Merge branch 'feat/new-account-page' into deploy/dev

api/controllers/console/workspace/model_providers.py

@@ -125,6 +125,10 @@ class ModelProviderIconApi(Resource):
     """
     Get model provider icon
     """
+
+    @setup_required
+    @login_required
+    @account_initialization_required
     def get(self, provider: str, icon_type: str, lang: str):
         model_provider_service = ModelProviderService()
         icon, mimetype = model_provider_service.get_model_provider_icon(

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

@@ -1,8 +1,18 @@
 from collections.abc import Generator
 from typing import Optional, Union
 
-from core.model_runtime.entities.llm_entities import LLMResult
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.llm_entities import LLMMode, LLMResult
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
+from core.model_runtime.entities.model_entities import (
+    AIModelEntity,
+    FetchFrom,
+    ModelFeature,
+    ModelPropertyKey,
+    ModelType,
+    ParameterRule,
+    ParameterType,
+)
 from core.model_runtime.model_providers.openai_api_compatible.llm.llm import OAIAPICompatLargeLanguageModel
 
 
@@ -29,3 +39,53 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
     def _add_custom_parameters(cls, credentials: dict) -> None:
         credentials["mode"] = "chat"
         credentials["endpoint_url"] = "https://api.siliconflow.cn/v1"
+
+    def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+        return AIModelEntity(
+            model=model,
+            label=I18nObject(en_US=model, zh_Hans=model),
+            model_type=ModelType.LLM,
+            features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL]
+            if credentials.get("function_calling_type") == "tool_call"
+            else [],
+            fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+            model_properties={
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000)),
+                ModelPropertyKey.MODE: LLMMode.CHAT.value,
+            },
+            parameter_rules=[
+                ParameterRule(
+                    name="temperature",
+                    use_template="temperature",
+                    label=I18nObject(en_US="Temperature", zh_Hans="温度"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="max_tokens",
+                    use_template="max_tokens",
+                    default=512,
+                    min=1,
+                    max=int(credentials.get("max_tokens", 1024)),
+                    label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"),
+                    type=ParameterType.INT,
+                ),
+                ParameterRule(
+                    name="top_p",
+                    use_template="top_p",
+                    label=I18nObject(en_US="Top P", zh_Hans="Top P"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="top_k",
+                    use_template="top_k",
+                    label=I18nObject(en_US="Top K", zh_Hans="Top K"),
+                    type=ParameterType.FLOAT,
+                ),
+                ParameterRule(
+                    name="frequency_penalty",
+                    use_template="frequency_penalty",
+                    label=I18nObject(en_US="Frequency Penalty", zh_Hans="重复惩罚"),
+                    type=ParameterType.FLOAT,
+                ),
+            ],
+        )

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

@@ -20,6 +20,7 @@ supported_model_types:
   - speech2text
 configurate_methods:
   - predefined-model
+  - customizable-model
 provider_credential_schema:
   credential_form_schemas:
     - variable: api_key
@@ -30,3 +31,57 @@ provider_credential_schema:
       placeholder:
         zh_Hans: 在此输入您的 API Key
         en_US: Enter your API Key
+model_credential_schema:
+  model:
+    label:
+      en_US: Model Name
+      zh_Hans: 模型名称
+    placeholder:
+      en_US: Enter your model name
+      zh_Hans: 输入模型名称
+  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
+    - variable: context_size
+      label:
+        zh_Hans: 模型上下文长度
+        en_US: Model context size
+      required: true
+      type: text-input
+      default: '4096'
+      placeholder:
+        zh_Hans: 在此输入您的模型上下文长度
+        en_US: Enter your Model context size
+    - variable: max_tokens
+      label:
+        zh_Hans: 最大 token 上限
+        en_US: Upper bound for max tokens
+      default: '4096'
+      type: text-input
+      show_on:
+        - variable: __model_type
+          value: llm
+    - variable: function_calling_type
+      label:
+        en_US: Function calling
+      type: select
+      required: false
+      default: no_call
+      options:
+        - value: no_call
+          label:
+            en_US: Not Support
+            zh_Hans: 不支持
+        - value: function_call
+          label:
+            en_US: Support
+            zh_Hans: 支持
+      show_on:
+        - variable: __model_type
+          value: llm

api/core/model_runtime/model_providers/tongyi/rerank/_position.yaml

@@ -0,0 +1 @@
+- gte-rerank

api/core/model_runtime/model_providers/tongyi/rerank/gte-rerank.yaml

@@ -0,0 +1,4 @@
+model: gte-rerank
+model_type: rerank
+model_properties:
+  context_size: 4000

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

@@ -0,0 +1,136 @@
+from typing import Optional
+
+import dashscope
+from dashscope.common.error import (
+    AuthenticationError,
+    InvalidParameter,
+    RequestFailure,
+    ServiceUnavailableError,
+    UnsupportedHTTPMethod,
+    UnsupportedModel,
+)
+
+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 GTERerankModel(RerankModel):
+    """
+    Model class for GTE 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
+        :param user: unique user id
+        :return: rerank result
+        """
+        if len(docs) == 0:
+            return RerankResult(model=model, docs=docs)
+
+        # initialize client
+        dashscope.api_key = credentials["dashscope_api_key"]
+
+        response = dashscope.TextReRank.call(
+            query=query,
+            documents=docs,
+            model=model,
+            top_n=top_n,
+            return_documents=True,
+        )
+
+        rerank_documents = []
+        for _, result in enumerate(response.output.results):
+            # format document
+            rerank_document = RerankDocument(
+                index=result.index,
+                score=result.relevance_score,
+                text=result["document"]["text"],
+            )
+
+            # score threshold check
+            if score_threshold is not None:
+                if result.relevance_score >= score_threshold:
+                    rerank_documents.append(rerank_document)
+            else:
+                rerank_documents.append(rerank_document)
+
+        return RerankResult(model=model, docs=rerank_documents)
+
+    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:
+            print(ex)
+            raise CredentialsValidateFailedError(str(ex))
+
+    @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: [
+                RequestFailure,
+            ],
+            InvokeServerUnavailableError: [
+                ServiceUnavailableError,
+            ],
+            InvokeRateLimitError: [],
+            InvokeAuthorizationError: [
+                AuthenticationError,
+            ],
+            InvokeBadRequestError: [
+                InvalidParameter,
+                UnsupportedModel,
+                UnsupportedHTTPMethod,
+            ],
+        }

api/core/model_runtime/model_providers/tongyi/tongyi.yaml

@@ -18,6 +18,7 @@ supported_model_types:
   - llm
   - tts
   - text-embedding
+  - rerank
 configurate_methods:
   - predefined-model
   - customizable-model

api/core/rag/datasource/vdb/elasticsearch/elasticsearch_vector.py

@@ -1,5 +1,6 @@
 import json
 import logging
+import math
 from typing import Any, Optional
 from urllib.parse import urlparse
 
@@ -112,7 +113,8 @@ def delete(self) -> None:
 
     def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Document]:
         top_k = kwargs.get("top_k", 10)
-        knn = {"field": Field.VECTOR.value, "query_vector": query_vector, "k": top_k}
+        num_candidates = math.ceil(top_k * 1.5)
+        knn = {"field": Field.VECTOR.value, "query_vector": query_vector, "k": top_k, "num_candidates": num_candidates}
 
         results = self._client.search(index=self._collection_name, knn=knn, size=top_k)
 

api/core/rag/entities/context_entities.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 from pydantic import BaseModel
 
 
@@ -7,4 +9,4 @@ class DocumentContext(BaseModel):
     """
 
     content: str
-    score: float
+    score: Optional[float] = None

api/core/rag/retrieval/dataset_retrieval.py

@@ -231,6 +231,9 @@ def retrieve(
                                 source["content"] = segment.content
                             retrieval_resource_list.append(source)
         if hit_callback and retrieval_resource_list:
+            retrieval_resource_list = sorted(retrieval_resource_list, key=lambda x: x.get("score"), reverse=True)
+            for position, item in enumerate(retrieval_resource_list, start=1):
+                item["position"] = position
             hit_callback.return_retriever_resource_info(retrieval_resource_list)
         if document_context_list:
             document_context_list = sorted(document_context_list, key=lambda x: x.score, reverse=True)

api/core/tools/provider/builtin/jina/jina.yaml

@@ -6,9 +6,9 @@ identity:
     zh_Hans: Jina AI
     pt_BR: Jina AI
   description:
-    en_US: Convert any URL to an LLM-friendly input or perform searches on the web for grounding information. Experience improved output for your agent and RAG systems at no cost.
-    zh_Hans: 将任何URL转换为LLM易读的输入或在网页上搜索引擎上搜索引擎。
-    pt_BR: Converte qualquer URL em uma entrada LLm-fácil de ler ou realize pesquisas na web para obter informação de grounding. Tenha uma experiência melhor para seu agente e sistemas RAG sem custo.
+    en_US: Your Search Foundation, Supercharged!
+    zh_Hans: 您的搜索底座，从此不同!
+    pt_BR: Your Search Foundation, Supercharged!
   icon: icon.svg
   tags:
     - search

api/core/tools/provider/builtin/vanna/tools/vanna.py

@@ -111,9 +111,10 @@ def _invoke(
         # with "visualize" set to True (default behavior) leads to remote code execution.
         # Affected versions: <= 0.5.5
         #########################################################################################
-        generate_chart = False
-        # generate_chart = tool_parameters.get("generate_chart", True)
-        res = vn.ask(prompt, False, True, generate_chart)
+        allow_llm_to_see_data = tool_parameters.get("allow_llm_to_see_data", False)
+        res = vn.ask(
+            prompt, print_results=False, auto_train=True, visualize=False, allow_llm_to_see_data=allow_llm_to_see_data
+        )
 
         result = []
 

api/core/tools/provider/builtin/vanna/tools/vanna.yaml

@@ -200,14 +200,14 @@ parameters:
       en_US: If enabled, it will attempt to train on the metadata of that database
       zh_Hans: 是否自动从数据库获取元数据来训练
     form: form
-  - name: generate_chart
+  - name: allow_llm_to_see_data
     type: boolean
     required: false
-    default: True
+    default: false
     label:
-      en_US: Generate Charts
-      zh_Hans: 生成图表
+      en_US: Whether to allow the LLM to see the data
+      zh_Hans: 是否允许LLM查看数据
     human_description:
-      en_US: Generate Charts
-      zh_Hans: 是否生成图表
+      en_US: Whether to allow the LLM to see the data
+      zh_Hans: 是否允许LLM查看数据
     form: form