A blog post discussing the use of Llamafiles for embeddings in Retrieval-Augmented Generation (RAG) applications and recommending the best models based on performance on RAG-relevant tasks.
pip install 'ragna builtin » ' # Install ragna with all extensions
ragna config # Initialize configuration
ragna ui # Launch the web app
Service Development Kit that uses Terraform, AWS ECS, Rust, Actix App, Postgress RDS, LLM, RAG, Cloudflare
• step-by-step guide on how to set up the service development kit, including creating an SSL certificate, setting up Terraform, and configuring Cloudflare.
• Rust, LLM, and RAG in the service development kit.
In this tutorial, we will build a RAG system with a self-querying retriever in the LangChain framework. This will enable us to filter the retrieved movies using metadata, thus providing more meaningful movie recommendations.
Retrieval-Augmented Generation (RAG) models, which merge pre-trained parametric and non-parametric memory systems, such as Wikipedia, to enhance language generation in knowledge-intensive NLP tasks, outperforming traditional seq2seq models and setting new standards in open domain QA tasks
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