mirror of
https://github.com/EthanMarti/infio-copilot.git
synced 2026-01-16 08:21:55 +00:00
update save trans to database
This commit is contained in:
duanfuxiang
parent
f3a0252ab6
commit
4f5b3f5d04
@ -15,6 +15,7 @@ import { LLMProvider } from './contexts/LLMContext'
|
||||
import { McpHubProvider } from './contexts/McpHubContext'
|
||||
import { RAGProvider } from './contexts/RAGContext'
|
||||
import { SettingsProvider } from './contexts/SettingsContext'
|
||||
import { TransProvider } from './contexts/TransContext'
|
||||
import InfioPlugin from './main'
|
||||
import { MentionableBlockData } from './types/mentionable'
|
||||
import { InfioSettings } from './types/settings'
|
||||
@ -96,7 +97,8 @@ export class ChatView extends ItemView {
|
||||
>
|
||||
<DiffStrategyProvider diffStrategy={this.plugin.diffStrategy}>
|
||||
<RAGProvider getRAGEngine={() => this.plugin.getRAGEngine()}>
|
||||
<DataviewProvider dataviewManager={this.plugin.dataviewManager}>
|
||||
<TransProvider getTransEngine={() => this.plugin.getTransEngine()}>
|
||||
<DataviewProvider dataviewManager={this.plugin.dataviewManager}>
|
||||
<McpHubProvider getMcpHub={() => this.plugin.getMcpHub()}>
|
||||
<QueryClientProvider client={queryClient}>
|
||||
<React.StrictMode>
|
||||
@ -109,6 +111,7 @@ export class ChatView extends ItemView {
|
||||
</QueryClientProvider>
|
||||
</McpHubProvider>
|
||||
</DataviewProvider>
|
||||
</TransProvider>
|
||||
</RAGProvider>
|
||||
</DiffStrategyProvider>
|
||||
</DatabaseProvider>
|
||||
|
||||
@ -24,6 +24,7 @@ import { useLLM } from '../../contexts/LLMContext'
|
||||
import { useMcpHub } from '../../contexts/McpHubContext'
|
||||
import { useRAG } from '../../contexts/RAGContext'
|
||||
import { useSettings } from '../../contexts/SettingsContext'
|
||||
import { useTrans } from '../../contexts/TransContext'
|
||||
import { matchSearchUsingCorePlugin } from '../../core/file-search/match/coreplugin-match'
|
||||
import { matchSearchUsingOmnisearch } from '../../core/file-search/match/omnisearch-match'
|
||||
import { regexSearchUsingCorePlugin } from '../../core/file-search/regex/coreplugin-regex'
|
||||
@ -34,7 +35,7 @@ import {
|
||||
LLMBaseUrlNotSetException,
|
||||
LLMModelNotSetException,
|
||||
} from '../../core/llm/exception'
|
||||
import { TransformationType, runTransformation } from '../../core/transformations/run_trans'
|
||||
import { TransformationType } from '../../core/transformations/trans-engine'
|
||||
import { useChatHistory } from '../../hooks/use-chat-history'
|
||||
import { useCustomModes } from '../../hooks/use-custom-mode'
|
||||
import { t } from '../../lang/helpers'
|
||||
@ -118,6 +119,7 @@ const Chat = forwardRef<ChatRef, ChatProps>((props, ref) => {
|
||||
const app = useApp()
|
||||
const { settings, setSettings } = useSettings()
|
||||
const { getRAGEngine } = useRAG()
|
||||
const { getTransEngine } = useTrans()
|
||||
const diffStrategy = useDiffStrategy()
|
||||
const dataviewManager = useDataview()
|
||||
const { getMcpHub } = useMcpHub()
|
||||
@ -832,30 +834,24 @@ const Chat = forwardRef<ChatRef, ChatProps>((props, ref) => {
|
||||
} else if (toolArgs.type === 'call_transformations') {
|
||||
// Handling for the unified transformations tool
|
||||
try {
|
||||
const targetFile = app.vault.getFileByPath(toolArgs.path);
|
||||
if (!targetFile) {
|
||||
throw new Error(`File not found: ${toolArgs.path}`);
|
||||
}
|
||||
|
||||
const fileContent = await readTFileContentPdf(targetFile, app.vault, app);
|
||||
|
||||
// The transformation type is now passed directly in the arguments
|
||||
const transformationType = toolArgs.transformation as TransformationType;
|
||||
|
||||
console.log("call_transformations", toolArgs)
|
||||
// Validate that the transformation type is a valid enum member
|
||||
if (!Object.values(TransformationType).includes(transformationType)) {
|
||||
throw new Error(`Unsupported transformation type: ${transformationType}`);
|
||||
if (!Object.values(TransformationType).includes(toolArgs.transformation as TransformationType)) {
|
||||
throw new Error(`Unsupported transformation type: ${toolArgs.transformation}`);
|
||||
}
|
||||
|
||||
// Execute the transformation
|
||||
const transformationResult = await runTransformation({
|
||||
content: fileContent,
|
||||
transformationType,
|
||||
settings,
|
||||
const transformationType = toolArgs.transformation as TransformationType;
|
||||
const transEngine = await getTransEngine();
|
||||
|
||||
// Execute the transformation using the TransEngine
|
||||
const transformationResult = await transEngine.runTransformation({
|
||||
filePath: toolArgs.path,
|
||||
transformationType: transformationType,
|
||||
model: {
|
||||
provider: settings.applyModelProvider,
|
||||
modelId: settings.applyModelId,
|
||||
}
|
||||
},
|
||||
saveToDatabase: true
|
||||
});
|
||||
|
||||
if (!transformationResult.success) {
|
||||
@ -863,7 +859,7 @@ const Chat = forwardRef<ChatRef, ChatProps>((props, ref) => {
|
||||
}
|
||||
|
||||
// Build the result message
|
||||
let formattedContent = `[${transformationType}] transformation complete:\n\n${transformationResult.result}`;
|
||||
let formattedContent = `[${toolArgs.transformation}] transformation complete:\n\n${transformationResult.result}`;
|
||||
|
||||
if (transformationResult.truncated) {
|
||||
formattedContent += `\n\n*Note: The original content was too long (${transformationResult.originalTokens} tokens) and was truncated to ${transformationResult.processedTokens} tokens for processing.*`;
|
||||
|
||||
39
src/contexts/TransContext.tsx
Normal file
39
src/contexts/TransContext.tsx
Normal file
@ -0,0 +1,39 @@
|
||||
import {
|
||||
PropsWithChildren,
|
||||
createContext,
|
||||
useContext,
|
||||
useEffect,
|
||||
useMemo,
|
||||
} from 'react'
|
||||
|
||||
import { TransEngine } from '../core/transformations/trans-engine'
|
||||
|
||||
export type TransContextType = {
|
||||
getTransEngine: () => Promise<TransEngine>
|
||||
}
|
||||
|
||||
const TransContext = createContext<TransContextType | null>(null)
|
||||
|
||||
export function TransProvider({
|
||||
getTransEngine,
|
||||
children,
|
||||
}: PropsWithChildren<{ getTransEngine: () => Promise<TransEngine> }>) {
|
||||
useEffect(() => {
|
||||
// start initialization of transEngine in the background
|
||||
void getTransEngine()
|
||||
}, [getTransEngine])
|
||||
|
||||
const value = useMemo(() => {
|
||||
return { getTransEngine }
|
||||
}, [getTransEngine])
|
||||
|
||||
return <TransContext.Provider value={value}>{children}</TransContext.Provider>
|
||||
}
|
||||
|
||||
export function useTrans() {
|
||||
const context = useContext(TransContext)
|
||||
if (!context) {
|
||||
throw new Error('useTrans must be used within a TransProvider')
|
||||
}
|
||||
return context
|
||||
}
|
||||
@ -2,7 +2,7 @@ import { ToolArgs } from "./types"
|
||||
|
||||
export function getCallInsightsDescription(args: ToolArgs): string {
|
||||
return `## insights
|
||||
Description: Use for **Information Processing**. After reading a note's content, use this tool to process and distill the information in various ways. You must choose the most appropriate transformation type based on your goal.
|
||||
Description: Use for **Knowledge Synthesis and Retrieval**. This is your primary tool for "asking questions" to a document or a set of documents. Use it to query your notes and extract higher-level insights, summaries, and other conceptual abstractions. Instead of just finding raw text, this tool helps you understand and synthesize the information within your vault.
|
||||
Parameters:
|
||||
- path: (required) The path to the file or folder to be processed (relative to the current working directory: ${args.cwd}).
|
||||
- transformation: (required) The type of transformation to apply. Must be one of the following:
|
||||
@ -15,12 +15,12 @@ Parameters:
|
||||
Usage:
|
||||
<insights>
|
||||
<path>path/to/your/file.md</path>
|
||||
<type>simple_summary</type>
|
||||
<transformation>simple_summary</transformation>
|
||||
</insights>
|
||||
|
||||
Example: Getting the key insights from a project note
|
||||
<insights>
|
||||
<path>Projects/Project_Alpha_Retrospective.md</path>
|
||||
<type>key_insights</type>
|
||||
<transformation>key_insights</transformation>
|
||||
</insights>`
|
||||
}
|
||||
|
||||
@ -54,9 +54,9 @@ export function getToolDescriptionsForMode(
|
||||
customModes?: ModeConfig[],
|
||||
experiments?: Record<string, boolean>,
|
||||
): string {
|
||||
console.log("getToolDescriptionsForMode", mode, customModes)
|
||||
// console.log("getToolDescriptionsForMode", mode, customModes)
|
||||
const config = getModeConfig(mode, customModes)
|
||||
console.log("config", config)
|
||||
// console.log("config", config)
|
||||
const args: ToolArgs = {
|
||||
cwd,
|
||||
searchSettings,
|
||||
@ -73,7 +73,7 @@ export function getToolDescriptionsForMode(
|
||||
config.groups.forEach((groupEntry) => {
|
||||
const groupName = getGroupName(groupEntry)
|
||||
const toolGroup = TOOL_GROUPS[groupName]
|
||||
console.log("toolGroup", toolGroup)
|
||||
// console.log("toolGroup", toolGroup)
|
||||
if (toolGroup) {
|
||||
toolGroup.tools.forEach((tool) => {
|
||||
if (isToolAllowedForMode(tool, mode, customModes ?? [], experiments ?? {})) {
|
||||
@ -85,11 +85,11 @@ export function getToolDescriptionsForMode(
|
||||
|
||||
// Add always available tools
|
||||
ALWAYS_AVAILABLE_TOOLS.forEach((tool) => tools.add(tool))
|
||||
console.log("tools", tools)
|
||||
// console.log("tools", tools)
|
||||
// Map tool descriptions for allowed tools
|
||||
const descriptions = Array.from(tools).map((toolName) => {
|
||||
const descriptionFn = toolDescriptionMap[toolName]
|
||||
console.log("descriptionFn", descriptionFn)
|
||||
// console.log("descriptionFn", descriptionFn)
|
||||
if (!descriptionFn) {
|
||||
return undefined
|
||||
}
|
||||
|
||||
@ -1,389 +0,0 @@
|
||||
import { Result, err, ok } from "neverthrow";
|
||||
|
||||
import { LLMModel } from '../../types/llm/model';
|
||||
import { RequestMessage } from '../../types/llm/request';
|
||||
import { InfioSettings } from '../../types/settings';
|
||||
import { tokenCount } from '../../utils/token';
|
||||
import LLMManager from '../llm/manager';
|
||||
import { ANALYZE_PAPER_DESCRIPTION, ANALYZE_PAPER_PROMPT } from '../prompts/transformations/analyze-paper';
|
||||
import { DENSE_SUMMARY_DESCRIPTION, DENSE_SUMMARY_PROMPT } from '../prompts/transformations/dense-summary';
|
||||
import { KEY_INSIGHTS_DESCRIPTION, KEY_INSIGHTS_PROMPT } from '../prompts/transformations/key-insights';
|
||||
import { REFLECTIONS_DESCRIPTION, REFLECTIONS_PROMPT } from '../prompts/transformations/reflections';
|
||||
import { SIMPLE_SUMMARY_DESCRIPTION, SIMPLE_SUMMARY_PROMPT } from '../prompts/transformations/simple-summary';
|
||||
import { TABLE_OF_CONTENTS_DESCRIPTION, TABLE_OF_CONTENTS_PROMPT } from '../prompts/transformations/table-of-contents';
|
||||
|
||||
// 转换类型枚举
|
||||
export enum TransformationType {
|
||||
DENSE_SUMMARY = 'dense-summary',
|
||||
ANALYZE_PAPER = 'analyze-paper',
|
||||
SIMPLE_SUMMARY = 'simple-summary',
|
||||
KEY_INSIGHTS = 'key-insights',
|
||||
TABLE_OF_CONTENTS = 'table-of-contents',
|
||||
REFLECTIONS = 'reflections'
|
||||
}
|
||||
|
||||
// 转换配置接口
|
||||
export interface TransformationConfig {
|
||||
type: TransformationType;
|
||||
prompt: string;
|
||||
description: string;
|
||||
maxTokens?: number;
|
||||
}
|
||||
|
||||
// 所有可用的转换配置
|
||||
export const TRANSFORMATIONS: Record<TransformationType, TransformationConfig> = {
|
||||
[TransformationType.DENSE_SUMMARY]: {
|
||||
type: TransformationType.DENSE_SUMMARY,
|
||||
prompt: DENSE_SUMMARY_PROMPT,
|
||||
description: DENSE_SUMMARY_DESCRIPTION,
|
||||
maxTokens: 4000
|
||||
},
|
||||
[TransformationType.ANALYZE_PAPER]: {
|
||||
type: TransformationType.ANALYZE_PAPER,
|
||||
prompt: ANALYZE_PAPER_PROMPT,
|
||||
description: ANALYZE_PAPER_DESCRIPTION,
|
||||
maxTokens: 3000
|
||||
},
|
||||
[TransformationType.SIMPLE_SUMMARY]: {
|
||||
type: TransformationType.SIMPLE_SUMMARY,
|
||||
prompt: SIMPLE_SUMMARY_PROMPT,
|
||||
description: SIMPLE_SUMMARY_DESCRIPTION,
|
||||
maxTokens: 2000
|
||||
},
|
||||
[TransformationType.KEY_INSIGHTS]: {
|
||||
type: TransformationType.KEY_INSIGHTS,
|
||||
prompt: KEY_INSIGHTS_PROMPT,
|
||||
description: KEY_INSIGHTS_DESCRIPTION,
|
||||
maxTokens: 3000
|
||||
},
|
||||
[TransformationType.TABLE_OF_CONTENTS]: {
|
||||
type: TransformationType.TABLE_OF_CONTENTS,
|
||||
prompt: TABLE_OF_CONTENTS_PROMPT,
|
||||
description: TABLE_OF_CONTENTS_DESCRIPTION,
|
||||
maxTokens: 2000
|
||||
},
|
||||
[TransformationType.REFLECTIONS]: {
|
||||
type: TransformationType.REFLECTIONS,
|
||||
prompt: REFLECTIONS_PROMPT,
|
||||
description: REFLECTIONS_DESCRIPTION,
|
||||
maxTokens: 2500
|
||||
}
|
||||
};
|
||||
|
||||
// 转换参数接口
|
||||
export interface TransformationParams {
|
||||
content: string;
|
||||
transformationType: TransformationType;
|
||||
settings: InfioSettings;
|
||||
model?: LLMModel;
|
||||
maxContentTokens?: number;
|
||||
}
|
||||
|
||||
// 转换结果接口
|
||||
export interface TransformationResult {
|
||||
success: boolean;
|
||||
result?: string;
|
||||
error?: string;
|
||||
truncated?: boolean;
|
||||
originalTokens?: number;
|
||||
processedTokens?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM 客户端类，用于与语言模型交互
|
||||
*/
|
||||
class TransformationLLMClient {
|
||||
private llm: LLMManager;
|
||||
private model: LLMModel;
|
||||
|
||||
constructor(llm: LLMManager, model: LLMModel) {
|
||||
this.llm = llm;
|
||||
this.model = model;
|
||||
}
|
||||
|
||||
async queryChatModel(messages: RequestMessage[]): Promise<Result<string, Error>> {
|
||||
try {
|
||||
const stream = await this.llm.streamResponse(
|
||||
this.model,
|
||||
{
|
||||
messages: messages,
|
||||
model: this.model.modelId,
|
||||
stream: true,
|
||||
}
|
||||
);
|
||||
|
||||
let response_content = "";
|
||||
for await (const chunk of stream) {
|
||||
const content = chunk.choices[0]?.delta?.content ?? '';
|
||||
response_content += content;
|
||||
}
|
||||
return ok(response_content);
|
||||
} catch (error) {
|
||||
return err(error instanceof Error ? error : new Error(String(error)));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 文档内容处理类
|
||||
*/
|
||||
class DocumentProcessor {
|
||||
private static readonly DEFAULT_MAX_TOKENS = 12000; // 默认最大 token 数
|
||||
private static readonly MIN_CONTENT_LENGTH = 100; // 最小内容长度（字符数）
|
||||
|
||||
/**
|
||||
* 检查和处理文档内容大小
|
||||
*/
|
||||
static async processContent(content: string, maxTokens: number = this.DEFAULT_MAX_TOKENS): Promise<{
|
||||
processedContent: string;
|
||||
truncated: boolean;
|
||||
originalTokens: number;
|
||||
processedTokens: number;
|
||||
}> {
|
||||
const originalTokens = await tokenCount(content);
|
||||
|
||||
if (originalTokens <= maxTokens) {
|
||||
return {
|
||||
processedContent: content,
|
||||
truncated: false,
|
||||
originalTokens,
|
||||
processedTokens: originalTokens
|
||||
};
|
||||
}
|
||||
|
||||
// 智能截断：基于 token 数量和内容边界
|
||||
// 先按字符比例粗略估算截断位置
|
||||
const estimatedCharRatio = content.length / originalTokens;
|
||||
const estimatedCharLimit = Math.floor(maxTokens * estimatedCharRatio * 0.9); // 留一些缓冲
|
||||
|
||||
let truncatedContent = content.substring(0, estimatedCharLimit);
|
||||
|
||||
// 查找最后一个完整句子的结束位置
|
||||
const lastSentenceEnd = Math.max(
|
||||
truncatedContent.lastIndexOf('.'),
|
||||
truncatedContent.lastIndexOf('!'),
|
||||
truncatedContent.lastIndexOf('?'),
|
||||
truncatedContent.lastIndexOf('。'),
|
||||
truncatedContent.lastIndexOf('！'),
|
||||
truncatedContent.lastIndexOf('？')
|
||||
);
|
||||
|
||||
// 查找最后一个段落的结束位置
|
||||
const lastParagraphEnd = truncatedContent.lastIndexOf('\n\n');
|
||||
|
||||
// 选择最合适的截断位置
|
||||
const cutoffPosition = Math.max(lastSentenceEnd, lastParagraphEnd);
|
||||
|
||||
if (cutoffPosition > estimatedCharLimit * 0.8) { // 如果截断位置不会丢失太多内容
|
||||
truncatedContent = content.substring(0, cutoffPosition + 1);
|
||||
}
|
||||
|
||||
// 确保截断后的内容不会太短
|
||||
if (truncatedContent.length < this.MIN_CONTENT_LENGTH) {
|
||||
// 按字符比例回退到安全长度
|
||||
const safeCharLimit = Math.max(this.MIN_CONTENT_LENGTH, Math.floor(maxTokens * estimatedCharRatio * 0.8));
|
||||
truncatedContent = content.substring(0, Math.min(safeCharLimit, content.length));
|
||||
}
|
||||
|
||||
// 验证最终的 token 数量
|
||||
const finalTokens = await tokenCount(truncatedContent);
|
||||
|
||||
// 如果仍然超过限制，进行更精确的截断
|
||||
if (finalTokens > maxTokens) {
|
||||
const adjustedRatio = truncatedContent.length / finalTokens;
|
||||
const adjustedCharLimit = Math.floor(maxTokens * adjustedRatio);
|
||||
truncatedContent = content.substring(0, adjustedCharLimit);
|
||||
}
|
||||
|
||||
const processedTokens = await tokenCount(truncatedContent);
|
||||
|
||||
return {
|
||||
processedContent: truncatedContent,
|
||||
truncated: true,
|
||||
originalTokens,
|
||||
processedTokens
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 验证内容是否适合处理
|
||||
*/
|
||||
static validateContent(content: string): Result<void, Error> {
|
||||
if (!content || content.trim().length === 0) {
|
||||
return err(new Error('内容不能为空'));
|
||||
}
|
||||
|
||||
if (content.length < this.MIN_CONTENT_LENGTH) {
|
||||
return err(new Error(`内容长度至少需要 ${this.MIN_CONTENT_LENGTH} 个字符`));
|
||||
}
|
||||
|
||||
return ok(undefined);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 主要的转换执行函数
|
||||
*/
|
||||
export async function runTransformation(params: TransformationParams): Promise<TransformationResult> {
|
||||
const { content, transformationType, settings, model, maxContentTokens } = params;
|
||||
|
||||
try {
|
||||
// 验证内容
|
||||
const contentValidation = DocumentProcessor.validateContent(content);
|
||||
if (contentValidation.isErr()) {
|
||||
return {
|
||||
success: false,
|
||||
error: contentValidation.error.message
|
||||
};
|
||||
}
|
||||
|
||||
// 获取转换配置
|
||||
const transformationConfig = TRANSFORMATIONS[transformationType];
|
||||
if (!transformationConfig) {
|
||||
return {
|
||||
success: false,
|
||||
error: `不支持的转换类型: ${transformationType}`
|
||||
};
|
||||
}
|
||||
|
||||
// 处理文档内容（检查 token 数量并截断）
|
||||
const tokenLimit = maxContentTokens || DocumentProcessor['DEFAULT_MAX_TOKENS'];
|
||||
const processedDocument = await DocumentProcessor.processContent(content, tokenLimit);
|
||||
|
||||
// 使用默认模型或传入的模型
|
||||
const llmModel: LLMModel = model || {
|
||||
provider: settings.applyModelProvider,
|
||||
modelId: settings.applyModelId,
|
||||
};
|
||||
|
||||
// 创建 LLM 管理器和客户端
|
||||
const llmManager = new LLMManager(settings);
|
||||
const client = new TransformationLLMClient(llmManager, llmModel);
|
||||
|
||||
// 构建请求消息
|
||||
const messages: RequestMessage[] = [
|
||||
{
|
||||
role: 'system',
|
||||
content: transformationConfig.prompt
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: processedDocument.processedContent
|
||||
}
|
||||
];
|
||||
|
||||
// 调用 LLM 执行转换
|
||||
const result = await client.queryChatModel(messages);
|
||||
|
||||
if (result.isErr()) {
|
||||
return {
|
||||
success: false,
|
||||
error: `LLM 调用失败: ${result.error.message}`,
|
||||
truncated: processedDocument.truncated,
|
||||
originalTokens: processedDocument.originalTokens,
|
||||
processedTokens: processedDocument.processedTokens
|
||||
};
|
||||
}
|
||||
|
||||
// 后处理结果
|
||||
const processedResult = postProcessResult(result.value, transformationType);
|
||||
|
||||
return {
|
||||
success: true,
|
||||
result: processedResult,
|
||||
truncated: processedDocument.truncated,
|
||||
originalTokens: processedDocument.originalTokens,
|
||||
processedTokens: processedDocument.processedTokens
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
error: `转换过程中出现错误: ${error instanceof Error ? error.message : String(error)}`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 后处理转换结果
|
||||
*/
|
||||
function postProcessResult(result: string, transformationType: TransformationType): string {
|
||||
let processed = result.trim();
|
||||
|
||||
// 移除可能的 markdown 代码块标记
|
||||
processed = processed.replace(/^```[\w]*\n/, '').replace(/\n```$/, '');
|
||||
|
||||
// 根据转换类型进行特定的后处理
|
||||
switch (transformationType) {
|
||||
case TransformationType.KEY_INSIGHTS:
|
||||
// 确保 insights 格式正确
|
||||
if (!processed.includes('INSIGHTS')) {
|
||||
processed = `# INSIGHTS\n\n${processed}`;
|
||||
}
|
||||
break;
|
||||
|
||||
case TransformationType.REFLECTIONS:
|
||||
// 确保 reflections 格式正确
|
||||
if (!processed.includes('REFLECTIONS')) {
|
||||
processed = `# REFLECTIONS\n\n${processed}`;
|
||||
}
|
||||
break;
|
||||
|
||||
case TransformationType.ANALYZE_PAPER: {
|
||||
// 确保论文分析包含所有必需的部分
|
||||
const requiredSections = ['PURPOSE', 'CONTRIBUTION', 'KEY FINDINGS', 'IMPLICATIONS', 'LIMITATIONS'];
|
||||
const hasAllSections = requiredSections.every(section =>
|
||||
processed.toUpperCase().includes(section)
|
||||
);
|
||||
|
||||
if (!hasAllSections) {
|
||||
// 如果缺少某些部分，添加提示
|
||||
processed += '\n\n*注意：某些分析部分可能不完整，建议重新处理或检查原始内容。*';
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return processed;
|
||||
}
|
||||
|
||||
/**
|
||||
* 批量执行转换
|
||||
*/
|
||||
export async function runBatchTransformations(
|
||||
content: string,
|
||||
transformationTypes: TransformationType[],
|
||||
settings: InfioSettings,
|
||||
model?: LLMModel
|
||||
): Promise<Record<string, TransformationResult>> {
|
||||
const results: Record<string, TransformationResult> = {};
|
||||
|
||||
// 并行执行所有转换
|
||||
const promises = transformationTypes.map(async (type) => {
|
||||
const result = await runTransformation({
|
||||
content,
|
||||
transformationType: type,
|
||||
settings,
|
||||
model
|
||||
});
|
||||
return { type, result };
|
||||
});
|
||||
|
||||
const completedResults = await Promise.all(promises);
|
||||
|
||||
for (const { type, result } of completedResults) {
|
||||
results[type] = result;
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取所有可用的转换类型和描述
|
||||
*/
|
||||
export function getAvailableTransformations(): Array<{ type: TransformationType, description: string }> {
|
||||
return Object.values(TRANSFORMATIONS).map(config => ({
|
||||
type: config.type,
|
||||
description: config.description
|
||||
}));
|
||||
}
|
||||
683
src/core/transformations/trans-engine.ts
Normal file
683
src/core/transformations/trans-engine.ts
Normal file
@ -0,0 +1,683 @@
|
||||
import { Result, err, ok } from "neverthrow";
|
||||
import { App } from 'obsidian';
|
||||
|
||||
import { DBManager } from '../../database/database-manager';
|
||||
import { InsightManager } from '../../database/modules/insight/insight-manager';
|
||||
import { EmbeddingModel } from '../../types/embedding';
|
||||
import { LLMModel } from '../../types/llm/model';
|
||||
import { RequestMessage } from '../../types/llm/request';
|
||||
import { InfioSettings } from '../../types/settings';
|
||||
import { readTFileContentPdf } from '../../utils/obsidian';
|
||||
import { tokenCount } from '../../utils/token';
|
||||
import LLMManager from '../llm/manager';
|
||||
import { ANALYZE_PAPER_DESCRIPTION, ANALYZE_PAPER_PROMPT } from '../prompts/transformations/analyze-paper';
|
||||
import { DENSE_SUMMARY_DESCRIPTION, DENSE_SUMMARY_PROMPT } from '../prompts/transformations/dense-summary';
|
||||
import { KEY_INSIGHTS_DESCRIPTION, KEY_INSIGHTS_PROMPT } from '../prompts/transformations/key-insights';
|
||||
import { REFLECTIONS_DESCRIPTION, REFLECTIONS_PROMPT } from '../prompts/transformations/reflections';
|
||||
import { SIMPLE_SUMMARY_DESCRIPTION, SIMPLE_SUMMARY_PROMPT } from '../prompts/transformations/simple-summary';
|
||||
import { TABLE_OF_CONTENTS_DESCRIPTION, TABLE_OF_CONTENTS_PROMPT } from '../prompts/transformations/table-of-contents';
|
||||
import { getEmbeddingModel } from '../rag/embedding';
|
||||
|
||||
// 转换类型枚举
|
||||
export enum TransformationType {
|
||||
DENSE_SUMMARY = 'dense_summary',
|
||||
ANALYZE_PAPER = 'analyze_paper',
|
||||
SIMPLE_SUMMARY = 'simple_summary',
|
||||
KEY_INSIGHTS = 'key_insights',
|
||||
TABLE_OF_CONTENTS = 'table_of_contents',
|
||||
REFLECTIONS = 'reflections'
|
||||
}
|
||||
|
||||
// 转换配置接口
|
||||
export interface TransformationConfig {
|
||||
type: TransformationType;
|
||||
prompt: string;
|
||||
description: string;
|
||||
maxTokens?: number;
|
||||
}
|
||||
|
||||
// 所有可用的转换配置
|
||||
export const TRANSFORMATIONS: Record<TransformationType, TransformationConfig> = {
|
||||
[TransformationType.DENSE_SUMMARY]: {
|
||||
type: TransformationType.DENSE_SUMMARY,
|
||||
prompt: DENSE_SUMMARY_PROMPT,
|
||||
description: DENSE_SUMMARY_DESCRIPTION,
|
||||
maxTokens: 4000
|
||||
},
|
||||
[TransformationType.ANALYZE_PAPER]: {
|
||||
type: TransformationType.ANALYZE_PAPER,
|
||||
prompt: ANALYZE_PAPER_PROMPT,
|
||||
description: ANALYZE_PAPER_DESCRIPTION,
|
||||
maxTokens: 3000
|
||||
},
|
||||
[TransformationType.SIMPLE_SUMMARY]: {
|
||||
type: TransformationType.SIMPLE_SUMMARY,
|
||||
prompt: SIMPLE_SUMMARY_PROMPT,
|
||||
description: SIMPLE_SUMMARY_DESCRIPTION,
|
||||
maxTokens: 2000
|
||||
},
|
||||
[TransformationType.KEY_INSIGHTS]: {
|
||||
type: TransformationType.KEY_INSIGHTS,
|
||||
prompt: KEY_INSIGHTS_PROMPT,
|
||||
description: KEY_INSIGHTS_DESCRIPTION,
|
||||
maxTokens: 3000
|
||||
},
|
||||
[TransformationType.TABLE_OF_CONTENTS]: {
|
||||
type: TransformationType.TABLE_OF_CONTENTS,
|
||||
prompt: TABLE_OF_CONTENTS_PROMPT,
|
||||
description: TABLE_OF_CONTENTS_DESCRIPTION,
|
||||
maxTokens: 2000
|
||||
},
|
||||
[TransformationType.REFLECTIONS]: {
|
||||
type: TransformationType.REFLECTIONS,
|
||||
prompt: REFLECTIONS_PROMPT,
|
||||
description: REFLECTIONS_DESCRIPTION,
|
||||
maxTokens: 2500
|
||||
}
|
||||
};
|
||||
|
||||
// 转换参数接口
|
||||
export interface TransformationParams {
|
||||
filePath: string; // 必须的文件路径
|
||||
contentType?: 'document' | 'tag' | 'folder';
|
||||
transformationType: TransformationType;
|
||||
model?: LLMModel;
|
||||
maxContentTokens?: number;
|
||||
saveToDatabase?: boolean;
|
||||
}
|
||||
|
||||
// 转换结果接口
|
||||
export interface TransformationResult {
|
||||
success: boolean;
|
||||
result?: string;
|
||||
error?: string;
|
||||
truncated?: boolean;
|
||||
originalTokens?: number;
|
||||
processedTokens?: number;
|
||||
}
|
||||
|
||||
/**
|
||||
* LLM 客户端类，用于与语言模型交互
|
||||
*/
|
||||
class TransformationLLMClient {
|
||||
private llm: LLMManager;
|
||||
private model: LLMModel;
|
||||
|
||||
constructor(llm: LLMManager, model: LLMModel) {
|
||||
this.llm = llm;
|
||||
this.model = model;
|
||||
}
|
||||
|
||||
async queryChatModel(messages: RequestMessage[]): Promise<Result<string, Error>> {
|
||||
try {
|
||||
const stream = await this.llm.streamResponse(
|
||||
this.model,
|
||||
{
|
||||
messages: messages,
|
||||
model: this.model.modelId,
|
||||
stream: true,
|
||||
}
|
||||
);
|
||||
|
||||
let response_content = "";
|
||||
for await (const chunk of stream) {
|
||||
const content = chunk.choices[0]?.delta?.content ?? '';
|
||||
response_content += content;
|
||||
}
|
||||
return ok(response_content);
|
||||
} catch (error) {
|
||||
return err(error instanceof Error ? error : new Error(String(error)));
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 文档内容处理类
|
||||
*/
|
||||
class DocumentProcessor {
|
||||
private static readonly DEFAULT_MAX_TOKENS = 12000; // 默认最大 token 数
|
||||
private static readonly MIN_CONTENT_LENGTH = 100; // 最小内容长度（字符数）
|
||||
|
||||
/**
|
||||
* 检查和处理文档内容大小
|
||||
*/
|
||||
static async processContent(content: string, maxTokens: number = this.DEFAULT_MAX_TOKENS): Promise<{
|
||||
processedContent: string;
|
||||
truncated: boolean;
|
||||
originalTokens: number;
|
||||
processedTokens: number;
|
||||
}> {
|
||||
const originalTokens = await tokenCount(content);
|
||||
|
||||
if (originalTokens <= maxTokens) {
|
||||
return {
|
||||
processedContent: content,
|
||||
truncated: false,
|
||||
originalTokens,
|
||||
processedTokens: originalTokens
|
||||
};
|
||||
}
|
||||
|
||||
// 智能截断：基于 token 数量和内容边界
|
||||
// 先按字符比例粗略估算截断位置
|
||||
const estimatedCharRatio = content.length / originalTokens;
|
||||
const estimatedCharLimit = Math.floor(maxTokens * estimatedCharRatio * 0.9); // 留一些缓冲
|
||||
|
||||
let truncatedContent = content.substring(0, estimatedCharLimit);
|
||||
|
||||
// 查找最后一个完整句子的结束位置
|
||||
const lastSentenceEnd = Math.max(
|
||||
truncatedContent.lastIndexOf('.'),
|
||||
truncatedContent.lastIndexOf('!'),
|
||||
truncatedContent.lastIndexOf('?'),
|
||||
truncatedContent.lastIndexOf('。'),
|
||||
truncatedContent.lastIndexOf('！'),
|
||||
truncatedContent.lastIndexOf('？')
|
||||
);
|
||||
|
||||
// 查找最后一个段落的结束位置
|
||||
const lastParagraphEnd = truncatedContent.lastIndexOf('\n\n');
|
||||
|
||||
// 选择最合适的截断位置
|
||||
const cutoffPosition = Math.max(lastSentenceEnd, lastParagraphEnd);
|
||||
|
||||
if (cutoffPosition > estimatedCharLimit * 0.8) { // 如果截断位置不会丢失太多内容
|
||||
truncatedContent = content.substring(0, cutoffPosition + 1);
|
||||
}
|
||||
|
||||
// 确保截断后的内容不会太短
|
||||
if (truncatedContent.length < this.MIN_CONTENT_LENGTH) {
|
||||
// 按字符比例回退到安全长度
|
||||
const safeCharLimit = Math.max(this.MIN_CONTENT_LENGTH, Math.floor(maxTokens * estimatedCharRatio * 0.8));
|
||||
truncatedContent = content.substring(0, Math.min(safeCharLimit, content.length));
|
||||
}
|
||||
|
||||
// 验证最终的 token 数量
|
||||
const finalTokens = await tokenCount(truncatedContent);
|
||||
|
||||
// 如果仍然超过限制，进行更精确的截断
|
||||
if (finalTokens > maxTokens) {
|
||||
const adjustedRatio = truncatedContent.length / finalTokens;
|
||||
const adjustedCharLimit = Math.floor(maxTokens * adjustedRatio);
|
||||
truncatedContent = content.substring(0, adjustedCharLimit);
|
||||
}
|
||||
|
||||
const processedTokens = await tokenCount(truncatedContent);
|
||||
|
||||
return {
|
||||
processedContent: truncatedContent,
|
||||
truncated: true,
|
||||
originalTokens,
|
||||
processedTokens
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 验证内容是否适合处理
|
||||
*/
|
||||
static validateContent(content: string): Result<void, Error> {
|
||||
if (!content || content.trim().length === 0) {
|
||||
return err(new Error('内容不能为空'));
|
||||
}
|
||||
|
||||
if (content.length < this.MIN_CONTENT_LENGTH) {
|
||||
return err(new Error(`内容长度至少需要 ${this.MIN_CONTENT_LENGTH} 个字符`));
|
||||
}
|
||||
|
||||
return ok(undefined);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 转换引擎类
|
||||
*/
|
||||
export class TransEngine {
|
||||
private app: App;
|
||||
private settings: InfioSettings;
|
||||
private llmManager: LLMManager;
|
||||
private insightManager: InsightManager | null = null;
|
||||
private embeddingModel: EmbeddingModel | null = null;
|
||||
|
||||
constructor(
|
||||
app: App,
|
||||
settings: InfioSettings,
|
||||
dbManager: DBManager,
|
||||
) {
|
||||
this.app = app;
|
||||
this.settings = settings;
|
||||
this.llmManager = new LLMManager(settings);
|
||||
this.insightManager = dbManager.getInsightManager();
|
||||
|
||||
// 初始化 embedding model
|
||||
if (settings.embeddingModelId && settings.embeddingModelId.trim() !== '') {
|
||||
try {
|
||||
this.embeddingModel = getEmbeddingModel(settings);
|
||||
} catch (error) {
|
||||
console.warn('Failed to initialize embedding model:', error);
|
||||
this.embeddingModel = null;
|
||||
}
|
||||
} else {
|
||||
this.embeddingModel = null;
|
||||
}
|
||||
}
|
||||
|
||||
cleanup() {
|
||||
this.embeddingModel = null;
|
||||
this.insightManager = null;
|
||||
}
|
||||
|
||||
setSettings(settings: InfioSettings) {
|
||||
this.settings = settings;
|
||||
this.llmManager = new LLMManager(settings);
|
||||
|
||||
// 重新初始化 embedding model
|
||||
if (settings.embeddingModelId && settings.embeddingModelId.trim() !== '') {
|
||||
try {
|
||||
this.embeddingModel = getEmbeddingModel(settings);
|
||||
} catch (error) {
|
||||
console.warn('Failed to initialize embedding model:', error);
|
||||
this.embeddingModel = null;
|
||||
}
|
||||
} else {
|
||||
this.embeddingModel = null;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文件元信息的方法
|
||||
*/
|
||||
private async getFileMetadata(filePath: string): Promise<
|
||||
| {
|
||||
success: true;
|
||||
fileExists: true;
|
||||
sourcePath: string;
|
||||
sourceMtime: number;
|
||||
}
|
||||
| {
|
||||
success: false;
|
||||
error: string;
|
||||
}
|
||||
> {
|
||||
const targetFile = this.app.vault.getFileByPath(filePath);
|
||||
if (!targetFile) {
|
||||
return {
|
||||
success: false,
|
||||
error: `文件不存在: ${filePath}`
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
success: true,
|
||||
fileExists: true,
|
||||
sourcePath: filePath,
|
||||
sourceMtime: targetFile.stat.mtime
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 检查数据库缓存的方法
|
||||
*/
|
||||
private async checkDatabaseCache(
|
||||
sourcePath: string,
|
||||
sourceMtime: number,
|
||||
transformationType: TransformationType
|
||||
): Promise<
|
||||
| {
|
||||
success: true;
|
||||
foundCache: true;
|
||||
result: TransformationResult;
|
||||
}
|
||||
| {
|
||||
success: true;
|
||||
foundCache: false;
|
||||
}
|
||||
> {
|
||||
// 如果没有必要的参数，跳过缓存检查
|
||||
if (!this.embeddingModel || !this.insightManager) {
|
||||
console.log("no embeddingModel or insightManager");
|
||||
return {
|
||||
success: true,
|
||||
foundCache: false
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
const existingInsights = await this.insightManager.getInsightsBySourcePath(sourcePath, this.embeddingModel);
|
||||
console.log("existingInsights", existingInsights);
|
||||
|
||||
// 查找匹配的转换类型和修改时间的洞察
|
||||
const matchingInsight = existingInsights.find(insight =>
|
||||
insight.insight_type === transformationType &&
|
||||
insight.source_mtime === sourceMtime
|
||||
);
|
||||
|
||||
if (matchingInsight) {
|
||||
// 找到匹配的缓存结果，直接返回
|
||||
console.log(`使用缓存的转换结果: ${transformationType} for ${sourcePath}`);
|
||||
return {
|
||||
success: true,
|
||||
foundCache: true,
|
||||
result: {
|
||||
success: true,
|
||||
result: matchingInsight.insight,
|
||||
truncated: false, // 缓存的结果不涉及截断
|
||||
originalTokens: 0, // 缓存结果不需要提供token信息
|
||||
processedTokens: 0
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
return {
|
||||
success: true,
|
||||
foundCache: false
|
||||
};
|
||||
} catch (cacheError) {
|
||||
console.warn('查询缓存失败，继续执行转换:', cacheError);
|
||||
// 缓存查询失败不影响主流程
|
||||
return {
|
||||
success: true,
|
||||
foundCache: false
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取文件内容的方法
|
||||
*/
|
||||
private async getFileContent(filePath: string): Promise<
|
||||
| {
|
||||
success: true;
|
||||
fileContent: string;
|
||||
}
|
||||
| {
|
||||
success: false;
|
||||
error: string;
|
||||
}
|
||||
> {
|
||||
const targetFile = this.app.vault.getFileByPath(filePath);
|
||||
if (!targetFile) {
|
||||
return {
|
||||
success: false,
|
||||
error: `文件不存在: ${filePath}`
|
||||
};
|
||||
}
|
||||
|
||||
try {
|
||||
const fileContent = await readTFileContentPdf(targetFile, this.app.vault, this.app);
|
||||
return {
|
||||
success: true,
|
||||
fileContent
|
||||
};
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
error: `读取文件失败: ${error instanceof Error ? error.message : String(error)}`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 保存转换结果到数据库的方法
|
||||
*/
|
||||
private async saveResultToDatabase(
|
||||
result: string,
|
||||
transformationType: TransformationType,
|
||||
sourcePath: string,
|
||||
sourceMtime: number,
|
||||
contentType: string
|
||||
): Promise<void> {
|
||||
if (!this.embeddingModel || !this.insightManager) {
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
// 生成洞察内容的嵌入向量
|
||||
const insightEmbedding = await this.embeddingModel.getEmbedding(result);
|
||||
|
||||
// 保存到数据库
|
||||
await this.insightManager.storeInsight(
|
||||
{
|
||||
insightType: transformationType,
|
||||
insight: result,
|
||||
sourceType: contentType,
|
||||
sourcePath: sourcePath,
|
||||
sourceMtime: sourceMtime,
|
||||
embedding: insightEmbedding,
|
||||
},
|
||||
this.embeddingModel
|
||||
);
|
||||
|
||||
console.log(`转换结果已成功保存到数据库: ${transformationType} for ${sourcePath}`);
|
||||
} catch (dbError) {
|
||||
console.warn('保存洞察到数据库失败:', dbError);
|
||||
// 后台任务失败不影响主要的转换结果
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 主要的转换执行方法
|
||||
*/
|
||||
async runTransformation(params: TransformationParams): Promise<TransformationResult> {
|
||||
console.log("runTransformation", params);
|
||||
const {
|
||||
filePath,
|
||||
contentType = 'document',
|
||||
transformationType,
|
||||
model,
|
||||
maxContentTokens,
|
||||
saveToDatabase = false
|
||||
} = params;
|
||||
|
||||
try {
|
||||
// 第一步：获取文件元信息
|
||||
const metadataResult = await this.getFileMetadata(filePath);
|
||||
|
||||
if (!metadataResult.success) {
|
||||
return {
|
||||
success: false,
|
||||
error: metadataResult.error
|
||||
};
|
||||
}
|
||||
|
||||
// 此时TypeScript知道metadataResult.success为true
|
||||
const { sourcePath, sourceMtime } = metadataResult;
|
||||
|
||||
// 第二步：检查数据库缓存
|
||||
const cacheCheckResult = await this.checkDatabaseCache(
|
||||
sourcePath,
|
||||
sourceMtime,
|
||||
transformationType
|
||||
);
|
||||
|
||||
if (cacheCheckResult.foundCache) {
|
||||
return cacheCheckResult.result;
|
||||
}
|
||||
|
||||
// 第三步：获取文件内容（只有在没有缓存时才执行）
|
||||
const fileContentResult = await this.getFileContent(filePath);
|
||||
|
||||
if (!fileContentResult.success) {
|
||||
return {
|
||||
success: false,
|
||||
error: fileContentResult.error
|
||||
};
|
||||
}
|
||||
|
||||
// 此时TypeScript知道fileContentResult.success为true
|
||||
const { fileContent } = fileContentResult;
|
||||
|
||||
// 验证内容
|
||||
const contentValidation = DocumentProcessor.validateContent(fileContent);
|
||||
if (contentValidation.isErr()) {
|
||||
return {
|
||||
success: false,
|
||||
error: contentValidation.error.message
|
||||
};
|
||||
}
|
||||
|
||||
// 获取转换配置
|
||||
const transformationConfig = TRANSFORMATIONS[transformationType];
|
||||
if (!transformationConfig) {
|
||||
return {
|
||||
success: false,
|
||||
error: `不支持的转换类型: ${transformationType}`
|
||||
};
|
||||
}
|
||||
|
||||
// 处理文档内容（检查 token 数量并截断）
|
||||
const tokenLimit = maxContentTokens || DocumentProcessor['DEFAULT_MAX_TOKENS'];
|
||||
const processedDocument = await DocumentProcessor.processContent(fileContent, tokenLimit);
|
||||
|
||||
// 使用默认模型或传入的模型
|
||||
const llmModel: LLMModel = model || {
|
||||
provider: this.settings.applyModelProvider,
|
||||
modelId: this.settings.applyModelId,
|
||||
};
|
||||
|
||||
// 创建 LLM 客户端
|
||||
const client = new TransformationLLMClient(this.llmManager, llmModel);
|
||||
|
||||
// 构建请求消息
|
||||
const messages: RequestMessage[] = [
|
||||
{
|
||||
role: 'system',
|
||||
content: transformationConfig.prompt
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: processedDocument.processedContent
|
||||
}
|
||||
];
|
||||
|
||||
// 调用 LLM 执行转换
|
||||
const result = await client.queryChatModel(messages);
|
||||
|
||||
if (result.isErr()) {
|
||||
return {
|
||||
success: false,
|
||||
error: `LLM 调用失败: ${result.error.message}`,
|
||||
truncated: processedDocument.truncated,
|
||||
originalTokens: processedDocument.originalTokens,
|
||||
processedTokens: processedDocument.processedTokens
|
||||
};
|
||||
}
|
||||
|
||||
// 后处理结果
|
||||
const processedResult = this.postProcessResult(result.value, transformationType);
|
||||
|
||||
// 保存转换结果到数据库（后台任务，不阻塞主流程）
|
||||
if (saveToDatabase) {
|
||||
// 创建后台任务，不使用 await
|
||||
(async () => {
|
||||
await this.saveResultToDatabase(
|
||||
processedResult,
|
||||
transformationType,
|
||||
sourcePath,
|
||||
sourceMtime,
|
||||
contentType
|
||||
);
|
||||
})(); // 立即执行异步函数，但不等待其完成
|
||||
}
|
||||
|
||||
return {
|
||||
success: true,
|
||||
result: processedResult,
|
||||
truncated: processedDocument.truncated,
|
||||
originalTokens: processedDocument.originalTokens,
|
||||
processedTokens: processedDocument.processedTokens
|
||||
};
|
||||
|
||||
} catch (error) {
|
||||
return {
|
||||
success: false,
|
||||
error: `转换过程中出现错误: ${error instanceof Error ? error.message : String(error)}`
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 后处理转换结果
|
||||
*/
|
||||
private postProcessResult(result: string, transformationType: TransformationType): string {
|
||||
let processed = result.trim();
|
||||
|
||||
// 移除可能的 markdown 代码块标记
|
||||
processed = processed.replace(/^```[\w]*\n/, '').replace(/\n```$/, '');
|
||||
|
||||
// 根据转换类型进行特定的后处理
|
||||
switch (transformationType) {
|
||||
case TransformationType.KEY_INSIGHTS:
|
||||
// 确保 insights 格式正确
|
||||
if (!processed.includes('INSIGHTS')) {
|
||||
processed = `# INSIGHTS\n\n${processed}`;
|
||||
}
|
||||
break;
|
||||
|
||||
case TransformationType.REFLECTIONS:
|
||||
// 确保 reflections 格式正确
|
||||
if (!processed.includes('REFLECTIONS')) {
|
||||
processed = `# REFLECTIONS\n\n${processed}`;
|
||||
}
|
||||
break;
|
||||
|
||||
case TransformationType.ANALYZE_PAPER: {
|
||||
// 确保论文分析包含所有必需的部分
|
||||
const requiredSections = ['PURPOSE', 'CONTRIBUTION', 'KEY FINDINGS', 'IMPLICATIONS', 'LIMITATIONS'];
|
||||
const hasAllSections = requiredSections.every(section =>
|
||||
processed.toUpperCase().includes(section)
|
||||
);
|
||||
|
||||
if (!hasAllSections) {
|
||||
// 如果缺少某些部分，添加提示
|
||||
processed += '\n\n*注意：某些分析部分可能不完整，建议重新处理或检查原始内容。*';
|
||||
}
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return processed;
|
||||
}
|
||||
|
||||
/**
|
||||
* 批量执行转换
|
||||
*/
|
||||
async runBatchTransformations(
|
||||
filePath: string,
|
||||
transformationTypes: TransformationType[],
|
||||
options?: {
|
||||
model?: LLMModel;
|
||||
saveToDatabase?: boolean;
|
||||
}
|
||||
): Promise<Record<string, TransformationResult>> {
|
||||
const results: Record<string, TransformationResult> = {};
|
||||
|
||||
// 并行执行所有转换
|
||||
const promises = transformationTypes.map(async (type) => {
|
||||
const result = await this.runTransformation({
|
||||
filePath: filePath,
|
||||
transformationType: type,
|
||||
model: options?.model,
|
||||
saveToDatabase: options?.saveToDatabase
|
||||
});
|
||||
return { type, result };
|
||||
});
|
||||
|
||||
const completedResults = await Promise.all(promises);
|
||||
|
||||
for (const { type, result } of completedResults) {
|
||||
results[type] = result;
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取所有可用的转换类型和描述
|
||||
*/
|
||||
static getAvailableTransformations(): Array<{ type: TransformationType, description: string }> {
|
||||
return Object.values(TRANSFORMATIONS).map(config => ({
|
||||
type: config.type,
|
||||
description: config.description
|
||||
}));
|
||||
}
|
||||
}
|
||||
@ -1,181 +0,0 @@
|
||||
import { InfioSettings } from '../../types/settings';
|
||||
|
||||
import {
|
||||
TransformationType,
|
||||
getAvailableTransformations,
|
||||
runBatchTransformations,
|
||||
runTransformation,
|
||||
} from './run_trans';
|
||||
|
||||
/**
|
||||
* 使用示例：单个转换
|
||||
*/
|
||||
export async function exampleSingleTransformation(settings: InfioSettings) {
|
||||
const sampleContent = `
|
||||
人工智能技术正在快速发展，特别是大型语言模型的出现，彻底改变了我们与计算机交互的方式。
|
||||
这些模型能够理解和生成人类语言，在多个领域展现出令人印象深刻的能力。
|
||||
|
||||
然而，随着AI技术的普及，我们也面临着新的挑战，包括伦理问题、隐私保护、
|
||||
以及如何确保AI技术的安全和可控发展。这些问题需要全社会的共同关注和努力。
|
||||
|
||||
未来，人工智能将继续在教育、医疗、商业等领域发挥重要作用，
|
||||
但我们必须在推进技术发展的同时，确保技术服务于人类的福祉。
|
||||
`;
|
||||
|
||||
try {
|
||||
// 执行简单摘要转换
|
||||
const result = await runTransformation({
|
||||
content: sampleContent,
|
||||
transformationType: TransformationType.SIMPLE_SUMMARY,
|
||||
settings: settings
|
||||
});
|
||||
|
||||
if (result.success) {
|
||||
console.log('转换成功！');
|
||||
console.log('结果:', result.result);
|
||||
|
||||
if (result.truncated) {
|
||||
console.log(`注意：内容被截断 (${result.originalLength} -> ${result.processedLength} 字符)`);
|
||||
}
|
||||
} else {
|
||||
console.error('转换失败:', result.error);
|
||||
}
|
||||
|
||||
return result;
|
||||
} catch (error) {
|
||||
console.error('执行转换时出错:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用示例：批量转换
|
||||
*/
|
||||
export async function exampleBatchTransformations(settings: InfioSettings) {
|
||||
const sampleContent = `
|
||||
区块链技术作为一种分布式账本技术，具有去中心化、不可篡改、透明公开等特点。
|
||||
它最初是为比特币而设计的底层技术，但现在已经扩展到各个行业和应用场景。
|
||||
|
||||
在金融领域，区块链可以用于跨境支付、供应链金融、数字货币等；
|
||||
在供应链管理中，它能够提供产品溯源和防伪验证；
|
||||
在数字身份认证方面，区块链可以建立更安全可靠的身份管理系统。
|
||||
|
||||
尽管区块链技术有很多优势，但它也面临着可扩展性、能耗、监管等挑战。
|
||||
随着技术的不断成熟和完善，相信这些问题会逐步得到解决。
|
||||
区块链技术的未来发展值得期待，它将为数字经济的发展提供重要的技术支撑。
|
||||
`;
|
||||
|
||||
try {
|
||||
// 同时执行多种转换
|
||||
const transformationTypes = [
|
||||
TransformationType.SIMPLE_SUMMARY,
|
||||
TransformationType.KEY_INSIGHTS,
|
||||
TransformationType.TABLE_OF_CONTENTS
|
||||
];
|
||||
|
||||
const results = await runBatchTransformations(
|
||||
sampleContent,
|
||||
transformationTypes,
|
||||
settings
|
||||
);
|
||||
|
||||
console.log('批量转换完成！');
|
||||
|
||||
for (const [type, result] of Object.entries(results)) {
|
||||
console.log(`\n=== ${type.toUpperCase()} ===`);
|
||||
if (result.success) {
|
||||
console.log(result.result);
|
||||
} else {
|
||||
console.error('失败:', result.error);
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
} catch (error) {
|
||||
console.error('执行批量转换时出错:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用示例：处理长文档（会被截断）
|
||||
*/
|
||||
export async function exampleLongDocumentProcessing(settings: InfioSettings) {
|
||||
// 模拟一个很长的文档
|
||||
const longContent = '这是一个很长的文档内容。'.repeat(10000); // 约50万字符
|
||||
|
||||
try {
|
||||
const result = await runTransformation({
|
||||
content: longContent,
|
||||
transformationType: TransformationType.DENSE_SUMMARY,
|
||||
settings: settings,
|
||||
maxContentLength: 30000 // 设置最大内容长度
|
||||
});
|
||||
|
||||
if (result.success) {
|
||||
console.log('长文档转换成功！');
|
||||
console.log('原始长度:', result.originalLength);
|
||||
console.log('处理后长度:', result.processedLength);
|
||||
console.log('是否被截断:', result.truncated);
|
||||
console.log('结果长度:', result.result?.length);
|
||||
} else {
|
||||
console.error('转换失败:', result.error);
|
||||
}
|
||||
|
||||
return result;
|
||||
} catch (error) {
|
||||
console.error('处理长文档时出错:', error);
|
||||
throw error;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用示例：获取所有可用的转换类型
|
||||
*/
|
||||
export function exampleGetAvailableTransformations() {
|
||||
const availableTransformations = getAvailableTransformations();
|
||||
|
||||
console.log('可用的转换类型:');
|
||||
availableTransformations.forEach((transformation, index) => {
|
||||
console.log(`${index + 1}. ${transformation.type}: ${transformation.description}`);
|
||||
});
|
||||
|
||||
return availableTransformations;
|
||||
}
|
||||
|
||||
/**
|
||||
* 使用示例：错误处理
|
||||
*/
|
||||
export async function exampleErrorHandling(settings: InfioSettings) {
|
||||
try {
|
||||
// 测试空内容
|
||||
const emptyResult = await runTransformation({
|
||||
content: '',
|
||||
transformationType: TransformationType.SIMPLE_SUMMARY,
|
||||
settings: settings
|
||||
});
|
||||
|
||||
console.log('空内容测试:', emptyResult);
|
||||
|
||||
// 测试太短的内容
|
||||
const shortResult = await runTransformation({
|
||||
content: '太短',
|
||||
transformationType: TransformationType.SIMPLE_SUMMARY,
|
||||
settings: settings
|
||||
});
|
||||
|
||||
console.log('短内容测试:', shortResult);
|
||||
|
||||
// 测试无效的转换类型（需要类型断言来测试）
|
||||
const invalidResult = await runTransformation({
|
||||
content: '这是一些测试内容，用于测试无效的转换类型处理。',
|
||||
transformationType: 'invalid-type' as TransformationType,
|
||||
settings: settings
|
||||
});
|
||||
|
||||
console.log('无效类型测试:', invalidResult);
|
||||
|
||||
} catch (error) {
|
||||
console.error('错误处理测试时出错:', error);
|
||||
}
|
||||
}
|
||||
@ -1,8 +1,8 @@
|
||||
import { App, TFile } from 'obsidian'
|
||||
|
||||
import { InsertSourceInsight, SelectSourceInsight } from '../../schema'
|
||||
import { EmbeddingModel } from '../../../types/embedding'
|
||||
import { DBManager } from '../../database-manager'
|
||||
import { InsertSourceInsight, SelectSourceInsight } from '../../schema'
|
||||
|
||||
import { InsightRepository } from './insight-repository'
|
||||
|
||||
@ -51,6 +51,7 @@ export class InsightManager {
|
||||
insight: string
|
||||
sourceType: 'document' | 'tag' | 'folder'
|
||||
sourcePath: string
|
||||
sourceMtime: number
|
||||
embedding: number[]
|
||||
},
|
||||
embeddingModel: EmbeddingModel,
|
||||
@ -60,6 +61,7 @@ export class InsightManager {
|
||||
insight: insightData.insight,
|
||||
source_type: insightData.sourceType,
|
||||
source_path: insightData.sourcePath,
|
||||
source_mtime: insightData.sourceMtime,
|
||||
embedding: insightData.embedding,
|
||||
}
|
||||
|
||||
@ -75,6 +77,7 @@ export class InsightManager {
|
||||
insight: string
|
||||
sourceType: 'document' | 'tag' | 'folder'
|
||||
sourcePath: string
|
||||
sourceMtime: number
|
||||
embedding: number[]
|
||||
}>,
|
||||
embeddingModel: EmbeddingModel,
|
||||
@ -84,6 +87,7 @@ export class InsightManager {
|
||||
insight: data.insight,
|
||||
source_type: data.sourceType,
|
||||
source_path: data.sourcePath,
|
||||
source_mtime: data.sourceMtime,
|
||||
embedding: data.embedding,
|
||||
}))
|
||||
|
||||
@ -100,6 +104,7 @@ export class InsightManager {
|
||||
insight?: string
|
||||
sourceType?: 'document' | 'tag' | 'folder'
|
||||
sourcePath?: string
|
||||
sourceMtime?: number
|
||||
embedding?: number[]
|
||||
},
|
||||
embeddingModel: EmbeddingModel,
|
||||
@ -118,6 +123,9 @@ export class InsightManager {
|
||||
if (updates.sourcePath !== undefined) {
|
||||
updateData.source_path = updates.sourcePath
|
||||
}
|
||||
if (updates.sourceMtime !== undefined) {
|
||||
updateData.source_mtime = updates.sourceMtime
|
||||
}
|
||||
if (updates.embedding !== undefined) {
|
||||
updateData.embedding = updates.embedding
|
||||
}
|
||||
@ -318,4 +326,26 @@ export class InsightManager {
|
||||
|
||||
return filteredInsights
|
||||
}
|
||||
|
||||
// /**
|
||||
// * 根据源文件修改时间范围获取洞察
|
||||
// */
|
||||
// async getInsightsByMtimeRange(
|
||||
// minMtime: number,
|
||||
// maxMtime: number,
|
||||
// embeddingModel: EmbeddingModel,
|
||||
// ): Promise<SelectSourceInsight[]> {
|
||||
// return await this.repository.getInsightsByMtimeRange(minMtime, maxMtime, embeddingModel)
|
||||
// }
|
||||
|
||||
// /**
|
||||
// * 根据源文件修改时间获取需要更新的洞察
|
||||
// */
|
||||
// async getOutdatedInsights(
|
||||
// sourcePath: string,
|
||||
// currentMtime: number,
|
||||
// embeddingModel: EmbeddingModel,
|
||||
// ): Promise<SelectSourceInsight[]> {
|
||||
// return await this.repository.getOutdatedInsights(sourcePath, currentMtime, embeddingModel)
|
||||
// }
|
||||
}
|
||||
|
||||
@ -139,8 +139,8 @@ export class InsightRepository {
|
||||
|
||||
// 构建批量插入的 SQL
|
||||
const values = data.map((insight, index) => {
|
||||
const offset = index * 6
|
||||
return `($${offset + 1}, $${offset + 2}, $${offset + 3}, $${offset + 4}, $${offset + 5}, $${offset + 6})`
|
||||
const offset = index * 7
|
||||
return `($${offset + 1}, $${offset + 2}, $${offset + 3}, $${offset + 4}, $${offset + 5}, $${offset + 6}, $${offset + 7})`
|
||||
}).join(',')
|
||||
|
||||
const params = data.flatMap(insight => [
|
||||
@ -148,12 +148,13 @@ export class InsightRepository {
|
||||
insight.insight.replace(/\0/g, ''), // 清理null字节
|
||||
insight.source_type,
|
||||
insight.source_path,
|
||||
insight.source_mtime,
|
||||
`[${insight.embedding.join(',')}]`, // 转换为PostgreSQL vector格式
|
||||
new Date() // updated_at
|
||||
])
|
||||
|
||||
await this.db.query(
|
||||
`INSERT INTO "${tableName}" (insight_type, insight, source_type, source_path, embedding, updated_at)
|
||||
`INSERT INTO "${tableName}" (insight_type, insight, source_type, source_path, source_mtime, embedding, updated_at)
|
||||
VALUES ${values}`,
|
||||
params
|
||||
)
|
||||
@ -197,6 +198,12 @@ export class InsightRepository {
|
||||
paramIndex++
|
||||
}
|
||||
|
||||
if (data.source_mtime !== undefined) {
|
||||
fields.push(`source_mtime = $${paramIndex}`)
|
||||
params.push(data.source_mtime)
|
||||
paramIndex++
|
||||
}
|
||||
|
||||
if (data.embedding !== undefined) {
|
||||
fields.push(`embedding = $${paramIndex}`)
|
||||
params.push(`[${data.embedding.join(',')}]`)
|
||||
@ -235,7 +242,7 @@ export class InsightRepository {
|
||||
}
|
||||
const tableName = this.getTableName(embeddingModel)
|
||||
|
||||
let whereConditions = ['1 - (embedding <=> $1::vector) > $2']
|
||||
const whereConditions: string[] = ['1 - (embedding <=> $1::vector) > $2']
|
||||
const params: unknown[] = [`[${queryVector.join(',')}]`, options.minSimilarity, options.limit]
|
||||
let paramIndex = 4
|
||||
|
||||
@ -259,7 +266,7 @@ export class InsightRepository {
|
||||
|
||||
const query = `
|
||||
SELECT
|
||||
id, insight_type, insight, source_type, source_path, created_at, updated_at,
|
||||
id, insight_type, insight, source_type, source_path, source_mtime, created_at, updated_at,
|
||||
1 - (embedding <=> $1::vector) as similarity
|
||||
FROM "${tableName}"
|
||||
WHERE ${whereConditions.join(' AND ')}
|
||||
@ -271,4 +278,36 @@ export class InsightRepository {
|
||||
const result = await this.db.query<SearchResult>(query, params)
|
||||
return result.rows
|
||||
}
|
||||
|
||||
// async getInsightsByMtimeRange(
|
||||
// minMtime: number,
|
||||
// maxMtime: number,
|
||||
// embeddingModel: EmbeddingModel,
|
||||
// ): Promise<SelectSourceInsight[]> {
|
||||
// if (!this.db) {
|
||||
// throw new DatabaseNotInitializedException()
|
||||
// }
|
||||
// const tableName = this.getTableName(embeddingModel)
|
||||
// const result = await this.db.query<SelectSourceInsight>(
|
||||
// `SELECT * FROM "${tableName}" WHERE source_mtime >= $1 AND source_mtime <= $2 ORDER BY created_at DESC`,
|
||||
// [minMtime, maxMtime]
|
||||
// )
|
||||
// return result.rows
|
||||
// }
|
||||
|
||||
// async getOutdatedInsights(
|
||||
// sourcePath: string,
|
||||
// currentMtime: number,
|
||||
// embeddingModel: EmbeddingModel,
|
||||
// ): Promise<SelectSourceInsight[]> {
|
||||
// if (!this.db) {
|
||||
// throw new DatabaseNotInitializedException()
|
||||
// }
|
||||
// const tableName = this.getTableName(embeddingModel)
|
||||
// const result = await this.db.query<SelectSourceInsight>(
|
||||
// `SELECT * FROM "${tableName}" WHERE source_path = $1 AND source_mtime < $2 ORDER BY created_at DESC`,
|
||||
// [sourcePath, currentMtime]
|
||||
// )
|
||||
// return result.rows
|
||||
// }
|
||||
}
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
import { SerializedLexicalNode } from 'lexical'
|
||||
|
||||
import { SUPPORT_EMBEDDING_SIMENTION } from '../constants'
|
||||
import { ApplyStatus } from '../types/apply'
|
||||
// import { EmbeddingModelId } from '../types/embedding'
|
||||
|
||||
// PostgreSQL column types
|
||||
@ -184,6 +183,7 @@ export type SourceInsightRecord = {
|
||||
insight: string
|
||||
source_type: 'document' | 'tag' | 'folder'
|
||||
source_path: string
|
||||
source_mtime: number
|
||||
embedding: number[]
|
||||
created_at: Date
|
||||
updated_at: Date
|
||||
@ -203,6 +203,7 @@ const createSourceInsightTable = (dimension: number): TableDefinition => {
|
||||
insight: { type: 'TEXT', notNull: true },
|
||||
source_type: { type: 'TEXT', notNull: true },
|
||||
source_path: { type: 'TEXT', notNull: true },
|
||||
source_mtime: { type: 'BIGINT', notNull: true },
|
||||
embedding: { type: 'VECTOR', dimensions: dimension },
|
||||
created_at: { type: 'TIMESTAMP', notNull: true, defaultNow: true },
|
||||
updated_at: { type: 'TIMESTAMP', notNull: true, defaultNow: true }
|
||||
|
||||
@ -104,6 +104,7 @@ export const migrations: Record<string, SqlMigration> = {
|
||||
"insight" text NOT NULL,
|
||||
"source_type" text NOT NULL,
|
||||
"source_path" text NOT NULL,
|
||||
"source_mtime" bigint NOT NULL,
|
||||
"embedding" vector(1536),
|
||||
"created_at" timestamp DEFAULT now() NOT NULL,
|
||||
"updated_at" timestamp DEFAULT now() NOT NULL
|
||||
@ -115,6 +116,7 @@ export const migrations: Record<string, SqlMigration> = {
|
||||
"insight" text NOT NULL,
|
||||
"source_type" text NOT NULL,
|
||||
"source_path" text NOT NULL,
|
||||
"source_mtime" bigint NOT NULL,
|
||||
"embedding" vector(1024),
|
||||
"created_at" timestamp DEFAULT now() NOT NULL,
|
||||
"updated_at" timestamp DEFAULT now() NOT NULL
|
||||
@ -126,6 +128,7 @@ export const migrations: Record<string, SqlMigration> = {
|
||||
"insight" text NOT NULL,
|
||||
"source_type" text NOT NULL,
|
||||
"source_path" text NOT NULL,
|
||||
"source_mtime" bigint NOT NULL,
|
||||
"embedding" vector(768),
|
||||
"created_at" timestamp DEFAULT now() NOT NULL,
|
||||
"updated_at" timestamp DEFAULT now() NOT NULL
|
||||
@ -137,6 +140,7 @@ export const migrations: Record<string, SqlMigration> = {
|
||||
"insight" text NOT NULL,
|
||||
"source_type" text NOT NULL,
|
||||
"source_path" text NOT NULL,
|
||||
"source_mtime" bigint NOT NULL,
|
||||
"embedding" vector(512),
|
||||
"created_at" timestamp DEFAULT now() NOT NULL,
|
||||
"updated_at" timestamp DEFAULT now() NOT NULL
|
||||
@ -148,6 +152,7 @@ export const migrations: Record<string, SqlMigration> = {
|
||||
"insight" text NOT NULL,
|
||||
"source_type" text NOT NULL,
|
||||
"source_path" text NOT NULL,
|
||||
"source_mtime" bigint NOT NULL,
|
||||
"embedding" vector(384),
|
||||
"created_at" timestamp DEFAULT now() NOT NULL,
|
||||
"updated_at" timestamp DEFAULT now() NOT NULL
|
||||
@ -245,5 +250,16 @@ export const migrations: Record<string, SqlMigration> = {
|
||||
"created_at" timestamp DEFAULT now() NOT NULL
|
||||
);
|
||||
`
|
||||
},
|
||||
add_source_mtime: {
|
||||
description: "Adds missing source_mtime column to existing source insight tables",
|
||||
sql: `
|
||||
-- Add source_mtime column to existing source insight tables if it doesn't exist
|
||||
ALTER TABLE "source_insight_1536" ADD COLUMN IF NOT EXISTS "source_mtime" bigint NOT NULL DEFAULT 0;
|
||||
ALTER TABLE "source_insight_1024" ADD COLUMN IF NOT EXISTS "source_mtime" bigint NOT NULL DEFAULT 0;
|
||||
ALTER TABLE "source_insight_768" ADD COLUMN IF NOT EXISTS "source_mtime" bigint NOT NULL DEFAULT 0;
|
||||
ALTER TABLE "source_insight_512" ADD COLUMN IF NOT EXISTS "source_mtime" bigint NOT NULL DEFAULT 0;
|
||||
ALTER TABLE "source_insight_384" ADD COLUMN IF NOT EXISTS "source_mtime" bigint NOT NULL DEFAULT 0;
|
||||
`
|
||||
}
|
||||
};
|
||||
|
||||
25
src/main.ts
25
src/main.ts
@ -11,6 +11,7 @@ import { getDiffStrategy } from "./core/diff/DiffStrategy"
|
||||
import { InlineEdit } from './core/edit/inline-edit-processor'
|
||||
import { McpHub } from './core/mcp/McpHub'
|
||||
import { RAGEngine } from './core/rag/rag-engine'
|
||||
import { TransEngine } from './core/transformations/trans-engine'
|
||||
import { DBManager } from './database/database-manager'
|
||||
import { migrateToJsonDatabase } from './database/json/migrateToJsonDatabase'
|
||||
import EventListener from "./event-listener"
|
||||
@ -41,6 +42,7 @@ export default class InfioPlugin extends Plugin {
|
||||
private activeLeafChangeUnloadFn: (() => void) | null = null
|
||||
private dbManagerInitPromise: Promise<DBManager> | null = null
|
||||
private ragEngineInitPromise: Promise<RAGEngine> | null = null
|
||||
private transEngineInitPromise: Promise<TransEngine> | null = null
|
||||
private mcpHubInitPromise: Promise<McpHub> | null = null
|
||||
settings: InfioSettings
|
||||
settingTab: InfioSettingTab
|
||||
@ -49,6 +51,7 @@ export default class InfioPlugin extends Plugin {
|
||||
dbManager: DBManager | null = null
|
||||
mcpHub: McpHub | null = null
|
||||
ragEngine: RAGEngine | null = null
|
||||
transEngine: TransEngine | null = null
|
||||
inlineEdit: InlineEdit | null = null
|
||||
diffStrategy?: DiffStrategy
|
||||
dataviewManager: DataviewManager | null = null
|
||||
@ -422,10 +425,14 @@ export default class InfioPlugin extends Plugin {
|
||||
// Promise cleanup
|
||||
this.dbManagerInitPromise = null
|
||||
this.ragEngineInitPromise = null
|
||||
this.transEngineInitPromise = null
|
||||
this.mcpHubInitPromise = null
|
||||
// RagEngine cleanup
|
||||
this.ragEngine?.cleanup()
|
||||
this.ragEngine = null
|
||||
// TransEngine cleanup
|
||||
this.transEngine?.cleanup()
|
||||
this.transEngine = null
|
||||
// Database cleanup
|
||||
this.dbManager?.cleanup()
|
||||
this.dbManager = null
|
||||
@ -445,6 +452,7 @@ export default class InfioPlugin extends Plugin {
|
||||
this.settings = newSettings
|
||||
await this.saveData(newSettings)
|
||||
this.ragEngine?.setSettings(newSettings)
|
||||
this.transEngine?.setSettings(newSettings)
|
||||
this.settingsListeners.forEach((listener) => listener(newSettings))
|
||||
}
|
||||
|
||||
@ -572,6 +580,23 @@ export default class InfioPlugin extends Plugin {
|
||||
return this.ragEngineInitPromise
|
||||
}
|
||||
|
||||
async getTransEngine(): Promise<TransEngine> {
|
||||
if (this.transEngine) {
|
||||
return this.transEngine
|
||||
}
|
||||
|
||||
if (!this.transEngineInitPromise) {
|
||||
this.transEngineInitPromise = (async () => {
|
||||
const dbManager = await this.getDbManager()
|
||||
this.transEngine = new TransEngine(this.app, this.settings, dbManager)
|
||||
return this.transEngine
|
||||
})()
|
||||
}
|
||||
|
||||
// if initialization is running, wait for it to complete instead of creating a new initialization promise
|
||||
return this.transEngineInitPromise
|
||||
}
|
||||
|
||||
private async migrateToJsonStorage() {
|
||||
try {
|
||||
const dbManager = await this.getDbManager()
|
||||
|
||||
@ -736,7 +736,7 @@ export function parseMsgBlocks(
|
||||
if (childNode.nodeName === 'path' && childNode.childNodes.length > 0) {
|
||||
// @ts-expect-error - parse5 node value type
|
||||
path = childNode.childNodes[0].value
|
||||
} else if (childNode.nodeName === 'type' && childNode.childNodes.length > 0) {
|
||||
} else if (childNode.nodeName === 'transformation' && childNode.childNodes.length > 0) {
|
||||
// @ts-expect-error - parse5 node value type
|
||||
transformation = childNode.childNodes[0].value
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user