GitHub topics: rag-systems
bazilicum/GraphLTM
Turn any LLM into a self-extending knowledge agent powered by a graph-structured memory - complete with PDF-to-graph ingestion, budget-aware optimisation, and dual-engine orchestration.
Language: Python - Size: 61.5 KB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 0 - Forks: 0

feld-m/rag_blueprint
A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.
Language: Python - Size: 18.2 MB - Last synced at: 11 days ago - Pushed at: 11 days ago - Stars: 7 - Forks: 6

bazilicum/pdf-query
This project processes and retrieves information from PDF file or PDF collection. It leverages Qdrant as a vector database for similarity searches and employs a Retrieval-Augmented Generation (RAG).
Language: Python - Size: 33.2 KB - Last synced at: about 24 hours ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

gedankrayze/training-data-generator
Training Data Generator for SPLADE Model Fine-tuning
Language: Python - Size: 37.1 KB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

pngo1997/Retrieval-Augmented-Retrieval-RAG-for-Cleantech-Media
Implements a Retrieval-Augmented Generation (RAG) system.
Language: Jupyter Notebook - Size: 21.7 MB - Last synced at: 5 days ago - Pushed at: 5 months ago - Stars: 0 - Forks: 0

ksm26/Retrieval-Optimization-From-Tokenization-to-Vector-Quantization
The course provides a comprehensive guide to optimizing retrieval systems in large-scale RAG applications. It covers tokenization, vector quantization, and search optimization techniques to enhance search quality, reduce memory usage, and balance performance in vector search systems.
Language: Jupyter Notebook - Size: 4.25 MB - Last synced at: 3 months ago - Pushed at: 6 months ago - Stars: 1 - Forks: 1

ksm26/Embedding-Models-From-Architecture-to-Implementation
Understand and build embedding models, focusing on word and sentence embeddings, dual encoder architectures. Learn to train embedding models using contrastive loss, implement them in semantic search and RAG systems.
Language: Jupyter Notebook - Size: 2 MB - Last synced at: 3 months ago - Pushed at: 10 months ago - Stars: 3 - Forks: 0
