SemanticScuttle - klotz.me

klotz: embeddings* + rag*

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1. Discovering Semantic Search and RAG with Large Language Models (LLMs)

   Foundational concepts, practical implementation of semantic search, and the workflow of RAG, highlighting its advantages and versatile applications.
   
   The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.

   2024-10-04 Tags: llm, semantic search, rag, nlp, embeddings, asymmetric by klotz
2. Advanced RAG Techniques

   This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.

   2024-08-01 Tags: rag, nlp, machine learning, information retrieval, natural language processing, llm, embeddings, semantic search by klotz
3. timescale/pgai: pgai on GitHub

   pgai brings AI workflows to your PostgreSQL database. It simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL by bringing embedding and generation AI models closer to the database.

   2024-06-21 Tags: pgai, postgresql, ai, database, embeddings, generation, rag, openai, ollama by klotz
4. Using Llamafiles for Embeddings in Local RAG Applications

   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.

   2024-05-17 Tags: llamafiles, embeddings, rag, text embedding models, llm by klotz

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