cauvang32
9c180bdd89
- Removed the `analyze_data_file` function from tool definitions to streamline functionality. - Enhanced the `execute_python_code` function description to clarify auto-installation of packages and file handling. - Deleted the `python_executor.py` module to simplify the codebase and improve maintainability. - Introduced a new `token_counter.py` module for efficient token counting for OpenAI API requests, including support for Discord image links and cost estimation.
321 lines
16 KiB
Markdown
321 lines
16 KiB
Markdown
# Complete Implementation Summary
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## ✅ All Requirements Implemented
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### 1. ✅ File Storage with User Limits
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- **Location**: `/tmp/bot_code_interpreter/user_files/{user_id}/`
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- **Per-User Limit**: `MAX_FILES_PER_USER` in `.env` (default: 20 files)
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- **Auto-Cleanup**: When limit reached, oldest file automatically deleted
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- **Expiration**: Files expire after `FILE_EXPIRATION_HOURS` (default: 48 hours, -1 for permanent)
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- **Metadata**: MongoDB stores file_id, filename, file_type, expires_at, etc.
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### 2. ✅ Universal File Access
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- **By Code Interpreter**: All files accessible via `load_file(file_id)`
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- **By AI Model**: File info in conversation context with file_id
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- **Smart Loading**: Auto-detects file type and loads appropriately
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- **200+ File Types**: CSV, Excel, JSON, Parquet, HDF5, NumPy, Images, Audio, Video, etc.
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### 3. ✅ All Work Through Code Interpreter
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- **Single Execution Path**: Everything runs through `execute_python_code`
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- **Removed**: Deprecated `analyze_data_file` tool
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- **Unified**: Data analysis, Python code, file processing - all in one place
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- **Auto-Install**: Packages auto-install when imported
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- **Auto-Capture**: Generated files automatically sent to user
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### 4. ✅ 200+ File Types Support
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- **Tabular**: CSV, Excel, Parquet, Feather, etc.
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- **Structured**: JSON, YAML, XML, TOML, etc.
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- **Binary**: HDF5, Pickle, NumPy, MATLAB, etc.
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- **Media**: Images, Audio, Video (20+ formats each)
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- **Code**: 50+ programming languages
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- **Scientific**: DICOM, NIfTI, FITS, VTK, etc.
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- **Geospatial**: GeoJSON, Shapefile, KML, etc.
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- **Archives**: ZIP, TAR, 7Z, etc.
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### 5. ✅ Configurable Code Execution Timeout
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- **Configuration**: `CODE_EXECUTION_TIMEOUT` in `.env` (default: 300 seconds)
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- **Smart Timeout**: Only counts actual code execution time
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- **Excluded from Timeout**:
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- Environment setup
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- Package installation
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- File upload/download
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- Result collection
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- **User-Friendly**: Clear timeout error messages
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---
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## 📊 Architecture Overview
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```
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┌─────────────────────────────────────────────────────────────────┐
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│ User Uploads File │
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│ (Any of 200+ file types) │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ upload_discord_attachment() │
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│ • Detects file type (200+ types) │
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│ • Checks user file limit (MAX_FILES_PER_USER) │
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│ • Deletes oldest if limit reached │
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│ • Saves to /tmp/bot_code_interpreter/user_files/{user_id}/ │
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│ • Stores metadata in MongoDB │
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│ • Sets expiration (FILE_EXPIRATION_HOURS) │
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│ • Returns file_id │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ MongoDB (Metadata) │
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│ { │
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│ file_id: "abc123", │
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│ user_id: "12345", │
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│ filename: "data.csv", │
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│ file_type: "csv", │
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│ file_size: 1234567, │
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│ file_path: "/tmp/.../abc123.csv", │
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│ uploaded_at: "2025-10-02T10:00:00", │
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│ expires_at: "2025-10-04T10:00:00" │
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│ } │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ User Asks to Process File │
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│ "Analyze this data", "Create plots", etc. │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ AI Model (GPT-4) │
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│ • Sees file context with file_id in conversation │
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│ • Generates Python code: │
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│ df = load_file('abc123') │
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│ df.describe() │
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│ plt.plot(df['x'], df['y']) │
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│ plt.savefig('plot.png') │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ execute_python_code() │
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│ 1. Validate code security │
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│ 2. Ensure venv ready (NOT counted in timeout) │
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│ 3. Install packages if needed (NOT counted in timeout) │
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│ 4. Fetch all user files from DB │
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│ 5. Inject load_file() function with file_id mappings │
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│ 6. Write code to temp file │
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│ 7. ⏱️ START TIMEOUT TIMER │
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│ 8. Execute Python code in isolated venv │
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│ 9. ⏱️ END TIMEOUT TIMER │
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│ 10. Capture stdout, stderr, generated files │
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│ 11. Return results │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ Isolated Python Execution │
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│ │
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│ FILES = {'abc123': '/tmp/.../abc123.csv'} │
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│ │
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│ def load_file(file_id): │
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│ path = FILES[file_id] │
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│ # Smart auto-detection: │
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│ if path.endswith('.csv'): │
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│ return pd.read_csv(path) │
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│ elif path.endswith('.xlsx'): │
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│ return pd.read_excel(path) │
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│ elif path.endswith('.parquet'): │
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│ return pd.read_parquet(path) │
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│ # ... 200+ file types handled ... │
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│ │
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│ # User's code executes here with timeout │
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│ df = load_file('abc123') # Auto: pd.read_csv() │
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│ print(df.describe()) │
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│ plt.plot(df['x'], df['y']) │
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│ plt.savefig('plot.png') # Auto-captured! │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ Auto-Capture Results │
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│ • stdout/stderr output │
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│ • Generated files: plot.png, results.csv, etc. │
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│ • Execution time │
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│ • Success/error status │
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└────────────────────────────┬────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ Send Results to Discord │
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│ • Text output (stdout) │
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│ • Generated files as attachments │
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│ • Error messages if any │
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│ • Execution time │
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└─────────────────────────────────────────────────────────────────┘
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│
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↓
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┌─────────────────────────────────────────────────────────────────┐
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│ Background Cleanup │
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│ • After FILE_EXPIRATION_HOURS: Delete expired files │
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│ • When user exceeds MAX_FILES_PER_USER: Delete oldest │
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│ • Remove from disk and MongoDB │
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└─────────────────────────────────────────────────────────────────┘
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```
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---
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## 📝 Configuration (.env)
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```bash
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# Discord & API Keys
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DISCORD_TOKEN=your_token_here
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OPENAI_API_KEY=your_api_key_here
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OPENAI_BASE_URL=https://models.github.ai/inference
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MONGODB_URI=your_mongodb_uri_here
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# File Management
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FILE_EXPIRATION_HOURS=48 # Files expire after 48 hours (-1 = never)
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MAX_FILES_PER_USER=20 # Maximum 20 files per user
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# Code Execution
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CODE_EXECUTION_TIMEOUT=300 # 5 minutes timeout for code execution
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```
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---
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## 🎯 Key Features
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### 1. Universal File Support
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- ✅ 200+ file types
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- ✅ Smart auto-detection
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- ✅ Automatic loading
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### 2. Intelligent File Management
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- ✅ Per-user limits
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- ✅ Automatic cleanup
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- ✅ Expiration handling
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### 3. Unified Execution
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- ✅ Single code interpreter
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- ✅ Auto-install packages
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- ✅ Auto-capture outputs
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### 4. Smart Timeout
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- ✅ Configurable duration
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- ✅ Only counts code runtime
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- ✅ Excludes setup/install
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### 5. Production Ready
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- ✅ Security validation
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- ✅ Error handling
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- ✅ Resource management
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---
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## 🧪 Testing Examples
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### Test 1: CSV File Analysis
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```python
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# Upload data.csv
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# Ask: "Analyze this CSV file"
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# AI generates:
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import pandas as pd
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import matplotlib.pyplot as plt
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df = load_file('file_id') # Auto: pd.read_csv()
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print(df.describe())
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df.hist(figsize=(12, 8))
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plt.savefig('histograms.png')
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```
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### Test 2: Parquet File Processing
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```python
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# Upload large_data.parquet
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# Ask: "Show correlations"
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# AI generates:
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import pandas as pd
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import seaborn as sns
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df = load_file('file_id') # Auto: pd.read_parquet()
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corr = df.corr()
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sns.heatmap(corr, annot=True)
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plt.savefig('correlation.png')
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```
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### Test 3: Multiple File Types
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```python
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# Upload: data.csv, config.yaml, model.pkl
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# Ask: "Load all files and process"
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# AI generates:
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import pandas as pd
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import yaml
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import pickle
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df = load_file('csv_id') # Auto: pd.read_csv()
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config = load_file('yaml_id') # Auto: yaml.safe_load()
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model = load_file('pkl_id') # Auto: pickle.load()
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predictions = model.predict(df)
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results = pd.DataFrame({'predictions': predictions})
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results.to_csv('predictions.csv')
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```
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### Test 4: Timeout Handling
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```python
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# Set CODE_EXECUTION_TIMEOUT=60
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# Upload data.csv
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# Ask: "Run complex computation"
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# AI generates code that takes 70 seconds
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# Result: TimeoutError after 60 seconds with clear message
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```
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---
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## 📚 Documentation Files
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1. **UNIFIED_FILE_SYSTEM_SUMMARY.md** - Complete file system overview
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2. **ALL_FILE_TYPES_AND_TIMEOUT_UPDATE.md** - Detailed implementation
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3. **QUICK_REFERENCE_FILE_TYPES_TIMEOUT.md** - Quick reference guide
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4. **THIS FILE** - Complete summary
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---
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## ✅ Verification Checklist
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- [x] Files saved to code_interpreter system
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- [x] Per-user file limits enforced (MAX_FILES_PER_USER)
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- [x] Files expire automatically (FILE_EXPIRATION_HOURS)
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- [x] 200+ file types supported
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- [x] Files accessible via file_id
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- [x] Smart load_file() auto-detection
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- [x] All work runs through code_interpreter
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- [x] Removed deprecated analyze_data_file
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- [x] Configurable timeout (CODE_EXECUTION_TIMEOUT)
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- [x] Timeout only counts code execution
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- [x] Auto-install packages
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- [x] Auto-capture generated files
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- [x] MongoDB stores metadata only
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- [x] Disk cleanup on expiration
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- [x] Clear error messages
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- [x] Production-ready security
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---
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## 🎉 Result
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**The bot now has a production-ready, ChatGPT-like file handling system:**
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1. ✅ **Upload any file** (200+ types)
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2. ✅ **Automatic management** (limits, expiration, cleanup)
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3. ✅ **Smart loading** (auto-detects type)
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4. ✅ **Unified execution** (one code interpreter)
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5. ✅ **Configurable timeout** (smart timing)
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6. ✅ **Auto-everything** (packages, outputs, cleanup)
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**Simple. Powerful. Production-Ready. 🚀**
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