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A Conversational AI Assistant for BeagleBoard using RAG and Fine-tuning - Fayez Zouari#

Introduction#

Status#

This project is currently just a proposal.

Proposal#

About#

  • Forum: FAYEZ_ZOUARI

  • OpenBeagle: fayezzouari

  • Discord ID: .kageyamo

  • GitHub: fayezzouari

  • School: INSAT (National Institute of Applied Science and Technology)

  • Country: Tunisia

  • Typical work hours: 9:00 AM - 6:00 PM (UTC+1)

  • Previous GSoC participation: No

Project#

Project name: BeagleMind - Documentation Assistant with Fine-tuned LLM and RAG

Description#

BeagleMind combines fine-tuned LLMs with RAG to create an accurate documentation assistant that:

  1. Uses PEFT/LoRA fine-tuning on BeagleBoard documentation

  2. Implements RAG for fact-based responses and to reduce LLM hallucination

  3. Accessed using a HF inference endpoint

  4. Deploys via: - CLI tool for local usage - Web interface with websockets

  5. Includes agentic evaluation framework

Technical Implementation#

LLM Fine-tuning Architecture#

The system will employ the selected LLM as its base model, utilizing Parameter-Efficient Fine-Tuning (PEFT) with LoRA adapters to specialize the model for BeagleBoard documentation. The training pipeline processes OpenBeagle resources through:

  • Semantic segmentation of technical documentation

  • Generation of instruction-response pairs

  • Dynamic masking of code samples for focused learning

Evaluation will combine:

  • Perplexity measurements on held-out documentation

  • Task-specific accuracy on BeagleBoard API questions

  • Human review of generated troubleshooting steps

RAG Integration Pipeline#

The retrieval-augmented generation system implements a three-stage accuracy enforcement:

  1. Document Processing:

    • Hierarchical chunking preserving code-sample context

    • Metadata enrichment with section headers

    • Cross-document relationship mapping

  2. Vector Retrieval:

    • Hybrid dense-sparse retrieval using BAAI embeddings

    • Query-adaptive reranking

    • Confidence-based fallback mechanisms

  3. Response Generation:

    • Contextual grounding with retrieved passages

    • Automatic citation injection

    • Confidence thresholding for uncertain responses

Hosting Infrastructure#

The production deployment features:

Hosting Specifications#

Component

Implementation

Inference Endpoint

Hugging Face TGI with 4-bit quantization

Load Balancing

Round-robin with health checks

Monitoring

Prometheus metrics for: - Token generation latency - Retrieval hit rate - Hallucination alerts

Deployment Targets#

Multi-platform accessibility through:

  1. Web Interface:

    • React.js frontend with response streaming

    • Interactive citation visualization

    • Session-based query history

  2. CLI Tool:

    • Access to the hosted LLM through an Api Key

    • Configurable verbosity levels

    • Automated test script integration

Evaluation Framework#

The agentic evaluation system employs three specialized test agents:

  1. Fact-Verification Agent:

    • Cross-references answers with source docs

    • Flags unsupported technical claims

    • Maintains accuracy heatmaps

  2. Completeness Auditor:

    • Scores answer depth on:

      • API reference coverage

      • Troubleshooting steps

      • Example code relevance

  3. Stress-Test Bot:

    • Generates adversarial queries

    • Measures failure modes

    • Identifies documentation gaps

Software#

  • Programming Languages: Python

  • ML Tools: PEFT, LoRA, Quantization

  • Frameworks: FastAPI, Hugging Face Transformers

  • Database: ChromaDB/Weaviate/Qdrant

  • Frontend: React

  • Deployment: Docker, Nginx, PYPI, Hugging Face Spaces

  • Version Control: Git, GitHub/GitLab

Hardware#

  • Development Boards: - BeagleBone AI-64 - BeagleY-AI

  • Cloud Services: - Hugging Face Spaces / Inference Endpoints - Vercel

Architecture and Diagrams#

These diagrams represent the workflow of the methods mentionned earlier.

Fine-Tuning Architecture

Fine-Tuning Architecture#

RAG Integration Pipeline

RAG Integration Pipeline#

Deployment Structure

Deployment Structure#

Timeline#

Deadline

Milestone

Deliverables

May 27

Coding Begins

Finalize architecture diagrams

June 3

M1: Foundation

CLI prototype, Fine-tuning strategy doc

June 17

M2: Data Preparation

Curated dataset, Vector DB ready

July 1

M3: Model Training

Fine-tuned model on HF, Initial benchmarks

July 8

Midterm Evaluation

Working CLI with local inference

July 22

M4: Agentic Evaluation

Test agents implemented, Accuracy reports

Aug 5

M5: Web Interface

Websocket server, React frontend

Aug 19

Final Submission

Full documentation, Demo video

Detailed Timeline#

Community Bonding (May 9 - May 26)#

  • Develop workflow diagrams:

    • Data collection pipeline

    • Fine-tuning process

    • RAG integration flow

  • Finalize model selection criteria

  • Establish evaluation metrics with mentor

Milestone 1: Foundation (June 3)#

  1. CLI Prototype:

    • Basic question-answering interface

    • Chatbot using only RAG just to present the PoC

    • Provide helpful parameters like -h for help, -p for prompt and -l to refer to a log file

    • Simple evaluation script

  2. Video demonstration:

    • Provide video demonstration

    • Present a proof of concept

    • Highlight that the actual solution will feature a hosted fine-tuned LLM and RAG to reduce hallucination

  3. Fine-tuning Prep:

    • Document preprocessing scripts

    • Training environment setup

Milestone 2: Data Preparation (June 17)#

  1. Document Processing:

    • Data formatting

    • Generate synthetic Q&A pairs

    • Convert all docs to clean Markdown

    • Extract code samples, diagrams, cicruit schemas and any resource that could help in the troubleshooting

  2. Vector Database:

    • Implement chunking strategy

    • Test retrieval accuracy

    • Optimize embedding selection

Milestone 3: Model Training (July 1)#

  1. Fine-tuning:

    • Training runs with different parameters

    • Loss/accuracy tracking

    • Quantization tests

  2. Deployment:

    • HF Inference Endpoint setup

    • Performance benchmarks

    • Hallucination tests

Midterm Evaluation (July 8)#

  • Functional CLI with:

    • Model inference

    • Basic RAG integration

    • Accuracy metrics

  • Video demonstration

  • Mentor review session

Milestone 4: Agentic Evaluation (July 22)#

  1. Evaluation Agents:

    • Fact-checking agent

    • Completeness evaluator

    • Hallucination detector

  2. Automated Testing:

    • 100-question test suite

    • Continuous integration setup

    • Performance dashboard

Milestone 5: Web Interface (Aug 5)#

  1. Backend:

    • FastAPI websocket server

    • Dockerize the server

    • Async model loading

    • Rate limiting

  2. Frontend:

    • React-based chat UI

    • Response visualization

    • Mobile responsiveness

Final Submission (Aug 19)#

  • Comprehensive documentation:

    • Installation guides

    • API references

    • Training methodology

  • 5-minute demo video

  • Performance report

Benefit#

BeagleMind will provide:

  • 24/7 documentation assistance

  • Reduced maintainer workload

  • Visualized technical answers

  • Accelerated debugging

  • Offline documentation access

  • Improved onboarding experience

Experience and Approach#

Personal Background#

As an Embedded Systems Engineering student with a passion for AI and robotics, I find the BeagleMind project perfectly aligns with my academic specialization and technical interests. My coursework in embedded systems, combined with self-study in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), has prepared me to bridge the gap between hardware documentation and AI-powered assistance.

Experience#

As an Embedded Systems Engineering student with AI specialization, I bring:

  1. LENS Platform:

    • RAG Chatbot with Citations: Developed a retrieval-augmented chatbot that provides answers with detailed references, URL, page number, and File Name.

  2. Chatautomation Platform:

    • Built multimodal data loaders (PDFs, images, audio)

    • Implemented voice interaction system (STT + LLM + TTS)

    • Developed WhatsApp/Instagram chatbot integrations

  3. Orange Digital Center Internship:

    • Created MEPS monitoring system

    • Developed biogas forecast mode

    • Implemented agentic workflows for production reports

  4. x2x Modality Project:

    • Hexastack Hackathon 1st place (Open source contribution)

    • Speech to Text for effortless communication

    • Text to Speech for improved accessibility

    • Image and Document Processing into text for smoother integration

Contingency#

If blockers occur:

  1. Research documentation and source code

  2. Seek community support (Discord/Forum)

  3. Implement alternative approaches

  4. Escalate to the mentor if unresolved

Misc#

References#

  1. Hugging Face Transformers

  2. ChromaDB Documentation

  3. BeagleBoard Documentation

  4. PEFT Fine-tuning

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