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462 lines
13 KiB
TypeScript
462 lines
13 KiB
TypeScript
import { type LLMModelItemType } from '@fastgpt/global/core/ai/model.d';
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import type {
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ChatCompletionCreateParamsNonStreaming,
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ChatCompletionCreateParamsStreaming,
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CompletionFinishReason,
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StreamChatType,
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UnStreamChatType,
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CompletionUsage,
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ChatCompletionMessageToolCall
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} from '@fastgpt/global/core/ai/type';
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import { getLLMModel } from './model';
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import { getLLMDefaultUsage } from '@fastgpt/global/core/ai/constants';
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import { getNanoid } from '@fastgpt/global/common/string/tools';
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import json5 from 'json5';
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/*
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Count response max token
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*/
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export const computedMaxToken = ({
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maxToken,
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model
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}: {
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maxToken?: number;
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model: LLMModelItemType;
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}) => {
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if (maxToken === undefined) return;
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maxToken = Math.min(maxToken, model.maxResponse);
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return maxToken;
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};
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// FastGPT temperature range: [0,10], ai temperature:[0,2],{0,1]……
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export const computedTemperature = ({
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model,
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temperature
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}: {
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model: LLMModelItemType;
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temperature: number;
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}) => {
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if (typeof model.maxTemperature !== 'number') return undefined;
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temperature = +(model.maxTemperature * (temperature / 10)).toFixed(2);
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temperature = Math.max(temperature, 0.01);
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return temperature;
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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 InferCompletionsBody<T> = T extends { stream: true }
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? ChatCompletionCreateParamsStreaming
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: T extends { stream: false }
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? ChatCompletionCreateParamsNonStreaming
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: ChatCompletionCreateParamsNonStreaming | ChatCompletionCreateParamsStreaming;
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export const llmCompletionsBodyFormat = <T extends CompletionsBodyType>(
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body: T & {
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stop?: string;
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},
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model: string | LLMModelItemType
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): InferCompletionsBody<T> => {
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const modelData = typeof model === 'string' ? getLLMModel(model) : model;
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if (!modelData) {
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return body as unknown as InferCompletionsBody<T>;
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}
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const response_format = (() => {
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if (!body.response_format?.type) return undefined;
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if (body.response_format.type === 'json_schema') {
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try {
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return {
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type: 'json_schema',
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json_schema: json5.parse(body.response_format?.json_schema as unknown as string)
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};
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} catch (error) {
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throw new Error('Json schema error');
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}
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}
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if (body.response_format.type) {
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return {
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type: body.response_format.type
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};
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}
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return undefined;
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})();
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const stop = body.stop ?? undefined;
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const requestBody: T = {
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...body,
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model: modelData.model,
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temperature:
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typeof body.temperature === 'number'
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? computedTemperature({
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model: modelData,
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temperature: body.temperature
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})
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: undefined,
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...modelData?.defaultConfig,
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response_format,
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stop: stop?.split('|')
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};
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// field map
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if (modelData.fieldMap) {
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Object.entries(modelData.fieldMap).forEach(([sourceKey, targetKey]) => {
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// @ts-ignore
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requestBody[targetKey] = body[sourceKey];
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// @ts-ignore
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delete requestBody[sourceKey];
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});
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}
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return requestBody as unknown as InferCompletionsBody<T>;
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};
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export const llmStreamResponseToAnswerText = async (
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response: StreamChatType
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): Promise<{
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text: string;
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usage?: CompletionUsage;
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toolCalls?: ChatCompletionMessageToolCall[];
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}> => {
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let answer = '';
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let usage = getLLMDefaultUsage();
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let toolCalls: ChatCompletionMessageToolCall[] = [];
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let callingTool: { name: string; arguments: string } | null = null;
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for await (const part of response) {
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usage = part.usage || usage;
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const responseChoice = part.choices?.[0]?.delta;
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const content = responseChoice?.content || '';
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answer += content;
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// Tool calls
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if (responseChoice?.tool_calls?.length) {
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responseChoice.tool_calls.forEach((toolCall, i) => {
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const index = toolCall.index ?? i;
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if (toolCall.id || callingTool) {
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// 有 id，代表新 call 工具
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if (toolCall.id) {
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callingTool = {
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name: toolCall.function?.name || '',
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arguments: toolCall.function?.arguments || ''
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};
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} else if (callingTool) {
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// Continue call(Perhaps the name of the previous function was incomplete)
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callingTool.name += toolCall.function?.name || '';
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callingTool.arguments += toolCall.function?.arguments || '';
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}
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if (!callingTool) {
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return;
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}
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// New tool, add to list.
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const toolId = getNanoid();
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toolCalls[index] = {
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...toolCall,
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id: toolId,
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type: 'function',
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function: callingTool
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};
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callingTool = null;
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} else {
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/* arg 追加到当前工具的参数里 */
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const arg: string = toolCall?.function?.arguments ?? '';
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const currentTool = toolCalls[index];
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if (currentTool && arg) {
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currentTool.function.arguments += arg;
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}
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}
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});
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}
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}
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return {
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text: parseReasoningContent(answer)[1],
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usage,
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toolCalls
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};
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};
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export const llmUnStreamResponseToAnswerText = async (
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response: UnStreamChatType
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): Promise<{
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text: string;
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toolCalls?: ChatCompletionMessageToolCall[];
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usage?: CompletionUsage;
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}> => {
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const answer = response.choices?.[0]?.message?.content || '';
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const toolCalls = response.choices?.[0]?.message?.tool_calls;
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return {
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text: answer,
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usage: response.usage,
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toolCalls
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};
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};
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export const formatLLMResponse = async (response: StreamChatType | UnStreamChatType) => {
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if ('iterator' in response) {
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return llmStreamResponseToAnswerText(response);
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}
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return llmUnStreamResponseToAnswerText(response);
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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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export const removeDatasetCiteText = (text: string, retainDatasetCite: boolean) => {
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return retainDatasetCite ? text : text.replace(/\[([a-f0-9]{24})\](?:\([^\)]*\)?)?/g, '');
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};
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// Parse llm stream part
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export const parseLLMStreamResponse = () => {
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let isInThinkTag: boolean | undefined = undefined;
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let startTagBuffer = '';
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let endTagBuffer = '';
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const thinkStartChars = '<think>';
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const thinkEndChars = '</think>';
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let citeBuffer = '';
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const maxCiteBufferLength = 32; // [Object](CITE)总长度为32
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/*
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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。
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retainDatasetCite -
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*/
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const parsePart = ({
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part,
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parseThinkTag = true,
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retainDatasetCite = true
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}: {
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part: {
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choices: {
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delta: {
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content?: string | null;
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reasoning_content?: string;
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};
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finish_reason?: CompletionFinishReason;
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}[];
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};
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parseThinkTag?: boolean;
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retainDatasetCite?: boolean;
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}): {
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reasoningContent: string;
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content: string;
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responseContent: string;
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finishReason: CompletionFinishReason;
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} => {
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const finishReason = part.choices?.[0]?.finish_reason || null;
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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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const isStreamEnd = !!finishReason;
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// Parse think
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const { reasoningContent: parsedThinkReasoningContent, content: parsedThinkContent } = (() => {
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if (reasoningContent || !parseThinkTag) {
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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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reasoningContent: '',
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content: ''
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};
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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 {
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reasoningContent: '',
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content
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};
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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 < thinkStartChars.length) {
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if (isStreamEnd) {
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const tmpContent = startTagBuffer;
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startTagBuffer = '';
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return {
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reasoningContent: '',
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content: tmpContent
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};
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}
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return {
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reasoningContent: '',
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content: ''
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};
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}
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if (startTagBuffer.startsWith(thinkStartChars)) {
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isInThinkTag = true;
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return {
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reasoningContent: startTagBuffer.slice(thinkStartChars.length),
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content: ''
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};
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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 {
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reasoningContent: '',
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content: startTagBuffer
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};
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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(thinkEndChars)) {
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isInThinkTag = false;
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const answer = endTagBuffer.slice(thinkEndChars.length);
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return {
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reasoningContent: '',
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content: answer
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};
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} else if (endTagBuffer.length >= thinkEndChars.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 {
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reasoningContent: tmp,
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content: ''
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};
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}
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return {
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reasoningContent: '',
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content: ''
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};
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} else if (content.includes(thinkEndChars)) {
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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(thinkEndChars);
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return {
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reasoningContent: think,
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content: answer
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};
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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 < thinkEndChars.length; i++) {
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const partialEndTag = thinkEndChars.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 {
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reasoningContent: think,
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content: ''
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};
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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 {
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reasoningContent: content,
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content: ''
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};
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})();
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// Parse datset cite
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if (retainDatasetCite) {
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return {
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reasoningContent: parsedThinkReasoningContent,
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content: parsedThinkContent,
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responseContent: parsedThinkContent,
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finishReason
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};
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}
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// 缓存包含 [ 的字符串，直到超出 maxCiteBufferLength 再一次性返回
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const parseCite = (text: string) => {
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// 结束时，返回所有剩余内容
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if (isStreamEnd) {
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const content = citeBuffer + text;
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return {
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content: removeDatasetCiteText(content, false)
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};
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}
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// 新内容包含 [，初始化缓冲数据
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if (text.includes('[')) {
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const index = text.indexOf('[');
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const beforeContent = citeBuffer + text.slice(0, index);
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citeBuffer = text.slice(index);
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// beforeContent 可能是：普通字符串，带 [ 的字符串
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return {
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content: removeDatasetCiteText(beforeContent, false)
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};
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}
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// 处于 Cite 缓冲区，判断是否满足条件
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else if (citeBuffer) {
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citeBuffer += text;
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// 检查缓冲区长度是否达到完整Quote长度或已经流结束
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if (citeBuffer.length >= maxCiteBufferLength) {
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const content = removeDatasetCiteText(citeBuffer, false);
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citeBuffer = '';
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return {
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content
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};
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} else {
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// 暂时不返回内容
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return { content: '' };
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}
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}
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return {
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content: text
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};
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};
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const { content: pasedCiteContent } = parseCite(parsedThinkContent);
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return {
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reasoningContent: parsedThinkReasoningContent,
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content: parsedThinkContent,
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responseContent: pasedCiteContent,
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finishReason
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};
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};
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return {
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parsePart
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};
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};
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