Mistral 7B and BAAI on Workers AI vs. OpenAI Models for RAG

In the evolving domain of AI-powered applications, selecting the appropriate model is crucial, particularly for Retrieval Augmented Generation (RAG) pipelines that enhance Large Language Models (LLMs) by supplying external research to inform their responses. This analysis compares the performance of Mistral 7B, a promising open-source alternative to OpenAI's GPT models, against BAAI models in RAG applications. Open-source models like Mistral 7B offer transparency in training processes and outputs, addressing security concerns associated with proprietary models. The Mistral 7B model, which outperforms models like Llama 2 in reasoning, mathematics, and code generation, is noted for its efficiency and ease of deployment, making it a viable option for those seeking alternatives to GPT models. However, in tests, gpt-3.5-turbo performed better than mistral-7b-instruct-v0.1, indicating potential for further improvements in newer models. Additionally, the analysis of embedding models such as BGE and text-embedding-ada-002 revealed that while they often produce similar context results, they have distinct characteristics that affect semantic search outcomes. The findings underscore the importance of choosing the right model based on specific application needs, with the Mistral 7B model showing promise but also room for growth in bridging performance gaps with established models.