refactor(middleware): add extractReasoningMiddleware for enhanced text processing (#5637)

src/renderer/src/middlewares/extractReasoningMiddleware.ts

@@ -0,0 +1,118 @@
+// Modified from https://github.com/vercel/ai/blob/845080d80b8538bb9c7e527d2213acb5f33ac9c2/packages/ai/core/middleware/extract-reasoning-middleware.ts
+
+import { getPotentialStartIndex } from '../utils/getPotentialIndex'
+
+export interface ExtractReasoningMiddlewareOptions {
+  openingTag: string
+  closingTag: string
+  separator?: string
+  enableReasoning?: boolean
+}
+
+function escapeRegExp(str: string) {
+  return str.replace(/[.*+?^${}()|[\\]\\]/g, '\\$&')
+}
+
+// 支持泛型 T，默认 T = { type: string; textDelta: string }
+export function extractReasoningMiddleware<T extends { type: string } = { type: string; textDelta: string }>({
+  openingTag,
+  closingTag,
+  separator = '\n',
+  enableReasoning
+}: ExtractReasoningMiddlewareOptions) {
+  const openingTagEscaped = escapeRegExp(openingTag)
+  const closingTagEscaped = escapeRegExp(closingTag)
+
+  return {
+    wrapGenerate: async ({ doGenerate }: { doGenerate: () => Promise<{ text: string } & Record<string, any>> }) => {
+      const { text: rawText, ...rest } = await doGenerate()
+      if (rawText == null) {
+        return { text: rawText, ...rest }
+      }
+      const text = rawText
+      const regexp = new RegExp(`${openingTagEscaped}(.*?)${closingTagEscaped}`, 'gs')
+      const matches = Array.from(text.matchAll(regexp))
+      if (!matches.length) {
+        return { text, ...rest }
+      }
+      const reasoning = matches.map((match: RegExpMatchArray) => match[1]).join(separator)
+      let textWithoutReasoning = text
+      for (let i = matches.length - 1; i >= 0; i--) {
+        const match = matches[i] as RegExpMatchArray
+        const beforeMatch = textWithoutReasoning.slice(0, match.index as number)
+        const afterMatch = textWithoutReasoning.slice((match.index as number) + match[0].length)
+        textWithoutReasoning =
+          beforeMatch + (beforeMatch.length > 0 && afterMatch.length > 0 ? separator : '') + afterMatch
+      }
+      return { ...rest, text: textWithoutReasoning, reasoning }
+    },
+    wrapStream: async ({
+      doStream
+    }: {
+      doStream: () => Promise<{ stream: ReadableStream<T> } & Record<string, any>>
+    }) => {
+      const { stream, ...rest } = await doStream()
+      if (!enableReasoning) {
+        return {
+          stream,
+          ...rest
+        }
+      }
+      let isFirstReasoning = true
+      let isFirstText = true
+      let afterSwitch = false
+      let isReasoning = false
+      let buffer = ''
+      return {
+        stream: stream.pipeThrough(
+          new TransformStream<T, T>({
+            transform: (chunk, controller) => {
+              if (chunk.type !== 'text-delta') {
+                controller.enqueue(chunk)
+                return
+              }
+              // @ts-expect-error: textDelta 只在 text-delta/reasoning chunk 上
+              buffer += chunk.textDelta
+              function publish(text: string) {
+                if (text.length > 0) {
+                  const prefix = afterSwitch && (isReasoning ? !isFirstReasoning : !isFirstText) ? separator : ''
+                  controller.enqueue({
+                    ...chunk,
+                    type: isReasoning ? 'reasoning' : 'text-delta',
+                    textDelta: prefix + text
+                  })
+                  afterSwitch = false
+                  if (isReasoning) {
+                    isFirstReasoning = false
+                  } else {
+                    isFirstText = false
+                  }
+                }
+              }
+              while (true) {
+                const nextTag = isReasoning ? closingTag : openingTag
+                const startIndex = getPotentialStartIndex(buffer, nextTag)
+                if (startIndex == null) {
+                  publish(buffer)
+                  buffer = ''
+                  break
+                }
+                publish(buffer.slice(0, startIndex))
+                const foundFullMatch = startIndex + nextTag.length <= buffer.length
+                if (foundFullMatch) {
+                  buffer = buffer.slice(startIndex + nextTag.length)
+                  isReasoning = !isReasoning
+                  afterSwitch = true
+                } else {
+                  buffer = buffer.slice(startIndex)
+                  break
+                }
+              }
+            }
+          })
+        ),
+        ...rest
+      }
+    }
+  }
+}

src/renderer/src/providers/AiProvider/BaseProvider.ts

@@ -15,7 +15,6 @@ import type { Message } from '@renderer/types/newMessage'
 import { delay, isJSON, parseJSON } from '@renderer/utils'
 import { addAbortController, removeAbortController } from '@renderer/utils/abortController'
 import { formatApiHost } from '@renderer/utils/api'
-import { glmZeroPreviewProcessor, thinkTagProcessor, ThoughtProcessor } from '@renderer/utils/formats'
 import { getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { isEmpty } from 'lodash'
 import type OpenAI from 'openai'
@@ -230,51 +229,4 @@ export default abstract class BaseProvider {
       cleanup
     }
   }
-
-  /**
-   * Finds the appropriate thinking processor for a given text chunk and model.
-   * Returns the processor if found, otherwise undefined.
-   */
-  protected findThinkingProcessor(chunkText: string, model: Model | undefined): ThoughtProcessor | undefined {
-    if (!model) return undefined
-
-    const processors: ThoughtProcessor[] = [thinkTagProcessor, glmZeroPreviewProcessor]
-    return processors.find((p) => p.canProcess(chunkText, model))
-  }
-
-  /**
-   * Returns a function closure that handles incremental reasoning text for a specific stream.
-   * The returned function processes a chunk, emits THINKING_DELTA for new reasoning,
-   * and returns the associated content part.
-   */
-  protected handleThinkingTags() {
-    let memoizedReasoning = ''
-    // Returns a function that handles a single chunk potentially containing thinking tags
-    return (chunkText: string, processor: ThoughtProcessor, onChunk: (chunk: any) => void): string => {
-      // Returns the processed content part
-      const { reasoning, content } = processor.process(chunkText)
-      let deltaReasoning = ''
-
-      if (reasoning && reasoning.trim()) {
-        // Check if the new reasoning starts with the previous one
-        if (reasoning.startsWith(memoizedReasoning)) {
-          deltaReasoning = reasoning.substring(memoizedReasoning.length)
-        } else {
-          // If not a continuation, send the whole new reasoning
-          deltaReasoning = reasoning
-          // console.warn("Thinking content did not start with previous memoized version. Sending full content.")
-        }
-        memoizedReasoning = reasoning // Update memoized state
-      } else {
-        // If no reasoning, reset memoized state? Let's reset.
-        memoizedReasoning = ''
-      }
-
-      if (deltaReasoning) {
-        onChunk({ type: ChunkType.THINKING_DELTA, text: deltaReasoning })
-      }
-
-      return content // Return the content part for TEXT_DELTA emission
-    }
-  }
 }

src/renderer/src/providers/AiProvider/OpenAICompatibleProvider.ts

@@ -18,6 +18,7 @@ import {
 } from '@renderer/config/models'
 import { getStoreSetting } from '@renderer/hooks/useSettings'
 import i18n from '@renderer/i18n'
+import { extractReasoningMiddleware } from '@renderer/middlewares/extractReasoningMiddleware'
 import { getAssistantSettings, getDefaultModel, getTopNamingModel } from '@renderer/services/AssistantService'
 import { EVENT_NAMES } from '@renderer/services/EventService'
 import {
@@ -51,6 +52,7 @@ import {
 import { mcpToolCallResponseToOpenAICompatibleMessage, parseAndCallTools } from '@renderer/utils/mcp-tools'
 import { findFileBlocks, findImageBlocks, getMainTextContent } from '@renderer/utils/messageUtils/find'
 import { buildSystemPrompt } from '@renderer/utils/prompt'
+import { asyncGeneratorToReadableStream, readableStreamAsyncIterable } from '@renderer/utils/stream'
 import { isEmpty, takeRight } from 'lodash'
 import OpenAI, { AzureOpenAI } from 'openai'
 import {
@@ -62,6 +64,11 @@ import {
 import { CompletionsParams } from '.'
 import OpenAIProvider from './OpenAIProvider'
 
+// 1. 定义联合类型
+export type OpenAIStreamChunk =
+  | { type: 'reasoning' | 'text-delta'; textDelta: string }
+  | { type: 'finish'; finishReason: any; usage: any; delta: any; chunk: any }
+
 export default class OpenAICompatibleProvider extends OpenAIProvider {
   constructor(provider: Provider) {
     super(provider)
@@ -331,6 +338,10 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
     const { contextCount, maxTokens, streamOutput } = getAssistantSettings(assistant)
     const isEnabledWebSearch = assistant.enableWebSearch || !!assistant.webSearchProviderId
     messages = addImageFileToContents(messages)
+    const enableReasoning =
+      ((isSupportedThinkingTokenModel(model) || isSupportedReasoningEffortModel(model)) &&
+        assistant.settings?.reasoning_effort !== undefined) ||
+      (isReasoningModel(model) && (!isSupportedThinkingTokenModel(model) || !isSupportedReasoningEffortModel(model)))
     let systemMessage = { role: 'system', content: assistant.prompt || '' }
     if (isSupportedReasoningEffortOpenAIModel(model)) {
       systemMessage = {
@@ -357,52 +368,7 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
       return streamOutput
     }
 
-    let hasReasoningContent = false
-    let lastChunk = ''
-    const isReasoningJustDone = (
-      delta: OpenAI.Chat.Completions.ChatCompletionChunk.Choice.Delta & {
-        reasoning_content?: string
-        reasoning?: string
-        thinking?: string
-      }
-    ) => {
-      if (!delta?.content) {
-        return false
-      }
-
-      // 检查当前chunk和上一个chunk的组合是否形成###Response标记
-      const combinedChunks = lastChunk + delta.content
-      lastChunk = delta.content
-
-      // 检测思考结束
-      if (combinedChunks.includes('###Response') || delta.content === '</think>') {
-        return true
-      }
-
-      // 如果有reasoning_content或reasoning，说明是在思考中
-      if (delta?.reasoning_content || delta?.reasoning || delta?.thinking) {
-        hasReasoningContent = true
-      }
-
-      // 如果之前有reasoning_content或reasoning，现在有普通content，说明思考结束
-      return !!(hasReasoningContent && delta.content)
-    }
-
-    let time_first_token_millsec = 0
-    let time_first_token_millsec_delta = 0
-    let time_first_content_millsec = 0
     const start_time_millsec = new Date().getTime()
-    console.log(
-      `completions start_time_millsec ${new Date(start_time_millsec).toLocaleString(undefined, {
-        year: 'numeric',
-        month: 'numeric',
-        day: 'numeric',
-        hour: 'numeric',
-        minute: 'numeric',
-        second: 'numeric',
-        fractionalSecondDigits: 3
-      })}`
-    )
     const lastUserMessage = _messages.findLast((m) => m.role === 'user')
     const { abortController, cleanup, signalPromise } = this.createAbortController(lastUserMessage?.id, true)
     const { signal } = abortController
@@ -436,9 +402,7 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
         } as ChatCompletionMessageParam)
         toolResults.forEach((ts) => reqMessages.push(ts as ChatCompletionMessageParam))
 
-        console.debug('[tool] reqMessages before processing', model.id, reqMessages)
         reqMessages = processReqMessages(model, reqMessages)
-        console.debug('[tool] reqMessages', model.id, reqMessages)
         const newStream = await this.sdk.chat.completions
           // @ts-ignore key is not typed
           .create(
@@ -499,193 +463,192 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
       // let isThinkingInContent: ThoughtProcessor | undefined = undefined
       // const processThinkingChunk = this.handleThinkingTags()
       let isFirstChunk = true
-      let isFirstThinkingChunk = true
+      let time_first_token_millsec = 0
-      for await (const chunk of stream) {
+      let time_first_token_millsec_delta = 0
-        if (window.keyv.get(EVENT_NAMES.CHAT_COMPLETION_PAUSED)) {
+      let time_first_content_millsec = 0
-          break
+      let time_thinking_start = 0
-        }
 
-        const delta = chunk.choices[0]?.delta
+      // 1. 初始化中间件
-        const finishReason = chunk.choices[0]?.finish_reason
+      const reasoningTags = [
-
+        { openingTag: '<think>', closingTag: '</think>', separator: '\n' },
-        // --- Incremental onChunk calls ---
+        { openingTag: '###Thinking', closingTag: '###Response', separator: '\n' }
-
+      ]
-        // 1. Reasoning Content
+      const getAppropriateTag = (model: Model) => {
-        const reasoningContent = delta?.reasoning_content || delta?.reasoning
+        if (model.id.includes('qwen3')) return reasoningTags[0]
-        const currentTime = new Date().getTime() // Get current time for each chunk
+        return reasoningTags[0]
-
+      }
-        if (time_first_token_millsec === 0 && isFirstThinkingChunk && reasoningContent) {
+      const reasoningTag = getAppropriateTag(model)
-          // 记录第一个token的时间
+      async function* openAIChunkToTextDelta(stream: any): AsyncGenerator<OpenAIStreamChunk> {
-          time_first_token_millsec = currentTime
+        for await (const chunk of stream) {
-          // 记录第一个token的时间差
+          if (window.keyv.get(EVENT_NAMES.CHAT_COMPLETION_PAUSED)) {
-          time_first_token_millsec_delta = currentTime - start_time_millsec
+            break
-          console.log(
-            `completions time_first_token_millsec ${new Date(currentTime).toLocaleString(undefined, {
-              year: 'numeric',
-              month: 'numeric',
-              day: 'numeric',
-              hour: 'numeric',
-              minute: 'numeric',
-              second: 'numeric',
-              fractionalSecondDigits: 3
-            })}`
-          )
-          isFirstThinkingChunk = false
-        }
-        if (reasoningContent) {
-          thinkingContent += reasoningContent
-          hasReasoningContent = true // Keep track if reasoning occurred
-
-          // Calculate thinking time as time elapsed since start until this chunk
-          const thinking_time = currentTime - time_first_token_millsec
-          onChunk({ type: ChunkType.THINKING_DELTA, text: reasoningContent, thinking_millsec: thinking_time })
-        }
-        if (isReasoningJustDone(delta) && time_first_content_millsec === 0) {
-          time_first_content_millsec = currentTime
-          final_time_thinking_millsec_delta = time_first_content_millsec - time_first_token_millsec
-          onChunk({
-            type: ChunkType.THINKING_COMPLETE,
-            text: thinkingContent,
-            thinking_millsec: final_time_thinking_millsec_delta
-          })
-
-          thinkingContent = ''
-          isFirstThinkingChunk = true
-          hasReasoningContent = false
-        }
-
-        // 2. Text Content
-        if (delta?.content) {
-          if (isEnabledWebSearch) {
-            if (delta?.annotations) {
-              delta.content = convertLinks(delta.content || '', isFirstChunk)
-            } else if (assistant.model?.provider === 'openrouter') {
-              delta.content = convertLinksToOpenRouter(delta.content || '', isFirstChunk)
-            } else if (isZhipuModel(assistant.model)) {
-              delta.content = convertLinksToZhipu(delta.content || '', isFirstChunk)
-            } else if (isHunyuanSearchModel(assistant.model)) {
-              delta.content = convertLinksToHunyuan(
-                delta.content || '',
-                chunk.search_info.search_results || [],
-                isFirstChunk
-              )
-            }
-          }
-          // 说明前面没有思考内容
-          if (isFirstChunk && time_first_token_millsec === 0 && time_first_token_millsec_delta === 0) {
-            isFirstChunk = false
-            time_first_token_millsec = currentTime
-            time_first_token_millsec_delta = time_first_token_millsec - start_time_millsec
-          }
-          content += delta.content // Still accumulate for processToolUses
-
-          // isThinkingInContent = this.findThinkingProcessor(content, model)
-          // if (isThinkingInContent) {
-          //   processThinkingChunk(content, isThinkingInContent, onChunk)
-          onChunk({ type: ChunkType.TEXT_DELTA, text: delta.content })
-          // } else {
-          // }
-        }
-        // console.log('delta?.finish_reason', delta?.finish_reason)
-        if (!isEmpty(finishReason) || delta?.annotations) {
-          onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
-          final_time_completion_millsec_delta = currentTime - start_time_millsec
-          console.log(
-            `completions final_time_completion_millsec ${new Date(currentTime).toLocaleString(undefined, {
-              year: 'numeric',
-              month: 'numeric',
-              day: 'numeric',
-              hour: 'numeric',
-              minute: 'numeric',
-              second: 'numeric',
-              fractionalSecondDigits: 3
-            })}`
-          )
-          // 6. Usage (If provided per chunk) - Capture the last known usage
-          if (chunk.usage) {
-            // console.log('chunk.usage', chunk.usage)
-            lastUsage = chunk.usage // Update with the latest usage info
-            // Send incremental usage update if needed by UI (optional, keep if useful)
-            // onChunk({ type: 'block_in_progress', response: { usage: chunk.usage } })
           }
 
-          // 3. Web Search
+          const delta = chunk.choices[0]?.delta
-          if (delta?.annotations) {
+          if (delta?.reasoning_content || delta?.reasoning) {
-            onChunk({
+            yield { type: 'reasoning', textDelta: delta.reasoning_content || delta.reasoning }
-              type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+          }
-              llm_web_search: {
+          if (delta?.content) {
-                results: delta.annotations,
+            yield { type: 'text-delta', textDelta: delta.content }
-                source: WebSearchSource.OPENAI_COMPATIBLE
-              }
-            } as LLMWebSearchCompleteChunk)
           }
 
-          if (assistant.model?.provider === 'perplexity') {
+          const finishReason = chunk.choices[0]?.finish_reason
-            const citations = chunk.citations
+          if (!isEmpty(finishReason)) {
-            if (citations) {
+            yield { type: 'finish', finishReason, usage: chunk.usage, delta, chunk }
-              onChunk({
+            break
-                type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
-                llm_web_search: {
-                  results: citations,
-                  source: WebSearchSource.PERPLEXITY
-                }
-              } as LLMWebSearchCompleteChunk)
-            }
-          }
-          if (isEnabledWebSearch && isZhipuModel(model) && finishReason === 'stop' && chunk?.web_search) {
-            onChunk({
-              type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
-              llm_web_search: {
-                results: chunk.web_search,
-                source: WebSearchSource.ZHIPU
-              }
-            } as LLMWebSearchCompleteChunk)
-          }
-          if (isEnabledWebSearch && isHunyuanSearchModel(model) && chunk?.search_info?.search_results) {
-            onChunk({
-              type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
-              llm_web_search: {
-                results: chunk.search_info.search_results,
-                source: WebSearchSource.HUNYUAN
-              }
-            } as LLMWebSearchCompleteChunk)
           }
         }
+      }
 
-        // --- End of Incremental onChunk calls ---
+      // 2. 使用中间件
-      } // End of for await loop
+      const { stream: processedStream } = await extractReasoningMiddleware<OpenAIStreamChunk>({
-
+        openingTag: reasoningTag?.openingTag,
-      // Call processToolUses AFTER the loop finishes processing the main stream content
+        closingTag: reasoningTag?.closingTag,
-      // Note: parseAndCallTools inside processToolUses should handle its own onChunk for tool responses
+        separator: reasoningTag?.separator,
-      await processToolUses(content, idx)
+        enableReasoning
-
+      }).wrapStream({
-      // Send the final block_complete chunk with accumulated data
+        doStream: async () => ({
-      onChunk({
+          stream: asyncGeneratorToReadableStream(openAIChunkToTextDelta(stream))
-        type: ChunkType.BLOCK_COMPLETE,
+        })
-        response: {
-          // Use the enhanced usage object
-          usage: lastUsage,
-          metrics: {
-            // Get completion tokens from the last usage object if available
-            completion_tokens: lastUsage?.completion_tokens,
-            time_completion_millsec: final_time_completion_millsec_delta,
-            time_first_token_millsec: time_first_token_millsec_delta,
-            time_thinking_millsec: final_time_thinking_millsec_delta
-          }
-        }
       })
 
-      // FIXME: 临时方案，重置时间戳和思考内容
+      // 3. 消费 processedStream，分发 onChunk
-      time_first_token_millsec = 0
+      for await (const chunk of readableStreamAsyncIterable(processedStream)) {
-      time_first_content_millsec = 0
+        const currentTime = new Date().getTime()
+        const delta = chunk.type === 'finish' ? chunk.delta : chunk
+        const rawChunk = chunk.type === 'finish' ? chunk.chunk : chunk
 
-      // OpenAI stream typically doesn't provide a final summary chunk easily.
+        switch (chunk.type) {
-      // We are sending per-chunk usage if available.
+          case 'reasoning': {
+            if (time_thinking_start === 0) {
+              time_thinking_start = currentTime
+              time_first_token_millsec = currentTime
+              time_first_token_millsec_delta = currentTime - start_time_millsec
+            }
+            thinkingContent += chunk.textDelta
+            const thinking_time = currentTime - time_thinking_start
+            onChunk({ type: ChunkType.THINKING_DELTA, text: chunk.textDelta, thinking_millsec: thinking_time })
+            break
+          }
+          case 'text-delta': {
+            let textDelta = chunk.textDelta
+
+            if (assistant.enableWebSearch && delta) {
+              const originalDelta = rawChunk?.choices?.[0]?.delta
+
+              if (originalDelta?.annotations) {
+                textDelta = convertLinks(textDelta, isFirstChunk)
+              } else if (assistant.model?.provider === 'openrouter') {
+                textDelta = convertLinksToOpenRouter(textDelta, isFirstChunk)
+              } else if (isZhipuModel(assistant.model)) {
+                textDelta = convertLinksToZhipu(textDelta, isFirstChunk)
+              } else if (isHunyuanSearchModel(assistant.model)) {
+                const searchResults = rawChunk?.search_info?.search_results || []
+                textDelta = convertLinksToHunyuan(textDelta, searchResults, isFirstChunk)
+              }
+            }
+            if (isFirstChunk) {
+              isFirstChunk = false
+              if (time_first_token_millsec === 0) {
+                time_first_token_millsec = currentTime
+                time_first_token_millsec_delta = currentTime - start_time_millsec
+              }
+            }
+            content += textDelta
+            if (time_thinking_start > 0 && time_first_content_millsec === 0) {
+              time_first_content_millsec = currentTime
+              final_time_thinking_millsec_delta = time_first_content_millsec - time_thinking_start
+
+              onChunk({
+                type: ChunkType.THINKING_COMPLETE,
+                text: thinkingContent,
+                thinking_millsec: final_time_thinking_millsec_delta
+              })
+            }
+            onChunk({ type: ChunkType.TEXT_DELTA, text: textDelta })
+            break
+          }
+          case 'finish': {
+            const finishReason = chunk.finishReason
+            const usage = chunk.usage
+            const originalFinishDelta = chunk.delta
+            const originalFinishRawChunk = chunk.chunk
+
+            if (!isEmpty(finishReason)) {
+              onChunk({ type: ChunkType.TEXT_COMPLETE, text: content })
+              final_time_completion_millsec_delta = currentTime - start_time_millsec
+              if (usage) {
+                lastUsage = usage
+              }
+              if (originalFinishDelta?.annotations) {
+                onChunk({
+                  type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+                  llm_web_search: {
+                    results: originalFinishDelta.annotations,
+                    source: WebSearchSource.OPENAI
+                  }
+                } as LLMWebSearchCompleteChunk)
+              }
+              if (assistant.model?.provider === 'perplexity') {
+                const citations = originalFinishRawChunk.citations
+                if (citations) {
+                  onChunk({
+                    type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+                    llm_web_search: {
+                      results: citations,
+                      source: WebSearchSource.PERPLEXITY
+                    }
+                  } as LLMWebSearchCompleteChunk)
+                }
+              }
+              if (
+                isEnabledWebSearch &&
+                isZhipuModel(model) &&
+                finishReason === 'stop' &&
+                originalFinishRawChunk?.web_search
+              ) {
+                onChunk({
+                  type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+                  llm_web_search: {
+                    results: originalFinishRawChunk.web_search,
+                    source: WebSearchSource.ZHIPU
+                  }
+                } as LLMWebSearchCompleteChunk)
+              }
+              if (
+                isEnabledWebSearch &&
+                isHunyuanSearchModel(model) &&
+                originalFinishRawChunk?.search_info?.search_results
+              ) {
+                onChunk({
+                  type: ChunkType.LLM_WEB_SEARCH_COMPLETE,
+                  llm_web_search: {
+                    results: originalFinishRawChunk.search_info.search_results,
+                    source: WebSearchSource.HUNYUAN
+                  }
+                } as LLMWebSearchCompleteChunk)
+              }
+            }
+            await processToolUses(content, idx)
+            onChunk({
+              type: ChunkType.BLOCK_COMPLETE,
+              response: {
+                usage: lastUsage,
+                metrics: {
+                  completion_tokens: lastUsage?.completion_tokens,
+                  time_completion_millsec: final_time_completion_millsec_delta,
+                  time_first_token_millsec: time_first_token_millsec_delta,
+                  time_thinking_millsec: final_time_thinking_millsec_delta
+                }
+              }
+            })
+            break
+          }
+        }
+      }
     }
 
-    console.debug('[completions] reqMessages before processing', model.id, reqMessages)
     reqMessages = processReqMessages(model, reqMessages)
-    console.debug('[completions] reqMessages', model.id, reqMessages)
     // 等待接口返回流
     onChunk({ type: ChunkType.LLM_RESPONSE_CREATED })
     const stream = await this.sdk.chat.completions
@@ -835,7 +798,6 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
 
     await this.checkIsCopilot()
 
-    console.debug('[summaries] reqMessages', model.id, [systemMessage, userMessage])
     // @ts-ignore key is not typed
     const response = await this.sdk.chat.completions.create({
       model: model.id,
@@ -877,10 +839,8 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
       role: 'user',
       content: userMessageContent
     }
-    console.debug('[summaryForSearch] reqMessages', model.id, [systemMessage, userMessage])
 
     const lastUserMessage = messages[messages.length - 1]
-    console.log('lastUserMessage?.id', lastUserMessage?.id)
     const { abortController, cleanup } = this.createAbortController(lastUserMessage?.id)
     const { signal } = abortController
 
@@ -989,7 +949,6 @@ export default class OpenAICompatibleProvider extends OpenAIProvider {
 
     try {
       await this.checkIsCopilot()
-      console.debug('[checkModel] body', model.id, body)
       if (!stream) {
         const response = await this.sdk.chat.completions.create(body as ChatCompletionCreateParamsNonStreaming)
         if (!response?.choices[0].message) {

src/renderer/src/utils/formats.ts

@@ -1,4 +1,3 @@
-import { Model } from '@renderer/types'
 import type { Message } from '@renderer/types/newMessage'
 
 import { findImageBlocks, getMainTextContent } from './messageUtils/find'
@@ -99,75 +98,6 @@ export function removeSvgEmptyLines(text: string): string {
 //   return content
 // }
 
-export interface ThoughtProcessor {
-  canProcess: (content: string, model?: Model) => boolean
-  process: (content: string) => { reasoning: string; content: string }
-}
-
-export const glmZeroPreviewProcessor: ThoughtProcessor = {
-  canProcess: (content: string, model?: Model) => {
-    if (!model) return false
-
-    const modelId = model.id || ''
-    const modelName = model.name || ''
-    const isGLMZeroPreview =
-      modelId.toLowerCase().includes('glm-zero-preview') || modelName.toLowerCase().includes('glm-zero-preview')
-
-    return isGLMZeroPreview && content.includes('###Thinking')
-  },
-  process: (content: string) => {
-    const parts = content.split('###')
-    const thinkingMatch = parts.find((part) => part.trim().startsWith('Thinking'))
-    const responseMatch = parts.find((part) => part.trim().startsWith('Response'))
-
-    return {
-      reasoning: thinkingMatch ? thinkingMatch.replace('Thinking', '').trim() : '',
-      content: responseMatch ? responseMatch.replace('Response', '').trim() : ''
-    }
-  }
-}
-
-export const thinkTagProcessor: ThoughtProcessor = {
-  canProcess: (content: string, model?: Model) => {
-    if (!model) return false
-
-    return content.startsWith('<think>') || content.includes('</think>')
-  },
-  process: (content: string) => {
-    // 处理正常闭合的 think 标签
-    const thinkPattern = /^<think>(.*?)<\/think>/s
-    const matches = content.match(thinkPattern)
-    if (matches) {
-      return {
-        reasoning: matches[1].trim(),
-        content: content.replace(thinkPattern, '').trim()
-      }
-    }
-
-    // 处理只有结束标签的情况
-    if (content.includes('</think>') && !content.startsWith('<think>')) {
-      const parts = content.split('</think>')
-      return {
-        reasoning: parts[0].trim(),
-        content: parts.slice(1).join('</think>').trim()
-      }
-    }
-
-    // 处理只有开始标签的情况
-    if (content.startsWith('<think>')) {
-      return {
-        reasoning: content.slice(7).trim(), // 跳过 '<think>' 标签
-        content: ''
-      }
-    }
-
-    return {
-      reasoning: '',
-      content
-    }
-  }
-}
-
 export function withGenerateImage(message: Message): { content: string; images?: string[] } {
   const originalContent = getMainTextContent(message)
   const imagePattern = new RegExp(`!\\[[^\\]]*\\]\\((.*?)\\s*("(?:.*[^"])")?\\s*\\)`)

src/renderer/src/utils/getPotentialIndex.ts

@@ -0,0 +1,29 @@
+// Copied from https://github.com/vercel/ai/blob/main/packages/ai/core/util/get-potential-start-index.ts
+
+/**
+ * Returns the index of the start of the searchedText in the text, or null if it
+ * is not found.
+ */
+export function getPotentialStartIndex(text: string, searchedText: string): number | null {
+  // Return null immediately if searchedText is empty.
+  if (searchedText.length === 0) {
+    return null
+  }
+
+  // Check if the searchedText exists as a direct substring of text.
+  const directIndex = text.indexOf(searchedText)
+  if (directIndex !== -1) {
+    return directIndex
+  }
+
+  // Otherwise, look for the largest suffix of "text" that matches
+  // a prefix of "searchedText". We go from the end of text inward.
+  for (let i = text.length - 1; i >= 0; i--) {
+    const suffix = text.substring(i)
+    if (searchedText.startsWith(suffix)) {
+      return i
+    }
+  }
+
+  return null
+}

src/renderer/src/utils/stream.ts

@@ -0,0 +1,25 @@
+export function readableStreamAsyncIterable<T>(stream: ReadableStream<T>): AsyncIterable<T> {
+  const reader = stream.getReader()
+  return {
+    [Symbol.asyncIterator](): AsyncIterator<T> {
+      return {
+        async next(): Promise<IteratorResult<T>> {
+          return reader.read() as Promise<IteratorResult<T>>
+        }
+      }
+    }
+  }
+}
+
+export function asyncGeneratorToReadableStream<T>(gen: AsyncGenerator<T>): ReadableStream<T> {
+  return new ReadableStream<T>({
+    async pull(controller) {
+      const { value, done } = await gen.next()
+      if (done) {
+        controller.close()
+      } else {
+        controller.enqueue(value)
+      }
+    }
+  })
+}