# CodeLens - Intelligent Code Analysis Tool
CodeLens is an intelligent code analysis tool designed to generate LLM-friendly context from your codebase. With seamless integration and insightful output, it helps developers analyze their projects effectively.
---
## Features
- **Multi-language support**: Analyzes Python and JavaScript/TypeScript codebases.
- **LLM-optimized analysis**: Extracts key elements like functions, classes, dependencies, and comments.
- **Token-friendly outputs**: Splits large file contents into token-limited chunks for LLM compatibility.
- **Seamless CLI**: Easy-to-use command-line interface with multiple options.
- **TODO tracking**: Highlights TODOs and FIXMEs for better code maintenance.
- **Pre-commit hook integration**: Automatically runs tests before committing to ensure code quality.
---
## Installation
To install CodeLens, use pip:
```bash
pip install llm-code-lens
```
---
## Usage
### Basic Usage
Analyze the current directory:
```bash
llmcl
```
Analyze a specific directory:
```bash
llmcl path/to/your/code
```
Specify output format (default is `txt`):
```bash
llmcl --format json
```
### Advanced Options
- Export full file contents in token-limited chunks:
```bash
llmcl --full
```
- Enable debug output:
```bash
llmcl --debug
```
- Customize the output directory:
```bash
llmcl --output /path/to/output
```
---
## Configuration
CodeLens requires no additional configuration. However, you can integrate it with pre-commit hooks for seamless testing workflows.
### Setting up Pre-commit Hooks
1. Navigate to the `scripts/` directory.
2. Run the following script to install the pre-commit hook:
```bash
python scripts/install-hooks.py
```
3. The pre-commit hook will automatically run tests using `pytest` before committing.
---
## Output Structure
CodeLens creates a `.codelens` directory containing the following:
- **`analysis.txt` (or `.json`)**: Complete codebase analysis, including:
- Project summary
- Key insights
- File structure and context
- Dependencies
- TODOs and comments
- **Full file content files**: When using the `--full` option, the full content of files is exported in token-limited chunks.
---
## SQL Server Integration
CodeLens supports analyzing SQL Server databases including stored procedures, views, and functions.
### Prerequisites
1. **ODBC Driver**: Install the Microsoft ODBC Driver for SQL Server:
- Windows: Install "ODBC Driver 17 for SQL Server" from Microsoft
- Linux: Follow [Microsoft's instructions](https://learn.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server) for your distribution
2. **Database Access**: Ensure you have appropriate permissions to view database objects
### Configuration
Three ways to configure SQL Server access:
1. **Environment Variables**:
```bash
export MSSQL_SERVER=your_server
export MSSQL_DATABASE=your_database # Optional
export MSSQL_USERNAME=your_username
export MSSQL_PASSWORD=your_password
```
2. **Command Line Options**:
```bash
llmcl --sql-server "server_name" --sql-database "database_name"
```
3. **Configuration File** (recommended for teams):
Create a `sql-config.json`:
```json
{
"server": "server_name",
"database": "database_name",
"env": {
"MSSQL_USERNAME": "username",
"MSSQL_PASSWORD": "password"
}
}
```
Then use:
```bash
llmcl --sql-config sql-config.json
```
### Usage Examples
Analyze both files and SQL Server:
```bash
llmcl --sql-server "server_name" --sql-database "database_name"
```
Export full object definitions:
```bash
llmcl --sql-server "server_name" --sql-database "database_name" --full
```
Analyze specific database with JSON output:
```bash
llmcl --sql-config sql-config.json --format json
```
### Output Structure
SQL analysis includes:
- Object inventory (procedures, views, functions)
- Dependencies between objects
- Complexity metrics
- TODOs and comments
- Parameter analysis
- Full object definitions (with --full flag)
### Security Notes
- Never commit SQL configuration files with credentials
- Use environment variables or secure secret management
- Consider using integrated security when possible
- Ensure minimum required permissions for analysis
---
## Requirements
- Python >= 3.8
---
## Development
### Setting up the Environment
1. Clone the repository:
```bash
git clone https://github.com/SikamikanikoBG/codelens.git
```
2. Navigate to the project directory:
```bash
cd codelens
```
3. Create a virtual environment and activate it:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
4. Install dependencies:
```bash
pip install -r requirements.txt
```
### Running Tests
Run the test suite using:
```bash
pytest
```
---
## Contributing
We welcome contributions! To get started:
1. Fork the repository.
2. Create a new branch for your feature or fix.
3. Submit a pull request with a detailed description of your changes.
---
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
---
## Support
For issues or feature requests, please visit our [GitHub Issues](https://github.com/SikamikanikoBG/codelens/issues).
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"description": "# CodeLens - Intelligent Code Analysis Tool\n\nCodeLens is an intelligent code analysis tool designed to generate LLM-friendly context from your codebase. With seamless integration and insightful output, it helps developers analyze their projects effectively.\n\n---\n\n## Features\n\n- **Multi-language support**: Analyzes Python and JavaScript/TypeScript codebases.\n- **LLM-optimized analysis**: Extracts key elements like functions, classes, dependencies, and comments.\n- **Token-friendly outputs**: Splits large file contents into token-limited chunks for LLM compatibility.\n- **Seamless CLI**: Easy-to-use command-line interface with multiple options.\n- **TODO tracking**: Highlights TODOs and FIXMEs for better code maintenance.\n- **Pre-commit hook integration**: Automatically runs tests before committing to ensure code quality.\n\n---\n\n## Installation\n\nTo install CodeLens, use pip:\n\n```bash\npip install llm-code-lens\n```\n\n---\n\n## Usage\n\n### Basic Usage\nAnalyze the current directory:\n```bash\nllmcl\n```\n\nAnalyze a specific directory:\n```bash\nllmcl path/to/your/code\n```\n\nSpecify output format (default is `txt`):\n```bash\nllmcl --format json\n```\n\n### Advanced Options\n- Export full file contents in token-limited chunks:\n ```bash\n llmcl --full\n ```\n\n- Enable debug output:\n ```bash\n llmcl --debug\n ```\n\n- Customize the output directory:\n ```bash\n llmcl --output /path/to/output\n ```\n\n---\n\n## Configuration\n\nCodeLens requires no additional configuration. However, you can integrate it with pre-commit hooks for seamless testing workflows.\n\n### Setting up Pre-commit Hooks\n\n1. Navigate to the `scripts/` directory.\n2. Run the following script to install the pre-commit hook:\n ```bash\n python scripts/install-hooks.py\n ```\n3. The pre-commit hook will automatically run tests using `pytest` before committing.\n\n---\n\n## Output Structure\n\nCodeLens creates a `.codelens` directory containing the following:\n- **`analysis.txt` (or `.json`)**: Complete codebase analysis, including:\n - Project summary\n - Key insights\n - File structure and context\n - Dependencies\n - TODOs and comments\n- **Full file content files**: When using the `--full` option, the full content of files is exported in token-limited chunks.\n\n---\n\n## SQL Server Integration\n\nCodeLens supports analyzing SQL Server databases including stored procedures, views, and functions.\n\n### Prerequisites\n\n1. **ODBC Driver**: Install the Microsoft ODBC Driver for SQL Server:\n - Windows: Install \"ODBC Driver 17 for SQL Server\" from Microsoft\n - Linux: Follow [Microsoft's instructions](https://learn.microsoft.com/en-us/sql/connect/odbc/linux-mac/installing-the-microsoft-odbc-driver-for-sql-server) for your distribution\n\n2. **Database Access**: Ensure you have appropriate permissions to view database objects\n\n### Configuration\n\nThree ways to configure SQL Server access:\n\n1. **Environment Variables**:\n ```bash\n export MSSQL_SERVER=your_server\n export MSSQL_DATABASE=your_database # Optional\n export MSSQL_USERNAME=your_username\n export MSSQL_PASSWORD=your_password\n ```\n\n2. **Command Line Options**:\n ```bash\n llmcl --sql-server \"server_name\" --sql-database \"database_name\"\n ```\n\n3. **Configuration File** (recommended for teams):\n Create a `sql-config.json`:\n ```json\n {\n \"server\": \"server_name\",\n \"database\": \"database_name\",\n \"env\": {\n \"MSSQL_USERNAME\": \"username\",\n \"MSSQL_PASSWORD\": \"password\"\n }\n }\n ```\n Then use:\n ```bash\n llmcl --sql-config sql-config.json\n ```\n\n### Usage Examples\n\nAnalyze both files and SQL Server:\n```bash\nllmcl --sql-server \"server_name\" --sql-database \"database_name\"\n```\n\nExport full object definitions:\n```bash\nllmcl --sql-server \"server_name\" --sql-database \"database_name\" --full\n```\n\nAnalyze specific database with JSON output:\n```bash\nllmcl --sql-config sql-config.json --format json\n```\n\n### Output Structure\n\nSQL analysis includes:\n- Object inventory (procedures, views, functions)\n- Dependencies between objects\n- Complexity metrics\n- TODOs and comments\n- Parameter analysis\n- Full object definitions (with --full flag)\n\n### Security Notes\n\n- Never commit SQL configuration files with credentials\n- Use environment variables or secure secret management\n- Consider using integrated security when possible\n- Ensure minimum required permissions for analysis\n\n---\n\n## Requirements\n\n- Python >= 3.8\n\n---\n\n## Development\n\n### Setting up the Environment\n\n1. Clone the repository:\n ```bash\n git clone https://github.com/SikamikanikoBG/codelens.git\n ```\n2. Navigate to the project directory:\n ```bash\n cd codelens\n ```\n3. Create a virtual environment and activate it:\n ```bash\n python -m venv venv\n source venv/bin/activate # On Windows: venv\\Scripts\\activate\n ```\n4. Install dependencies:\n ```bash\n pip install -r requirements.txt\n ```\n\n### Running Tests\n\nRun the test suite using:\n```bash\npytest\n```\n\n---\n\n## Contributing\n\nWe welcome contributions! To get started:\n1. Fork the repository.\n2. Create a new branch for your feature or fix.\n3. Submit a pull request with a detailed description of your changes.\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\n## Support\n\nFor issues or feature requests, please visit our [GitHub Issues](https://github.com/SikamikanikoBG/codelens/issues).\n\n",
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