Verba is an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. It supports various RAG techniques, data types, LLM providers, and offers Docker support and a fully-customizable frontend.
This is a local LLM chatbot project with RAG for processing PDF input files
In this article, Dr. Leon Eversberg explains how to build an advanced Local Language Model (LLM) Retrieval-Augmented Generation (RAG) pipeline using open-source bi-encoders and cross-encoders for better chatbot performance.
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.
- Proxy fine-tuning is a method to improve large pre-trained language models without directly accessing their weights.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
- It operates on top of black-box LLMs by utilizing only their predictions.
- The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
- Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.
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.
• 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) 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.
ColBERT is a new way of scoring passage relevance using a BERT language model that substantially solves the problems with dense passage retrieval.
In this notebook, we will explore a typical RAG solution where we will utilize an open-source model and the vector database Chroma DB. However, we will integrate a semantic cache system that will store various user queries and decide whether to generate the prompt enriched with information from the vector database or the cache.
