RAG_Techniques
Showcases advanced Retrieval-Augmented Generation (RAG) techniques with detailed notebook tutorials.
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
109views this month
Decision brief
RAG_Techniques offers detailed Jupyter Notebook tutorials on advanced Retrieval-Augmented Generation techniques, focusing on embeddings, semantic search, and integration with LLMs like GPT and LLaMA.
Good fit when
- When you need comprehensive tutorials on RAG techniques, including embeddings and semantic search, with practical examples in Jupyter Notebooks.
- If your project requires integration with specific LLMs such as GPT and LLaMA, and you seek detailed guidance on how to implement these integrations.
Avoid when
- If you are looking for a repository that focuses on a broader range of AI techniques beyond RAG, as RAG_Techniques is specialized in Retrieval-Augmented Generation.
- If you require a repository that supports commercial use, as RAG_Techniques is licensed under a custom non-commercial license.
- Pricing:
- freemium - Free to use for non-commercial purposes.
- Requirements:
- Min 8 GB RAM; Requires a Python environment and Jupyter Notebook for running the tutorials.
Observed Sep 18, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (2d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 18, 2026
- Security (OSV)
- No lockfile
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/NirDiamant/RAG_TechniquesHow it fits your stack(27)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Depends on
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository provides detailed tutorials on various advanced Retrieval-Augmented Generation (RAG) techniques through Jupyter Notebook tutorials. It covers topics such as embeddings, semantic search, and integration with LLMs like GPT and LLaMA.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 18, 2026
Categories
Graph entities
Tags
README
Getting Started To begin implementing these advanced RAG techniques in your projects: 1. Clone this repository: 2. Navigate to the technique you're interested in: 3. Follow the detailed implementation guide in each technique's directory. License This project is licensed under a c...
For agents
This page has a .md twin and JSON over the API.