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chore: update dependencies and remove unused patches

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- Updated various package versions in yarn.lock for improved compatibility and performance.
- Removed obsolete patches for antd and openai, streamlining the dependency management.
- Adjusted icon imports in Dropdown and useIcons to utilize Lucide icons for better visual consistency.
This commit is contained in:
MyPrototypeWhat 2025-08-15 11:47:24 +08:00
parent d05ed94702
commit 628919b562
4 changed files with 3255 additions and 2600 deletions
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69
.yarn/patches/antd-npm-5.24.7-356a553ae5.patch vendored
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@ -1,69 +0,0 @@
diff --git a/es/dropdown/dropdown.js b/es/dropdown/dropdown.js
index 986877a762b9ad0aca596a8552732cd12d2eaabb..1f18aa2ea745e68950e4cee16d4d655f5c835fd5 100644
--- a/es/dropdown/dropdown.js
+++ b/es/dropdown/dropdown.js
@@ -2,7 +2,7 @@
import * as React from 'react';
import LeftOutlined from "@ant-design/icons/es/icons/LeftOutlined";
-import RightOutlined from "@ant-design/icons/es/icons/RightOutlined";
+import { ChevronRight } from 'lucide-react';
import classNames from 'classnames';
import RcDropdown from 'rc-dropdown';
import useEvent from "rc-util/es/hooks/useEvent";
@@ -158,8 +158,10 @@ const Dropdown = props => {
className: `${prefixCls}-menu-submenu-arrow`
}, direction === 'rtl' ? (/*#__PURE__*/React.createElement(LeftOutlined, {
className: `${prefixCls}-menu-submenu-arrow-icon`
- })) : (/*#__PURE__*/React.createElement(RightOutlined, {
- className: `${prefixCls}-menu-submenu-arrow-icon`
+ })) : (/*#__PURE__*/React.createElement(ChevronRight, {
+ size: 16,
+ strokeWidth: 1.8,
+ className: `${prefixCls}-menu-submenu-arrow-icon lucide-custom`
}))),
mode: "vertical",
selectable: false,
diff --git a/es/dropdown/style/index.js b/es/dropdown/style/index.js
index 768c01783002c6901c85a73061ff6b3e776a60ce..39b1b95a56cdc9fb586a193c3adad5141f5cf213 100644
--- a/es/dropdown/style/index.js
+++ b/es/dropdown/style/index.js
@@ -240,7 +240,8 @@ const genBaseStyle = token => {
marginInlineEnd: '0 !important',
color: token.colorTextDescription,
fontSize: fontSizeIcon,
- fontStyle: 'normal'
+ fontStyle: 'normal',
+ marginTop: 3,
}
}
}),
diff --git a/es/select/useIcons.js b/es/select/useIcons.js
index 959115be936ef8901548af2658c5dcfdc5852723..c812edd52123eb0faf4638b1154fcfa1b05b513b 100644
--- a/es/select/useIcons.js
+++ b/es/select/useIcons.js
@@ -4,10 +4,10 @@ import * as React from 'react';
import CheckOutlined from "@ant-design/icons/es/icons/CheckOutlined";
import CloseCircleFilled from "@ant-design/icons/es/icons/CloseCircleFilled";
import CloseOutlined from "@ant-design/icons/es/icons/CloseOutlined";
-import DownOutlined from "@ant-design/icons/es/icons/DownOutlined";
import LoadingOutlined from "@ant-design/icons/es/icons/LoadingOutlined";
import SearchOutlined from "@ant-design/icons/es/icons/SearchOutlined";
import { devUseWarning } from '../_util/warning';
+import { ChevronDown } from 'lucide-react';
export default function useIcons(_ref) {
let {
suffixIcon,
@@ -56,8 +56,10 @@ export default function useIcons(_ref) {
className: iconCls
}));
}
- return getSuffixIconNode(/*#__PURE__*/React.createElement(DownOutlined, {
- className: iconCls
+ return getSuffixIconNode(/*#__PURE__*/React.createElement(ChevronDown, {
+ size: 16,
+ strokeWidth: 1.8,
+ className: `${iconCls} lucide-custom`
}));
};
}

279
.yarn/patches/openai-npm-5.1.0-0e7b3ccb07.patch vendored
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@ -1,279 +0,0 @@
diff --git a/client.js b/client.js
index 33b4ff6309d5f29187dab4e285d07dac20340bab..8f568637ee9e4677585931fb0284c8165a933f69 100644
--- a/client.js
+++ b/client.js
@@ -433,7 +433,7 @@ class OpenAI {
'User-Agent': this.getUserAgent(),
'X-Stainless-Retry-Count': String(retryCount),
...(options.timeout ? { 'X-Stainless-Timeout': String(Math.trunc(options.timeout / 1000)) } : {}),
- ...(0, detect_platform_1.getPlatformHeaders)(),
+ // ...(0, detect_platform_1.getPlatformHeaders)(),
'OpenAI-Organization': this.organization,
'OpenAI-Project': this.project,
},
diff --git a/client.mjs b/client.mjs
index c34c18213073540ebb296ea540b1d1ad39527906..1ce1a98256d7e90e26ca963582f235b23e996e73 100644
--- a/client.mjs
+++ b/client.mjs
@@ -430,7 +430,7 @@ export class OpenAI {
'User-Agent': this.getUserAgent(),
'X-Stainless-Retry-Count': String(retryCount),
...(options.timeout ? { 'X-Stainless-Timeout': String(Math.trunc(options.timeout / 1000)) } : {}),
- ...getPlatformHeaders(),
+ // ...getPlatformHeaders(),
'OpenAI-Organization': this.organization,
'OpenAI-Project': this.project,
},
diff --git a/core/error.js b/core/error.js
index a12d9d9ccd242050161adeb0f82e1b98d9e78e20..fe3a5462480558bc426deea147f864f12b36f9bd 100644
--- a/core/error.js
+++ b/core/error.js
@@ -40,7 +40,7 @@ class APIError extends OpenAIError {
if (!status || !headers) {
return new APIConnectionError({ message, cause: (0, errors_1.castToError)(errorResponse) });
}
- const error = errorResponse?.['error'];
+ const error = errorResponse?.['error'] || errorResponse;
if (status === 400) {
return new BadRequestError(status, error, message, headers);
}
diff --git a/core/error.mjs b/core/error.mjs
index 83cefbaffeb8c657536347322d8de9516af479a2..63334b7972ec04882aa4a0800c1ead5982345045 100644
--- a/core/error.mjs
+++ b/core/error.mjs
@@ -36,7 +36,7 @@ export class APIError extends OpenAIError {
if (!status || !headers) {
return new APIConnectionError({ message, cause: castToError(errorResponse) });
}
- const error = errorResponse?.['error'];
+ const error = errorResponse?.['error'] || errorResponse;
if (status === 400) {
return new BadRequestError(status, error, message, headers);
}
diff --git a/resources/embeddings.js b/resources/embeddings.js
index 2404264d4ba0204322548945ebb7eab3bea82173..8f1bc45cc45e0797d50989d96b51147b90ae6790 100644
--- a/resources/embeddings.js
+++ b/resources/embeddings.js
@@ -5,52 +5,64 @@ exports.Embeddings = void 0;
const resource_1 = require("../core/resource.js");
const utils_1 = require("../internal/utils.js");
class Embeddings extends resource_1.APIResource {
- /**
- * Creates an embedding vector representing the input text.
- *
- * @example
- * ```ts
- * const createEmbeddingResponse =
- * await client.embeddings.create({
- * input: 'The quick brown fox jumped over the lazy dog',
- * model: 'text-embedding-3-small',
- * });
- * ```
- */
- create(body, options) {
- const hasUserProvidedEncodingFormat = !!body.encoding_format;
- // No encoding_format specified, defaulting to base64 for performance reasons
- // See https://github.com/openai/openai-node/pull/1312
- let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
- if (hasUserProvidedEncodingFormat) {
- (0, utils_1.loggerFor)(this._client).debug('embeddings/user defined encoding_format:', body.encoding_format);
- }
- const response = this._client.post('/embeddings', {
- body: {
- ...body,
- encoding_format: encoding_format,
- },
- ...options,
- });
- // if the user specified an encoding_format, return the response as-is
- if (hasUserProvidedEncodingFormat) {
- return response;
- }
- // in this stage, we are sure the user did not specify an encoding_format
- // and we defaulted to base64 for performance reasons
- // we are sure then that the response is base64 encoded, let's decode it
- // the returned result will be a float32 array since this is OpenAI API's default encoding
- (0, utils_1.loggerFor)(this._client).debug('embeddings/decoding base64 embeddings from base64');
- return response._thenUnwrap((response) => {
- if (response && response.data) {
- response.data.forEach((embeddingBase64Obj) => {
- const embeddingBase64Str = embeddingBase64Obj.embedding;
- embeddingBase64Obj.embedding = (0, utils_1.toFloat32Array)(embeddingBase64Str);
- });
- }
- return response;
- });
- }
+ /**
+ * Creates an embedding vector representing the input text.
+ *
+ * @example
+ * ```ts
+ * const createEmbeddingResponse =
+ * await client.embeddings.create({
+ * input: 'The quick brown fox jumped over the lazy dog',
+ * model: 'text-embedding-3-small',
+ * });
+ * ```
+ */
+ create(body, options) {
+ const hasUserProvidedEncodingFormat = !!body.encoding_format;
+ // No encoding_format specified, defaulting to base64 for performance reasons
+ // See https://github.com/openai/openai-node/pull/1312
+ let encoding_format = hasUserProvidedEncodingFormat
+ ? body.encoding_format
+ : "base64";
+ if (body.model.includes("jina")) {
+ encoding_format = undefined;
+ }
+ if (hasUserProvidedEncodingFormat) {
+ (0, utils_1.loggerFor)(this._client).debug(
+ "embeddings/user defined encoding_format:",
+ body.encoding_format
+ );
+ }
+ const response = this._client.post("/embeddings", {
+ body: {
+ ...body,
+ encoding_format: encoding_format,
+ },
+ ...options,
+ });
+ // if the user specified an encoding_format, return the response as-is
+ if (hasUserProvidedEncodingFormat || body.model.includes("jina")) {
+ return response;
+ }
+ // in this stage, we are sure the user did not specify an encoding_format
+ // and we defaulted to base64 for performance reasons
+ // we are sure then that the response is base64 encoded, let's decode it
+ // the returned result will be a float32 array since this is OpenAI API's default encoding
+ (0, utils_1.loggerFor)(this._client).debug(
+ "embeddings/decoding base64 embeddings from base64"
+ );
+ return response._thenUnwrap((response) => {
+ if (response && response.data && typeof response.data[0]?.embedding === 'string') {
+ response.data.forEach((embeddingBase64Obj) => {
+ const embeddingBase64Str = embeddingBase64Obj.embedding;
+ embeddingBase64Obj.embedding = (0, utils_1.toFloat32Array)(
+ embeddingBase64Str
+ );
+ });
+ }
+ return response;
+ });
+ }
}
exports.Embeddings = Embeddings;
//# sourceMappingURL=embeddings.js.map
diff --git a/resources/embeddings.mjs b/resources/embeddings.mjs
index 19dcaef578c194a89759c4360073cfd4f7dd2cbf..0284e9cc615c900eff508eb595f7360a74bd9200 100644
--- a/resources/embeddings.mjs
+++ b/resources/embeddings.mjs
@@ -2,51 +2,61 @@
import { APIResource } from "../core/resource.mjs";
import { loggerFor, toFloat32Array } from "../internal/utils.mjs";
export class Embeddings extends APIResource {
- /**
- * Creates an embedding vector representing the input text.
- *
- * @example
- * ```ts
- * const createEmbeddingResponse =
- * await client.embeddings.create({
- * input: 'The quick brown fox jumped over the lazy dog',
- * model: 'text-embedding-3-small',
- * });
- * ```
- */
- create(body, options) {
- const hasUserProvidedEncodingFormat = !!body.encoding_format;
- // No encoding_format specified, defaulting to base64 for performance reasons
- // See https://github.com/openai/openai-node/pull/1312
- let encoding_format = hasUserProvidedEncodingFormat ? body.encoding_format : 'base64';
- if (hasUserProvidedEncodingFormat) {
- loggerFor(this._client).debug('embeddings/user defined encoding_format:', body.encoding_format);
- }
- const response = this._client.post('/embeddings', {
- body: {
- ...body,
- encoding_format: encoding_format,
- },
- ...options,
- });
- // if the user specified an encoding_format, return the response as-is
- if (hasUserProvidedEncodingFormat) {
- return response;
- }
- // in this stage, we are sure the user did not specify an encoding_format
- // and we defaulted to base64 for performance reasons
- // we are sure then that the response is base64 encoded, let's decode it
- // the returned result will be a float32 array since this is OpenAI API's default encoding
- loggerFor(this._client).debug('embeddings/decoding base64 embeddings from base64');
- return response._thenUnwrap((response) => {
- if (response && response.data) {
- response.data.forEach((embeddingBase64Obj) => {
- const embeddingBase64Str = embeddingBase64Obj.embedding;
- embeddingBase64Obj.embedding = toFloat32Array(embeddingBase64Str);
- });
- }
- return response;
- });
- }
+ /**
+ * Creates an embedding vector representing the input text.
+ *
+ * @example
+ * ```ts
+ * const createEmbeddingResponse =
+ * await client.embeddings.create({
+ * input: 'The quick brown fox jumped over the lazy dog',
+ * model: 'text-embedding-3-small',
+ * });
+ * ```
+ */
+ create(body, options) {
+ const hasUserProvidedEncodingFormat = !!body.encoding_format;
+ // No encoding_format specified, defaulting to base64 for performance reasons
+ // See https://github.com/openai/openai-node/pull/1312
+ let encoding_format = hasUserProvidedEncodingFormat
+ ? body.encoding_format
+ : "base64";
+ if (body.model.includes("jina")) {
+ encoding_format = undefined;
+ }
+ if (hasUserProvidedEncodingFormat) {
+ loggerFor(this._client).debug(
+ "embeddings/user defined encoding_format:",
+ body.encoding_format
+ );
+ }
+ const response = this._client.post("/embeddings", {
+ body: {
+ ...body,
+ encoding_format: encoding_format,
+ },
+ ...options,
+ });
+ // if the user specified an encoding_format, return the response as-is
+ if (hasUserProvidedEncodingFormat || body.model.includes("jina")) {
+ return response;
+ }
+ // in this stage, we are sure the user did not specify an encoding_format
+ // and we defaulted to base64 for performance reasons
+ // we are sure then that the response is base64 encoded, let's decode it
+ // the returned result will be a float32 array since this is OpenAI API's default encoding
+ loggerFor(this._client).debug(
+ "embeddings/decoding base64 embeddings from base64"
+ );
+ return response._thenUnwrap((response) => {
+ if (response && response.data && typeof response.data[0]?.embedding === 'string') {
+ response.data.forEach((embeddingBase64Obj) => {
+ const embeddingBase64Str = embeddingBase64Obj.embedding;
+ embeddingBase64Obj.embedding = toFloat32Array(embeddingBase64Str);
+ });
+ }
+ return response;
+ });
+ }
}
//# sourceMappingURL=embeddings.mjs.map

3
src/renderer/src/services/ApiService.ts
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@ -12,7 +12,7 @@ import { getStoreSetting } from '@renderer/hooks/useSettings'
import i18n from '@renderer/i18n'
import store from '@renderer/store'
import { Assistant, MCPServer, MCPTool, Model, Provider } from '@renderer/types'
import { type Chunk } from '@renderer/types/chunk'
import { type Chunk, ChunkType } from '@renderer/types/chunk'
import { Message } from '@renderer/types/newMessage'
import { SdkModel } from '@renderer/types/sdk'
import { removeSpecialCharactersForTopicName } from '@renderer/utils'
@ -157,6 +157,7 @@ export async function fetchChatCompletion({
// onChunkReceived({ type: ChunkType.LLM_WEB_SEARCH_IN_PROGRESS })
// }
// --- Call AI Completions ---
onChunkReceived({ type: ChunkType.LLM_RESPONSE_CREATED })
// 在 AI SDK 调用时设置正确的 OpenTelemetry 上下文
if (topicId) {

5504
yarn.lock
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