PyVoiceAgent

# PyVoiceAgent

A powerful, local-first interactive voice assistant built for **offline capability** and **complete control**. This project orchestrates state-of-the-art local AI models to provide a seamless voice-to-voice experience without relying on third-party cloud APIs.

## Key Highlights

-   **Full Voice Interaction**: Talk to the agent and hear it speak back naturally.
-   **Persistent Memory**: Remembers your previous conversations across sessions using a robust SQLite database.
-   **Local Intelligence**: Powered by **DeepSeek R1** (via Ollama) for reasoning and **Faster Whisper** for transcription.
-   **Intelligent Summarization**: Automatically summarizes interactions to maintain concise context.

## Pros & Cons

| Advantages | Trade-offs |
| :--- | :--- |
| **Zero Cost**: No recurring API fees; runs entirely on your hardware. | **Hardware Dependent**: Performance scales with your CPU/GPU power. |
| **Offline**: Works completely without an internet connection (after initial setup). | **Setup**: Requires installing and managing local models (Ollama, etc.). |
| **Customizable**: Full access to modify the graph, prompts, and memory logic. | **Model Capability**: Local models (e.g., 8B) are powerful but may lag behind massive cloud models (e.g., GPT-4) in complex reasoning. |
| **Low Latency**: Eliminates network latency constraints. | **Resource Usage**: Can be memory and compute intensive during inference. |

## Architecture

The system uses **LangGraph** to manage the conversational flow:

1.  **Transcribe**: `Faster Whisper` converts your voice to text.
2.  **Context retrieval**: Fetches conversation history and session context from `SQLite`.
3.  **Process**: `DeepSeek R1` generates a response and "thinks" through the problem.
4.  **Synthesize**: `Chatterbox TTS` converts the text response back to audio.
5.  **Save & Summarize**: The interaction is logged, and a summary is generated for future context.

## Quick Start

### Prerequisites
-   **Python 3.