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refactor: Align python-tools-strategy.md with intelligent-tools-strategy format
- Transform from command reference to strategic decision framework - Add core framework with decision principles and execution rules - Include universal command template with PURPOSE/TASK/CONTEXT/EXPECTED structure - Add tool selection matrix and workflow integration patterns - Include planning checklist and best practices sections - Add hierarchical vector system details and migration benefits - Provide quick setup guide and decision framework 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
catlog22
@@ -1,451 +1,223 @@
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---
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name: python-tools-strategy
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description: Command reference for Python-based tool invocation
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type: command-reference
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description: Strategic framework for Python-based intelligent tool selection
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type: strategic-guideline
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---
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# Python Tools Command Reference
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# Python Tools Selection Strategy
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## ⚡ Quick Commands
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## ⚡ Core Framework
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**Smart Analysis**: `pycli --analyze --query "search term" --tool [gemini/codex]`
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**Direct Tool Invocation**: `pycli --analyze --tool [gemini/codex] -p "prompt"`
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**Vector Database Setup**: `pycli --init`
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**Vector Database Update**: `pycli --update-embeddings`
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**Python CLI (pycli)**: Unified interface for intelligent context discovery and tool execution
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**Vector Database**: Hierarchical semantic search with automatic parent discovery
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**Smart Analysis**: Context-aware file selection with similarity scoring
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## ⏰ When to Use What
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### Decision Principles
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- **Context first** - Use vector search for intelligent file discovery
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- **Hierarchical by default** - Leverage parent directory vector databases automatically
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- **Semantic over syntactic** - Find relevant code by meaning, not just keywords
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- **Tool integration** - Seamlessly combine context discovery with Gemini/Codex execution
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### 🔄 Vector Database Timing
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### Quick Decision Rules
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1. **Need context discovery?** → Start with `pycli --analyze --query`
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2. **Know exact files?** → Use `pycli --analyze -p` directly
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3. **First time in project?** → Run `pycli --init` first
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4. **Files changed?** → Update with `pycli --update-embeddings`
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### Core Execution Rules
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- **Default Tool**: Gemini for analysis, Codex for development
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- **Similarity Threshold**: 0.3 minimum for relevant results
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- **Hierarchical Search**: Automatic parent directory vector database discovery
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- **Command Pattern**: Always use `pycli` wrapper for consistent interface
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## 🎯 Universal Command Template
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### Standard Format (REQUIRED)
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```bash
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# FIRST TIME (run once per project)
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# Smart Context Discovery
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pycli --analyze --query "
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PURPOSE: [clear analysis goal]
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SEARCH: [semantic search terms]
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TOOL: [gemini/codex/both]
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EXPECTED: [expected context and output]
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" --tool [gemini/codex]
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# Direct Tool Execution
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pycli --analyze --tool [gemini/codex] -p "
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PURPOSE: [clear execution goal]
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TASK: [specific execution task]
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CONTEXT: [known file references]
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EXPECTED: [expected deliverables]
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"
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```
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### Template Structure
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- [ ] **PURPOSE** - Clear goal and intent for analysis
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- [ ] **SEARCH/TASK** - Semantic search terms or specific task
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- [ ] **TOOL** - Gemini for analysis, Codex for development
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- [ ] **CONTEXT** - File references and project context
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- [ ] **EXPECTED** - Clear expected results and format
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## 📊 Tool Selection Matrix
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| Task Type | Command | Use Case | Context Strategy |
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|-----------|---------|----------|------------------|
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| **Context Discovery** | `pycli --analyze --query` | Code exploration, pattern finding | Vector similarity search |
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| **Targeted Analysis** | `pycli --analyze --tool gemini -p` | Architecture review, understanding | Known file analysis |
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| **Development** | `pycli --analyze --tool codex -p` | Feature implementation, bug fixes | Smart context + execution |
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| **Setup** | `pycli --init` | Project initialization | Vector database creation |
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| **Maintenance** | `pycli --update-embeddings` | Index updates after changes | Incremental vectorization |
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| **Health Check** | `pycli --status` | System verification | Database validation |
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## 🚀 Usage Patterns
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### Workflow Integration (REQUIRED)
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When planning any coding task, **ALWAYS** integrate Python CLI tools:
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1. **Discovery Phase**: Use `pycli --analyze --query` for context
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2. **Analysis Phase**: Use Gemini for understanding with smart context
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3. **Implementation Phase**: Use Codex for development with relevant files
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4. **Validation Phase**: Update embeddings and verify results
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### Common Scenarios
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```bash
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# Project Context Discovery
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pycli --analyze --query "
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PURPOSE: Understand authentication architecture
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SEARCH: authentication patterns, login systems, user management
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TOOL: gemini
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EXPECTED: Architecture overview and key implementation files
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" --tool gemini
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# Feature Development with Context
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pycli --analyze --query "
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PURPOSE: Implement user registration
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SEARCH: user creation, validation patterns, database models
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TOOL: codex
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EXPECTED: Complete registration module with tests
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" --tool codex
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# Code Quality Analysis
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pycli --analyze --query "
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PURPOSE: Review error handling patterns
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SEARCH: exception handling, error middleware, logging
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TOOL: gemini
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EXPECTED: Error handling assessment and recommendations
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" --tool gemini
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```
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## 📋 Planning Checklist
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For every development task:
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- [ ] **Discovery completed** - Context discovery with vector search
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- [ ] **Purpose defined** - Clear goal and intent documented
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- [ ] **Tool selected** - Gemini for analysis, Codex for development
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- [ ] **Context gathered** - Relevant files identified through similarity
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- [ ] **Template applied** - Standard command format used
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- [ ] **Embeddings updated** - Vector database reflects current state
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- [ ] **Results validated** - Output quality and relevance verified
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## 🎯 Key Features
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### Python CLI (pycli)
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- **Command**: `pycli --analyze`
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- **Strengths**: Hierarchical vector search, semantic similarity, context discovery
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- **Best For**: Intelligent file selection, context-aware analysis, project exploration
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### Vector Database
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- **Hierarchical**: Automatic parent directory discovery
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- **Semantic**: Meaning-based similarity scoring
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- **Efficient**: Incremental updates and smart caching
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- **Scalable**: Project-wide context with subdirectory support
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### Context Patterns
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- Query-based: `--query "semantic search terms"`
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- Direct prompt: `-p "specific task"`
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- Tool selection: `--tool [gemini|codex|both]`
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- Similarity control: `--top-k N --similarity-threshold X`
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## 🔧 Best Practices
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- **Start with discovery** - Use `--query` for context before direct prompts
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- **Be semantic** - Use meaning-based search terms, not just keywords
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- **Update regularly** - Run `--update-embeddings` after file changes
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- **Validate context** - Check similarity scores and relevance before proceeding
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- **Document patterns** - Reference successful query patterns for reuse
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- **Leverage hierarchy** - Work in subdirectories, let parent DBs provide context
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## 📁 Hierarchical Vector System
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**Base Structure**: `~/.claude/vector_db/[project-path]/`
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### Automatic Discovery
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```
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Project Structure Vector Database Usage
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/project/ Creates: ~/.claude/vector_db/project/
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├── src/ → Uses parent DB automatically
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│ ├── auth/ → Uses parent DB automatically
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│ └── api/ → Uses parent DB automatically
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└── tests/ → Uses parent DB automatically
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```
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### Smart Context Integration
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- **Parent Discovery**: Subdirectories automatically use parent vector DB
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- **Semantic Search**: Find files by meaning, not just filename patterns
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- **Similarity Scoring**: Relevance-based file selection with configurable thresholds
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- **Incremental Updates**: Efficient re-indexing of only changed files
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### Migration Benefits
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```bash
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# Enhanced Context Discovery (vs traditional grep/find)
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# OLD: find . -name "*auth*" | head -10
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# NEW: pycli --analyze --query "authentication patterns" --tool gemini
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# OLD: grep -r "login" src/ | head -20
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# NEW: pycli --analyze --query "login implementation" --tool codex
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# OLD: ~/.claude/scripts/gemini-wrapper -p "analyze auth"
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# NEW: pycli --analyze --query "authentication architecture" --tool gemini
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```
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## 🎯 Quick Setup Guide
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### Project Setup (One-time per project)
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```bash
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# 1. Navigate to project root
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cd /path/to/project
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# 2. Initialize vector database
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pycli --init
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# DAILY (when files change)
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# 3. Verify setup
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pycli --status
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```
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### Daily Workflow
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```bash
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# 1. Update embeddings (after file changes)
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pycli --update-embeddings
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# BEFORE ANALYSIS (check status)
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pycli --status
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# 2. Smart context discovery
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pycli --analyze --query "your search terms" --tool gemini
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# 3. Targeted development
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pycli --analyze --query "implementation patterns" --tool codex
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```
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### 🎯 Tool Selection Timing
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- **Code Discovery** → Use `pycli --analyze --query` to find relevant files
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- **Direct Analysis** → Use `pycli --analyze -p` when you know what to analyze
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- **Development** → Use `--tool codex` for implementation tasks
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- **Understanding** → Use `--tool gemini` for analysis and exploration
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## 🎯 Core Commands
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### Smart Analysis (Recommended)
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```bash
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# Find similar code patterns and analyze
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pycli --analyze --query "authentication patterns" --tool gemini
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# Search with development context
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pycli --analyze --query "error handling" --tool codex
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# Both discovery and analysis
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pycli --analyze --query "database connections" --tool both
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```
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### Direct Tool Invocation
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```bash
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# Direct analysis with known context
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pycli --analyze --tool gemini -p "analyze authentication patterns"
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# Direct development task
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pycli --analyze --tool codex -p "implement user login"
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# Status and testing
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pycli --status
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pycli --test-search
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```
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### Vector Database Operations
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```bash
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# Initial setup (run once per project)
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pycli --init
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# Daily updates (run when files change)
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pycli --update-embeddings
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# Status check
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pycli --status
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```
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## 📊 Command Matrix
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| What You Want | Command | Use Case |
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|---------------|---------|----------|
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| **Smart analysis** | `pycli --analyze --query "pattern" --tool gemini` | Code discovery & analysis |
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| **Direct analysis** | `pycli --analyze --tool gemini -p "prompt"` | Known target analysis |
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| **Generate code** | `pycli --analyze --tool codex -p "task"` | Development |
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| **Setup project** | `pycli --init` | First time setup |
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| **Update search index** | `pycli --update-embeddings` | Maintenance |
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| **Check status** | `pycli --status` | System health |
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## 🚀 Usage Examples
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### Replace Gemini Wrapper
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```bash
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# OLD: ~/.claude/scripts/gemini-wrapper -p "analyze auth patterns"
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# NEW: pycli --analyze --tool gemini -p "analyze auth patterns"
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```
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### Replace Codex Commands
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```bash
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# OLD: codex --full-auto exec "implement login"
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# NEW: pycli --analyze --tool codex -p "implement login"
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```
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### Smart Context Discovery
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```bash
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# Find relevant files first, then analyze
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pycli --analyze --query "user authentication" --tool gemini
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# Results include:
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# - Hierarchical vector database search
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# - Semantically similar files from project and parent directories
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# - Generated tool command with intelligent context
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# - Executed analysis with smart file selection
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```
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## 🔧 Command Options
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### pycli (Unified Interface)
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```bash
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pycli [command] [options]
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Commands:
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--init Initialize vector database for current project
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--analyze Run analysis with AI tools
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--status Show system status and health
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--test-search Test vector search functionality
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--update-embeddings Update vector embeddings for changed files
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Analysis Options:
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--tool [gemini|codex|both] Which AI tool to use (default: gemini)
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-p, --prompt TEXT Direct prompt for analysis
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--query TEXT Semantic search query for context discovery
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--top-k INTEGER Number of similar files to find (default: 10)
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--similarity-threshold FLOAT Minimum similarity score (0.0-1.0)
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Output Options:
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--quiet Suppress progress output
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--verbose Show detailed analysis information
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--output [patterns|json] Output format (default: patterns)
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```
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### Installation & Setup
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```bash
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# Install pycli system
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bash D:/Claude_dms3/.claude/scripts/install_pycli.sh
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# The script will automatically add ~/.claude/scripts/ to your PATH
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# Reload your shell configuration
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source ~/.bashrc # or ~/.zshrc
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# Verify installation - now you can use pycli directly
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pycli --help
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```
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## 📋 Common Workflows
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### 🚀 First-Time Setup (Vector Database)
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```bash
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# 1. Install pycli system
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bash D:/Claude_dms3/.claude/scripts/install_pycli.sh
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# 2. Initialize vector database for project
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cd /path/to/your/project
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pycli --init
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# 3. Verify setup works
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pycli --status
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# 4. Test search functionality
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pycli --test-search
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```
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### 🎯 Analysis Workflow (Recommended)
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```bash
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# 1. Update vectors (if files changed)
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pycli --update-embeddings
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# 2. Smart analysis with context discovery
|
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pycli --analyze --query "what you're looking for" --tool gemini
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# 3. Development with context
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pycli --analyze --query "related patterns" --tool codex
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```
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### ⏰ When to Run Commands
|
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#### 🔄 Vector Database Maintenance
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```bash
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# WHEN: First time using system
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pycli --init
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# WHEN: Files have been added/modified (daily/after coding)
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pycli --update-embeddings
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# WHEN: Before starting analysis (check if system ready)
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pycli --status
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```
|
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|
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#### 🎯 Analysis Timing
|
||||
```bash
|
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# WHEN: You need to find relevant code patterns
|
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pycli --analyze --query "search term" --tool gemini
|
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|
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# WHEN: You have specific prompt and know context
|
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pycli --analyze --tool gemini -p "specific prompt"
|
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|
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# WHEN: You want to develop/implement something
|
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pycli --analyze --query "similar implementations" --tool codex
|
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```
|
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|
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### Integration with Existing Tools
|
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```bash
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# In place of gemini-wrapper
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pycli --analyze --tool gemini -p "$YOUR_PROMPT"
|
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|
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# In place of codex commands
|
||||
pycli --analyze --tool codex -p "$YOUR_TASK"
|
||||
|
||||
# Enhanced with hierarchical context discovery
|
||||
pycli --analyze --query "relevant context" --tool both
|
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```
|
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|
||||
## 🎯 Quick Reference
|
||||
|
||||
### 🚀 Most Common Commands
|
||||
```bash
|
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# 1. Smart analysis (recommended first choice)
|
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pycli --analyze --query "what you're looking for" --tool gemini
|
||||
|
||||
# 2. Direct tool call (when you know exactly what to analyze)
|
||||
pycli --analyze --tool codex -p "what you want to do"
|
||||
|
||||
# 3. Keep embeddings updated (run after file changes)
|
||||
pycli --update-embeddings
|
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```
|
||||
|
||||
### ⚙️ Configuration (config.yaml)
|
||||
```yaml
|
||||
# Essential settings only
|
||||
embeddings:
|
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enabled: true
|
||||
similarity_threshold: 0.3
|
||||
|
||||
tools:
|
||||
default_tool: "gemini"
|
||||
timeout: 300
|
||||
```
|
||||
|
||||
### 🐛 Troubleshooting
|
||||
```bash
|
||||
# Check if everything works
|
||||
pycli --status
|
||||
|
||||
# Rebuild if issues
|
||||
pycli --init
|
||||
|
||||
# Test search functionality
|
||||
pycli --test-search
|
||||
```
|
||||
|
||||
## 🎪 Integration Decision Tree
|
||||
## 🎪 Decision Framework
|
||||
|
||||
```
|
||||
Need to analyze code?
|
||||
├─ Do you know specific files to analyze?
|
||||
│ ├─ YES → Use: pycli --analyze --tool [gemini/codex] -p "prompt"
|
||||
│ └─ NO → Use: pycli --analyze --query "search term" --tool [gemini/codex]
|
||||
└─ Is vector database updated?
|
||||
├─ UNSURE → Run: pycli --status
|
||||
├─ NO → Run: pycli --update-embeddings
|
||||
└─ YES → Proceed with analysis
|
||||
Need intelligent code analysis?
|
||||
├─ Know specific files to analyze?
|
||||
│ ├─ YES → pycli --analyze --tool [gemini/codex] -p "prompt"
|
||||
│ └─ NO → pycli --analyze --query "semantic search" --tool [gemini/codex]
|
||||
├─ Vector database updated?
|
||||
│ ├─ UNSURE → pycli --status
|
||||
│ ├─ NO → pycli --update-embeddings
|
||||
│ └─ YES → Proceed with analysis
|
||||
└─ First time in project?
|
||||
└─ Run pycli --init first
|
||||
```
|
||||
|
||||
## 🏗️ Hierarchical Vector Database
|
||||
|
||||
### Key Features
|
||||
- **Automatic Parent Discovery**: Subdirectories automatically use parent's vector database
|
||||
- **No Redundant Vectorization**: Avoids duplicate processing in project subdirectories
|
||||
- **Central Storage**: All vector databases stored in `~/.claude/vector_db/`
|
||||
- **Path-based Organization**: Vector DBs organized by project directory structure
|
||||
|
||||
### How It Works
|
||||
```bash
|
||||
# Project structure
|
||||
/home/user/myproject/
|
||||
├── src/
|
||||
│ └── auth/ # Uses parent's vector DB
|
||||
└── tests/ # Uses parent's vector DB
|
||||
|
||||
# Vector database structure
|
||||
~/.claude/vector_db/
|
||||
└── home_user_myproject/ # Single DB for entire project
|
||||
├── embeddings.pkl
|
||||
└── index.json
|
||||
```
|
||||
|
||||
### Usage Examples
|
||||
```bash
|
||||
# Initialize at project root
|
||||
cd /home/user/myproject
|
||||
pycli --init
|
||||
|
||||
# Work in subdirectory (automatically finds parent DB)
|
||||
cd src/auth
|
||||
pycli --analyze --query "authentication patterns" # Uses parent's DB
|
||||
|
||||
# Work in another subdirectory
|
||||
cd ../../tests
|
||||
pycli --analyze --query "test patterns" # Uses same parent DB
|
||||
```
|
||||
|
||||
## 🔧 Vector Database Setup & Maintenance
|
||||
|
||||
### ⚡ One-Time System Setup
|
||||
```bash
|
||||
# 1. Install dependencies (first time only)
|
||||
cd .claude/python_script && pip install -r requirements.txt
|
||||
|
||||
# 2. Initialize vector database (creates embeddings)
|
||||
python indexer.py --rebuild-index --update-embeddings
|
||||
|
||||
# 3. Verify setup works
|
||||
python cli.py --status
|
||||
|
||||
# 4. Test search functionality
|
||||
python cli.py --test-search
|
||||
```
|
||||
|
||||
### 📋 What Happens During Setup
|
||||
1. **File Indexing**: Scans project files and creates index
|
||||
2. **Model Download**: Downloads AI model (first time only, ~500MB)
|
||||
3. **Embedding Generation**: Creates vector representations of code
|
||||
4. **Cache Creation**: Saves embeddings to `.claude/cache/embeddings/`
|
||||
|
||||
### 🎯 Verification Checklist
|
||||
After setup, verify these work:
|
||||
- [ ] `python cli.py --status` shows "System ready"
|
||||
- [ ] `python cli.py --test-search` returns results
|
||||
- [ ] Files exist: `.claude/cache/embeddings/embeddings.pkl`
|
||||
- [ ] Search works: `python analyzer.py --query "test"`
|
||||
|
||||
### 🐛 Common Issues & Fixes
|
||||
|
||||
#### Nothing works / Setup failed
|
||||
```bash
|
||||
# Nuclear option - reset everything
|
||||
rm -rf .claude/cache/embeddings/*
|
||||
python indexer.py --rebuild-index --update-embeddings
|
||||
```
|
||||
|
||||
#### Slow performance
|
||||
```yaml
|
||||
# In config.yaml - reduce batch size
|
||||
embeddings:
|
||||
batch_size: 16
|
||||
```
|
||||
|
||||
#### No search results found
|
||||
```yaml
|
||||
# In config.yaml - lower similarity threshold
|
||||
embeddings:
|
||||
similarity_threshold: 0.1
|
||||
```
|
||||
|
||||
#### Memory errors during setup
|
||||
```yaml
|
||||
# In config.yaml - use smaller batches
|
||||
embeddings:
|
||||
batch_size: 8
|
||||
```
|
||||
|
||||
#### Model download fails
|
||||
```bash
|
||||
# Manual model download
|
||||
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')"
|
||||
```
|
||||
|
||||
## 📋 Usage Rules & Best Practices
|
||||
|
||||
### 🎯 Core Rules
|
||||
|
||||
1. **Always check status first** - Run `python cli.py --status` before analysis
|
||||
2. **Update after file changes** - Run `indexer.py --update-embeddings` when files modified
|
||||
3. **Use vector search for discovery** - Use `analyzer.py --query` when exploring code
|
||||
4. **Use direct tools for known targets** - Use `cli.py --analyze` for specific analysis
|
||||
5. **Prefer context-aware tools** - Enhanced Python tools over legacy shell scripts
|
||||
|
||||
### ⏰ Maintenance Schedule
|
||||
|
||||
```bash
|
||||
# DAILY (or after coding sessions)
|
||||
python .claude/python_script/indexer.py --update-embeddings
|
||||
|
||||
# WEEKLY (or when config changes)
|
||||
python .claude/python_script/cli.py --status # Check system health
|
||||
|
||||
# MONTHLY (or after major project changes)
|
||||
python .claude/python_script/indexer.py --rebuild-index --update-embeddings
|
||||
```
|
||||
|
||||
### 🎯 Tool Selection Rules
|
||||
|
||||
#### Use `cli.py --analyze --query` when:
|
||||
- ✅ Exploring unfamiliar codebase
|
||||
- ✅ Looking for similar code patterns
|
||||
- ✅ Need context discovery for complex tasks
|
||||
- ✅ Want smart file selection for tool execution
|
||||
|
||||
#### Use `cli.py --analyze -p` when:
|
||||
- ✅ You know exactly what files to analyze
|
||||
- ✅ Direct prompt execution without context search
|
||||
- ✅ Quick tool invocation with known targets
|
||||
|
||||
#### Use `indexer.py` when:
|
||||
- ✅ First time setup
|
||||
- ✅ Files have been added/modified
|
||||
- ✅ System performance degraded
|
||||
- ✅ Configuration changed
|
||||
|
||||
|
||||
### 🚀 Migration from Legacy Tools
|
||||
|
||||
#### Replace gemini-wrapper
|
||||
```bash
|
||||
# OLD (shell-based)
|
||||
~/.claude/scripts/gemini-wrapper -p "analyze authentication"
|
||||
|
||||
# NEW (Python-based with hierarchical vector context)
|
||||
pycli --analyze --query "authentication" --tool gemini
|
||||
```
|
||||
|
||||
#### Replace codex commands
|
||||
```bash
|
||||
# OLD (direct execution)
|
||||
codex --full-auto exec "implement user login"
|
||||
|
||||
# NEW (context-aware development with hierarchical DB)
|
||||
pycli --analyze --query "login implementation patterns" --tool codex
|
||||
```
|
||||
|
||||
#### Integration workflow
|
||||
1. **Install pycli** - Run installation script once
|
||||
2. **Initialize projects** - Run `pycli --init` in each project root
|
||||
3. **Replace commands** - Update scripts to use `pycli` instead of direct Python calls
|
||||
4. **Enjoy hierarchical benefits** - Automatic parent DB discovery in subdirectories
|
||||
|
||||
## 🎉 Advanced Features
|
||||
|
||||
### Bash Wrapper Benefits
|
||||
- **Unified Interface**: Single `pycli` command for all operations
|
||||
- **Smart Path Detection**: Automatically finds project roots and vector databases
|
||||
- **Environment Management**: Configurable Python interpreter path
|
||||
- **Hierarchical Support**: Intelligent parent directory discovery
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user