RAG Implementation using Mistral 7B, Haystack, Weaviate, and FastAPI

In this tutorial, I show you how to implement the cutting-edge RAG (Retrieval Augmented Generation) model using Mistral 7B, Haystack, Weaviate, and FastAPI.

Mistral 7B, with its impressive 7.3 billion parameters, takes center stage. It's a powerhouse model that outperforms even larger models on various benchmarks. Notably, it excels in English tasks while also holding its ground in code-related tasks. Mistral 7B utilizes Grouped-query attention (GQA) for swift inference and employs Sliding Window Attention (SWA) to handle longer sequences more efficiently. And the best part? It's open-source under the Apache 2.0 license, offering unrestricted access.

Haystack is our trusty companion, an open-source framework that simplifies the development of production-ready applications. It provides a seamless experience for leveraging the latest Large Language Models (LLMs), like Mistral 7B, for various tasks. With Haystack, you can effortlessly manage preprocessing, pipelines, agents, and evaluation. Plus, it offers flexibility in choosing databases, from Elasticsearch to Weaviate, allowing you to scale up to handling millions of documents with ease.

Speaking of Weaviate, it plays a crucial role in our tutorial. Weaviate is an open-source vector database, designed for storing data objects and vector embeddings from your favorite ML models. Whether you're working with a handful of data objects or scaling up to billions, Weaviate has got you covered.

Last but not least, we harness the power of FastAPI, a robust framework you're already familiar with, to build our RAG implementation app.

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Mistral 7B: https://mistral.ai/news/announcing-mistral-7b/
Weaviate: https://weaviate.io/
Haystack: https://haystack.deepset.ai/
GitHub Repo: https://github.com/AIAnytime/Haystack-and-Mistral-7B-RAG-Implementation
LLM Playlist: https://www.youtube.com/playlist?list=PLrLEqwuz-mRJiYH51G0C3-pyP7doo79Sv

#mistral #generativeai #ai