使用Gemma 7B LLM和Upstash矢量数据库构建RAG应用程序

Retrieval-Augmented Generation (RAG) is the concept of providing large language models (LLMs) with additional information from an external knowledge source. This allows them to generate more accurate and contextual answers while reducing hallucinations. In this article, we will provide a step-by-step guide to building a complete RAG application using the latest open-source LLM by Google Gemma 7B and Upstash serverless vector database.

Table of Contents:

1. Getting Started & Setting Up Working Environment
2. Download & Split the Cosmopedia Dataset
3. Generating Embedding with Sentence Transformers Model
4. Store the Embeddings in the Upstash Vector Database
5. Introduce & Use Gemma 7B LLM
6. Querying the RAG Application

You can try Upstash Vector Database for free from here:

Upstash: Serverless Data for Redis and Kafka

Designed for the serverless with per-request pricing and Redis API on durable storage.

console.upstash.com