contract-bot

# ⚖️ Contract Buddy

An asynchronous, multimodal, and fully localized AI legal assistant deployed via Telegram. Designed with privacy and performance in mind, this project demonstrates advanced Agentic AI architecture, combining Retrieval-Augmented Generation (RAG) with local multimodal vision models to analyze, query, and summarize legal contracts. 

## 🧠 Architecture Overview

Built to run entirely locally, ensuring zero data leakage for sensitive legal documents. The system dynamically manages VRAM by swapping between reasoning and translation models on the fly.

### 🚀 Core Features (Updated for Milestone 1)

* **The Universal Translator Pipeline:** Natively processes legal documents in **English** and **6 Indic Languages** (Hindi, Marathi, Gujarati, Kannada, Tamil, Telugu).
* **Smart OCR Extraction:** Bypasses standard vision models in favor of `EasyOCR`, successfully extracting raw Devanagari and Latin scripts from physical document images and handwritten notes.
* **Dynamic AI Routing:** Uses a "Translate-Reason-Translate" workflow:
  * `langdetect` identifies the document language.
  * `Sarvam-1` (an Indic-optimized LLM) translates regional text into English to prevent hallucination.
  * `DeepSeek-R1` acts as the core legal brain, running risk analysis on the normalized English text.
  * `Sarvam-1` translates the final legal analysis back into the user's selected UI language.
* **Local RAG Pipeline:** Utilizes `sqlite-vec` for ultra-fast, lightweight vector storage and `sentence-transformers` for embedding contract chunks.
* **Stateful Memory:** Implements a custom chat_id-keyed conversational memory buffer, enabling contextual follow-up questions and executive session summaries.
* **Asynchronous Routing:** Built with `python-telegram-bot` and `asyncio.to_thread` to prevent API timeouts during heavy local GPU inference.
* **Output Sanitization:** Engineered regex layers to cleanly parse and remove internal `<think>` tags from reasoning models.

## 🛠️ Tech S