Update README.md
This commit is contained in:
Dhanji R. Prasanna
committed by
GitHub
GitHub
parent
b8a4f9ca7e
commit
826c0120fe
167
README.md
167
README.md
@@ -1,166 +1,3 @@
|
||||
# G3 General Purpose AI Agent
|
||||
# G3
|
||||
|
||||
A code-first AI agent that helps you complete tasks by writing and executing code or scripts.
|
||||
|
||||
## Philosophy
|
||||
|
||||
G3 doesn't just give you advice - **it writes and runs code to solve your problems**. Whether you need to:
|
||||
- Process data files
|
||||
- Automate workflows
|
||||
- Scrape websites
|
||||
- Manipulate files
|
||||
- Analyze logs
|
||||
- Generate reports
|
||||
- Set up environments
|
||||
|
||||
G3 will write the appropriate scripts (Python, Bash, JavaScript, etc.) and can execute them for you.
|
||||
|
||||
## Features
|
||||
|
||||
- **Code-First Approach**: Always tries to solve problems with executable code
|
||||
- **Multi-Language Support**: Generates Python, Bash, JavaScript, Rust, and more
|
||||
- **Modular Architecture**: Clean separation between CLI, core engine, and LLM providers
|
||||
- **Multiple LLM Providers**: Support for OpenAI, Anthropic, and embedded open-weights models
|
||||
- **Local Model Support**: Run completely offline with embedded GGUF models via llama.cpp
|
||||
- **Interactive Mode**: Chat with the AI and watch it solve problems in real-time
|
||||
- **Task Automation**: Create reusable automation scripts
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
cargo install --path .
|
||||
```
|
||||
|
||||
## Configuration
|
||||
|
||||
Create a configuration file at `~/.config/g3/config.toml`:
|
||||
|
||||
### Cloud Providers
|
||||
|
||||
```toml
|
||||
[providers]
|
||||
default_provider = "anthropic"
|
||||
|
||||
[providers.anthropic]
|
||||
api_key = "your-anthropic-api-key"
|
||||
model = "claude-3-5-sonnet-20241022"
|
||||
max_tokens = 4096
|
||||
temperature = 0.1
|
||||
|
||||
[providers.openai]
|
||||
api_key = "your-openai-api-key"
|
||||
model = "gpt-4"
|
||||
max_tokens = 2048
|
||||
temperature = 0.1
|
||||
```
|
||||
|
||||
### Local Embedded Models
|
||||
|
||||
For completely offline operation with open-weights models:
|
||||
|
||||
```toml
|
||||
[providers]
|
||||
default_provider = "embedded"
|
||||
|
||||
[providers.embedded]
|
||||
# Path to your GGUF model file
|
||||
model_path = "~/.cache/g3/models/codellama-7b-instruct.Q4_K_M.gguf"
|
||||
model_type = "codellama"
|
||||
context_length = 4096
|
||||
max_tokens = 2048
|
||||
temperature = 0.1
|
||||
# Number of layers to offload to GPU (0 for CPU only)
|
||||
gpu_layers = 32
|
||||
# Number of CPU threads to use
|
||||
threads = 8
|
||||
```
|
||||
|
||||
**Getting Models**: Download GGUF models from [Hugging Face](https://huggingface.co/models?library=gguf) (search for "GGUF"). Popular options:
|
||||
- [CodeLlama 7B Instruct](https://huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF)
|
||||
- [Llama 2 7B Chat](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF)
|
||||
- [Mistral 7B Instruct](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF)
|
||||
|
||||
### Agent Settings
|
||||
|
||||
```toml
|
||||
[agent]
|
||||
max_context_length = 8192
|
||||
enable_streaming = true
|
||||
timeout_seconds = 60
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
### Interactive Mode (Default)
|
||||
|
||||
Simply run G3 to start interactive mode:
|
||||
|
||||
```bash
|
||||
g3
|
||||
```
|
||||
|
||||
Example interactions:
|
||||
```
|
||||
g3> Process this CSV file and show me the top 10 customers by revenue
|
||||
# G3 writes Python pandas script to analyze the CSV
|
||||
|
||||
g3> Set up a backup script for my home directory
|
||||
# G3 creates a bash script with rsync/tar commands
|
||||
|
||||
g3> Download all images from this webpage
|
||||
# G3 writes a Python script with requests/BeautifulSoup
|
||||
|
||||
g3> Convert these JSON files to a SQLite database
|
||||
# G3 creates a Python script to parse JSON and insert into SQLite
|
||||
```
|
||||
|
||||
### Direct Commands
|
||||
|
||||
You can also use G3 with direct commands:
|
||||
|
||||
```bash
|
||||
# Solve any task with code
|
||||
g3 task "merge these PDF files into one"
|
||||
g3 task "find all TODO comments in my codebase"
|
||||
|
||||
# Create automation scripts
|
||||
g3 automate "daily backup of my projects folder"
|
||||
g3 automate "resize all images in a folder to 800px width"
|
||||
|
||||
# Data processing
|
||||
g3 data "analyze this log file for error patterns"
|
||||
g3 data "convert CSV to JSON with validation"
|
||||
|
||||
# Legacy code commands (still supported)
|
||||
g3 analyze src/main.rs
|
||||
g3 generate "fibonacci function" --output fib.py
|
||||
g3 review src/lib.rs
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
G3 follows the Unix philosophy with modular, composable components:
|
||||
|
||||
- **g3-cli**: Command-line interface
|
||||
- **g3-core**: Core agent logic and orchestration
|
||||
- **g3-providers**: LLM provider abstractions
|
||||
- **g3-config**: Configuration management
|
||||
|
||||
See [DESIGN.md](DESIGN.md) for detailed architecture documentation.
|
||||
|
||||
## Development
|
||||
|
||||
```bash
|
||||
# Build all crates
|
||||
cargo build
|
||||
|
||||
# Run tests
|
||||
cargo test
|
||||
|
||||
# Run with debug logging
|
||||
RUST_LOG=debug cargo run -- analyze src/
|
||||
```
|
||||
|
||||
## License
|
||||
|
||||
MIT
|
||||
An experiment in a code-first AI agent that helps you complete tasks by writing and executing code.
|
||||
|
||||
Reference in New Issue
Block a user