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A. Milestone 2

Core RAG Functionality

In this milestone, I focused on finalizing the core RAG functionality by implementing the previously discussed features.

I developed a Gradio interface to interact with the chatbot and visually inspect the results, including the sources used to answer each query. These sources are passed through a reranking model to ensure that only the most relevant ones are selected before being sent to the LLM.

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Chatbot Interface

• Developed using Gradio for interactive exploration of BeagleBoard documentation.

• Markdown-rendered answer area, supporting:

  • Clean formatting (bold text, bullet points, inline code)
  • Clickable links to documentation files
  • Embedded images and references

The user can ask questions like "How to blink an LED using BeagleBoard?", and the system responds with relevant reformulated documentation snippets.

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Sources Section

• Right panel labeled Sources & References displays:

  • File name, path, and clickable GitHub link
  • Scoring metrics: Composite, Rerank, and Content Quality
  • Formatted content preview for readability

Note: Reranking model implementation details are in retrieval.py.

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Repository Tree Overview

The src directory contains the core logic for the QA system:

• main.py: Entry point
• gradio_app.py: Gradio UI
• qa_system.py: Manages end-to-end QA pipeline
• retrieval.py: Document retrieval with scoring
• search_vectorstore.py: Searches Milvus vector store
• github_direct_ingester.py: Pulls data from GitHub repos
• graph_qa.py: Prototype for graph-based QA
• router.py: Routes requests
• config.py: Stores configs and model params

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CI Integration with GitLab

To streamline integration with OpenBeagle:

• Integrated with GitLab CI for continuous deployment.

• Pipeline (WIP) to include automated testing and linting to:

  • Maintain code quality
  • Catch errors early
  • Ensure deployment consistency

B. Milestone 1

Vector Database Implementation

• Chose Milvus for the vector store, based on benchmarking with VectorDBBench
• Prioritized scalability, performance, and ecosystem support

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Chunking Strategy

• Used Chonkie, which uses the CHOMP pipeline for modular and semantic chunking
• Improves granularity and chunk retrieval accuracy

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Retrieval Strategy

• Proposing a Graph-RAG approach:

  • Uses structured relationships (e.g., diagrams ↔ code ↔ documentation)
  • Inspired by GRAG Paper
  • Scripts in development to benchmark impact

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Embedding Models

• Primary: BAAI/bge-large-en
• Reranker Candidate: all-MiniLM-L6-v2

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Data Source

• Sourced from BeagleBoard GitHub repos (code, diagrams, documentation)
• Indexing metadata to enable semantically rich queries

C. Milestone 0

Model Selection

a. Mistral-Small-3.1-24B-Instruct-2503-GGUF

• 24B params, instruction-tuned, quantized
• Handles 128K tokens
• Ideal for local deployment (e.g., 4090 GPU)
• Strong multilingual and code performance

b. Phi-4 (14B)

• Developed by Microsoft
• Excels in reasoning and competitive programming
• Compact with performance matching larger models

c. Qwen2.5-Coder-7B

• Optimized for code gen and repair
• Supports 131K context length
• Memory-efficient with 4-bit quantization

d. Qwen3 30B-A3B

• Dual-mode reasoning
• Agentic task support
• Ideal for long-form processing and tool-based interaction

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Selection Criteria

• Model size
• MMLU, MBPP, EvalPlus, MATH
• Inference speed (tokens/s)

Fine-Tuning Environment

• Google Colab Pro: Good for prototyping and tuning
• HF Inference Endpoints: Considered for hosting (higher cost)
• Unsloth: For efficient 4-bit fine-tuning

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Data Collection Pipeline

• GitHub Scraper + API

• Markdown, code, PDF processing

• External sources:

  • eLinux.org
  • Datasheets
  • Community forums (if allowed)

Fine-Tuning Dataset Prep

• QA pair generation via:

  • Manual annotation
  • LLM-based synthetic prompts (verified)

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Fine-Tuned Model Evaluation

Agentic Evaluation

• Evaluates reasoning, planning, tool usage

Tools considered:

• DeepEval
• Opik
• JudgeLM
• AgentBench

Metric-Based Evaluation

• Perplexity
• BLEU / ROUGE
• F1 Score
• BERTScore
• Exact Match (EM)
• Latency / Throughput

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Demo Preparation

Working on improving the RAG architecture using advanced retrieval methods:

Techniques

1. Graph RAG (GRAG): Leverages structured entity relationships
2. Contextual Semantic Search: Uses semantic embeddings + cross-encoders
3. Dense Passage Retrieval (DPR): Efficient dual-encoder retrieval

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These approaches aim to outperform traditional RAG by improving the contextual relevance and accuracy of responses, especially on the BeagleBoard dataset.

➡️ Demo will be recorded in an introductory video.