mirror of
https://github.com/labring/FastGPT.git
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* feat: think tag parse * remove some model config * feat: parse think tag test
This commit is contained in:
14
docSite/content/zh-cn/docs/development/upgrading/490.md
Normal file
14
docSite/content/zh-cn/docs/development/upgrading/490.md
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@@ -0,0 +1,14 @@
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---
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title: 'V4.9.0(进行中)'
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description: 'FastGPT V4.9.0 更新说明'
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icon: 'upgrade'
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draft: false
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toc: true
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weight: 803
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---
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## 完整更新内容
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1. 新增 - AI 对话节点解析 <think></think> 标签内容,便于各类模型进行思考链输出。
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2. 修复 - 思考链流输出时,有时与正文顺序偏差。
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7
packages/global/core/ai/type.d.ts
vendored
7
packages/global/core/ai/type.d.ts
vendored
@@ -1,14 +1,12 @@
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import openai from 'openai';
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import type {
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ChatCompletionMessageToolCall,
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ChatCompletionChunk,
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ChatCompletionMessageParam as SdkChatCompletionMessageParam,
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ChatCompletionToolMessageParam,
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ChatCompletionContentPart as SdkChatCompletionContentPart,
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ChatCompletionUserMessageParam as SdkChatCompletionUserMessageParam,
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ChatCompletionToolMessageParam as SdkChatCompletionToolMessageParam,
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ChatCompletionAssistantMessageParam as SdkChatCompletionAssistantMessageParam,
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ChatCompletionContentPartText
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ChatCompletionAssistantMessageParam as SdkChatCompletionAssistantMessageParam
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} from 'openai/resources';
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import { ChatMessageTypeEnum } from './constants';
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import { WorkflowInteractiveResponseType } from '../workflow/template/system/interactive/type';
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@@ -71,7 +69,8 @@ export type ChatCompletionMessageFunctionCall =
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};
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// Stream response
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export type StreamChatType = Stream<ChatCompletionChunk>;
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export type StreamChatType = Stream<openai.Chat.Completions.ChatCompletionChunk>;
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export type UnStreamChatType = openai.Chat.Completions.ChatCompletion;
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export default openai;
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export * from 'openai';
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@@ -10,6 +10,7 @@ import { FlowNodeOutputItemType, ReferenceValueType } from '../type/io';
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import { ChatItemType, NodeOutputItemType } from '../../../core/chat/type';
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import { ChatItemValueTypeEnum, ChatRoleEnum } from '../../../core/chat/constants';
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import { replaceVariable, valToStr } from '../../../common/string/tools';
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import { ChatCompletionChunk } from 'openai/resources';
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export const getMaxHistoryLimitFromNodes = (nodes: StoreNodeItemType[]): number => {
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let limit = 10;
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@@ -419,3 +420,137 @@ export function rewriteNodeOutputByHistories(
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};
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});
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}
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// Parse <think></think> tags to think and answer - unstream response
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export const parseReasoningContent = (text: string): [string, string] => {
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const regex = /<think>([\s\S]*?)<\/think>/;
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const match = text.match(regex);
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if (!match) {
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return ['', text];
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}
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const thinkContent = match[1].trim();
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// Add answer (remaining text after think tag)
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const answerContent = text.slice(match.index! + match[0].length);
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return [thinkContent, answerContent];
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};
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// Parse <think></think> tags to think and answer - stream response
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export const parseReasoningStreamContent = () => {
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let isInThinkTag: boolean | undefined;
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const startTag = '<think>';
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let startTagBuffer = '';
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const endTag = '</think>';
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let endTagBuffer = '';
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/*
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parseReasoning - 只控制是否主动解析 <think></think>,如果接口已经解析了,仍然会返回 think 内容。
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*/
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const parsePart = (
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part: {
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choices: {
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delta: {
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content?: string;
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reasoning_content?: string;
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};
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}[];
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},
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parseReasoning = false
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): [string, string] => {
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const content = part.choices?.[0]?.delta?.content || '';
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// @ts-ignore
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const reasoningContent = part.choices?.[0]?.delta?.reasoning_content || '';
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if (reasoningContent || !parseReasoning) {
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isInThinkTag = false;
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return [reasoningContent, content];
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}
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if (!content) {
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return ['', ''];
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}
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// 如果不在 think 标签中,或者有 reasoningContent(接口已解析),则返回 reasoningContent 和 content
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if (isInThinkTag === false) {
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return ['', content];
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}
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// 检测是否为 think 标签开头的数据
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if (isInThinkTag === undefined) {
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// Parse content think and answer
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startTagBuffer += content;
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// 太少内容时候,暂时不解析
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if (startTagBuffer.length < startTag.length) {
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return ['', ''];
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}
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if (startTagBuffer.startsWith(startTag)) {
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isInThinkTag = true;
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return [startTagBuffer.slice(startTag.length), ''];
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}
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// 如果未命中 think 标签,则认为不在 think 标签中,返回 buffer 内容作为 content
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isInThinkTag = false;
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return ['', startTagBuffer];
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}
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// 确认是 think 标签内容,开始返回 think 内容,并实时检测 </think>
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/*
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检测 </think> 方案。
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存储所有疑似 </think> 的内容,直到检测到完整的 </think> 标签或超出 </think> 长度。
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content 返回值包含以下几种情况:
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abc - 完全未命中尾标签
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abc<th - 命中一部分尾标签
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abc</think> - 完全命中尾标签
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abc</think>abc - 完全命中尾标签
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</think>abc - 完全命中尾标签
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k>abc - 命中一部分尾标签
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*/
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// endTagBuffer 专门用来记录疑似尾标签的内容
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if (endTagBuffer) {
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endTagBuffer += content;
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if (endTagBuffer.includes(endTag)) {
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isInThinkTag = false;
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const answer = endTagBuffer.slice(endTag.length);
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return ['', answer];
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} else if (endTagBuffer.length >= endTag.length) {
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// 缓存内容超出尾标签长度,且仍未命中 </think>,则认为本次猜测 </think> 失败,仍处于 think 阶段。
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const tmp = endTagBuffer;
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endTagBuffer = '';
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return [tmp, ''];
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}
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return ['', ''];
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} else if (content.includes(endTag)) {
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// 返回内容,完整命中</think>,直接结束
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isInThinkTag = false;
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const [think, answer] = content.split(endTag);
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return [think, answer];
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} else {
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// 无 buffer,且未命中 </think>,开始疑似 </think> 检测。
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for (let i = 1; i < endTag.length; i++) {
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const partialEndTag = endTag.slice(0, i);
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// 命中一部分尾标签
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if (content.endsWith(partialEndTag)) {
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const think = content.slice(0, -partialEndTag.length);
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endTagBuffer += partialEndTag;
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return [think, ''];
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}
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}
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}
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// 完全未命中尾标签,还是 think 阶段。
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return [content, ''];
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};
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const getStartTagBuffer = () => startTagBuffer;
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return {
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parsePart,
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getStartTagBuffer
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};
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};
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|
@@ -1,7 +1,9 @@
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import OpenAI from '@fastgpt/global/core/ai';
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import {
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||||
ChatCompletionCreateParamsNonStreaming,
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ChatCompletionCreateParamsStreaming
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ChatCompletionCreateParamsStreaming,
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StreamChatType,
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UnStreamChatType
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||||
} from '@fastgpt/global/core/ai/type';
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import { getErrText } from '@fastgpt/global/common/error/utils';
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import { addLog } from '../../common/system/log';
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@@ -38,29 +40,30 @@ export const getAxiosConfig = (props?: { userKey?: OpenaiAccountType }) => {
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};
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||||
};
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type CompletionsBodyType =
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| ChatCompletionCreateParamsNonStreaming
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| ChatCompletionCreateParamsStreaming;
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type InferResponseType<T extends CompletionsBodyType> =
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T extends ChatCompletionCreateParamsStreaming
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? OpenAI.Chat.Completions.ChatCompletionChunk
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: OpenAI.Chat.Completions.ChatCompletion;
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export const createChatCompletion = async <T extends CompletionsBodyType>({
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export const createChatCompletion = async ({
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body,
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userKey,
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timeout,
|
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options
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}: {
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body: T;
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body: ChatCompletionCreateParamsNonStreaming | ChatCompletionCreateParamsStreaming;
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userKey?: OpenaiAccountType;
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timeout?: number;
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options?: OpenAI.RequestOptions;
|
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}): Promise<{
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||||
response: InferResponseType<T>;
|
||||
isStreamResponse: boolean;
|
||||
getEmptyResponseTip: () => string;
|
||||
}> => {
|
||||
}): Promise<
|
||||
{
|
||||
getEmptyResponseTip: () => string;
|
||||
} & (
|
||||
| {
|
||||
response: StreamChatType;
|
||||
isStreamResponse: true;
|
||||
}
|
||||
| {
|
||||
response: UnStreamChatType;
|
||||
isStreamResponse: false;
|
||||
}
|
||||
)
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||||
> => {
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||||
try {
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||||
const modelConstantsData = getLLMModel(body.model);
|
||||
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||||
@@ -96,9 +99,17 @@ export const createChatCompletion = async <T extends CompletionsBodyType>({
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||||
return i18nT('chat:LLM_model_response_empty');
|
||||
};
|
||||
|
||||
if (isStreamResponse) {
|
||||
return {
|
||||
response,
|
||||
isStreamResponse: true,
|
||||
getEmptyResponseTip
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
response: response as InferResponseType<T>,
|
||||
isStreamResponse,
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||||
response,
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||||
isStreamResponse: false,
|
||||
getEmptyResponseTip
|
||||
};
|
||||
} catch (error) {
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||||
|
@@ -1,461 +1,4 @@
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||||
{
|
||||
"provider": "PPIO",
|
||||
"list": [
|
||||
{
|
||||
"model": "deepseek/deepseek-r1/community",
|
||||
"name": "deepseek/deepseek-r1/community",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-v3/community",
|
||||
"name": "deepseek/deepseek-v3/community",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-r1",
|
||||
"name": "deepseek/deepseek-r1",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-v3",
|
||||
"name": "deepseek/deepseek-v3",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-r1-distill-llama-70b",
|
||||
"name": "deepseek/deepseek-r1-distill-llama-70b",
|
||||
"maxContext": 32000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-r1-distill-qwen-32b",
|
||||
"name": "deepseek/deepseek-r1-distill-qwen-32b",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-r1-distill-qwen-14b",
|
||||
"name": "deepseek/deepseek-r1-distill-qwen-14b",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "deepseek/deepseek-r1-distill-llama-8b",
|
||||
"name": "deepseek/deepseek-r1-distill-llama-8b",
|
||||
"maxContext": 32000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen/qwen-2.5-72b-instruct",
|
||||
"name": "qwen/qwen-2.5-72b-instruct",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen/qwen-2-vl-72b-instruct",
|
||||
"name": "qwen/qwen-2-vl-72b-instruct",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "meta-llama/llama-3.2-3b-instruct",
|
||||
"name": "meta-llama/llama-3.2-3b-instruct",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen/qwen2.5-32b-instruct",
|
||||
"name": "qwen/qwen2.5-32b-instruct",
|
||||
"maxContext": 32000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "baichuan/baichuan2-13b-chat",
|
||||
"name": "baichuan/baichuan2-13b-chat",
|
||||
"maxContext": 14336,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "meta-llama/llama-3.1-70b-instruct",
|
||||
"name": "meta-llama/llama-3.1-70b-instruct",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "meta-llama/llama-3.1-8b-instruct",
|
||||
"name": "meta-llama/llama-3.1-8b-instruct",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "01-ai/yi-1.5-34b-chat",
|
||||
"name": "01-ai/yi-1.5-34b-chat",
|
||||
"maxContext": 16384,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "01-ai/yi-1.5-9b-chat",
|
||||
"name": "01-ai/yi-1.5-9b-chat",
|
||||
"maxContext": 16384,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "thudm/glm-4-9b-chat",
|
||||
"name": "thudm/glm-4-9b-chat",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen/qwen-2-7b-instruct",
|
||||
"name": "qwen/qwen-2-7b-instruct",
|
||||
"maxContext": 32768,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 50000,
|
||||
"maxTemperature": 2,
|
||||
"vision": false,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
}
|
||||
]
|
||||
}
|
||||
"list": []
|
||||
}
|
||||
|
@@ -37,25 +37,26 @@ export const computedTemperature = ({
|
||||
return temperature;
|
||||
};
|
||||
|
||||
type CompletionsBodyType = (
|
||||
type CompletionsBodyType =
|
||||
| ChatCompletionCreateParamsNonStreaming
|
||||
| ChatCompletionCreateParamsStreaming
|
||||
) & {
|
||||
response_format?: any;
|
||||
json_schema?: string;
|
||||
stop?: string;
|
||||
};
|
||||
| ChatCompletionCreateParamsStreaming;
|
||||
type InferCompletionsBody<T> = T extends { stream: true }
|
||||
? ChatCompletionCreateParamsStreaming
|
||||
: ChatCompletionCreateParamsNonStreaming;
|
||||
: T extends { stream: false }
|
||||
? ChatCompletionCreateParamsNonStreaming
|
||||
: ChatCompletionCreateParamsNonStreaming | ChatCompletionCreateParamsStreaming;
|
||||
|
||||
export const llmCompletionsBodyFormat = <T extends CompletionsBodyType>(
|
||||
body: T,
|
||||
body: T & {
|
||||
response_format?: any;
|
||||
json_schema?: string;
|
||||
stop?: string;
|
||||
},
|
||||
model: string | LLMModelItemType
|
||||
): InferCompletionsBody<T> => {
|
||||
const modelData = typeof model === 'string' ? getLLMModel(model) : model;
|
||||
if (!modelData) {
|
||||
return body as InferCompletionsBody<T>;
|
||||
return body as unknown as InferCompletionsBody<T>;
|
||||
}
|
||||
|
||||
const response_format = body.response_format;
|
||||
@@ -91,9 +92,7 @@ export const llmCompletionsBodyFormat = <T extends CompletionsBodyType>(
|
||||
});
|
||||
}
|
||||
|
||||
// console.log(requestBody);
|
||||
|
||||
return requestBody as InferCompletionsBody<T>;
|
||||
return requestBody as unknown as InferCompletionsBody<T>;
|
||||
};
|
||||
|
||||
export const llmStreamResponseToText = async (response: StreamChatType) => {
|
||||
|
@@ -3,13 +3,13 @@ import { filterGPTMessageByMaxContext, loadRequestMessages } from '../../../chat
|
||||
import type { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/core/chat/type.d';
|
||||
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import { SseResponseEventEnum } from '@fastgpt/global/core/workflow/runtime/constants';
|
||||
import { textAdaptGptResponse } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import {
|
||||
parseReasoningContent,
|
||||
parseReasoningStreamContent,
|
||||
textAdaptGptResponse
|
||||
} from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
import { createChatCompletion } from '../../../ai/config';
|
||||
import type {
|
||||
ChatCompletion,
|
||||
ChatCompletionMessageParam,
|
||||
StreamChatType
|
||||
} from '@fastgpt/global/core/ai/type.d';
|
||||
import type { ChatCompletionMessageParam, StreamChatType } from '@fastgpt/global/core/ai/type.d';
|
||||
import { formatModelChars2Points } from '../../../../support/wallet/usage/utils';
|
||||
import type { LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
|
||||
import { postTextCensor } from '../../../../common/api/requestPlusApi';
|
||||
@@ -195,7 +195,13 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
});
|
||||
|
||||
const { answerText, reasoningText } = await (async () => {
|
||||
if (res && isStreamResponse) {
|
||||
if (isStreamResponse) {
|
||||
if (!res) {
|
||||
return {
|
||||
answerText: '',
|
||||
reasoningText: ''
|
||||
};
|
||||
}
|
||||
// sse response
|
||||
const { answer, reasoning } = await streamResponse({
|
||||
res,
|
||||
@@ -210,34 +216,49 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
reasoningText: reasoning
|
||||
};
|
||||
} else {
|
||||
const unStreamResponse = response as ChatCompletion;
|
||||
const answer = unStreamResponse.choices?.[0]?.message?.content || '';
|
||||
// @ts-ignore
|
||||
const reasoning = unStreamResponse.choices?.[0]?.message?.reasoning_content || '';
|
||||
const { content, reasoningContent } = (() => {
|
||||
const content = response.choices?.[0]?.message?.content || '';
|
||||
// @ts-ignore
|
||||
const reasoningContent: string = response.choices?.[0]?.message?.reasoning_content || '';
|
||||
|
||||
// API already parse reasoning content
|
||||
if (reasoningContent || !aiChatReasoning) {
|
||||
return {
|
||||
content,
|
||||
reasoningContent
|
||||
};
|
||||
}
|
||||
|
||||
const [think, answer] = parseReasoningContent(content);
|
||||
return {
|
||||
content: answer,
|
||||
reasoningContent: think
|
||||
};
|
||||
})();
|
||||
|
||||
// Some models do not support streaming
|
||||
if (stream) {
|
||||
if (isResponseAnswerText && answer) {
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answer
|
||||
reasoning_content: reasoningContent
|
||||
})
|
||||
});
|
||||
}
|
||||
if (aiChatReasoning && reasoning) {
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: reasoning
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
answerText: answer,
|
||||
reasoningText: reasoning
|
||||
answerText: content,
|
||||
reasoningText: reasoningContent
|
||||
};
|
||||
}
|
||||
})();
|
||||
@@ -267,7 +288,7 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
});
|
||||
|
||||
return {
|
||||
answerText,
|
||||
answerText: answerText.trim(),
|
||||
reasoningText,
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: {
|
||||
totalPoints: externalProvider.openaiAccount?.key ? 0 : totalPoints,
|
||||
@@ -500,26 +521,18 @@ async function streamResponse({
|
||||
});
|
||||
let answer = '';
|
||||
let reasoning = '';
|
||||
const { parsePart, getStartTagBuffer } = parseReasoningStreamContent();
|
||||
|
||||
for await (const part of stream) {
|
||||
if (res.closed) {
|
||||
stream.controller?.abort();
|
||||
break;
|
||||
}
|
||||
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
const [reasoningContent, content] = parsePart(part, aiChatReasoning);
|
||||
answer += content;
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
const reasoningContent = part.choices?.[0]?.delta?.reasoning_content || '';
|
||||
reasoning += reasoningContent;
|
||||
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
@@ -529,6 +542,21 @@ async function streamResponse({
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
// if answer is empty, try to get value from startTagBuffer. (Cause: The response content is too short to exceed the minimum parse length)
|
||||
if (answer === '') {
|
||||
answer = getStartTagBuffer();
|
||||
}
|
||||
|
||||
return { answer, reasoning };
|
||||
|
@@ -243,12 +243,17 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
|
||||
chatAssistantResponse = chatAssistantResponse.concat(assistantResponses);
|
||||
} else {
|
||||
if (reasoningText) {
|
||||
chatAssistantResponse.push({
|
||||
type: ChatItemValueTypeEnum.reasoning,
|
||||
reasoning: {
|
||||
content: reasoningText
|
||||
}
|
||||
});
|
||||
const isResponseReasoningText = inputs.find(
|
||||
(item) => item.key === NodeInputKeyEnum.aiChatReasoning
|
||||
)?.value;
|
||||
if (isResponseReasoningText) {
|
||||
chatAssistantResponse.push({
|
||||
type: ChatItemValueTypeEnum.reasoning,
|
||||
reasoning: {
|
||||
content: reasoningText
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
if (answerText) {
|
||||
// save assistant text response
|
||||
|
145
projects/app/src/pages/api/v1/chat/utils.test.ts
Normal file
145
projects/app/src/pages/api/v1/chat/utils.test.ts
Normal file
@@ -0,0 +1,145 @@
|
||||
import '@/pages/api/__mocks__/base';
|
||||
import { parseReasoningStreamContent } from '@fastgpt/global/core/workflow/runtime/utils';
|
||||
|
||||
test('Parse reasoning stream content test', async () => {
|
||||
const partList = [
|
||||
{
|
||||
data: [{ content: '你好1' }, { content: '你好2' }, { content: '你好3' }],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ reasoning_content: '这是' },
|
||||
{ reasoning_content: '思考' },
|
||||
{ reasoning_content: '过程' },
|
||||
{ content: '你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<t' },
|
||||
{ content: 'hink>' },
|
||||
{ content: '这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程' },
|
||||
{ content: '</think>' },
|
||||
{ content: '你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>' },
|
||||
{ content: '这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程' },
|
||||
{ content: '</think>' },
|
||||
{ content: '你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程' },
|
||||
{ content: '</think>' },
|
||||
{ content: '你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程</' },
|
||||
{ content: 'think>' },
|
||||
{ content: '你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程</think>' },
|
||||
{ content: '你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程</think>你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程</th' },
|
||||
{ content: '假的' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' },
|
||||
{ content: '过程</think>你好1' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '你好1你好2你好3', reasoning: '这是思考过程</th假的你好2你好3过程' }
|
||||
},
|
||||
{
|
||||
data: [
|
||||
{ content: '<think>这是' },
|
||||
{ content: '思考' },
|
||||
{ content: '过程</th' },
|
||||
{ content: '假的' },
|
||||
{ content: '你好2' },
|
||||
{ content: '你好3' }
|
||||
],
|
||||
correct: { answer: '', reasoning: '这是思考过程</th假的你好2你好3' }
|
||||
}
|
||||
];
|
||||
|
||||
partList.forEach((part) => {
|
||||
const { parsePart } = parseReasoningStreamContent();
|
||||
|
||||
let answer = '';
|
||||
let reasoning = '';
|
||||
part.data.forEach((item) => {
|
||||
const formatPart = {
|
||||
choices: [
|
||||
{
|
||||
delta: {
|
||||
role: 'assistant',
|
||||
content: item.content,
|
||||
reasoning_content: item.reasoning_content
|
||||
}
|
||||
}
|
||||
]
|
||||
};
|
||||
const [reasoningContent, content] = parsePart(formatPart, true);
|
||||
answer += content;
|
||||
reasoning += reasoningContent;
|
||||
});
|
||||
|
||||
expect(answer).toBe(part.correct.answer);
|
||||
expect(reasoning).toBe(part.correct.reasoning);
|
||||
});
|
||||
});
|
@@ -24,7 +24,11 @@ export type StreamResponseType = {
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: ChatHistoryItemResType[];
|
||||
};
|
||||
type ResponseQueueItemType =
|
||||
| { event: SseResponseEventEnum.fastAnswer | SseResponseEventEnum.answer; text: string }
|
||||
| {
|
||||
event: SseResponseEventEnum.fastAnswer | SseResponseEventEnum.answer;
|
||||
text?: string;
|
||||
reasoningText?: string;
|
||||
}
|
||||
| { event: SseResponseEventEnum.interactive; [key: string]: any }
|
||||
| {
|
||||
event:
|
||||
@@ -79,7 +83,7 @@ export const streamFetch = ({
|
||||
if (abortCtrl.signal.aborted) {
|
||||
responseQueue.forEach((item) => {
|
||||
onMessage(item);
|
||||
if (isAnswerEvent(item.event)) {
|
||||
if (isAnswerEvent(item.event) && item.text) {
|
||||
responseText += item.text;
|
||||
}
|
||||
});
|
||||
@@ -91,7 +95,7 @@ export const streamFetch = ({
|
||||
for (let i = 0; i < fetchCount; i++) {
|
||||
const item = responseQueue[i];
|
||||
onMessage(item);
|
||||
if (isAnswerEvent(item.event)) {
|
||||
if (isAnswerEvent(item.event) && item.text) {
|
||||
responseText += item.text;
|
||||
}
|
||||
}
|
||||
@@ -180,7 +184,7 @@ export const streamFetch = ({
|
||||
// console.log(parseJson, event);
|
||||
if (event === SseResponseEventEnum.answer) {
|
||||
const reasoningText = parseJson.choices?.[0]?.delta?.reasoning_content || '';
|
||||
onMessage({
|
||||
pushDataToQueue({
|
||||
event,
|
||||
reasoningText
|
||||
});
|
||||
@@ -194,7 +198,7 @@ export const streamFetch = ({
|
||||
}
|
||||
} else if (event === SseResponseEventEnum.fastAnswer) {
|
||||
const reasoningText = parseJson.choices?.[0]?.delta?.reasoning_content || '';
|
||||
onMessage({
|
||||
pushDataToQueue({
|
||||
event,
|
||||
reasoningText
|
||||
});
|
||||
|
Reference in New Issue
Block a user