This repository offers a hands-on guide for technical teams and individuals with basic technical backgrounds to build conversational AI solutions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). It bridges theoretical concepts with practical code implementations, aiming to demystify AI development.

How It Works

The guide systematically covers core components of RAG systems. It begins with LLM fundamentals, transformer architectures (specifically Hugging Face), and prompt engineering techniques. It then delves into embeddings, vector stores (comparing Chroma, Milvus, Weaviate, and Faiss), and document chunking strategies. Quantization methods for efficient model loading and advanced generation configurations are also explored, alongside LangChain memory management and agent/tool integration.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and setting up Python environments. Specific code execution commands are detailed within the usecase-1 and usecase-2 directories.
• Prerequisites: Python 3.x, Hugging Face libraries (transformers, datasets, tokenizers), LangChain, and potentially specific vector database clients (e.g., chromadb). GPU acceleration is recommended for performance.
• Resources: Setup time depends on environment configuration and dependency downloads. Running models, especially larger ones, requires significant RAM and VRAM.
• Links: usecase-1, usecase-2

Highlighted Details

• Comprehensive coverage of RAG components from LLMs to vector stores and chunking.
• Practical walkthroughs for integrating LLMs with chatbots for tasks like calendar booking and weather retrieval.
• Explores advanced topics like quantization (4-bit, 8-bit) and efficient model loading with bitsandbytes.
• Includes benchmarks and performance analysis for different hardware configurations (A4000, A100) and platforms (Groq LPU).

Maintenance & Community

The project is maintained by zahaby. Community contributions are encouraged. Contact information is provided for feedback.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The content is largely compiled from various online resources, indicating a focus on curation rather than novel research. While it covers many foundational aspects, advanced or highly specific RAG optimizations may require consulting external, more specialized documentation.