Files
FastGPT/packages/service/core/chat/utils.ts
Archer 9d27de154b 4.7-alpha2 (#1027)
* feat: stop toolCall and rename some field. (#46)

* perf: node delete tip;pay tip

* fix: toolCall cannot save child answer

* feat: stop tool

* fix: team modal

* fix feckbackMoal  auth bug (#47)

* 简单的支持提示词运行tool。优化workflow模板 (#49)

* remove templates

* fix: request body undefined

* feat: prompt tool run

* feat: workflow tamplates modal

* perf: plugin start

* 4.7 (#50)

* fix docker-compose download url (#994)

original code is a bad url with '404 NOT FOUND' return.
fix docker-compose download url, add 'v' before docker-compose version

* Update ai_settings.md (#1000)

* Update configuration.md

* Update configuration.md

* Fix history in classifyQuestion and extract modules (#1012)

* Fix history in classifyQuestion and extract modules

* Add chatValue2RuntimePrompt import and update text formatting

* flow controller to packages

* fix: rerank select

* modal ui

* perf: modal code path

* point not sufficient

* feat: http url support variable

* fix http key

* perf: prompt

* perf: ai setting modal

* simple edit ui

---------

Co-authored-by: entorick <entorick11@qq.com>
Co-authored-by: liujianglc <liujianglc@163.com>
Co-authored-by: Fengrui Liu <liufengrui.work@bytedance.com>

* fix team share redirect to login (#51)

* feat: support openapi import plugins (#48)

* feat: support openapi import plugins

* feat: import from url

* fix: add body params parse

* fix build

* fix

* fix

* fix

* tool box ui (#52)

* fix: training queue

* feat: simple edit tool select

* perf: simple edit dataset prompt

* fix: chatbox tool ux

* feat: quote prompt module

* perf: plugin tools sign

* perf: model avatar

* tool selector ui

* feat: max histories

* perf: http plugin import (#53)

* perf: plugin http import

* chatBox ui

* perf: name

* fix: Node template card (#54)

* fix: ts

* setting modal

* package

* package

* feat: add plugins search (#57)

* feat: add plugins search

* perf: change http plugin header input

* Yjl (#56)

* perf: prompt tool call

* perf: chat box ux

* doc

* doc

* price tip

* perf: tool selector

* ui'

* fix: vector queue

* fix: empty tool and empty response

* fix: empty msg

* perf: pg index

* perf: ui tip

* doc

* tool tip

---------

Co-authored-by: yst <77910600+yu-and-liu@users.noreply.github.com>
Co-authored-by: entorick <entorick11@qq.com>
Co-authored-by: liujianglc <liujianglc@163.com>
Co-authored-by: Fengrui Liu <liufengrui.work@bytedance.com>
Co-authored-by: heheer <71265218+newfish-cmyk@users.noreply.github.com>
2024-03-21 13:32:31 +08:00

250 lines
6.8 KiB
TypeScript
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import { ChatRoleEnum, IMG_BLOCK_KEY } from '@fastgpt/global/core/chat/constants';
import { countGptMessagesTokens } from '@fastgpt/global/common/string/tiktoken';
import type {
ChatCompletionContentPart,
ChatCompletionMessageParam
} from '@fastgpt/global/core/ai/type.d';
import axios from 'axios';
import { ChatCompletionRequestMessageRoleEnum } from '@fastgpt/global/core/ai/constants';
/* slice chat context by tokens */
const filterEmptyMessages = (messages: ChatCompletionMessageParam[]) => {
return messages.filter((item) => {
if (item.role === ChatCompletionRequestMessageRoleEnum.System) return !!item.content;
if (item.role === ChatCompletionRequestMessageRoleEnum.User) return !!item.content;
if (item.role === ChatCompletionRequestMessageRoleEnum.Assistant)
return !!item.content || !!item.function_call || !!item.tool_calls;
return true;
});
};
export function filterGPTMessageByMaxTokens({
messages = [],
maxTokens
}: {
messages: ChatCompletionMessageParam[];
maxTokens: number;
}) {
if (!Array.isArray(messages)) {
return [];
}
const rawTextLen = messages.reduce((sum, item) => {
if (typeof item.content === 'string') {
return sum + item.content.length;
}
if (Array.isArray(item.content)) {
return (
sum +
item.content.reduce((sum, item) => {
if (item.type === 'text') {
return sum + item.text.length;
}
return sum;
}, 0)
);
}
return sum;
}, 0);
// If the text length is less than half of the maximum token, no calculation is required
if (rawTextLen < maxTokens * 0.5) {
return filterEmptyMessages(messages);
}
// filter startWith system prompt
const chatStartIndex = messages.findIndex(
(item) => item.role !== ChatCompletionRequestMessageRoleEnum.System
);
const systemPrompts: ChatCompletionMessageParam[] = messages.slice(0, chatStartIndex);
const chatPrompts: ChatCompletionMessageParam[] = messages.slice(chatStartIndex);
// reduce token of systemPrompt
maxTokens -= countGptMessagesTokens(systemPrompts);
// Save the last chat prompt(question)
const question = chatPrompts.pop();
if (!question) {
return systemPrompts;
}
const chats: ChatCompletionMessageParam[] = [question];
// 从后往前截取对话内容, 每次需要截取2个
while (1) {
const assistant = chatPrompts.pop();
const user = chatPrompts.pop();
if (!assistant || !user) {
break;
}
const tokens = countGptMessagesTokens([assistant, user]);
maxTokens -= tokens;
/* 整体 tokens 超出范围,截断 */
if (maxTokens < 0) {
break;
}
chats.unshift(assistant);
chats.unshift(user);
if (chatPrompts.length === 0) {
break;
}
}
return filterEmptyMessages([...systemPrompts, ...chats]);
}
export const formatGPTMessagesInRequestBefore = (messages: ChatCompletionMessageParam[]) => {
return messages
.map((item) => {
if (!item.content) return;
if (typeof item.content === 'string') {
return {
...item,
content: item.content.trim()
};
}
// array
if (item.content.length === 0) return;
if (item.content.length === 1 && item.content[0].type === 'text') {
return {
...item,
content: item.content[0].text
};
}
return item;
})
.filter(Boolean) as ChatCompletionMessageParam[];
};
/**
string to vision model. Follow the markdown code block rule for interception:
@rule:
```img-block
{src:""}
{src:""}
```
```file-block
{name:"",src:""},
{name:"",src:""}
```
@example:
Whats in this image?
```img-block
{src:"https://1.png"}
```
@return
[
{ type: 'text', text: 'Whats in this image?' },
{
type: 'image_url',
image_url: {
url: 'https://1.png'
}
}
]
*/
export async function formatStr2ChatContent(str: string) {
const content: ChatCompletionContentPart[] = [];
let lastIndex = 0;
const regex = new RegExp(`\`\`\`(${IMG_BLOCK_KEY})\\n([\\s\\S]*?)\`\`\``, 'g');
const imgKey: 'image_url' = 'image_url';
let match;
while ((match = regex.exec(str)) !== null) {
// add previous text
if (match.index > lastIndex) {
const text = str.substring(lastIndex, match.index).trim();
if (text) {
content.push({ type: 'text', text });
}
}
const blockType = match[1].trim();
if (blockType === IMG_BLOCK_KEY) {
const blockContentLines = match[2].trim().split('\n');
const jsonLines = blockContentLines.map((item) => {
try {
return JSON.parse(item) as { src: string };
} catch (error) {
return { src: '' };
}
});
for (const item of jsonLines) {
if (!item.src) throw new Error("image block's content error");
}
content.push(
...jsonLines.map((item) => ({
type: imgKey,
image_url: {
url: item.src
}
}))
);
}
lastIndex = regex.lastIndex;
}
// add remaining text
if (lastIndex < str.length) {
const remainingText = str.substring(lastIndex).trim();
if (remainingText) {
content.push({ type: 'text', text: remainingText });
}
}
// Continuous text type content, if type=text, merge them
for (let i = 0; i < content.length - 1; i++) {
const currentContent = content[i];
const nextContent = content[i + 1];
if (currentContent.type === 'text' && nextContent.type === 'text') {
currentContent.text += nextContent.text;
content.splice(i + 1, 1);
i--;
}
}
if (content.length === 1 && content[0].type === 'text') {
return content[0].text;
}
if (!content) return null;
// load img to base64
for await (const item of content) {
if (item.type === imgKey && item[imgKey]?.url) {
const response = await axios.get(item[imgKey].url, {
responseType: 'arraybuffer'
});
const base64 = Buffer.from(response.data).toString('base64');
item[imgKey].url = `data:${response.headers['content-type']};base64,${base64}`;
}
}
return content ? content : null;
}
export const loadChatImgToBase64 = async (content: string | ChatCompletionContentPart[]) => {
if (typeof content === 'string') {
return content;
}
return Promise.all(
content.map(async (item) => {
if (item.type === 'text') return item;
// load image
const response = await axios.get(item.image_url.url, {
responseType: 'arraybuffer'
});
const base64 = Buffer.from(response.data).toString('base64');
item.image_url.url = `data:${response.headers['content-type']};base64,${base64}`;
return item;
})
);
};