feat: simplify the code and optimize prompts for complex tasks (#6)

* feat: simplify the code, remove unnecessary prompts, and optimize prompts for complex tasks

* fix: context7 query with topic

Author: interstellarmt

src/tools/extract_antv_topic.ts

@@ -1,10 +1,6 @@
 /**
- * AntV Intelligent Assistant Preprocessing Tool
- *
- * 处理所有与 AntV 可视化库相关的用户查询，智能识别、解析并结构化用户需求。
- * 支持自动识别库类型、提取技术主题关键词、判断用户意图，并为后续 query_antv_document 工具准备结构化信息。
+ * AntV Topic Extraction Tool - Preprocesses user queries for AntV libraries
  */
-
 import { z } from 'zod';
 import type { AntVLibrary } from '../types';
 import { logger } from '../utils';
@@ -51,251 +47,117 @@ interface ExtractAntVTopicArgs {
 }
 
 function generateExtractionPrompt(args: ExtractAntVTopicArgs): string {
-  const libraryContext = args.library
-    ? getLibraryConfig(args.library)
-    : undefined;
-  return generateUnifiedPrompt(args.query, args.maxTopics, libraryContext);
-}
-
-function generateUnifiedPrompt(
-  query: string,
-  maxTopics: number,
-  libraryContext?: { id: AntVLibrary; name: string },
-): string {
-  const isLibrarySpecified = !!libraryContext;
-  const allLibraries = Object.values(ANTV_LIBRARY_META);
-  const libraryDeterminationSection = isLibrarySpecified
-    ? generateLibrarySpecifiedSection(libraryContext)
-    : generateLibraryDetectionSection(allLibraries);
-  const extractionRulesSection = generateExtractionRulesSection(
-    maxTopics,
-    libraryContext,
-  );
-  const outputFormatSection = generateOutputFormatSection();
-  const examplesSection = generateExamplesSection();
-  return `# AntV ${isLibrarySpecified ? 'Topic and Intent Extraction' : 'Smart Library Detection and Topic Extraction'} Task
-
-## Task Objective
-${
-  isLibrarySpecified
-    ? `Extract the most relevant technical topic phrases and user intent from user queries for ${libraryContext.name} documentation retrieval.`
-    : `1. First: Detect and recommend the most suitable AntV library based on query intent and installed dependencies
-    2. Second: Extract relevant technical topics and user intent for the determined library`
-}
-
-## User Query
-\`\`\`
-${query}
-\`\`\`
-
-${libraryDeterminationSection}
-
-${extractionRulesSection}
-
-## Output Format
-
-${outputFormatSection}
-
-## Examples
-
-${examplesSection}
-
-Now please begin ${isLibrarySpecified ? 'extraction' : 'the detection and extraction process'}:
+  const { query, library, maxTopics } = args;
 
-## Important Notice
-**MANDATORY NEXT STEP**: After completing this task, immediately call the \`query_antv_document\` tool with the extracted parameters.
-
-**Requirements:**
-- Always use extracted library, topic, intent parameters with \`query_antv_document\` tool
-- Never skip \`query_antv_document\` tool call and answer directly
-- For complex tasks: pass subTasks parameter; for simple tasks: use basic parameters`;
-}
-
-function generateLibrarySpecifiedSection(libraryContext: {
-  id: AntVLibrary;
-  name: string;
-}): string {
-  return `## Phase 1: Library Context
-  **Target Library**: ${libraryContext.name} (${libraryContext.id})`;
-}
-
-function generateLibraryDetectionSection(
-  allLibraries: Array<{ id: AntVLibrary; name: string; description: string }>,
-): string {
-  // 动态生成库映射列表
+  // Generate library mappings
   const libraryMappings = Object.values(ANTV_LIBRARY_META)
-    .map((lib) => `   - \`@antv/${lib.id}\` → ${lib.name}`)
+    .map((lib) => `   - **${lib.name} (${lib.id})**: ${lib.description}`)
     .join('\n');
 
-  return `## Phase 1: Library Detection and Recommendation
-
-**Your first task is to determine the most suitable AntV library for this query.**
+  // Get library-specific context if specified
+  const libraryContext = library
+    ? {
+        name: getLibraryConfig(library).name,
+        keywords: getLibraryKeywords(library),
+      }
+    : null;
 
-### Step 1.1: Project Dependency Scan
-Scan the user's current project for installed AntV dependencies:
-1. **Check package.json**: Look for AntV-related dependencies in both \`dependencies\` and \`devDependencies\`
-2. **Look for patterns**:
-${libraryMappings}
+  const libraryTerminology = libraryContext
+    ? libraryContext.keywords
+    : 'Use the terminology and components specific to the determined target library';
 
-### Step 1.2: Smart Library Recommendation
-Based on query content and detected dependencies, recommend the most suitable library:
+  return `# AntV Query Analysis & Topic Extraction
 
-**Library Selection Rules (Priority Order):**
-1. **Query Intent Match**: Select library that best matches the technical requirements:
-   ${allLibraries
-     .map((lib) => `- **${lib.name} (${lib.id})**: ${lib.description}`)
-     .join('\n   ')}
+## User Query
+**Query**: ${query}
+**Max Topics**: ${maxTopics}
+${library ? `**Specified Library**: ${getLibraryConfig(library).name} (${library})` : '**Library**: Auto-detect'}
 
-2. **Installed Dependency Priority**: If multiple libraries match the intent, prioritize installed ones
-3. **Best Practice Guidance**: Consider the most commonly used library for the specific use case
+## Task Instructions
 
-**Output the recommended library and reason before proceeding to Phase 2.**`;
-}
+### Phase 1: Library Detection ${library ? '(Skipped - Library Specified)' : ''}
+${
+  library
+    ? `Using specified library: **${getLibraryConfig(library).name} (${library})**`
+    : `**Determine the most suitable AntV library:**
 
-function generateExtractionRulesSection(
-  maxTopics: number,
-  libraryContext?: { id: AntVLibrary; name: string },
-): string {
-  const libraryTerminology = libraryContext
-    ? getLibraryKeywords(libraryContext.id)
-    : 'Use the terminology and components specific to the determined target library';
-  const librarySpecificPriority = libraryContext
-    ? `${libraryContext.name} specific component concepts and APIs`
-    : 'Target library specific component concepts and APIs';
-  return `## Phase 2: Topic Extraction and Intent Recognition
+Available Libraries:
+${libraryMappings}
 
-Once you have the target library (either specified or determined), proceed with:
+**Selection Priority:**
+1. Match query intent with library purpose
+2. Check for existing project dependencies
+3. Consider common use cases for the query type`
+}
 
-### 2.1: Complex Task Detection
-Determine whether the user query is a complex task. Characteristics of complex tasks:
-- Contains multiple technical concepts or functional points
-- Requires multiple steps to complete
-- Involves multiple chart types or multiple functions
-- Has clear workflow or combination requirements
+### Phase 2: Topic Extraction & Analysis
 
-### 2.2: Topic Phrase Extraction
-- **Source Limitation**: Extract only from user query content, do not add concepts not present in the query
-- **CRITICAL: All topics MUST be in English** - Use official AntV English terminology from: ${
-    libraryContext
-      ? getLibraryKeywords(libraryContext.id)
-      : 'Use the terminology and components specific to the determined target library'
-  }
-- 翻译和提取时，务必优先使用上方列出的官方组件英文名称，保持与官方文档一致，避免自创或误译,保持技术准确性
-- **Quantity Requirement**: Maximum ${maxTopics} items, can be fewer than this number
-- **Format Requirements**:
-  - Extract meaningful phrase combinations (1-4 words)
-  - Avoid single words or overly long sentences
-  - Ensure diversity between terms, avoid repetition
-  - **MANDATORY**: Output only English technical terms for documentation search
-- **Components, Concepts, Terminology**: ${libraryTerminology}
-- **Priority**:
-  1. ${librarySpecificPriority}
-  2. Chart types and visualization concepts
-  3. Interaction, animation effects, data processing and configuration
+**Extract up to ${maxTopics} key technical topics covering ALL aspects mentioned:**
+- Extract only from user query content, no additional concepts
+- Use official AntV English terminology${libraryContext ? `: ${libraryContext.keywords}` : ''}
+- Format as meaningful phrases (1-4 words), output in English only
+- **Cover these technical aspects in priority order**:
+  1. Chart types and core components
+  2. Visual styling (colors, thickness, backgrounds, opacity)
+  3. Interactions (hover, click, tooltip, zoom)
+  4. Configurations (data processing, scales, axis, animations)
 
-### 2.3: User Intent Recognition
+**Determine User Intent:**
 Based on the tone and content of the query, select the most matching intent:
 - **learn**: Learning and understanding (e.g., what is, how to understand, introduce)
 - **implement**: Implementing functionality (e.g., how to create, how to implement, code examples)
 - **solve**: Solving problems (e.g., errors, not working, fixing issues)
 
-### 2.4: Complex Task Decomposition
-If determined to be a complex task, decompose into 2-4 subtasks, each subtask should:
+**Assess Task Complexity:**
+Determine if the query is complex based on:
+- Extracted topics exceed 5
+- Involves more than 2 components or features
+- Other cases where the query appears complex based on overall assessment (e.g., multi-step processes, combinations of features)
+
+
+If complex, decompose into 2-4 subtasks, each subtask should:
 - Focus on a specific technical point or functionality
 - Be able to independently obtain answers through documentation queries
 - Be arranged in logical order (basic → advanced)
 - Subtask topics must be clear, concise, and follow the format requirements above
 
-**Note**: For complex tasks, provide the decomposed subtask list to the query_antv_document tool for one-time processing.`;
-}
+### Phase 3: Output Format
 
-function generateOutputFormatSection(): string {
-  return `### Simple Task Output Format:
+**For Simple Tasks:**
 \`\`\`json
 {
-  "topic": "actually extracted topic1, topic2, topic3",
+  "library": "detected_or_specified_library",
+  "topic": "topic1, topic2, topic3",
   "intent": "learn|implement|solve",
-  "library": "the target library (either specified or determined)",
   "isComplexTask": false
 }
 \`\`\`
 
-### Complex Task Output Format:
+**For Complex Tasks:**
 \`\`\`json
 {
-  "topic": "summary of all related topics",
-  "intent": "overall intent",
-  "library": "the target library (either specified or determined)",
+  "library": "detected_or_specified_library",
+  "topic": "overall_topic_summary",
+  "intent": "overall_intent",
   "isComplexTask": true,
   "subTasks": [
     {
-      "query": "specific question for subtask 1",
-      "topic": "topic for subtask 1",
-      "intent": "intent for subtask 1"
+      "query": "specific_subtask_question_1",
+      "topic": "subtask_topic_1"
     },
     {
-      "query": "specific question for subtask 2",
-      "topic": "topic for subtask 2",
-      "intent": "intent for subtask 2"
+      "query": "specific_subtask_question_2",
+      "topic": "subtask_topic_2"
     }
   ]
 }
-\`\`\``;
-}
-
-function generateExamplesSection(): string {
-  return `**Example 1 - Simple Task (English Query)**
-Query: "What is G2?"
-Output:
-\`\`\`json
-{
-  "topic": "G2 introduction",
-  "intent": "learn",
-  "library": "g2",
-  "isComplexTask": false
-}
 \`\`\`
 
-**Example 2 - Simple Task (Chinese Query)**
-Query: "如何修改G2图表的颜色？"
-Output:
-\`\`\`json
-{
-  "topic": "chart color, styling",
-  "intent": "implement",
-  "library": "g2",
-  "isComplexTask": false
-}
-\`\`\`
+**Analyze the query and provide the structured output above.**
 
-**Example 3 - Complex Task**
-Query: "How to create an animated bar chart with hover interaction in G2?"
-Output:
-\`\`\`json
-{
-  "topic": "animated bar chart, chart animation, hover interaction, mouse events",
-  "intent": "implement",
-  "library": "g2",
-  "isComplexTask": true,
-  "subTasks": [
-    {
-      "query": "How to create a basic bar chart in G2?",
-      "topic": "bar chart, chart creation",
-      "intent": "implement"
-    },
-    {
-      "query": "How to add animation effects to G2 charts?",
-      "topic": "chart animation, animation effects",
-      "intent": "implement"
-    },
-    {
-      "query": "How to add hover interaction to G2 charts?",
-      "topic": "hover interaction, mouse events",
-      "intent": "implement"
-    }
-  ]
-}
-\`\`\``;
+## Important Notice
+**MANDATORY NEXT STEP**: After completing this task, immediately call the \`query_antv_document\` tool with the extracted parameters.
+`;
 }
 
 export const ExtractAntVTopicTool = {
@@ -321,8 +183,8 @@ Key features:
     const startTime = Date.now();
     try {
       const extractionPrompt = generateExtractionPrompt(args);
-      const maxTopics = args.maxTopics;
       const processingTime = Date.now() - startTime;
+
       return {
         content: [
           {
@@ -333,8 +195,8 @@ Key features:
         _meta: {
           topic: '', // Will be filled by LLM
           intent: '', // Will be filled by LLM
-          library: args.library || 'g2',
-          maxTopics,
+          library: args.library || 'auto-detect',
+          maxTopics: args.maxTopics,
           promptGenerated: true,
           next_tools: ['query_antv_document'],
           isComplexTask: false, // Will be determined and filled by LLM
@@ -344,18 +206,8 @@ Key features:
       };
     } catch (error) {
       logger.error('Failed to generate extraction prompt:', error);
-      // Use safe fallbacks since validation might have failed
-      const fallbackLibrary =
-        args.library && Object.keys(ANTV_LIBRARY_META).includes(args.library)
-          ? args.library
-          : 'g2';
-      const fallbackMaxTopics =
-        typeof args.maxTopics === 'number' &&
-        args.maxTopics >= 3 &&
-        args.maxTopics <= 8
-          ? args.maxTopics
-          : 5;
       const processingTime = Date.now() - startTime;
+
       return {
         content: [
           {
@@ -369,8 +221,8 @@ Key features:
         _meta: {
           topic: '',
           intent: '',
-          library: fallbackLibrary,
-          maxTopics: fallbackMaxTopics,
+          library: args.library || 'auto-detect',
+          maxTopics: args.maxTopics,
           promptGenerated: false,
           next_tools: ['query_antv_document'],
           isComplexTask: false,