mirror of
https://github.com/labring/FastGPT.git
synced 2025-07-22 04:06:18 +00:00
Test parse cite and add tool call parallel (#4737)
* add quote response filter (#4727) * chatting * add quote response filter * add test * remove comment * perf: cite hidden * perf: format llm response * feat: comment * update default chunk size * update default chunk size --------- Co-authored-by: heheer <heheer@sealos.io>
This commit is contained in:
Archer
18
packages/global/common/string/password.ts
Normal file
18
packages/global/common/string/password.ts
Normal file
@@ -0,0 +1,18 @@
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export const checkPasswordRule = (password: string) => {
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const patterns = [
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/\d/, // Contains digits
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/[a-z]/, // Contains lowercase letters
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/[A-Z]/, // Contains uppercase letters
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/[!@#$%^&*()_+=-]/ // Contains special characters
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];
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const validChars = /^[\dA-Za-z!@#$%^&*()_+=-]{6,100}$/;
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// Check length and valid characters
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if (!validChars.test(password)) return false;
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// Count how many patterns are satisfied
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const matchCount = patterns.filter((pattern) => pattern.test(password)).length;
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// Must satisfy at least 2 patterns
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return matchCount >= 2;
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};
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@@ -88,8 +88,8 @@ export const Prompt_userQuotePromptList: PromptTemplateItem[] = [
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- 保持答案与 <Reference></Reference> 中描述的一致。
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- 使用 Markdown 语法优化回答格式。
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- 使用与问题相同的语言回答。
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- 使用 [id](QUOTE) 格式来引用<Reference></Reference>中的知识，其中 QUOTE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](QUOTE)。"
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- 使用 [id](CITE) 格式来引用<Reference></Reference>中的知识，其中 CITE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](CITE)。"
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- 每段至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。`,
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['4.9.2']: `使用 <Reference></Reference> 标记中的内容作为本次对话的参考:
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@@ -146,8 +146,8 @@ export const Prompt_userQuotePromptList: PromptTemplateItem[] = [
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- 保持答案与 <Reference></Reference> 中描述的一致。
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- 使用 Markdown 语法优化回答格式。
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- 使用与问题相同的语言回答。
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- 使用 [id](QUOTE) 格式来引用<Reference></Reference>中的知识，其中 QUOTE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](QUOTE)。"
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- 使用 [id](CITE) 格式来引用<Reference></Reference>中的知识，其中 CITE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](CITE)。"
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- 每段至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。
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问题:"""{{question}}"""`,
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@@ -217,8 +217,8 @@ export const Prompt_systemQuotePromptList: PromptTemplateItem[] = [
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- 保持答案与 <Reference></Reference> 中描述的一致。
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- 使用 Markdown 语法优化回答格式。
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- 使用与问题相同的语言回答。
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- 使用 [id](QUOTE) 格式来引用<Reference></Reference>中的知识，其中 QUOTE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](QUOTE)。"
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- 使用 [id](CITE) 格式来引用<Reference></Reference>中的知识，其中 CITE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](CITE)。"
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- 每段至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。`,
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['4.9.2']: `使用 <Reference></Reference> 标记中的内容作为本次对话的参考:
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@@ -271,8 +271,8 @@ export const Prompt_systemQuotePromptList: PromptTemplateItem[] = [
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- 保持答案与 <Reference></Reference> 中描述的一致。
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- 使用 Markdown 语法优化回答格式。
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- 使用与问题相同的语言回答。
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- 使用 [id](QUOTE) 格式来引用<Reference></Reference>中的知识，其中 QUOTE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](QUOTE)。"
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- 使用 [id](CITE) 格式来引用<Reference></Reference>中的知识，其中 CITE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](CITE)。"
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- 每段至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。
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问题:"""{{question}}"""`,
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@@ -321,24 +321,13 @@ export const Prompt_systemQuotePromptList: PromptTemplateItem[] = [
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}
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];
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export const getQuotePrompt = (
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version?: string,
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role: 'user' | 'system' = 'user',
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parseQuote = true
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) => {
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export const getQuotePrompt = (version?: string, role: 'user' | 'system' = 'user') => {
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const quotePromptTemplates =
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role === 'user' ? Prompt_userQuotePromptList : Prompt_systemQuotePromptList;
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const defaultTemplate = quotePromptTemplates[0].value;
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return parseQuote
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? getPromptByVersion(version, defaultTemplate)
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: getPromptByVersion(version, defaultTemplate).replace(
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`- 使用 [id](QUOTE) 格式来引用<Reference></Reference>中的知识，其中 QUOTE 是固定常量, id 为引文中的 id。
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- 在每段结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](QUOTE)。"
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- 每段至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。`,
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''
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);
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return getPromptByVersion(version, defaultTemplate);
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};
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// Document quote prompt
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@@ -60,7 +60,7 @@ export const getExtractJsonToolPrompt = (version?: string) => {
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"""
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- {{description}}
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- 不是每个参数都是必须生成的，如果没有合适的参数值，不要生成该参数，或返回空字符串。
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- 需要结合前面的对话内容，一起生成合适的参数。
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- 需要结合历史记录，一起生成合适的参数。
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"""
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本次输入内容: """{{content}}"""
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@@ -1,6 +1,5 @@
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export const getDatasetSearchToolResponsePrompt = (parseQuote: boolean) => {
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return parseQuote
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? `## Role
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export const getDatasetSearchToolResponsePrompt = () => {
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return `## Role
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你是一个知识库回答助手，可以 "quotes" 中的内容作为本次对话的参考。为了使回答结果更加可信并且可追溯，你需要在每段话结尾添加引用标记。
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## Rules
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@@ -9,16 +8,7 @@ export const getDatasetSearchToolResponsePrompt = (parseQuote: boolean) => {
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- 保持答案与 "quotes" 中描述的一致。
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||||
- 使用 Markdown 语法优化回答格式。尤其是图片、表格、序列号等内容，需严格完整输出。
|
||||
- 使用与问题相同的语言回答。
|
||||
- 使用 [id](QUOTE) 格式来引用 "quotes" 中的知识，其中 QUOTE 是固定常量, id 为引文中的 id。
|
||||
- 在每段话结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](QUOTE)。"
|
||||
- 每段话至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。`
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: `## Role
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你是一个知识库回答助手，可以 "quotes" 中的内容作为本次对话的参考。
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## Rules
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- 如果你不清楚答案，你需要澄清。
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- 避免提及你是从 "quotes" 获取的知识。
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- 保持答案与 "quotes" 中描述的一致。
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- 使用 Markdown 语法优化回答格式。尤其是图片、表格、序列号等内容，需严格完整输出。
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- 使用与问题相同的语言回答。`;
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- 使用 [id](CITE) 格式来引用 "quotes" 中的知识，其中 CITE 是固定常量, id 为引文中的 id。
|
||||
- 在每段话结尾自然地整合引用。例如: "FastGPT 是一个基于大语言模型(LLM)的知识库问答系统[67e517e74767063e882d6861](CITE)。"
|
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- 每段话至少包含一个引用，也可根据内容需要加入多个引用，按顺序排列。`;
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};
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1
packages/global/core/ai/type.d.ts
vendored
1
packages/global/core/ai/type.d.ts
vendored
@@ -60,6 +60,7 @@ export type ChatCompletionAssistantToolParam = {
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tool_calls: ChatCompletionMessageToolCall[];
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};
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export type ChatCompletionMessageToolCall = ChatCompletionMessageToolCall & {
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index?: number;
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toolName?: string;
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toolAvatar?: string;
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||||
};
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@@ -1,9 +1,15 @@
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import { DispatchNodeResponseType } from '../workflow/runtime/type';
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import { FlowNodeTypeEnum } from '../workflow/node/constant';
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import { ChatItemValueTypeEnum, ChatRoleEnum, ChatSourceEnum } from './constants';
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import { ChatHistoryItemResType, ChatItemType, UserChatItemValueItemType } from './type.d';
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import {
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AIChatItemValueItemType,
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ChatHistoryItemResType,
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ChatItemType,
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UserChatItemValueItemType
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} from './type.d';
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import { sliceStrStartEnd } from '../../common/string/tools';
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import { PublishChannelEnum } from '../../support/outLink/constant';
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import { removeDatasetCiteText } from '../../../service/core/ai/utils';
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// Concat 2 -> 1, and sort by role
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export const concatHistories = (histories1: ChatItemType[], histories2: ChatItemType[]) => {
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@@ -77,6 +83,7 @@ export const getHistoryPreview = (
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});
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};
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// Filter workflow public response
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export const filterPublicNodeResponseData = ({
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flowResponses = [],
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responseDetail = false
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@@ -112,6 +119,40 @@ export const filterPublicNodeResponseData = ({
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});
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};
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// Remove dataset cite in ai response
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export const removeAIResponseCite = <T extends AIChatItemValueItemType[] | string>(
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value: T,
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retainCite: boolean
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): T => {
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if (retainCite) return value;
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if (typeof value === 'string') {
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return removeDatasetCiteText(value, false) as T;
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}
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return value.map<AIChatItemValueItemType>((item) => {
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if (item.text?.content) {
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return {
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||||
...item,
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||||
text: {
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||||
...item.text,
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||||
content: removeDatasetCiteText(item.text.content, false)
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||||
}
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||||
};
|
||||
}
|
||||
if (item.reasoning?.content) {
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||||
return {
|
||||
...item,
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||||
reasoning: {
|
||||
...item.reasoning,
|
||||
content: removeDatasetCiteText(item.reasoning.content, false)
|
||||
}
|
||||
};
|
||||
}
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||||
return item;
|
||||
}) as T;
|
||||
};
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||||
|
||||
export const removeEmptyUserInput = (input?: UserChatItemValueItemType[]) => {
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||||
return (
|
||||
input?.filter((item) => {
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||||
|
||||
@@ -8,7 +8,7 @@ import {
|
||||
export const minChunkSize = 64; // min index and chunk size
|
||||
|
||||
// Chunk size
|
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export const chunkAutoChunkSize = 1500;
|
||||
export const chunkAutoChunkSize = 1000;
|
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export const getMaxChunkSize = (model: LLMModelItemType) => {
|
||||
return Math.max(model.maxContext - model.maxResponse, 2000);
|
||||
};
|
||||
|
||||
@@ -58,7 +58,7 @@ export type ChatDispatchProps = {
|
||||
chatConfig: AppSchema['chatConfig'];
|
||||
lastInteractive?: WorkflowInteractiveResponseType; // last interactive response
|
||||
stream: boolean;
|
||||
parseQuote?: boolean;
|
||||
retainDatasetCite?: boolean;
|
||||
maxRunTimes: number;
|
||||
isToolCall?: boolean;
|
||||
workflowStreamResponse?: WorkflowResponseType;
|
||||
|
||||
@@ -54,7 +54,7 @@ export const DatasetSearchModule: FlowNodeTemplateType = {
|
||||
key: NodeInputKeyEnum.datasetMaxTokens,
|
||||
renderTypeList: [FlowNodeInputTypeEnum.hidden],
|
||||
label: '',
|
||||
value: 1500,
|
||||
value: 5000,
|
||||
valueType: WorkflowIOValueTypeEnum.number
|
||||
},
|
||||
{
|
||||
|
||||
@@ -1,79 +1,6 @@
|
||||
{
|
||||
"provider": "Qwen",
|
||||
"list": [
|
||||
{
|
||||
"model": "qwen-vl-plus",
|
||||
"name": "qwen-vl-plus",
|
||||
"maxContext": 32000,
|
||||
"maxResponse": 2000,
|
||||
"quoteMaxToken": 20000,
|
||||
"maxTemperature": 1.2,
|
||||
"vision": true,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen-plus",
|
||||
"name": "Qwen-plus",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 60000,
|
||||
"maxTemperature": 1,
|
||||
"vision": false,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"responseFormatList": ["text", "json_object"]
|
||||
},
|
||||
{
|
||||
"model": "qwen-turbo",
|
||||
"name": "Qwen-turbo",
|
||||
"maxContext": 128000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 100000,
|
||||
"maxTemperature": 1,
|
||||
"vision": false,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"responseFormatList": ["text", "json_object"]
|
||||
},
|
||||
|
||||
{
|
||||
"model": "qwen-max",
|
||||
"name": "Qwen-max",
|
||||
@@ -123,6 +50,78 @@
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen-plus",
|
||||
"name": "Qwen-plus",
|
||||
"maxContext": 64000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 60000,
|
||||
"maxTemperature": 1,
|
||||
"vision": false,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"responseFormatList": ["text", "json_object"]
|
||||
},
|
||||
{
|
||||
"model": "qwen-vl-plus",
|
||||
"name": "qwen-vl-plus",
|
||||
"maxContext": 32000,
|
||||
"maxResponse": 2000,
|
||||
"quoteMaxToken": 20000,
|
||||
"maxTemperature": 1.2,
|
||||
"vision": true,
|
||||
"toolChoice": false,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true
|
||||
},
|
||||
{
|
||||
"model": "qwen-turbo",
|
||||
"name": "Qwen-turbo",
|
||||
"maxContext": 128000,
|
||||
"maxResponse": 8000,
|
||||
"quoteMaxToken": 100000,
|
||||
"maxTemperature": 1,
|
||||
"vision": false,
|
||||
"toolChoice": true,
|
||||
"functionCall": false,
|
||||
"defaultSystemChatPrompt": "",
|
||||
"datasetProcess": true,
|
||||
"usedInClassify": true,
|
||||
"customCQPrompt": "",
|
||||
"usedInExtractFields": true,
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
"showStopSign": true,
|
||||
"responseFormatList": ["text", "json_object"]
|
||||
},
|
||||
{
|
||||
"model": "qwen3-235b-a22b",
|
||||
"name": "qwen3-235b-a22b",
|
||||
@@ -142,7 +141,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -168,7 +169,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -194,7 +197,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -220,7 +225,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -246,7 +253,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -272,7 +281,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -298,7 +309,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -324,7 +337,9 @@
|
||||
"usedInQueryExtension": true,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": true,
|
||||
@@ -350,7 +365,9 @@
|
||||
"usedInQueryExtension": false,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": false,
|
||||
@@ -375,7 +392,9 @@
|
||||
"usedInQueryExtension": false,
|
||||
"customExtractPrompt": "",
|
||||
"usedInToolCall": true,
|
||||
"defaultConfig": {},
|
||||
"defaultConfig": {
|
||||
"stream": true
|
||||
},
|
||||
"fieldMap": {},
|
||||
"type": "llm",
|
||||
"showTopP": false,
|
||||
|
||||
@@ -2,7 +2,7 @@ import type { ChatCompletionMessageParam } from '@fastgpt/global/core/ai/type.d'
|
||||
import { createChatCompletion } from '../config';
|
||||
import { countGptMessagesTokens, countPromptTokens } from '../../../common/string/tiktoken/index';
|
||||
import { loadRequestMessages } from '../../chat/utils';
|
||||
import { llmCompletionsBodyFormat, llmResponseToAnswerText } from '../utils';
|
||||
import { llmCompletionsBodyFormat, formatLLMResponse } from '../utils';
|
||||
import {
|
||||
QuestionGuidePrompt,
|
||||
QuestionGuideFooterPrompt
|
||||
@@ -42,12 +42,12 @@ export async function createQuestionGuide({
|
||||
temperature: 0.1,
|
||||
max_tokens: 200,
|
||||
messages: requestMessages,
|
||||
stream: false
|
||||
stream: true
|
||||
},
|
||||
model
|
||||
)
|
||||
});
|
||||
const { text: answer, usage } = await llmResponseToAnswerText(response);
|
||||
const { text: answer, usage } = await formatLLMResponse(response);
|
||||
|
||||
const start = answer.indexOf('[');
|
||||
const end = answer.lastIndexOf(']');
|
||||
|
||||
@@ -4,7 +4,7 @@ import { ChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { countGptMessagesTokens, countPromptTokens } from '../../../common/string/tiktoken/index';
|
||||
import { chats2GPTMessages } from '@fastgpt/global/core/chat/adapt';
|
||||
import { getLLMModel } from '../model';
|
||||
import { llmCompletionsBodyFormat, llmResponseToAnswerText } from '../utils';
|
||||
import { llmCompletionsBodyFormat, formatLLMResponse } from '../utils';
|
||||
import { addLog } from '../../../common/system/log';
|
||||
import { filterGPTMessageByMaxContext } from '../../chat/utils';
|
||||
import json5 from 'json5';
|
||||
@@ -170,7 +170,7 @@ assistant: ${chatBg}
|
||||
const { response } = await createChatCompletion({
|
||||
body: llmCompletionsBodyFormat(
|
||||
{
|
||||
stream: false,
|
||||
stream: true,
|
||||
model: modelData.model,
|
||||
temperature: 0.1,
|
||||
messages
|
||||
@@ -178,7 +178,7 @@ assistant: ${chatBg}
|
||||
modelData
|
||||
)
|
||||
});
|
||||
const { text: answer, usage } = await llmResponseToAnswerText(response);
|
||||
const { text: answer, usage } = await formatLLMResponse(response);
|
||||
const inputTokens = usage?.prompt_tokens || (await countGptMessagesTokens(messages));
|
||||
const outputTokens = usage?.completion_tokens || (await countPromptTokens(answer));
|
||||
|
||||
|
||||
@@ -1,145 +0,0 @@
|
||||
import { parseReasoningStreamContent } from './utils';
|
||||
import { expect, test } from 'vitest';
|
||||
|
||||
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);
|
||||
});
|
||||
});
|
||||
@@ -5,10 +5,12 @@ import {
|
||||
CompletionFinishReason,
|
||||
StreamChatType,
|
||||
UnStreamChatType,
|
||||
CompletionUsage
|
||||
CompletionUsage,
|
||||
ChatCompletionMessageToolCall
|
||||
} from '@fastgpt/global/core/ai/type';
|
||||
import { getLLMModel } from './model';
|
||||
import { getLLMDefaultUsage } from '@fastgpt/global/core/ai/constants';
|
||||
import { getNanoid } from '@fastgpt/global/common/string/tools';
|
||||
|
||||
/*
|
||||
Count response max token
|
||||
@@ -105,33 +107,84 @@ export const llmStreamResponseToAnswerText = async (
|
||||
): Promise<{
|
||||
text: string;
|
||||
usage?: CompletionUsage;
|
||||
toolCalls?: ChatCompletionMessageToolCall[];
|
||||
}> => {
|
||||
let answer = '';
|
||||
let usage = getLLMDefaultUsage();
|
||||
let toolCalls: ChatCompletionMessageToolCall[] = [];
|
||||
let callingTool: { name: string; arguments: string } | null = null;
|
||||
|
||||
for await (const part of response) {
|
||||
usage = part.usage || usage;
|
||||
const responseChoice = part.choices?.[0]?.delta;
|
||||
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
const content = responseChoice?.content || '';
|
||||
answer += content;
|
||||
|
||||
// Tool calls
|
||||
if (responseChoice?.tool_calls?.length) {
|
||||
responseChoice.tool_calls.forEach((toolCall) => {
|
||||
const index = toolCall.index;
|
||||
|
||||
if (toolCall.id || callingTool) {
|
||||
// 有 id，代表新 call 工具
|
||||
if (toolCall.id) {
|
||||
callingTool = {
|
||||
name: toolCall.function?.name || '',
|
||||
arguments: toolCall.function?.arguments || ''
|
||||
};
|
||||
} else if (callingTool) {
|
||||
// Continue call(Perhaps the name of the previous function was incomplete)
|
||||
callingTool.name += toolCall.function?.name || '';
|
||||
callingTool.arguments += toolCall.function?.arguments || '';
|
||||
}
|
||||
|
||||
if (!callingTool) {
|
||||
return;
|
||||
}
|
||||
|
||||
// New tool, add to list.
|
||||
const toolId = getNanoid();
|
||||
toolCalls[index] = {
|
||||
...toolCall,
|
||||
id: toolId,
|
||||
type: 'function',
|
||||
function: callingTool
|
||||
};
|
||||
callingTool = null;
|
||||
} else {
|
||||
/* arg 追加到当前工具的参数里 */
|
||||
const arg: string = toolCall?.function?.arguments ?? '';
|
||||
const currentTool = toolCalls[index];
|
||||
if (currentTool && arg) {
|
||||
currentTool.function.arguments += arg;
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
return {
|
||||
text: parseReasoningContent(answer)[1],
|
||||
usage
|
||||
usage,
|
||||
toolCalls
|
||||
};
|
||||
};
|
||||
export const llmUnStreamResponseToAnswerText = async (
|
||||
response: UnStreamChatType
|
||||
): Promise<{
|
||||
text: string;
|
||||
toolCalls?: ChatCompletionMessageToolCall[];
|
||||
usage?: CompletionUsage;
|
||||
}> => {
|
||||
const answer = response.choices?.[0]?.message?.content || '';
|
||||
const toolCalls = response.choices?.[0]?.message?.tool_calls;
|
||||
return {
|
||||
text: answer,
|
||||
usage: response.usage
|
||||
usage: response.usage,
|
||||
toolCalls
|
||||
};
|
||||
};
|
||||
export const llmResponseToAnswerText = async (response: StreamChatType | UnStreamChatType) => {
|
||||
export const formatLLMResponse = async (response: StreamChatType | UnStreamChatType) => {
|
||||
if ('iterator' in response) {
|
||||
return llmStreamResponseToAnswerText(response);
|
||||
}
|
||||
@@ -155,20 +208,31 @@ export const parseReasoningContent = (text: string): [string, string] => {
|
||||
return [thinkContent, answerContent];
|
||||
};
|
||||
|
||||
// Parse <think></think> tags to think and answer - stream response
|
||||
export const parseReasoningStreamContent = () => {
|
||||
let isInThinkTag: boolean | undefined;
|
||||
export const removeDatasetCiteText = (text: string, retainDatasetCite: boolean) => {
|
||||
return retainDatasetCite ? text : text.replace(/\[([a-f0-9]{24})\]\(CITE\)/g, '');
|
||||
};
|
||||
|
||||
const startTag = '<think>';
|
||||
// Parse llm stream part
|
||||
export const parseLLMStreamResponse = () => {
|
||||
let isInThinkTag: boolean | undefined = undefined;
|
||||
let startTagBuffer = '';
|
||||
|
||||
const endTag = '</think>';
|
||||
let endTagBuffer = '';
|
||||
|
||||
const thinkStartChars = '<think>';
|
||||
const thinkEndChars = '</think>';
|
||||
|
||||
let citeBuffer = '';
|
||||
const maxCiteBufferLength = 32; // [Object](CITE)总长度为32
|
||||
|
||||
/*
|
||||
parseThinkTag - 只控制是否主动解析 <think></think>，如果接口已经解析了，则不再解析。
|
||||
retainDatasetCite -
|
||||
*/
|
||||
const parsePart = (
|
||||
const parsePart = ({
|
||||
part,
|
||||
parseThinkTag = true,
|
||||
retainDatasetCite = true
|
||||
}: {
|
||||
part: {
|
||||
choices: {
|
||||
delta: {
|
||||
@@ -177,147 +241,209 @@ export const parseReasoningStreamContent = () => {
|
||||
};
|
||||
finish_reason?: CompletionFinishReason;
|
||||
}[];
|
||||
},
|
||||
parseThinkTag = false
|
||||
): {
|
||||
};
|
||||
parseThinkTag?: boolean;
|
||||
retainDatasetCite?: boolean;
|
||||
}): {
|
||||
reasoningContent: string;
|
||||
content: string;
|
||||
responseContent: string;
|
||||
finishReason: CompletionFinishReason;
|
||||
} => {
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
const finishReason = part.choices?.[0]?.finish_reason || null;
|
||||
|
||||
const content = part.choices?.[0]?.delta?.content || '';
|
||||
// @ts-ignore
|
||||
const reasoningContent = part.choices?.[0]?.delta?.reasoning_content || '';
|
||||
if (reasoningContent || !parseThinkTag) {
|
||||
isInThinkTag = false;
|
||||
return { reasoningContent, content, finishReason };
|
||||
}
|
||||
const isStreamEnd = !!finishReason;
|
||||
|
||||
if (!content) {
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: '',
|
||||
finishReason
|
||||
};
|
||||
}
|
||||
|
||||
// 如果不在 think 标签中，或者有 reasoningContent(接口已解析），则返回 reasoningContent 和 content
|
||||
if (isInThinkTag === false) {
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content,
|
||||
finishReason
|
||||
};
|
||||
}
|
||||
|
||||
// 检测是否为 think 标签开头的数据
|
||||
if (isInThinkTag === undefined) {
|
||||
// Parse content think and answer
|
||||
startTagBuffer += content;
|
||||
// 太少内容时候，暂时不解析
|
||||
if (startTagBuffer.length < startTag.length) {
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: '',
|
||||
finishReason
|
||||
};
|
||||
}
|
||||
|
||||
if (startTagBuffer.startsWith(startTag)) {
|
||||
isInThinkTag = true;
|
||||
return {
|
||||
reasoningContent: startTagBuffer.slice(startTag.length),
|
||||
content: '',
|
||||
finishReason
|
||||
};
|
||||
}
|
||||
|
||||
// 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
|
||||
isInThinkTag = false;
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: startTagBuffer,
|
||||
finishReason
|
||||
};
|
||||
}
|
||||
|
||||
// 确认是 think 标签内容，开始返回 think 内容，并实时检测 </think>
|
||||
/*
|
||||
检测 </think> 方案。
|
||||
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。
|
||||
content 返回值包含以下几种情况:
|
||||
abc - 完全未命中尾标签
|
||||
abc<th - 命中一部分尾标签
|
||||
abc</think> - 完全命中尾标签
|
||||
abc</think>abc - 完全命中尾标签
|
||||
</think>abc - 完全命中尾标签
|
||||
k>abc - 命中一部分尾标签
|
||||
*/
|
||||
// endTagBuffer 专门用来记录疑似尾标签的内容
|
||||
if (endTagBuffer) {
|
||||
endTagBuffer += content;
|
||||
if (endTagBuffer.includes(endTag)) {
|
||||
// Parse think
|
||||
const { reasoningContent: parsedThinkReasoningContent, content: parsedThinkContent } = (() => {
|
||||
if (reasoningContent || !parseThinkTag) {
|
||||
isInThinkTag = false;
|
||||
const answer = endTagBuffer.slice(endTag.length);
|
||||
return { reasoningContent, content };
|
||||
}
|
||||
|
||||
if (!content) {
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: answer,
|
||||
finishReason
|
||||
};
|
||||
} else if (endTagBuffer.length >= endTag.length) {
|
||||
// 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。
|
||||
const tmp = endTagBuffer;
|
||||
endTagBuffer = '';
|
||||
return {
|
||||
reasoningContent: tmp,
|
||||
content: '',
|
||||
finishReason
|
||||
content: ''
|
||||
};
|
||||
}
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: '',
|
||||
finishReason
|
||||
};
|
||||
} else if (content.includes(endTag)) {
|
||||
// 返回内容，完整命中</think>，直接结束
|
||||
isInThinkTag = false;
|
||||
const [think, answer] = content.split(endTag);
|
||||
return {
|
||||
reasoningContent: think,
|
||||
content: answer,
|
||||
finishReason
|
||||
};
|
||||
} else {
|
||||
// 无 buffer，且未命中 </think>，开始疑似 </think> 检测。
|
||||
for (let i = 1; i < endTag.length; i++) {
|
||||
const partialEndTag = endTag.slice(0, i);
|
||||
// 命中一部分尾标签
|
||||
if (content.endsWith(partialEndTag)) {
|
||||
const think = content.slice(0, -partialEndTag.length);
|
||||
endTagBuffer += partialEndTag;
|
||||
|
||||
// 如果不在 think 标签中，或者有 reasoningContent(接口已解析），则返回 reasoningContent 和 content
|
||||
if (isInThinkTag === false) {
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content
|
||||
};
|
||||
}
|
||||
|
||||
// 检测是否为 think 标签开头的数据
|
||||
if (isInThinkTag === undefined) {
|
||||
// Parse content think and answer
|
||||
startTagBuffer += content;
|
||||
// 太少内容时候，暂时不解析
|
||||
if (startTagBuffer.length < thinkStartChars.length) {
|
||||
if (isStreamEnd) {
|
||||
const tmpContent = startTagBuffer;
|
||||
startTagBuffer = '';
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: tmpContent
|
||||
};
|
||||
}
|
||||
return {
|
||||
reasoningContent: think,
|
||||
content: '',
|
||||
finishReason
|
||||
reasoningContent: '',
|
||||
content: ''
|
||||
};
|
||||
}
|
||||
|
||||
if (startTagBuffer.startsWith(thinkStartChars)) {
|
||||
isInThinkTag = true;
|
||||
return {
|
||||
reasoningContent: startTagBuffer.slice(thinkStartChars.length),
|
||||
content: ''
|
||||
};
|
||||
}
|
||||
|
||||
// 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
|
||||
isInThinkTag = false;
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: startTagBuffer
|
||||
};
|
||||
}
|
||||
|
||||
// 确认是 think 标签内容，开始返回 think 内容，并实时检测 </think>
|
||||
/*
|
||||
检测 </think> 方案。
|
||||
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。
|
||||
content 返回值包含以下几种情况:
|
||||
abc - 完全未命中尾标签
|
||||
abc<th - 命中一部分尾标签
|
||||
abc</think> - 完全命中尾标签
|
||||
abc</think>abc - 完全命中尾标签
|
||||
</think>abc - 完全命中尾标签
|
||||
k>abc - 命中一部分尾标签
|
||||
*/
|
||||
// endTagBuffer 专门用来记录疑似尾标签的内容
|
||||
if (endTagBuffer) {
|
||||
endTagBuffer += content;
|
||||
if (endTagBuffer.includes(thinkEndChars)) {
|
||||
isInThinkTag = false;
|
||||
const answer = endTagBuffer.slice(thinkEndChars.length);
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: answer
|
||||
};
|
||||
} else if (endTagBuffer.length >= thinkEndChars.length) {
|
||||
// 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。
|
||||
const tmp = endTagBuffer;
|
||||
endTagBuffer = '';
|
||||
return {
|
||||
reasoningContent: tmp,
|
||||
content: ''
|
||||
};
|
||||
}
|
||||
return {
|
||||
reasoningContent: '',
|
||||
content: ''
|
||||
};
|
||||
} else if (content.includes(thinkEndChars)) {
|
||||
// 返回内容，完整命中</think>，直接结束
|
||||
isInThinkTag = false;
|
||||
const [think, answer] = content.split(thinkEndChars);
|
||||
return {
|
||||
reasoningContent: think,
|
||||
content: answer
|
||||
};
|
||||
} else {
|
||||
// 无 buffer，且未命中 </think>，开始疑似 </think> 检测。
|
||||
for (let i = 1; i < thinkEndChars.length; i++) {
|
||||
const partialEndTag = thinkEndChars.slice(0, i);
|
||||
// 命中一部分尾标签
|
||||
if (content.endsWith(partialEndTag)) {
|
||||
const think = content.slice(0, -partialEndTag.length);
|
||||
endTagBuffer += partialEndTag;
|
||||
return {
|
||||
reasoningContent: think,
|
||||
content: ''
|
||||
};
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 完全未命中尾标签，还是 think 阶段。
|
||||
return {
|
||||
reasoningContent: content,
|
||||
content: ''
|
||||
};
|
||||
})();
|
||||
|
||||
// Parse datset cite
|
||||
if (retainDatasetCite) {
|
||||
return {
|
||||
reasoningContent: parsedThinkReasoningContent,
|
||||
content: parsedThinkContent,
|
||||
responseContent: parsedThinkContent,
|
||||
finishReason
|
||||
};
|
||||
}
|
||||
|
||||
// 完全未命中尾标签，还是 think 阶段。
|
||||
// 缓存包含 [ 的字符串，直到超出 maxCiteBufferLength 再一次性返回
|
||||
const parseCite = (text: string) => {
|
||||
// 结束时，返回所有剩余内容
|
||||
if (isStreamEnd) {
|
||||
const content = citeBuffer + text;
|
||||
return {
|
||||
content: removeDatasetCiteText(content, false)
|
||||
};
|
||||
}
|
||||
|
||||
// 新内容包含 [，初始化缓冲数据
|
||||
if (text.includes('[')) {
|
||||
const index = text.indexOf('[');
|
||||
const beforeContent = citeBuffer + text.slice(0, index);
|
||||
citeBuffer = text.slice(index);
|
||||
|
||||
// beforeContent 可能是：普通字符串，带 [ 的字符串
|
||||
return {
|
||||
content: removeDatasetCiteText(beforeContent, false)
|
||||
};
|
||||
}
|
||||
// 处于 Cite 缓冲区，判断是否满足条件
|
||||
else if (citeBuffer) {
|
||||
citeBuffer += text;
|
||||
|
||||
// 检查缓冲区长度是否达到完整Quote长度或已经流结束
|
||||
if (citeBuffer.length >= maxCiteBufferLength) {
|
||||
const content = removeDatasetCiteText(citeBuffer, false);
|
||||
citeBuffer = '';
|
||||
|
||||
return {
|
||||
content
|
||||
};
|
||||
} else {
|
||||
// 暂时不返回内容
|
||||
return { content: '' };
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
content: text
|
||||
};
|
||||
};
|
||||
const { content: pasedCiteContent } = parseCite(parsedThinkContent);
|
||||
|
||||
return {
|
||||
reasoningContent: content,
|
||||
content: '',
|
||||
reasoningContent: parsedThinkReasoningContent,
|
||||
content: parsedThinkContent,
|
||||
responseContent: pasedCiteContent,
|
||||
finishReason
|
||||
};
|
||||
};
|
||||
|
||||
const getStartTagBuffer = () => startTagBuffer;
|
||||
|
||||
return {
|
||||
parsePart,
|
||||
getStartTagBuffer
|
||||
parsePart
|
||||
};
|
||||
};
|
||||
|
||||
@@ -31,5 +31,6 @@ export const computedPluginUsage = async ({
|
||||
return plugin.hasTokenFee ? pluginCurrentCost + childrenUsages : pluginCurrentCost;
|
||||
}
|
||||
|
||||
// Personal plugins are charged regardless of whether they are successful or not
|
||||
return childrenUsages;
|
||||
};
|
||||
|
||||
@@ -19,7 +19,7 @@ import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/ty
|
||||
import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
|
||||
import { getHandleId } from '@fastgpt/global/core/workflow/utils';
|
||||
import { loadRequestMessages } from '../../../chat/utils';
|
||||
import { llmCompletionsBodyFormat, llmResponseToAnswerText } from '../../../ai/utils';
|
||||
import { llmCompletionsBodyFormat, formatLLMResponse } from '../../../ai/utils';
|
||||
import { addLog } from '../../../../common/system/log';
|
||||
import { ModelTypeEnum } from '../../../../../global/core/ai/model';
|
||||
import { replaceVariable } from '@fastgpt/global/common/string/tools';
|
||||
@@ -135,13 +135,13 @@ const completions = async ({
|
||||
model: cqModel.model,
|
||||
temperature: 0.01,
|
||||
messages: requestMessages,
|
||||
stream: false
|
||||
stream: true
|
||||
},
|
||||
cqModel
|
||||
),
|
||||
userKey: externalProvider.openaiAccount
|
||||
});
|
||||
const { text: answer, usage } = await llmResponseToAnswerText(response);
|
||||
const { text: answer, usage } = await formatLLMResponse(response);
|
||||
|
||||
// console.log(JSON.stringify(chats2GPTMessages({ messages, reserveId: false }), null, 2));
|
||||
// console.log(answer, '----');
|
||||
|
||||
@@ -30,7 +30,7 @@ import {
|
||||
import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
|
||||
import { DispatchNodeResultType } from '@fastgpt/global/core/workflow/runtime/type';
|
||||
import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
|
||||
import { llmCompletionsBodyFormat, llmResponseToAnswerText } from '../../../ai/utils';
|
||||
import { llmCompletionsBodyFormat, formatLLMResponse } from '../../../ai/utils';
|
||||
import { ModelTypeEnum } from '../../../../../global/core/ai/model';
|
||||
import {
|
||||
getExtractJsonPrompt,
|
||||
@@ -226,10 +226,10 @@ const toolChoice = async (props: ActionProps) => {
|
||||
}
|
||||
];
|
||||
|
||||
const { response } = (await createChatCompletion({
|
||||
const { response } = await createChatCompletion({
|
||||
body: llmCompletionsBodyFormat(
|
||||
{
|
||||
stream: false,
|
||||
stream: true,
|
||||
model: extractModel.model,
|
||||
temperature: 0.01,
|
||||
messages: filterMessages,
|
||||
@@ -239,16 +239,15 @@ const toolChoice = async (props: ActionProps) => {
|
||||
extractModel
|
||||
),
|
||||
userKey: externalProvider.openaiAccount
|
||||
})) as { response: UnStreamChatType };
|
||||
});
|
||||
const { toolCalls, usage } = await formatLLMResponse(response);
|
||||
|
||||
const arg: Record<string, any> = (() => {
|
||||
try {
|
||||
return json5.parse(
|
||||
response?.choices?.[0]?.message?.tool_calls?.[0]?.function?.arguments || ''
|
||||
);
|
||||
return json5.parse(toolCalls?.[0]?.function?.arguments || '');
|
||||
} catch (error) {
|
||||
console.log(agentFunction.parameters);
|
||||
console.log(response.choices?.[0]?.message?.tool_calls?.[0]?.function);
|
||||
console.log(toolCalls?.[0]?.function);
|
||||
console.log('Your model may not support tool_call', error);
|
||||
return {};
|
||||
}
|
||||
@@ -257,11 +256,10 @@ const toolChoice = async (props: ActionProps) => {
|
||||
const AIMessages: ChatCompletionMessageParam[] = [
|
||||
{
|
||||
role: ChatCompletionRequestMessageRoleEnum.Assistant,
|
||||
tool_calls: response.choices?.[0]?.message?.tool_calls
|
||||
tool_calls: toolCalls
|
||||
}
|
||||
];
|
||||
|
||||
const usage = response.usage;
|
||||
const inputTokens = usage?.prompt_tokens || (await countGptMessagesTokens(filterMessages, tools));
|
||||
const outputTokens = usage?.completion_tokens || (await countGptMessagesTokens(AIMessages));
|
||||
return {
|
||||
@@ -321,13 +319,13 @@ Human: ${content}`
|
||||
model: extractModel.model,
|
||||
temperature: 0.01,
|
||||
messages: requestMessages,
|
||||
stream: false
|
||||
stream: true
|
||||
},
|
||||
extractModel
|
||||
),
|
||||
userKey: externalProvider.openaiAccount
|
||||
});
|
||||
const { text: answer, usage } = await llmResponseToAnswerText(response);
|
||||
const { text: answer, usage } = await formatLLMResponse(response);
|
||||
const inputTokens = usage?.prompt_tokens || (await countMessagesTokens(messages));
|
||||
const outputTokens = usage?.completion_tokens || (await countPromptTokens(answer));
|
||||
|
||||
|
||||
@@ -26,7 +26,12 @@ import { getNanoid, sliceStrStartEnd } from '@fastgpt/global/common/string/tools
|
||||
import { AIChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
|
||||
import { formatToolResponse, initToolCallEdges, initToolNodes } from './utils';
|
||||
import { computedMaxToken, llmCompletionsBodyFormat } from '../../../../ai/utils';
|
||||
import {
|
||||
computedMaxToken,
|
||||
llmCompletionsBodyFormat,
|
||||
removeDatasetCiteText,
|
||||
parseLLMStreamResponse
|
||||
} from '../../../../ai/utils';
|
||||
import { toolValueTypeList } from '@fastgpt/global/core/workflow/constants';
|
||||
import { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
import { ChatItemValueTypeEnum } from '@fastgpt/global/core/chat/constants';
|
||||
@@ -48,6 +53,7 @@ export const runToolWithFunctionCall = async (
|
||||
runtimeEdges,
|
||||
externalProvider,
|
||||
stream,
|
||||
retainDatasetCite = true,
|
||||
workflowStreamResponse,
|
||||
params: {
|
||||
temperature,
|
||||
@@ -261,7 +267,8 @@ export const runToolWithFunctionCall = async (
|
||||
res,
|
||||
toolNodes,
|
||||
stream: aiResponse,
|
||||
workflowStreamResponse
|
||||
workflowStreamResponse,
|
||||
retainDatasetCite
|
||||
});
|
||||
|
||||
return {
|
||||
@@ -288,8 +295,18 @@ export const runToolWithFunctionCall = async (
|
||||
]
|
||||
: [];
|
||||
|
||||
const answer = result.choices?.[0]?.message?.content || '';
|
||||
if (answer) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: removeDatasetCiteText(answer, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
answer: result.choices?.[0]?.message?.content || '',
|
||||
answer,
|
||||
functionCalls: toolCalls,
|
||||
inputTokens: usage?.prompt_tokens,
|
||||
outputTokens: usage?.completion_tokens
|
||||
@@ -509,12 +526,14 @@ async function streamResponse({
|
||||
res,
|
||||
toolNodes,
|
||||
stream,
|
||||
workflowStreamResponse
|
||||
workflowStreamResponse,
|
||||
retainDatasetCite
|
||||
}: {
|
||||
res: NextApiResponse;
|
||||
toolNodes: ToolNodeItemType[];
|
||||
stream: StreamChatType;
|
||||
workflowStreamResponse?: WorkflowResponseType;
|
||||
retainDatasetCite?: boolean;
|
||||
}) {
|
||||
const write = responseWriteController({
|
||||
res,
|
||||
@@ -526,6 +545,8 @@ async function streamResponse({
|
||||
let functionId = getNanoid();
|
||||
let usage = getLLMDefaultUsage();
|
||||
|
||||
const { parsePart } = parseLLMStreamResponse();
|
||||
|
||||
for await (const part of stream) {
|
||||
usage = part.usage || usage;
|
||||
if (res.closed) {
|
||||
@@ -533,17 +554,21 @@ async function streamResponse({
|
||||
break;
|
||||
}
|
||||
|
||||
const { content: toolChoiceContent, responseContent } = parsePart({
|
||||
part,
|
||||
parseThinkTag: false,
|
||||
retainDatasetCite
|
||||
});
|
||||
|
||||
const responseChoice = part.choices?.[0]?.delta;
|
||||
textAnswer += toolChoiceContent;
|
||||
|
||||
if (responseChoice.content) {
|
||||
const content = responseChoice?.content || '';
|
||||
textAnswer += content;
|
||||
|
||||
if (responseContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
text: responseContent
|
||||
})
|
||||
});
|
||||
} else if (responseChoice.function_call) {
|
||||
|
||||
@@ -29,8 +29,9 @@ import { formatToolResponse, initToolCallEdges, initToolNodes } from './utils';
|
||||
import {
|
||||
computedMaxToken,
|
||||
llmCompletionsBodyFormat,
|
||||
removeDatasetCiteText,
|
||||
parseReasoningContent,
|
||||
parseReasoningStreamContent
|
||||
parseLLMStreamResponse
|
||||
} from '../../../../ai/utils';
|
||||
import { WorkflowResponseType } from '../../type';
|
||||
import { toolValueTypeList } from '@fastgpt/global/core/workflow/constants';
|
||||
@@ -60,6 +61,7 @@ export const runToolWithPromptCall = async (
|
||||
runtimeEdges,
|
||||
externalProvider,
|
||||
stream,
|
||||
retainDatasetCite = true,
|
||||
workflowStreamResponse,
|
||||
params: {
|
||||
temperature,
|
||||
@@ -275,7 +277,8 @@ export const runToolWithPromptCall = async (
|
||||
toolNodes,
|
||||
stream: aiResponse,
|
||||
workflowStreamResponse,
|
||||
aiChatReasoning
|
||||
aiChatReasoning,
|
||||
retainDatasetCite
|
||||
});
|
||||
|
||||
return {
|
||||
@@ -318,7 +321,7 @@ export const runToolWithPromptCall = async (
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: reasoning
|
||||
reasoning_content: removeDatasetCiteText(reasoning, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
@@ -344,7 +347,7 @@ export const runToolWithPromptCall = async (
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: replaceAnswer
|
||||
text: removeDatasetCiteText(replaceAnswer, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
@@ -566,13 +569,15 @@ async function streamResponse({
|
||||
res,
|
||||
stream,
|
||||
workflowStreamResponse,
|
||||
aiChatReasoning
|
||||
aiChatReasoning,
|
||||
retainDatasetCite
|
||||
}: {
|
||||
res: NextApiResponse;
|
||||
toolNodes: ToolNodeItemType[];
|
||||
stream: StreamChatType;
|
||||
workflowStreamResponse?: WorkflowResponseType;
|
||||
aiChatReasoning?: boolean;
|
||||
retainDatasetCite?: boolean;
|
||||
}) {
|
||||
const write = responseWriteController({
|
||||
res,
|
||||
@@ -585,7 +590,7 @@ async function streamResponse({
|
||||
let finish_reason: CompletionFinishReason = null;
|
||||
let usage = getLLMDefaultUsage();
|
||||
|
||||
const { parsePart, getStartTagBuffer } = parseReasoningStreamContent();
|
||||
const { parsePart } = parseLLMStreamResponse();
|
||||
|
||||
for await (const part of stream) {
|
||||
usage = part.usage || usage;
|
||||
@@ -595,11 +600,16 @@ async function streamResponse({
|
||||
break;
|
||||
}
|
||||
|
||||
const { reasoningContent, content, finishReason } = parsePart(part, aiChatReasoning);
|
||||
const { reasoningContent, content, responseContent, finishReason } = parsePart({
|
||||
part,
|
||||
parseThinkTag: aiChatReasoning,
|
||||
retainDatasetCite
|
||||
});
|
||||
finish_reason = finish_reason || finishReason;
|
||||
answer += content;
|
||||
reasoning += reasoningContent;
|
||||
|
||||
// Reasoning response
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
@@ -612,13 +622,15 @@ async function streamResponse({
|
||||
|
||||
if (content) {
|
||||
if (startResponseWrite) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
})
|
||||
});
|
||||
if (responseContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: responseContent
|
||||
})
|
||||
});
|
||||
}
|
||||
} else if (answer.length >= 3) {
|
||||
answer = answer.trimStart();
|
||||
if (/0(:|：)/.test(answer)) {
|
||||
@@ -640,22 +652,6 @@ async function streamResponse({
|
||||
}
|
||||
}
|
||||
|
||||
if (answer === '') {
|
||||
answer = getStartTagBuffer();
|
||||
if (/0(:|：)/.test(answer)) {
|
||||
// find first : index
|
||||
const firstIndex = answer.indexOf('0:') !== -1 ? answer.indexOf('0:') : answer.indexOf('0：');
|
||||
answer = answer.substring(firstIndex + 2).trim();
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answer
|
||||
})
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return { answer, reasoning, finish_reason, usage };
|
||||
}
|
||||
|
||||
|
||||
@@ -26,7 +26,12 @@ import { countGptMessagesTokens } from '../../../../../common/string/tiktoken/in
|
||||
import { GPTMessages2Chats } from '@fastgpt/global/core/chat/adapt';
|
||||
import { AIChatItemType } from '@fastgpt/global/core/chat/type';
|
||||
import { formatToolResponse, initToolCallEdges, initToolNodes } from './utils';
|
||||
import { computedMaxToken, llmCompletionsBodyFormat } from '../../../../ai/utils';
|
||||
import {
|
||||
computedMaxToken,
|
||||
llmCompletionsBodyFormat,
|
||||
removeDatasetCiteText,
|
||||
parseLLMStreamResponse
|
||||
} from '../../../../ai/utils';
|
||||
import { getNanoid, sliceStrStartEnd } from '@fastgpt/global/common/string/tools';
|
||||
import { toolValueTypeList } from '@fastgpt/global/core/workflow/constants';
|
||||
import { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
|
||||
@@ -89,12 +94,13 @@ export const runToolWithToolChoice = async (
|
||||
interactiveEntryToolParams,
|
||||
...workflowProps
|
||||
} = props;
|
||||
const {
|
||||
let {
|
||||
res,
|
||||
requestOrigin,
|
||||
runtimeNodes,
|
||||
runtimeEdges,
|
||||
stream,
|
||||
retainDatasetCite = true,
|
||||
externalProvider,
|
||||
workflowStreamResponse,
|
||||
params: {
|
||||
@@ -104,9 +110,11 @@ export const runToolWithToolChoice = async (
|
||||
aiChatTopP,
|
||||
aiChatStopSign,
|
||||
aiChatResponseFormat,
|
||||
aiChatJsonSchema
|
||||
aiChatJsonSchema,
|
||||
aiChatReasoning
|
||||
}
|
||||
} = workflowProps;
|
||||
aiChatReasoning = !!aiChatReasoning && !!toolModel.reasoning;
|
||||
|
||||
if (maxRunToolTimes <= 0 && response) {
|
||||
return response;
|
||||
@@ -279,6 +287,7 @@ export const runToolWithToolChoice = async (
|
||||
messages: requestMessages,
|
||||
tools,
|
||||
tool_choice: 'auto',
|
||||
parallel_tool_calls: true,
|
||||
temperature,
|
||||
max_tokens,
|
||||
top_p: aiChatTopP,
|
||||
@@ -288,7 +297,7 @@ export const runToolWithToolChoice = async (
|
||||
},
|
||||
toolModel
|
||||
);
|
||||
// console.log(JSON.stringify(filterMessages, null, 2), '==requestMessages');
|
||||
// console.log(JSON.stringify(requestBody, null, 2), '==requestMessages');
|
||||
/* Run llm */
|
||||
const {
|
||||
response: aiResponse,
|
||||
@@ -320,7 +329,9 @@ export const runToolWithToolChoice = async (
|
||||
res,
|
||||
workflowStreamResponse,
|
||||
toolNodes,
|
||||
stream: aiResponse
|
||||
stream: aiResponse,
|
||||
aiChatReasoning,
|
||||
retainDatasetCite
|
||||
});
|
||||
|
||||
return {
|
||||
@@ -335,11 +346,38 @@ export const runToolWithToolChoice = async (
|
||||
const finish_reason = result.choices?.[0]?.finish_reason as CompletionFinishReason;
|
||||
const calls = result.choices?.[0]?.message?.tool_calls || [];
|
||||
const answer = result.choices?.[0]?.message?.content || '';
|
||||
// @ts-ignore
|
||||
const reasoningContent = result.choices?.[0]?.message?.reasoning_content || '';
|
||||
const usage = result.usage;
|
||||
|
||||
// 加上name和avatar
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: removeDatasetCiteText(reasoningContent, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
// 格式化 toolCalls
|
||||
const toolCalls = calls.map((tool) => {
|
||||
const toolNode = toolNodes.find((item) => item.nodeId === tool.function?.name);
|
||||
|
||||
// 不支持 stream 模式的模型的这里需要补一个响应给客户端
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolCall,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
toolName: toolNode?.name || '',
|
||||
toolAvatar: toolNode?.avatar || '',
|
||||
functionName: tool.function.name,
|
||||
params: tool.function?.arguments ?? '',
|
||||
response: ''
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
return {
|
||||
...tool,
|
||||
toolName: toolNode?.name || '',
|
||||
@@ -347,27 +385,11 @@ export const runToolWithToolChoice = async (
|
||||
};
|
||||
});
|
||||
|
||||
// 不支持 stream 模式的模型的流失响应
|
||||
toolCalls.forEach((tool) => {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolCall,
|
||||
data: {
|
||||
tool: {
|
||||
id: tool.id,
|
||||
toolName: tool.toolName,
|
||||
toolAvatar: tool.toolAvatar,
|
||||
functionName: tool.function.name,
|
||||
params: tool.function?.arguments ?? '',
|
||||
response: ''
|
||||
}
|
||||
}
|
||||
});
|
||||
});
|
||||
if (answer) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answer
|
||||
text: removeDatasetCiteText(answer, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
@@ -627,12 +649,16 @@ async function streamResponse({
|
||||
res,
|
||||
toolNodes,
|
||||
stream,
|
||||
workflowStreamResponse
|
||||
workflowStreamResponse,
|
||||
aiChatReasoning,
|
||||
retainDatasetCite
|
||||
}: {
|
||||
res: NextApiResponse;
|
||||
toolNodes: ToolNodeItemType[];
|
||||
stream: StreamChatType;
|
||||
workflowStreamResponse?: WorkflowResponseType;
|
||||
aiChatReasoning: boolean;
|
||||
retainDatasetCite?: boolean;
|
||||
}) {
|
||||
const write = responseWriteController({
|
||||
res,
|
||||
@@ -642,105 +668,130 @@ async function streamResponse({
|
||||
let textAnswer = '';
|
||||
let callingTool: { name: string; arguments: string } | null = null;
|
||||
let toolCalls: ChatCompletionMessageToolCall[] = [];
|
||||
let finishReason: CompletionFinishReason = null;
|
||||
let finish_reason: CompletionFinishReason = null;
|
||||
let usage = getLLMDefaultUsage();
|
||||
|
||||
const { parsePart } = parseLLMStreamResponse();
|
||||
|
||||
for await (const part of stream) {
|
||||
usage = part.usage || usage;
|
||||
if (res.closed) {
|
||||
stream.controller?.abort();
|
||||
finishReason = 'close';
|
||||
finish_reason = 'close';
|
||||
break;
|
||||
}
|
||||
|
||||
const {
|
||||
reasoningContent,
|
||||
content: toolChoiceContent,
|
||||
responseContent,
|
||||
finishReason
|
||||
} = parsePart({
|
||||
part,
|
||||
parseThinkTag: true,
|
||||
retainDatasetCite
|
||||
});
|
||||
textAnswer += toolChoiceContent;
|
||||
finish_reason = finishReason || finish_reason;
|
||||
|
||||
const responseChoice = part.choices?.[0]?.delta;
|
||||
const finish_reason = part.choices?.[0]?.finish_reason as CompletionFinishReason;
|
||||
finishReason = finishReason || finish_reason;
|
||||
|
||||
if (responseChoice?.content) {
|
||||
const content = responseChoice.content || '';
|
||||
textAnswer += content;
|
||||
|
||||
// Reasoning response
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
reasoning_content: reasoningContent
|
||||
})
|
||||
});
|
||||
}
|
||||
if (responseChoice?.tool_calls?.[0]) {
|
||||
// @ts-ignore
|
||||
const toolCall: ChatCompletionMessageToolCall = responseChoice.tool_calls[0];
|
||||
// In a stream response, only one tool is returned at a time. If have id, description is executing a tool
|
||||
if (toolCall.id || callingTool) {
|
||||
// Start call tool
|
||||
if (toolCall.id) {
|
||||
callingTool = {
|
||||
name: toolCall.function?.name || '',
|
||||
arguments: toolCall.function?.arguments || ''
|
||||
};
|
||||
} else if (callingTool) {
|
||||
// Continue call
|
||||
callingTool.name += toolCall.function.name || '';
|
||||
callingTool.arguments += toolCall.function.arguments || '';
|
||||
}
|
||||
if (responseContent) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: responseContent
|
||||
})
|
||||
});
|
||||
}
|
||||
// Parse tool calls
|
||||
if (responseChoice?.tool_calls?.length) {
|
||||
responseChoice.tool_calls.forEach((toolCall) => {
|
||||
const index = toolCall.index;
|
||||
|
||||
const toolFunction = callingTool!;
|
||||
// Call new tool
|
||||
if (toolCall.id || callingTool) {
|
||||
// 有 id，代表新 call 工具
|
||||
if (toolCall.id) {
|
||||
callingTool = {
|
||||
name: toolCall.function?.name || '',
|
||||
arguments: toolCall.function?.arguments || ''
|
||||
};
|
||||
} else if (callingTool) {
|
||||
// Continue call(Perhaps the name of the previous function was incomplete)
|
||||
callingTool.name += toolCall.function?.name || '';
|
||||
callingTool.arguments += toolCall.function?.arguments || '';
|
||||
}
|
||||
|
||||
const toolNode = toolNodes.find((item) => item.nodeId === toolFunction.name);
|
||||
if (!callingTool) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (toolNode) {
|
||||
// New tool, add to list.
|
||||
const toolId = getNanoid();
|
||||
toolCalls.push({
|
||||
...toolCall,
|
||||
id: toolId,
|
||||
type: 'function',
|
||||
function: toolFunction,
|
||||
toolName: toolNode.name,
|
||||
toolAvatar: toolNode.avatar
|
||||
});
|
||||
const toolNode = toolNodes.find((item) => item.nodeId === callingTool!.name);
|
||||
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolCall,
|
||||
data: {
|
||||
tool: {
|
||||
id: toolId,
|
||||
toolName: toolNode.name,
|
||||
toolAvatar: toolNode.avatar,
|
||||
functionName: toolFunction.name,
|
||||
params: toolFunction?.arguments ?? '',
|
||||
response: ''
|
||||
if (toolNode) {
|
||||
// New tool, add to list.
|
||||
const toolId = getNanoid();
|
||||
toolCalls[index] = {
|
||||
...toolCall,
|
||||
id: toolId,
|
||||
type: 'function',
|
||||
function: callingTool,
|
||||
toolName: toolNode.name,
|
||||
toolAvatar: toolNode.avatar
|
||||
};
|
||||
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.toolCall,
|
||||
data: {
|
||||
tool: {
|
||||
id: toolId,
|
||||
toolName: toolNode.name,
|
||||
toolAvatar: toolNode.avatar,
|
||||
functionName: callingTool.name,
|
||||
params: callingTool?.arguments ?? '',
|
||||
response: ''
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
callingTool = null;
|
||||
}
|
||||
} else {
|
||||
/* arg 插入最后一个工具的参数里 */
|
||||
const arg: string = toolCall?.function?.arguments ?? '';
|
||||
const currentTool = toolCalls[toolCalls.length - 1];
|
||||
if (currentTool && arg) {
|
||||
currentTool.function.arguments += arg;
|
||||
});
|
||||
callingTool = null;
|
||||
}
|
||||
} else {
|
||||
/* arg 追加到当前工具的参数里 */
|
||||
const arg: string = toolCall?.function?.arguments ?? '';
|
||||
const currentTool = toolCalls[index];
|
||||
if (currentTool && arg) {
|
||||
currentTool.function.arguments += arg;
|
||||
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.toolParams,
|
||||
data: {
|
||||
tool: {
|
||||
id: currentTool.id,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: arg,
|
||||
response: ''
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.toolParams,
|
||||
data: {
|
||||
tool: {
|
||||
id: currentTool.id,
|
||||
toolName: '',
|
||||
toolAvatar: '',
|
||||
params: arg,
|
||||
response: ''
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return { answer: textAnswer, toolCalls, finish_reason: finishReason, usage };
|
||||
return { answer: textAnswer, toolCalls: toolCalls.filter(Boolean), finish_reason, usage };
|
||||
}
|
||||
|
||||
@@ -4,7 +4,11 @@ import type { ChatItemType, UserChatItemValueItemType } from '@fastgpt/global/co
|
||||
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 } from '../../../ai/utils';
|
||||
import {
|
||||
removeDatasetCiteText,
|
||||
parseReasoningContent,
|
||||
parseLLMStreamResponse
|
||||
} from '../../../ai/utils';
|
||||
import { createChatCompletion } from '../../../ai/config';
|
||||
import type {
|
||||
ChatCompletionMessageParam,
|
||||
@@ -75,7 +79,7 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
res,
|
||||
requestOrigin,
|
||||
stream = false,
|
||||
parseQuote = true,
|
||||
retainDatasetCite = true,
|
||||
externalProvider,
|
||||
histories,
|
||||
node: { name, version },
|
||||
@@ -159,8 +163,7 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
userChatInput,
|
||||
systemPrompt,
|
||||
userFiles,
|
||||
documentQuoteText,
|
||||
parseQuote
|
||||
documentQuoteText
|
||||
}),
|
||||
// Censor = true and system key, will check content
|
||||
(() => {
|
||||
@@ -223,7 +226,8 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
aiChatReasoning,
|
||||
parseThinkTag: modelConstantsData.reasoning,
|
||||
isResponseAnswerText,
|
||||
workflowStreamResponse
|
||||
workflowStreamResponse,
|
||||
retainDatasetCite
|
||||
});
|
||||
|
||||
return {
|
||||
@@ -258,23 +262,21 @@ export const dispatchChatCompletion = async (props: ChatProps): Promise<ChatResp
|
||||
})();
|
||||
|
||||
// Some models do not support streaming
|
||||
if (stream) {
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: reasoningContent
|
||||
})
|
||||
});
|
||||
}
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: content
|
||||
})
|
||||
});
|
||||
}
|
||||
if (aiChatReasoning && reasoningContent) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
reasoning_content: removeDatasetCiteText(reasoningContent, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
if (isResponseAnswerText && content) {
|
||||
workflowStreamResponse?.({
|
||||
event: SseResponseEventEnum.fastAnswer,
|
||||
data: textAdaptGptResponse({
|
||||
text: removeDatasetCiteText(content, retainDatasetCite)
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
@@ -452,8 +454,7 @@ async function getChatMessages({
|
||||
systemPrompt,
|
||||
userChatInput,
|
||||
userFiles,
|
||||
documentQuoteText,
|
||||
parseQuote = true
|
||||
documentQuoteText
|
||||
}: {
|
||||
model: LLMModelItemType;
|
||||
maxTokens?: number;
|
||||
@@ -470,14 +471,13 @@ async function getChatMessages({
|
||||
|
||||
userFiles: UserChatItemValueItemType['file'][];
|
||||
documentQuoteText?: string; // document quote
|
||||
parseQuote?: boolean;
|
||||
}) {
|
||||
// Dataset prompt ====>
|
||||
// User role or prompt include question
|
||||
const quoteRole =
|
||||
aiChatQuoteRole === 'user' || datasetQuotePrompt.includes('{{question}}') ? 'user' : 'system';
|
||||
|
||||
const defaultQuotePrompt = getQuotePrompt(version, quoteRole, parseQuote);
|
||||
const defaultQuotePrompt = getQuotePrompt(version, quoteRole);
|
||||
|
||||
const datasetQuotePromptTemplate = datasetQuotePrompt || defaultQuotePrompt;
|
||||
|
||||
@@ -539,7 +539,8 @@ async function streamResponse({
|
||||
workflowStreamResponse,
|
||||
aiChatReasoning,
|
||||
parseThinkTag,
|
||||
isResponseAnswerText
|
||||
isResponseAnswerText,
|
||||
retainDatasetCite = true
|
||||
}: {
|
||||
res: NextApiResponse;
|
||||
stream: StreamChatType;
|
||||
@@ -547,6 +548,7 @@ async function streamResponse({
|
||||
aiChatReasoning?: boolean;
|
||||
parseThinkTag?: boolean;
|
||||
isResponseAnswerText?: boolean;
|
||||
retainDatasetCite: boolean;
|
||||
}) {
|
||||
const write = responseWriteController({
|
||||
res,
|
||||
@@ -557,7 +559,7 @@ async function streamResponse({
|
||||
let finish_reason: CompletionFinishReason = null;
|
||||
let usage: CompletionUsage = getLLMDefaultUsage();
|
||||
|
||||
const { parsePart, getStartTagBuffer } = parseReasoningStreamContent();
|
||||
const { parsePart } = parseLLMStreamResponse();
|
||||
|
||||
for await (const part of stream) {
|
||||
usage = part.usage || usage;
|
||||
@@ -568,7 +570,11 @@ async function streamResponse({
|
||||
break;
|
||||
}
|
||||
|
||||
const { reasoningContent, content, finishReason } = parsePart(part, parseThinkTag);
|
||||
const { reasoningContent, content, responseContent, finishReason } = parsePart({
|
||||
part,
|
||||
parseThinkTag,
|
||||
retainDatasetCite
|
||||
});
|
||||
finish_reason = finish_reason || finishReason;
|
||||
answer += content;
|
||||
reasoning += reasoningContent;
|
||||
@@ -583,26 +589,12 @@ async function streamResponse({
|
||||
});
|
||||
}
|
||||
|
||||
if (isResponseAnswerText && content) {
|
||||
if (isResponseAnswerText && responseContent) {
|
||||
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();
|
||||
if (isResponseAnswerText && answer) {
|
||||
workflowStreamResponse?.({
|
||||
write,
|
||||
event: SseResponseEventEnum.answer,
|
||||
data: textAdaptGptResponse({
|
||||
text: answer
|
||||
text: responseContent
|
||||
})
|
||||
});
|
||||
}
|
||||
|
||||
@@ -21,7 +21,7 @@ export async function dispatchDatasetConcat(
|
||||
props: DatasetConcatProps
|
||||
): Promise<DatasetConcatResponse> {
|
||||
const {
|
||||
params: { limit = 1500, ...quoteMap }
|
||||
params: { limit = 6000, ...quoteMap }
|
||||
} = props as DatasetConcatProps;
|
||||
|
||||
const quoteList = Object.values(quoteMap).filter((list) => Array.isArray(list));
|
||||
|
||||
@@ -55,11 +55,10 @@ export async function dispatchDatasetSearch(
|
||||
runningUserInfo: { tmbId },
|
||||
histories,
|
||||
node,
|
||||
parseQuote = true,
|
||||
params: {
|
||||
datasets = [],
|
||||
similarity,
|
||||
limit = 1500,
|
||||
limit = 5000,
|
||||
userChatInput = '',
|
||||
authTmbId = false,
|
||||
collectionFilterMatch,
|
||||
@@ -114,7 +113,6 @@ export async function dispatchDatasetSearch(
|
||||
if (datasetIds.length === 0) {
|
||||
return emptyResult;
|
||||
}
|
||||
// console.log(concatQueries, rewriteQuery, aiExtensionResult);
|
||||
|
||||
// get vector
|
||||
const vectorModel = getEmbeddingModel(
|
||||
@@ -267,7 +265,7 @@ export async function dispatchDatasetSearch(
|
||||
[DispatchNodeResponseKeyEnum.nodeResponse]: responseData,
|
||||
nodeDispatchUsages,
|
||||
[DispatchNodeResponseKeyEnum.toolResponses]: {
|
||||
prompt: getDatasetSearchToolResponsePrompt(parseQuote),
|
||||
prompt: getDatasetSearchToolResponsePrompt(),
|
||||
quotes: searchRes.map((item) => ({
|
||||
id: item.id,
|
||||
sourceName: item.sourceName,
|
||||
|
||||
@@ -135,7 +135,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
|
||||
timezone,
|
||||
externalProvider,
|
||||
stream = false,
|
||||
parseQuote = true,
|
||||
retainDatasetCite = true,
|
||||
version = 'v1',
|
||||
responseDetail = true,
|
||||
responseAllData = true,
|
||||
@@ -607,7 +607,7 @@ export async function dispatchWorkFlow(data: Props): Promise<DispatchFlowRespons
|
||||
timezone,
|
||||
externalProvider,
|
||||
stream,
|
||||
parseQuote,
|
||||
retainDatasetCite,
|
||||
node,
|
||||
runtimeNodes,
|
||||
runtimeEdges,
|
||||
|
||||
@@ -1,10 +1,7 @@
|
||||
import { getErrText } from '@fastgpt/global/common/error/utils';
|
||||
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
|
||||
import type { ChatItemType } from '@fastgpt/global/core/chat/type.d';
|
||||
import {
|
||||
WorkflowIOValueTypeEnum,
|
||||
NodeOutputKeyEnum
|
||||
} from '@fastgpt/global/core/workflow/constants';
|
||||
import { NodeOutputKeyEnum } from '@fastgpt/global/core/workflow/constants';
|
||||
import {
|
||||
RuntimeEdgeItemType,
|
||||
RuntimeNodeItemType,
|
||||
@@ -34,31 +31,22 @@ export const getWorkflowResponseWrite = ({
|
||||
return ({
|
||||
write,
|
||||
event,
|
||||
data,
|
||||
stream
|
||||
data
|
||||
}: {
|
||||
write?: (text: string) => void;
|
||||
event: SseResponseEventEnum;
|
||||
data: Record<string, any>;
|
||||
stream?: boolean; // Focus set stream response
|
||||
}) => {
|
||||
const useStreamResponse = stream ?? streamResponse;
|
||||
const useStreamResponse = streamResponse;
|
||||
|
||||
if (!res || res.closed || !useStreamResponse) return;
|
||||
|
||||
// Forbid show detail
|
||||
const detailEvent: Record<string, 1> = {
|
||||
[SseResponseEventEnum.error]: 1,
|
||||
[SseResponseEventEnum.flowNodeStatus]: 1,
|
||||
[SseResponseEventEnum.flowResponses]: 1,
|
||||
[SseResponseEventEnum.interactive]: 1,
|
||||
[SseResponseEventEnum.toolCall]: 1,
|
||||
[SseResponseEventEnum.toolParams]: 1,
|
||||
[SseResponseEventEnum.toolResponse]: 1,
|
||||
[SseResponseEventEnum.updateVariables]: 1,
|
||||
[SseResponseEventEnum.flowNodeResponse]: 1
|
||||
const notDetailEvent: Record<string, 1> = {
|
||||
[SseResponseEventEnum.answer]: 1,
|
||||
[SseResponseEventEnum.fastAnswer]: 1
|
||||
};
|
||||
if (!detail && detailEvent[event]) return;
|
||||
if (!detail && !notDetailEvent[event]) return;
|
||||
|
||||
// Forbid show running status
|
||||
const statusEvent: Record<string, 1> = {
|
||||
|
||||
@@ -308,7 +308,7 @@
|
||||
"key": "limit",
|
||||
"renderTypeList": ["hidden"],
|
||||
"label": "",
|
||||
"value": 1500,
|
||||
"value": 5000,
|
||||
"valueType": "number"
|
||||
},
|
||||
{
|
||||
|
||||
@@ -211,7 +211,7 @@
|
||||
"key": "limit",
|
||||
"renderTypeList": ["hidden"],
|
||||
"label": "",
|
||||
"value": 1500,
|
||||
"value": 5000,
|
||||
"valueType": "number"
|
||||
},
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user