :param credentials: model credentials
+        :param texts: texts to embed
+        :param user: unique user id
+        :param input_type: input type
+        :return: embeddings result
+        """
         client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30)
 
         if "model_version" in credentials:

api/core/model_runtime/model_providers/sagemaker/llm/llm.py

@@ -84,9 +84,9 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
     Model class for Cohere large language model.
     """
 
-    sagemaker_client: Any = None
-    sagemaker_sess: Any = None
+    sagemaker_session: Any = None
     predictor: Any = None
+    sagemaker_endpoint: str = None
 
     def _handle_chat_generate_response(
         self,
@@ -212,27 +212,29 @@ class SageMakerLargeLanguageModel(LargeLanguageModel):
         :param user: unique user id
         :return: full response or stream response chunk generator result
         """
-        if not self.sagemaker_client:
-            access_key = credentials.get("access_key")
-            secret_key = credentials.get("secret_key")
+        if not self.sagemaker_session:
+            access_key = credentials.get("aws_access_key_id")
+            secret_key = credentials.get("aws_secret_access_key")
             aws_region = credentials.get("aws_region")
+            boto_session = None
             if aws_region:
                 if access_key and secret_key:
-                    self.sagemaker_client = boto3.client(
-                        "sagemaker-runtime",
-                        aws_access_key_id=access_key,
-                        aws_secret_access_key=secret_key,
-                        region_name=aws_region,
+                    boto_session = boto3.Session(
+                        aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=aws_region
                     )
                 else:
-                    self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region)
+                    boto_session = boto3.Session(region_name=aws_region)
             else:
-                self.sagemaker_client = boto3.client("sagemaker-runtime")
+                boto_session = boto3.Session()
 
-            sagemaker_session = Session(sagemaker_runtime_client=self.sagemaker_client)
+            sagemaker_client = boto_session.client("sagemaker")
+            self.sagemaker_session = Session(boto_session=boto_session, sagemaker_client=sagemaker_client)
+
+        if self.sagemaker_endpoint != credentials.get("sagemaker_endpoint"):
+            self.sagemaker_endpoint = credentials.get("sagemaker_endpoint")
             self.predictor = Predictor(
-                endpoint_name=credentials.get("sagemaker_endpoint"),
-                sagemaker_session=sagemaker_session,
+                endpoint_name=self.sagemaker_endpoint,
+                sagemaker_session=self.sagemaker_session,
                 serializer=serializers.JSONSerializer(),
             )
 

api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py

@@ -6,6 +6,7 @@ from typing import Any, Optional
 
 import boto3
 
+from core.embedding.embedding_constant import EmbeddingInputType
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
@@ -53,7 +54,12 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
         return embeddings
 
     def _invoke(
-        self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None
+        self,
+        model: str,
+        credentials: dict,
+        texts: list[str],
+        user: Optional[str] = None,
+        input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT,
     ) -> TextEmbeddingResult:
         """
         Invoke text embedding model
@@ -62,6 +68,7 @@ class SageMakerEmbeddingModel(TextEmbeddingModel):
         :param credentials: model credentials
         :param texts: texts to embed
         :param user: unique user id
+        :param input_type: input type
         :return: embeddings result
         """
         # get model properties

api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml

@@ -1,25 +1,38 @@
-- Qwen/Qwen2.5-7B-Instruct
-- Qwen/Qwen2.5-14B-Instruct
-- Qwen/Qwen2.5-32B-Instruct
 - Qwen/Qwen2.5-72B-Instruct
+- Qwen/Qwen2.5-Math-72B-Instruct
+- Qwen/Qwen2.5-32B-Instruct
+- Qwen/Qwen2.5-14B-Instruct
+- Qwen/Qwen2.5-7B-Instruct
+- Qwen/Qwen2.5-Coder-7B-Instruct
+- deepseek-ai/DeepSeek-V2.5
 - Qwen/Qwen2-72B-Instruct
 - Qwen/Qwen2-57B-A14B-Instruct
 - Qwen/Qwen2-7B-Instruct
 - Qwen/Qwen2-1.5B-Instruct
-- 01-ai/Yi-1.5-34B-Chat
-- 01-ai/Yi-1.5-9B-Chat-16K
-- 01-ai/Yi-1.5-6B-Chat
-- THUDM/glm-4-9b-chat
-- deepseek-ai/DeepSeek-V2.5
 - deepseek-ai/DeepSeek-V2-Chat
 - deepseek-ai/DeepSeek-Coder-V2-Instruct
+- THUDM/glm-4-9b-chat
+- THUDM/chatglm3-6b
+- 01-ai/Yi-1.5-34B-Chat-16K
+- 01-ai/Yi-1.5-9B-Chat-16K
+- 01-ai/Yi-1.5-6B-Chat
+- internlm/internlm2_5-20b-chat
 - internlm/internlm2_5-7b-chat
-- google/gemma-2-27b-it
-- google/gemma-2-9b-it
-- meta-llama/Meta-Llama-3-70B-Instruct
-- meta-llama/Meta-Llama-3-8B-Instruct
 - meta-llama/Meta-Llama-3.1-405B-Instruct
 - meta-llama/Meta-Llama-3.1-70B-Instruct
 - meta-llama/Meta-Llama-3.1-8B-Instruct
-- mistralai/Mixtral-8x7B-Instruct-v0.1
+- meta-llama/Meta-Llama-3-70B-Instruct
+- meta-llama/Meta-Llama-3-8B-Instruct
+- google/gemma-2-27b-it
+- google/gemma-2-9b-it
 - mistralai/Mistral-7B-Instruct-v0.2
+- Pro/Qwen/Qwen2-7B-Instruct
+- Pro/Qwen/Qwen2-1.5B-Instruct
+- Pro/THUDM/glm-4-9b-chat
+- Pro/THUDM/chatglm3-6b
+- Pro/01-ai/Yi-1.5-9B-Chat-16K
+- Pro/01-ai/Yi-1.5-6B-Chat
+- Pro/internlm/internlm2_5-7b-chat
+- Pro/meta-llama/Meta-Llama-3.1-8B-Instruct
+- Pro/meta-llama/Meta-Llama-3-8B-Instruct
+- Pro/google/gemma-2-9b-it

api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml

@@ -28,3 +28,4 @@ pricing:
   output: '0'
   unit: '0.000001'
   currency: RMB
+deprecated: true