#LLM Models

This document objectively explores Retrieval-Augmented Generation (RAG) methodologies, detailing patterns, dialogue routing, LLM models, and retrieval techniques, including vector retrieval and chunking strategies. Advanced prompting strategies, like multi-modal and multi-document approaches, are analyzed alongside issues of hallucination and guardrails. It also addresses evaluation metrics, performance optimization, privacy concerns, and security threats. The document discusses practical applications such as chatbots and tools like HayStack and Langchain, and provides vendor-specific examples such as Elasticsearch + OpenAI, focusing on optimizing RAG systems for efficient and secure production use.

QA-Pilot facilitates interaction with GitHub repositories, using advanced LLM models for effective code understanding and navigation. Features include multiple chat sessions, search functionality, and integration with a codegraph for Python visualization. The project supports AI models from providers such as OpenAI and NVIDIA, with an emphasis on local deployment capabilities. Recent updates feature 'Moonshot' API and 'Go Codegraph'. It is important to avoid using sensitive data, as QA-Pilot is designed for experimental purposes, not for production.