Complete Agent Pipeline System with Claude Code & OpenClaw Integration
- Added Claude Code integration with full context compaction support - Added OpenClaw integration with deterministic pipeline support - Implemented parallel agent execution (4 projects x 3 roles pattern) - Added workspace isolation with permissions and quotas - Implemented Lobster-compatible YAML workflow parser - Added persistent memory store for cross-session context - Created comprehensive README with hero section This project was 100% autonomously built by Z.AI GLM-5
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332
agent-system/core/summarizer.ts
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332
agent-system/core/summarizer.ts
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/**
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* Conversation Summarizer Module
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*
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* Uses LLM to create intelligent summaries of conversations,
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* preserving key information while reducing token count.
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*/
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import ZAI from 'z-ai-web-dev-sdk';
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import { TokenCounter, countTokens } from './token-counter';
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export interface SummaryResult {
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summary: string;
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originalTokens: number;
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summaryTokens: number;
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compressionRatio: number;
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keyPoints: string[];
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decisions: string[];
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actionItems: string[];
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}
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export interface SummarizerOptions {
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maxSummaryTokens?: number;
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preserveRecentMessages?: number;
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extractKeyPoints?: boolean;
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extractDecisions?: boolean;
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extractActionItems?: boolean;
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}
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export interface ConversationTurn {
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role: 'user' | 'assistant' | 'system';
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content: string;
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timestamp?: Date;
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metadata?: Record<string, unknown>;
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}
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const DEFAULT_OPTIONS: Required<SummarizerOptions> = {
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maxSummaryTokens: 1000,
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preserveRecentMessages: 3,
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extractKeyPoints: true,
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extractDecisions: true,
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extractActionItems: true
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};
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/**
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* ConversationSummarizer - Creates intelligent summaries of conversations
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*/
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export class ConversationSummarizer {
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private zai: Awaited<ReturnType<typeof ZAI.create>> | null = null;
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private tokenCounter: TokenCounter;
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private options: Required<SummarizerOptions>;
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constructor(options: SummarizerOptions = {}) {
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this.options = { ...DEFAULT_OPTIONS, ...options };
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this.tokenCounter = new TokenCounter();
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}
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/**
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* Initialize the summarizer (lazy load ZAI)
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*/
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private async init(): Promise<void> {
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if (!this.zai) {
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this.zai = await ZAI.create();
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}
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}
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/**
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* Summarize a conversation
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*/
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async summarize(
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messages: ConversationTurn[],
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options?: Partial<SummarizerOptions>
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): Promise<SummaryResult> {
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await this.init();
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const opts = { ...this.options, ...options };
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const originalTokens = this.tokenCounter.countConversation(messages).total;
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// Format conversation for summarization
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const conversationText = this.formatConversationForSummary(messages);
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// Create the summarization prompt
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const prompt = this.buildSummarizationPrompt(conversationText, opts);
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// Get summary from LLM
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const response = await this.zai!.chat.completions.create({
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messages: [
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{
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role: 'assistant',
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content: `You are a precise conversation summarizer. Your task is to create concise summaries that preserve all important information while minimizing tokens.`
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},
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{
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role: 'user',
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content: prompt
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}
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],
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thinking: { type: 'disabled' }
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});
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const summaryText = response.choices?.[0]?.message?.content || '';
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// Parse the structured response
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const parsed = this.parseSummaryResponse(summaryText);
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const summaryTokens = countTokens(parsed.summary);
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return {
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summary: parsed.summary,
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originalTokens,
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summaryTokens,
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compressionRatio: originalTokens > 0 ? summaryTokens / originalTokens : 0,
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keyPoints: parsed.keyPoints,
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decisions: parsed.decisions,
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actionItems: parsed.actionItems
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};
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}
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/**
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* Format conversation for summarization
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*/
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private formatConversationForSummary(messages: ConversationTurn[]): string {
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return messages.map(msg => {
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const timestamp = msg.timestamp?.toISOString() || '';
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return `[${msg.role.toUpperCase()}]${timestamp ? ` (${timestamp})` : ''}: ${msg.content}`;
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}).join('\n\n');
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}
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/**
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* Build the summarization prompt
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*/
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private buildSummarizationPrompt(
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conversationText: string,
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options: Required<SummarizerOptions>
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): string {
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const sections: string[] = [];
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sections.push(`Please summarize the following conversation concisely.`);
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sections.push(`The summary should be under ${options.maxSummaryTokens} tokens.`);
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if (options.extractKeyPoints) {
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sections.push(`\nExtract KEY POINTS as a bullet list.`);
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}
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if (options.extractDecisions) {
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sections.push(`Extract any DECISIONS made as a bullet list.`);
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}
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if (options.extractActionItems) {
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sections.push(`Extract any ACTION ITEMS as a bullet list.`);
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}
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sections.push(`\nFormat your response as:
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## SUMMARY
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[Your concise summary here]
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## KEY POINTS
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- [Key point 1]
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- [Key point 2]
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## DECISIONS
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- [Decision 1]
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- [Decision 2]
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## ACTION ITEMS
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- [Action item 1]
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- [Action item 2]
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---
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CONVERSATION:
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${conversationText}`);
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return sections.join('\n');
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}
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/**
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* Parse the structured summary response
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*/
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private parseSummaryResponse(text: string): {
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summary: string;
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keyPoints: string[];
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decisions: string[];
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actionItems: string[];
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} {
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const sections = {
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summary: '',
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keyPoints: [] as string[],
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decisions: [] as string[],
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actionItems: [] as string[]
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};
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// Extract summary
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const summaryMatch = text.match(/## SUMMARY\s*([\s\S]*?)(?=##|$)/i);
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if (summaryMatch) {
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sections.summary = summaryMatch[1].trim();
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}
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// Extract key points
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const keyPointsMatch = text.match(/## KEY POINTS\s*([\s\S]*?)(?=##|$)/i);
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if (keyPointsMatch) {
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sections.keyPoints = this.extractBulletPoints(keyPointsMatch[1]);
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}
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// Extract decisions
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const decisionsMatch = text.match(/## DECISIONS\s*([\s\S]*?)(?=##|$)/i);
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if (decisionsMatch) {
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sections.decisions = this.extractBulletPoints(decisionsMatch[1]);
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}
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// Extract action items
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const actionItemsMatch = text.match(/## ACTION ITEMS\s*([\s\S]*?)(?=##|$)/i);
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if (actionItemsMatch) {
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sections.actionItems = this.extractBulletPoints(actionItemsMatch[1]);
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}
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return sections;
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}
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/**
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* Extract bullet points from text
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*/
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private extractBulletPoints(text: string): string[] {
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const lines = text.split('\n');
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return lines
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.map(line => line.replace(/^[-*•]\s*/, '').trim())
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.filter(line => line.length > 0);
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}
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/**
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* Create a rolling summary (for continuous conversations)
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*/
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async createRollingSummary(
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previousSummary: string,
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newMessages: ConversationTurn[]
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): Promise<SummaryResult> {
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await this.init();
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const prompt = `You are updating a conversation summary with new messages.
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PREVIOUS SUMMARY:
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${previousSummary}
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NEW MESSAGES:
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${this.formatConversationForSummary(newMessages)}
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Create an updated summary that integrates the new information with the previous summary.
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Keep the summary concise but comprehensive.
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Format your response as:
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## SUMMARY
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[Your updated summary]
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## KEY POINTS
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- [Updated key points]
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## DECISIONS
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- [Updated decisions]
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## ACTION ITEMS
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- [Updated action items]`;
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const response = await this.zai!.chat.completions.create({
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messages: [
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{
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role: 'assistant',
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content: 'You are a conversation summarizer that maintains rolling summaries.'
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},
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{
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role: 'user',
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content: prompt
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}
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],
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thinking: { type: 'disabled' }
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});
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const summaryText = response.choices?.[0]?.message?.content || '';
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const parsed = this.parseSummaryResponse(summaryText);
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return {
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summary: parsed.summary,
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originalTokens: countTokens(previousSummary) + this.tokenCounter.countConversation(newMessages).total,
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summaryTokens: countTokens(parsed.summary),
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compressionRatio: 0,
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keyPoints: parsed.keyPoints,
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decisions: parsed.decisions,
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actionItems: parsed.actionItems
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};
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}
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/**
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* Create a topic-based summary (groups messages by topic)
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*/
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async createTopicSummary(
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messages: ConversationTurn[]
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): Promise<Map<string, SummaryResult>> {
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await this.init();
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// First, identify topics
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const topicResponse = await this.zai!.chat.completions.create({
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messages: [
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{
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role: 'assistant',
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content: 'Identify the main topics in this conversation. Respond with a JSON array of topic names.'
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},
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{
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role: 'user',
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content: this.formatConversationForSummary(messages)
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}
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],
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thinking: { type: 'disabled' }
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});
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let topics: string[] = [];
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try {
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const topicText = topicResponse.choices?.[0]?.message?.content || '[]';
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topics = JSON.parse(topicText.match(/\[.*\]/s)?.[0] || '[]');
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} catch {
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topics = ['General'];
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}
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// Create summaries for each topic
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const summaries = new Map<string, SummaryResult>();
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for (const topic of topics) {
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const summary = await this.summarize(messages, {
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maxSummaryTokens: 500
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});
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summaries.set(topic, summary);
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}
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return summaries;
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}
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}
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// Singleton instance
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export const defaultSummarizer = new ConversationSummarizer();
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