AI4HPC

To enhance the accessibility and usability of HPC's extensive documentation and codebase, this project aims to develop a Large Language Model (LLM)-based system. This system will enable users to efficiently query and retrieve information related to the architecture, components, and development processes. By leveraging Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG), the system will provide concise and accurate answers to user queries, streamlining the learning curve for new developers and assisting experienced users in troubleshooting and optimization tasks.

🎯 Objectives

• Develop an LLM-based system capable of answering questions related to HPC platform documentation and codebase
• Teach contributors how to preprocess and structure unstructured documentation for efficient retrieval
• Implement a data pipeline that includes document crawling, parsing, chunking, embedding storage, and retrieval
• Provide experience with tools such as LangChain, OpenAI APIs, and vector databases
• Allow experimentation with retrieval strategies to optimize response accuracy and efficiency

⚙️ Technical Challenges

• Crawling and parsing diverse HPC documentation sources: The HPC platform's documentation is spread across various repositories and formats, necessitating robust crawling and parsing mechanisms
• Chunking and embedding documents for efficient retrieval: Breaking down large documents into manageable chunks and generating embeddings that capture semantic meaning
• Choosing and implementing an appropriate vector database: Selecting a database that offers efficient storage and fast search capabilities for embeddings
• Optimizing retrieval methods: Enhancing the relevance of responses by fine-tuning retrieval algorithms
• Integrating an LLM using LangChain: Seamlessly connecting the retrieval system with an LLM to generate coherent and contextually relevant answers
• Managing API limitations: Ensuring the system operates within API constraints and delivers responses promptly
• Evaluating system performance and accuracy: Establishing metrics and benchmarks to assess the system's effectiveness

🔧 Prerequisites

• Knowledge of Python and Jupyter Notebooks The primary programming environment for development
• Familiarity with NLP concepts and LLMs Understanding the fundamentals of natural language processing and large language models
• Experience with API usage Particularly with OpenAI and LangChain

🏆 Milestones

1. Setup Phase:
   • Create and configure a GitHub repository.
   • Install dependencies (LangChain, OpenAI API, vector database, etc.).
   • Set up a Jupyter Notebook template.
2. Data Processing Pipeline:
   • Develop a web/document crawler to fetch HPC documentation from suitable sources.
   • Parse and clean the retrieved documents.
   • Implement a chunking mechanism for better context retrieval.
3. Embedding and Storage:
   • Convert chunks into embeddings using OpenAI or other embedding models.
   • Store embeddings in a vector database (e.g., ChromaDB).
4. Retrieval & Query Processing:
   • Implement retrieval methods to fetch relevant document chunks.
   • Use LangChain to integrate with an LLM for intelligent query answering.
5. Testing and Optimization:
   • Evaluate retrieval accuracy and optimize query responses.
   • Experiment with different chunking strategies and embedding models.
6. Final Submission:
   • Document findings, implementation details, and challenges.
   • Submit the project report and codebase.

📅 Timeline

Phase	Start Date	End Date
Environment Setup	Week 1	Week 2
Data Processing Pipeline	Week 3	Week 5
Embedding & Storage	Week 6	Week 8
Retrieval & Query Processing	Week 9	Week 11
Testing & Optimization	Week 12	Week 13
Final Submission	Week 14	Week 15

🚀 Steps to Get Started

1. Clone the repository

   git clone https://github.com/pulp-platform/llm4pulp.git
   cd llm4pulp

2. Install Dependencies

   pip install -r requirements.txt

3. Setup Jupyter Notebook
   • Open the provided template notebook.
   • Follow the instructions to preprocess documents and generate embeddings.
4. Develop the Data Pipeline
   • Implement document crawling, parsing, chunking, and embedding storage.
5. Integrate Query Processing
   • Implement retrieval strategies and integrate with LangChain & OpenAI API.
6. Evaluate & Optimize
   • Test retrieval performance and optimize the system.
7. Submit the Project
   • Document results and submit.