Files
FastGPT/packages/service/core/chat/saveChat.ts
T
Archer 7a6601394d perf: agent pause (#6588)
* doc

* feat: Pause Recovery (#6494)

* feat: Pause Recovery

* agent pause

* agent pause

* fix:agent pause

* fix:agent pause

* perf: pause agent call

* fix: test

---------

Co-authored-by: archer <545436317@qq.com>

* fix: image read and json error (Agent) (#6502)

* fix:
1.image read
2.JSON parsing error

* dataset cite and pause

* perf: plancall second parse

* add test

---------

Co-authored-by: archer <545436317@qq.com>

* master message

* remove invalid code

* fix: pause agent (#6595)

* fix: ask and step result

* delete console

* udpate pnpm version

* prettier

---------

Co-authored-by: YeYuheng <57035043+YYH211@users.noreply.github.com>
2026-03-20 18:07:29 +08:00

672 lines
18 KiB
TypeScript

import type {
AIChatItemType,
ChatHistoryItemResType,
UserChatItemType
} from '@fastgpt/global/core/chat/type';
import type { ChatSourceEnum } from '@fastgpt/global/core/chat/constants';
import { ChatRoleEnum } from '@fastgpt/global/core/chat/constants';
import { MongoChatItem } from './chatItemSchema';
import { MongoChat } from './chatSchema';
import { mongoSessionRun } from '../../common/mongo/sessionRun';
import { type StoreNodeItemType } from '@fastgpt/global/core/workflow/type/node';
import { getAppChatConfig, getGuideModule } from '@fastgpt/global/core/workflow/utils';
import { type AppChatConfigType, type VariableItemType } from '@fastgpt/global/core/app/type';
import {
checkInteractiveResponseStatus,
mergeChatResponseData
} from '@fastgpt/global/core/chat/utils';
import { pushChatLog } from './pushChatLog';
import {
FlowNodeTypeEnum,
FlowNodeInputTypeEnum
} from '@fastgpt/global/core/workflow/node/constant';
import { extractDeepestInteractive } from '@fastgpt/global/core/workflow/runtime/utils';
import { MongoAppChatLog } from '../app/logs/chatLogsSchema';
import { writePrimary } from '../../common/mongo/utils';
import { getLogger, LogCategories } from '../../common/logger';
const logger = getLogger(LogCategories.MODULE.CHAT.HISTORY);
import { MongoChatItemResponse } from './chatItemResponseSchema';
import { chatValue2RuntimePrompt } from '@fastgpt/global/core/chat/adapt';
import type { ClientSession } from '../../common/mongo';
import { removeS3TTL } from '../../common/s3/utils';
import { VariableInputEnum } from '@fastgpt/global/core/workflow/constants';
import { encryptSecretValue, anyValueDecrypt } from '../../common/secret/utils';
import type { SecretValueType } from '@fastgpt/global/common/secret/type';
import type { WorkflowInteractiveResponseType } from '@fastgpt/global/core/workflow/template/system/interactive/type';
import { getFlatAppResponses } from '@fastgpt/global/core/chat/utils';
export type Props = {
chatId: string;
appId: string;
versionId?: string;
teamId: string;
tmbId: string;
nodes: StoreNodeItemType[];
appChatConfig?: AppChatConfigType;
variables?: Record<string, any>;
newTitle: string;
source: `${ChatSourceEnum}`;
sourceName?: string;
shareId?: string;
outLinkUid?: string;
userContent: UserChatItemType & { dataId?: string };
aiContent: AIChatItemType & { dataId?: string };
metadata?: Record<string, any>;
durationSeconds: number; //s
errorMsg?: string;
};
const beforProcess = (props: Props) => {
// Remove url
props.userContent.value.forEach((item) => {
if (item.file?.key) {
item.file.url = '';
}
});
};
const afterProcess = async ({
contents,
variables,
variableList,
session
}: {
contents: (UserChatItemType | AIChatItemType)[];
variables?: Record<string, any>;
variableList?: VariableItemType[];
session: ClientSession;
}) => {
const contentFileKeys = contents
.map((item) => {
if (item.value && Array.isArray(item.value)) {
return item.value.flatMap((valueItem) => {
const keys: string[] = [];
// 1. chat file
if ('file' in valueItem && valueItem.file?.key) {
keys.push(valueItem.file.key);
}
// 2. plugin input
if ('text' in valueItem && valueItem.text?.content) {
try {
const parsed = JSON.parse(valueItem.text.content);
// 2.1 plugin input - 数组格式
if (Array.isArray(parsed)) {
parsed.forEach((field) => {
if (field.value && Array.isArray(field.value)) {
field.value.forEach((file: { key: string }) => {
if (file.key && typeof file.key === 'string') {
keys.push(file.key);
}
});
}
});
}
// 2.2 form input - 对象格式 { "字段名": [{ key, url, ... }] }
else if (parsed && typeof parsed === 'object' && !Array.isArray(parsed)) {
Object.values(parsed).forEach((fieldValue) => {
if (Array.isArray(fieldValue)) {
fieldValue.forEach((file: any) => {
if (
file &&
typeof file === 'object' &&
file.key &&
typeof file.key === 'string'
) {
keys.push(file.key);
}
});
}
});
}
} catch (err) {}
}
return keys;
});
}
return [];
})
.flat()
.filter(Boolean) as string[];
const variableFileKeys: string[] = [];
if (variables && variableList) {
variableList.forEach((varItem) => {
if (varItem.type === VariableInputEnum.file) {
const varValue = variables[varItem.key];
if (Array.isArray(varValue)) {
variableFileKeys.push(...varValue.map((item) => item.key));
}
}
});
}
const allFileKeys = [...new Set([...contentFileKeys, ...variableFileKeys])];
if (allFileKeys.length > 0) {
await removeS3TTL({ key: allFileKeys, bucketName: 'private', session });
}
};
const formatAiContent = ({
aiContent,
durationSeconds,
errorMsg
}: {
aiContent: AIChatItemType & { dataId?: string };
durationSeconds: number;
errorMsg?: string;
}) => {
const { responseData, ...aiResponse } = aiContent;
const citeCollectionIds = new Set<string>();
const dealResponseData = (responseItem: ChatHistoryItemResType) => {
if (responseItem.moduleType === FlowNodeTypeEnum.datasetSearchNode && responseItem.quoteList) {
// @ts-ignore
responseItem.quoteList = responseItem.quoteList.map((quote) => {
citeCollectionIds.add(quote.collectionId);
return {
id: quote.id,
chunkIndex: quote.chunkIndex,
datasetId: quote.datasetId,
collectionId: quote.collectionId,
sourceId: quote.sourceId,
sourceName: quote.sourceName,
score: quote.score
};
});
}
};
getFlatAppResponses(responseData || []).forEach(dealResponseData);
const errorCount = responseData?.filter((item) => item.errorText).length ?? 0;
return {
aiResponse: {
...aiResponse,
durationSeconds,
errorMsg,
citeCollectionIds: Array.from(citeCollectionIds)
},
nodeResponses: responseData,
citeCollectionIds,
errorCount
};
};
const getChatDataLog = async ({
nodeResponses
}: {
nodeResponses: ReturnType<typeof formatAiContent>['nodeResponses'];
}) => {
const now = new Date();
const fifteenMinutesAgo = new Date(now.getTime() - 15 * 60 * 1000);
const errorCount = nodeResponses?.some((item) => item.errorText) ? 1 : 0;
const totalPoints =
nodeResponses?.reduce((sum: number, item: any) => sum + (item.totalPoints || 0), 0) || 0;
return {
fifteenMinutesAgo,
errorCount,
totalPoints,
now
};
};
export const pushChatRecords = async (props: Props) => {
beforProcess(props);
const {
chatId,
appId,
versionId,
teamId,
tmbId,
nodes,
appChatConfig,
variables,
newTitle,
source,
sourceName,
shareId,
outLinkUid,
userContent,
aiContent,
durationSeconds,
errorMsg,
metadata = {}
} = props;
if (!chatId || chatId === 'NO_RECORD_HISTORIES') return;
try {
const chat = await MongoChat.findOne(
{
appId,
chatId
},
'_id metadata'
);
const metadataUpdate = {
...chat?.metadata,
...metadata
};
const { welcomeText, variables: variableList } = getAppChatConfig({
chatConfig: appChatConfig,
systemConfigNode: getGuideModule(nodes),
isPublicFetch: false
});
const pluginInputs = nodes?.find(
(node) => node.flowNodeType === FlowNodeTypeEnum.pluginInput
)?.inputs;
// Format save chat content: Remove quote q/a
const { aiResponse, nodeResponses, errorCount } = formatAiContent({
aiContent,
durationSeconds,
errorMsg
});
const processedContent = [userContent, aiResponse];
await mongoSessionRun(async (session) => {
const [{ _id: chatItemIdHuman }, { _id: chatItemIdAi, dataId }] = await MongoChatItem.create(
processedContent.map((item) => ({
chatId,
teamId,
tmbId,
appId,
...item
})),
{ session, ordered: true, ...writePrimary }
);
// Create chat item respones
if (nodeResponses) {
await MongoChatItemResponse.create(
nodeResponses.map((item) => ({
teamId,
appId,
chatId,
chatItemDataId: dataId,
data: item
})),
{ session, ordered: true, ...writePrimary }
);
}
await MongoChat.updateOne(
{
appId,
chatId
},
{
$set: {
teamId,
tmbId,
appId,
appVersionId: versionId,
chatId,
variableList,
welcomeText,
variables: variables || {},
pluginInputs,
title: newTitle,
source,
sourceName,
shareId,
outLinkUid,
metadata: metadataUpdate,
updateTime: new Date()
},
$setOnInsert: {
createTime: new Date()
},
...(errorCount > 0 && { $inc: { errorCount: errorCount } })
},
{
session,
upsert: true,
...writePrimary
}
);
await afterProcess({
contents: processedContent,
variables,
variableList,
session
});
pushChatLog({
chatId,
chatItemIdHuman: String(chatItemIdHuman),
chatItemIdAi: String(chatItemIdAi),
appId
});
});
// Create chat data log
try {
const { fifteenMinutesAgo, errorCount, totalPoints, now } = await getChatDataLog({
nodeResponses
});
const userId = String(outLinkUid || tmbId);
const hasHistoryChat = await MongoAppChatLog.exists({
teamId,
appId,
userId,
createTime: { $lt: now }
});
await MongoAppChatLog.updateOne(
{
teamId,
appId,
chatId,
updateTime: { $gte: fifteenMinutesAgo }
},
{
$inc: {
chatItemCount: 1,
errorCount,
totalPoints,
totalResponseTime: durationSeconds
},
$set: {
updateTime: now,
sourceName
},
$setOnInsert: {
appId,
teamId,
chatId,
userId,
source,
createTime: now,
goodFeedbackCount: 0,
badFeedbackCount: 0,
isFirstChat: !hasHistoryChat
}
},
{
upsert: true,
...writePrimary
}
);
} catch (error) {
logger.error('Failed to push chat log', { chatId, error });
}
} catch (error) {
logger.error('Failed to update chat history', { chatId, error });
}
};
/*
更新交互节点,包含两种情况:
1. 更新当前的 items,并把 value 追加到当前 items
2. 新增 items, 次数只需要改当前的 items 里的交互节点值即可,其他属性追加在新增的 items 里
*/
export const updateInteractiveChat = async ({
interactive,
...props
}: Props & {
interactive: WorkflowInteractiveResponseType;
}) => {
beforProcess(props);
const {
teamId,
chatId,
appId,
nodes,
appChatConfig,
userContent,
aiContent,
variables,
durationSeconds,
errorMsg
} = props;
if (!chatId) return;
const { variables: variableList } = getAppChatConfig({
chatConfig: appChatConfig,
systemConfigNode: getGuideModule(nodes),
isPublicFetch: false
});
const chatItem = await MongoChatItem.findOne({ appId, chatId, obj: ChatRoleEnum.AI }).sort({
_id: -1
});
if (!chatItem || chatItem.obj !== ChatRoleEnum.AI) return;
// Get interactive value
interactive.params = interactive.params || {};
// Get interactive response
const { text: userInteractiveVal } = chatValue2RuntimePrompt(userContent.value);
// 如果是发送一条新的 user 消息,则直接用推送记录的方式
const status = checkInteractiveResponseStatus({
interactive,
input: userInteractiveVal
});
// 提取嵌套在子流程里的交互节点
const finalInteractive = extractDeepestInteractive(interactive);
if (status === 'query') {
// 特殊处理:
{
// 1. AskQuery 需要把用户答案回填到上一条 interactive,避免后续多轮恢复时丢失 answer。
if (finalInteractive.type === 'agentPlanAskQuery') {
finalInteractive.params.answer = userInteractiveVal;
chatItem.value[chatItem.value.length - 1].interactive = interactive;
chatItem.markModified('value');
await chatItem.save();
// 追加 PlanId 给 userItem(便于适配器会跳过转化该条消息)
props.userContent.value.forEach((item) => {
item.planId = finalInteractive.planId;
});
}
}
return await pushChatRecords(props);
}
const parsedUserInteractiveVal = (() => {
try {
return JSON.parse(userInteractiveVal);
} catch (err) {
return userInteractiveVal;
}
})();
const { aiResponse, nodeResponses, errorCount } = formatAiContent({
aiContent,
durationSeconds,
errorMsg
});
/*
在原来 chat_items 上更新。
1. 更新交互响应结果
2. 合并 chat_item 数据
3. 合并 chat_item_response 数据
*/
// Update interactive value
{
if (
finalInteractive.type === 'userSelect' ||
finalInteractive.type === 'agentPlanAskUserSelect'
) {
finalInteractive.params.userSelectedVal = userInteractiveVal;
} else if (
(finalInteractive.type === 'userInput' || finalInteractive.type === 'agentPlanAskUserForm') &&
typeof parsedUserInteractiveVal === 'object'
) {
finalInteractive.params.inputForm = finalInteractive.params.inputForm.map((item) => {
const itemValue = parsedUserInteractiveVal[item.key];
if (itemValue === undefined) return item;
// 如果是密码类型,加密后存储
if (item.type === FlowNodeInputTypeEnum.password) {
const decryptedVal = anyValueDecrypt(itemValue);
if (typeof decryptedVal === 'string') {
return {
...item,
value: encryptSecretValue({
value: decryptedVal,
secret: ''
} as SecretValueType)
};
}
return {
...item,
value: itemValue
};
}
return {
...item,
value: itemValue
};
});
finalInteractive.params.submitted = true;
} else if (finalInteractive.type === 'paymentPause') {
chatItem.value.pop();
} else if (finalInteractive.type === 'agentPlanCheck') {
finalInteractive.params.confirmed = true;
}
// 将最新的 interactive 赋值给最后一条消息(最后一条必然是带交互的消息)
chatItem.value[chatItem.value.length - 1].interactive = interactive;
}
// Update current items
{
if (aiContent.customFeedbacks) {
chatItem.customFeedbacks = chatItem.customFeedbacks
? [...chatItem.customFeedbacks, ...aiContent.customFeedbacks]
: aiContent.customFeedbacks;
}
if (aiContent.value) {
chatItem.value = chatItem.value ? [...chatItem.value, ...aiContent.value] : aiContent.value;
}
if (aiResponse.citeCollectionIds) {
chatItem.citeCollectionIds = chatItem.citeCollectionIds
? [...chatItem.citeCollectionIds, ...aiResponse.citeCollectionIds]
: aiResponse.citeCollectionIds;
}
if (aiContent.memories) {
chatItem.memories = {
...chatItem.memories,
...aiContent.memories
};
}
chatItem.durationSeconds = chatItem.durationSeconds
? +(chatItem.durationSeconds + durationSeconds).toFixed(2)
: durationSeconds;
}
chatItem.markModified('value');
await mongoSessionRun(async (session) => {
await chatItem.save({ session });
await MongoChat.updateOne(
{
appId,
chatId
},
{
$set: {
variables,
updateTime: new Date()
},
...(errorCount > 0 && { $inc: { errorCount: errorCount } })
},
{
session
}
);
// Create chat item respones
if (nodeResponses) {
/*
Merge with last response data
如果是从嵌套的 node 里触发的交互,这里需要进行一个合并,否则会导致出现两次相同的 node(child response 无法合并起来)
*/
const lastResponse = await MongoChatItemResponse.findOneAndDelete({
appId,
chatId,
chatItemDataId: chatItem.dataId
})
.sort({
_id: -1
})
.lean()
.session(session);
const newResponses = lastResponse?.data
? mergeChatResponseData([lastResponse?.data, ...nodeResponses])
: nodeResponses;
await MongoChatItemResponse.create(
newResponses.map((item) => ({
teamId,
appId,
chatId,
chatItemDataId: chatItem.dataId,
data: item
})),
{ session, ordered: true, ...writePrimary }
);
}
await afterProcess({
contents: [userContent, aiContent],
variables,
variableList,
session
});
});
// Push chat data logs
try {
const { fifteenMinutesAgo, errorCount, totalPoints, now } = await getChatDataLog({
nodeResponses
});
await MongoAppChatLog.updateOne(
{
teamId,
appId,
chatId,
updateTime: { $gte: fifteenMinutesAgo }
},
{
$inc: {
chatItemCount: 1,
errorCount,
totalPoints,
totalResponseTime: durationSeconds
},
$set: {
updateTime: now
}
},
{
...writePrimary
}
);
} catch (error) {
logger.error('Failed to update interactive chat log', { chatId, error });
}
};