add: type

Signed-off-by: yihong0618 <zouzou0208@gmail.com>

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

@@ -779,7 +779,7 @@ LLM_BASE_MODELS = [
                     name="frequency_penalty",
                     **PARAMETER_RULE_TEMPLATE[DefaultParameterName.FREQUENCY_PENALTY],
                 ),
-                _get_max_tokens(default=512, min_val=1, max_val=4096),
+                _get_max_tokens(default=512, min_val=1, max_val=16384),
                 ParameterRule(
                     name="seed",
                     label=I18nObject(zh_Hans="种子", en_US="Seed"),

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

@@ -122,7 +122,7 @@ class GiteeAIRerankModel(RerankModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512))},
         )
 
         return entity

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

@@ -140,7 +140,7 @@ class GPUStackRerankModel(RerankModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512))},
         )
 
         return entity

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

@@ -128,7 +128,7 @@ class JinaRerankModel(RerankModel):
             label=I18nObject(en_US=model),
             model_type=ModelType.RERANK,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
-            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000))},
         )
 
         return entity

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

@@ -193,7 +193,7 @@ class JinaTextEmbeddingModel(TextEmbeddingModel):
             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"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000))},
         )
 
         return entity

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

@@ -139,7 +139,7 @@ class OllamaEmbeddingModel(TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

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

@@ -176,7 +176,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

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

@@ -182,7 +182,7 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

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

@@ -173,7 +173,7 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel):
             model_type=ModelType.TEXT_EMBEDDING,
             fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
             model_properties={
-                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size")),
+                ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512)),
                 ModelPropertyKey.MAX_CHUNKS: 1,
             },
             parameter_rules=[],

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

@@ -166,7 +166,7 @@ class VoyageTextEmbeddingModel(TextEmbeddingModel):
             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"))},
+            model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 512))},
         )
 
         return entity

api/core/model_runtime/model_providers/x/llm/grok-beta.yaml

@@ -1,9 +1,12 @@
 model: grok-beta
 label:
-  en_US: Grok beta
+  en_US: Grok Beta
 model_type: llm
 features:
+  - agent-thought
+  - tool-call
   - multi-tool-call
+  - stream-tool-call
 model_properties:
   mode: chat
   context_size: 131072

api/core/model_runtime/model_providers/x/llm/grok-vision-beta.yaml

@@ -0,0 +1,64 @@
+model: grok-vision-beta
+label:
+  en_US: Grok Vision Beta
+model_type: llm
+features:
+  - agent-thought
+  - vision
+model_properties:
+  mode: chat
+  context_size: 8192
+parameter_rules:
+  - name: temperature
+    label:
+      en_US: "Temperature"
+      zh_Hans: "采样温度"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 2.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The randomness of the sampling temperature control output. The temperature value is within the range of [0.0, 1.0]. The higher the value, the more random and creative the output; the lower the value, the more stable it is. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样温度控制输出的随机性。温度值在 [0.0, 1.0] 范围内，值越高，输出越随机和创造性；值越低，输出越稳定。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: top_p
+    label:
+      en_US: "Top P"
+      zh_Hans: "Top P"
+    type: float
+    default: 0.7
+    min: 0.0
+    max: 1.0
+    precision: 1
+    required: true
+    help:
+      en_US: "The value range of the sampling method is [0.0, 1.0]. The top_p value determines that the model selects tokens from the top p% of candidate words with the highest probability; when top_p is 0, this parameter is invalid. It is recommended to adjust either top_p or temperature parameters according to your needs to avoid adjusting both at the same time."
+      zh_Hans: "采样方法的取值范围为 [0.0,1.0]。top_p 值确定模型从概率最高的前p%的候选词中选取 tokens；当 top_p 为 0 时，此参数无效。建议根据需求调整 top_p 或 temperature 参数，避免同时调整两者。"
+
+  - name: frequency_penalty
+    use_template: frequency_penalty
+    label:
+      en_US: "Frequency Penalty"
+      zh_Hans: "频率惩罚"
+    type: float
+    default: 0
+    min: 0
+    max: 2.0
+    precision: 1
+    required: false
+    help:
+      en_US: "Number between 0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim."
+      zh_Hans: "介于0和2.0之间的数字。正值会根据新标记在文本中迄今为止的现有频率来惩罚它们，从而降低模型一字不差地重复同一句话的可能性。"
+
+  - name: user
+    use_template: text
+    label:
+      en_US: "User"
+      zh_Hans: "用户"
+    type: string
+    required: false
+    help:
+      en_US: "Used to track and differentiate conversation requests from different users."
+      zh_Hans: "用于追踪和区分不同用户的对话请求。"

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

@@ -35,3 +35,5 @@ class XAILargeLanguageModel(OAIAPICompatLargeLanguageModel):
         credentials["endpoint_url"] = str(URL(credentials["endpoint_url"])) or "https://api.x.ai/v1"
         credentials["mode"] = LLMMode.CHAT.value
         credentials["function_calling_type"] = "tool_call"
+        credentials["stream_function_calling"] = "support"
+        credentials["vision_support"] = "support"

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

@@ -105,17 +105,6 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel):
 
         return [list(map(float, e)) for e in embeddings], embedding_used_tokens
 
-    def embed_query(self, text: str) -> list[float]:
-        """Call out to ZhipuAI's embedding endpoint.
-
-        Args:
-            text: The text to embed.
-
-        Returns:
-            Embeddings for the text.
-        """
-        return self.embed_documents([text])[0]
-
     def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
         """
         Calculate response usage

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

@@ -1,3 +1,4 @@
+import matplotlib
 import matplotlib.pyplot as plt
 from matplotlib.font_manager import FontProperties, fontManager
 
@@ -5,7 +6,7 @@ from core.tools.provider.builtin_tool_provider import BuiltinToolProviderControl
 
 
 def set_chinese_font():
-    font_list = [
+    to_find_fonts = [
         "PingFang SC",
         "SimHei",
         "Microsoft YaHei",
@@ -15,16 +16,16 @@ def set_chinese_font():
         "Noto Sans CJK SC",
         "Noto Sans CJK JP",
     ]
-
-    for font in font_list:
-        if font in fontManager.ttflist:
-            chinese_font = FontProperties(font)
-            if chinese_font.get_name() == font:
-                return chinese_font
+    installed_fonts = frozenset(fontInfo.name for fontInfo in fontManager.ttflist)
+    for font in to_find_fonts:
+        if font in installed_fonts:
+            return FontProperties(font)
 
     return FontProperties()
 
 
+# use non-interactive backend to prevent `RuntimeError: main thread is not in main loop`
+matplotlib.use("Agg")
 # use a business theme
 plt.style.use("seaborn-v0_8-darkgrid")
 plt.rcParams["axes.unicode_minus"] = False

api/services/conversation_service.py

@@ -1,3 +1,4 @@
+from collections.abc import Callable
 from datetime import UTC, datetime
 from typing import Optional, Union
 
@@ -74,14 +75,14 @@ class ConversationService:
         return InfiniteScrollPagination(data=conversations, limit=limit, has_more=has_more)
 
     @classmethod
-    def _get_sort_params(cls, sort_by: str) -> tuple[str, callable]:
+    def _get_sort_params(cls, sort_by: str):
         if sort_by.startswith("-"):
             return sort_by[1:], desc
         return sort_by, asc
 
     @classmethod
     def _build_filter_condition(
-        cls, sort_field: str, sort_direction: callable, reference_conversation: Conversation, is_next_page: bool = False
+        cls, sort_field: str, sort_direction: Callable, reference_conversation: Conversation, is_next_page: bool = False
     ):
         field_value = getattr(reference_conversation, sort_field)
         if (sort_direction == desc and not is_next_page) or (sort_direction == asc and is_next_page):
@@ -160,5 +161,5 @@ class ConversationService:
         conversation = cls.get_conversation(app_model, conversation_id, user)
 
         conversation.is_deleted = True
-        conversation.updated_at = datetime.now(timezone.utc).replace(tzinfo=None)
+        conversation.updated_at = datetime.now(UTC).replace(tzinfo=None)
         db.session.commit()

web/app/components/base/app-icon-picker/index.tsx

@@ -69,7 +69,7 @@ const AppIconPicker: FC<AppIconPickerProps> = ({
     },
   })
 
-  const [imageCropInfo, setImageCropInfo] = useState<{ tempUrl: string; croppedAreaPixels: Area; fileName: string }>()
+  const [imageCropInfo, setImageCropInfo] = useState<{ tempUrl: string; croppedAreaPixels: Area; fileName: string; file?: File }>()
   const handleImageCropped = async (tempUrl: string, croppedAreaPixels: Area, fileName: string) => {
     setImageCropInfo({ tempUrl, croppedAreaPixels, fileName })
   }
@@ -93,13 +93,15 @@ const AppIconPicker: FC<AppIconPickerProps> = ({
       if (!imageCropInfo && !uploadImageInfo)
         return
       setUploading(true)
-      if (imageCropInfo.file) {
+      if (imageCropInfo?.file) {
         handleLocalFileUpload(imageCropInfo.file)
         return
       }
-      const blob = await getCroppedImg(imageCropInfo.tempUrl, imageCropInfo.croppedAreaPixels, imageCropInfo.fileName)
-      const file = new File([blob], imageCropInfo.fileName, { type: blob.type })
-      handleLocalFileUpload(file)
+      if (imageCropInfo) {
+        const blob = await getCroppedImg(imageCropInfo.tempUrl, imageCropInfo.croppedAreaPixels, imageCropInfo.fileName)
+        const file = new File([blob], imageCropInfo.fileName, { type: blob.type })
+        handleLocalFileUpload(file)
+      }
     }
   }
 

web/app/components/base/app-icon-picker/utils.ts

@@ -116,11 +116,15 @@ export default async function getCroppedImg(
   })
 }
 
-export function checkIsAnimatedImage(file) {
+export function checkIsAnimatedImage(file: Blob) {
   return new Promise((resolve, reject) => {
     const fileReader = new FileReader()
 
     fileReader.onload = function (e) {
+      if (!e.target || !(e.target.result instanceof ArrayBuffer)) {
+        reject(new Error('File reading error'))
+        return
+      }
       const arr = new Uint8Array(e.target.result)
 
       // Check file extension
@@ -148,7 +152,7 @@ export function checkIsAnimatedImage(file) {
 }
 
 // Function to check for WebP signature
-function isWebP(arr) {
+function isWebP(arr: Uint8Array): boolean {
   return (
     arr[0] === 0x52 && arr[1] === 0x49 && arr[2] === 0x46 && arr[3] === 0x46
     && arr[8] === 0x57 && arr[9] === 0x45 && arr[10] === 0x42 && arr[11] === 0x50
@@ -156,7 +160,7 @@ function isWebP(arr) {
 }
 
 // Function to check if the WebP is animated (contains ANIM chunk)
-function checkWebPAnimation(arr) {
+function checkWebPAnimation(arr: string | any[] | Uint8Array) {
   // Search for the ANIM chunk in WebP to determine if it's animated
   for (let i = 12; i < arr.length - 4; i++) {
     if (arr[i] === 0x41 && arr[i + 1] === 0x4E && arr[i + 2] === 0x49 && arr[i + 3] === 0x4D)