This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.
- Approximate Tokens, Words and Characters Calculator for LLM's and Text Trimmer — Simple calculator to estimate tokens for Large Language Models and text editor to trim text
- Text File Merger for LLM — This tool combines multiple text files into a single document, with clear separation between files
- PDF to TXT Converter — Convert PDF documents to plain text format for use with LLMs and text analysis
- HTML to TXT Converter — Remove HTML tags and extract clean text content for LLM processing
- LLM System Prompt Generator — Generate optimized system prompts for different LLM model sizes (3B, 33B, 70B, etc.)
- Creative Idea Generator — AI-powered brainstorming tool for generating creative solutions and ideas
- Text File Merger for LLM — This tool combines multiple text files into a single document, with clear separation between files
- PDF to TXT Converter — Convert PDF documents to plain text format for use with LLMs and text analysis
- HTML to TXT Converter — Remove HTML tags and extract clean text content for LLM processing
- LLM System Prompt Generator — Generate optimized system prompts for different LLM model sizes (3B, 33B, 70B, etc.)
- Creative Idea Generator — AI-powered brainstorming tool for generating creative solutions and ideas
Researchers from Cornell University developed a technique called 'contextual document embeddings' to improve the performance of Retrieval-Augmented Generation (RAG) systems, enhancing the retrieval of relevant documents by making embedding models more context-aware.
Standard methods like bi-encoders often fail to account for context-specific details, leading to poor performance in application-specific datasets. Contextual document embeddings address this by enhancing the sensitivity of the embedding model to subtle differences in documents, particularly in specialized domains.
The researchers proposed two complementary methods to improve bi-encoders:
- Modifying the training process using contrastive learning to distinguish between similar documents.
- Modifying the bi-encoder architecture to incorporate corpus context during the embedding process.
These modifications allow the model to capture both the general context and specific details of documents, leading to better performance, especially in out-of-domain scenarios. The new technique has shown consistent improvements over standard bi-encoders and can be adapted for various applications beyond text-based models.
Standard methods like bi-encoders often fail to account for context-specific details, leading to poor performance in application-specific datasets. Contextual document embeddings address this by enhancing the sensitivity of the embedding model to subtle differences in documents, particularly in specialized domains.
The researchers proposed two complementary methods to improve bi-encoders:
- Modifying the training process using contrastive learning to distinguish between similar documents.
- Modifying the bi-encoder architecture to incorporate corpus context during the embedding process.
These modifications allow the model to capture both the general context and specific details of documents, leading to better performance, especially in out-of-domain scenarios. The new technique has shown consistent improvements over standard bi-encoders and can be adapted for various applications beyond text-based models.
"Generate 5 essential questions that, when answered, capture the main points and core meaning of the text. Focus on questions that:
Address the central theme or argument
Identify key supporting ideas
Highlight important facts or evidence
Reveal the author's purpose or perspective
Explore any significant implications or conclusions
Phrase the questions to encourage comprehensive yet concise answers. Present only the questions, numbered and without any additional text."
Address the central theme or argument
Identify key supporting ideas
Highlight important facts or evidence
Reveal the author's purpose or perspective
Explore any significant implications or conclusions
Phrase the questions to encourage comprehensive yet concise answers. Present only the questions, numbered and without any additional text."
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.
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.
The article explains semantic text chunking, a technique for automatically grouping similar pieces of text to be used in pre-processing stages for Retrieval Augmented Generation (RAG) or similar applications. It uses visualizations to understand the chunking process and explores extensions involving clustering and LLM-powered labeling.
This article discusses the importance of chunking, embedding, and indexing in RAGs (Recursive Auto-Segmented Graphs). The author compares recursive character splitting and semantic splitting techniques for text chunking and suggests the use of agentic chunking for superior RAG retrieval.
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.
This article provides a comprehensive guide on fine-tuning the Llama 3.1 language model using Unsloth for efficient parameter-efficient training. It covers concepts like supervised fine-tuning, LoRA, QLoRA, and practical steps for training on a high-quality dataset.
