labdmitriy/llm-rag

Jupyter Notebook

312

14 commits

updated Apr 28, 2025

See the code

README

LLM RAG

Configuration

Environment

Copy .env.example file to .env
Fill in the required values

Installation

Using `pip`

Create and activate new Python virtual environment
pip install -r requirements.txt
pip install -e . (basic packages)
or
pip install -e .[ragatouille] (with ragatouille for ColBERT)

Using `uv`

uv sync --group dev (basic packages)
or
uv sync --group dev --extra ragatouille (with ragatouille for ColBERT)
uv pip install -e .

RAG From Scratch

Author of original series - Lance Martin

Original Sources

Video

YouTube Playlist

Code

Main Repository
Corrective RAG (CRAG)
Self-RAG

Part	Name	Video	Slides	Jupyter Notebook	Python Script	LangGraph Studio
1	Overview	Watch	View	01-overview.ipynb	-	-
2	Indexing	Watch	View	02-indexing.ipynb	-	-
3	Retrieval	Watch	View	03-retrieval.ipynb	-	-
4	Generation	Watch	View	04-generation.ipynb	-	-
5	Query Translation - Multi-Query	Watch	View	05-multi-query.ipynb	multi_query.py	Query Translation - Multi-Query
6	Query Translation - RAG-Fusion	Watch	View	06-rag-fusion.ipynb	rag_fusion.py	Query Translation - RAG-Fusion
7	Query Translation - Decomposition	Watch	View	07-01-decomposition-recursive.ipynb 07-02-decomposition-parallel.ipynb	recursive.py parallel.py	Query Translation - Decomposition (Recursive) Query Translation - Decomposition (Parallel)
8	Query Translation - Step-Back Prompting	Watch	View	08-step-back.ipynb	step_back.py	Query Translation - Step-Back Prompting
9	Query Translation - HyDE	Watch	View	09-hyde.ipynb	hyde.py	Query Translation - HyDE
10	Routing	Watch	View	10-01-logical-routing.ipynb	logical.py semantic.py	Routing - Logical Routing Routing - Semantic Routing
11	Query Construction	Watch	View	11-query-construction.ipynb	self_query.py	Query Construction - Self-Query
12	Indexing - Multi-Representation Indexing	Watch	View	12-01-multi-vector-summary.ipynb 12-02-multi-vector-chunks.ipynb 12-03-multi-vector-hypothetical-questions.ipynb	summary.py chunks.py hypothetical_questions.py	Indexing - Multi-Vector - Summary Indexing - Multi-Vector - Chunks Indexing - Multi-Vector - Hypothetical Questions
13	Indexing - RAPTOR	Watch	View	13-raptor.ipynb	raptor.py	-
14	Indexing - ColBERT	Watch	View	14-colbert.ipynb	colbert_model.py	-
15	Retrieval - CRAG	Watch	-	15-crag.ipynb	crag.py	Retrieval - CRAG
16	Generation - Self-RAG	Watch	-	16-self-rag.ipynb	self_rag.py	Generation - Self-RAG

Query Translation

Indexing

Multi-Representation Indexing

Dense X Retrieval: What Retrieval Granularity Should We Use?

RAPTOR

RAPTOR: Recursive Abstractive Processing for Tre-Organized Retrieval Building long context RAG with RAPTOR from scratch

ColBERT

ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
RAGatouille
[Paper review] ColBERT, ColBERTv2
Overcoming the Limits of RAG with ColBERT
ColBERT Inference in the Browser

Agentic RAG

Self-Reflective RAG with LangGraph

CRAG

Corrective Retrieval Augmented Generation

Self-RAG

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Contributors

labdmitriy

14 commits

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labdmitriy/llm-rag

Jupyter Notebook

312

14 commits

updated Apr 28, 2025

See the code

README

LLM RAG

Configuration

Environment

Copy .env.example file to .env
Fill in the required values

Installation

Using `pip`

Create and activate new Python virtual environment
pip install -r requirements.txt
pip install -e . (basic packages)
or
pip install -e .[ragatouille] (with ragatouille for ColBERT)

Using `uv`

uv sync --group dev (basic packages)
or
uv sync --group dev --extra ragatouille (with ragatouille for ColBERT)
uv pip install -e .

RAG From Scratch

Author of original series - Lance Martin

Original Sources

Video

YouTube Playlist

Code

Main Repository
Corrective RAG (CRAG)
Self-RAG

Part	Name	Video	Slides	Jupyter Notebook	Python Script	LangGraph Studio
1	Overview	Watch	View	01-overview.ipynb	-	-
2	Indexing	Watch	View	02-indexing.ipynb	-	-
3	Retrieval	Watch	View	03-retrieval.ipynb	-	-
4	Generation	Watch	View	04-generation.ipynb	-	-
5	Query Translation - Multi-Query	Watch	View	05-multi-query.ipynb	multi_query.py	Query Translation - Multi-Query
6	Query Translation - RAG-Fusion	Watch	View	06-rag-fusion.ipynb	rag_fusion.py	Query Translation - RAG-Fusion
7	Query Translation - Decomposition	Watch	View	07-01-decomposition-recursive.ipynb 07-02-decomposition-parallel.ipynb	recursive.py parallel.py	Query Translation - Decomposition (Recursive) Query Translation - Decomposition (Parallel)
8	Query Translation - Step-Back Prompting	Watch	View	08-step-back.ipynb	step_back.py	Query Translation - Step-Back Prompting
9	Query Translation - HyDE	Watch	View	09-hyde.ipynb	hyde.py	Query Translation - HyDE
10	Routing	Watch	View	10-01-logical-routing.ipynb	logical.py semantic.py	Routing - Logical Routing Routing - Semantic Routing
11	Query Construction	Watch	View	11-query-construction.ipynb	self_query.py	Query Construction - Self-Query
12	Indexing - Multi-Representation Indexing	Watch	View	12-01-multi-vector-summary.ipynb 12-02-multi-vector-chunks.ipynb 12-03-multi-vector-hypothetical-questions.ipynb	summary.py chunks.py hypothetical_questions.py	Indexing - Multi-Vector - Summary Indexing - Multi-Vector - Chunks Indexing - Multi-Vector - Hypothetical Questions
13	Indexing - RAPTOR	Watch	View	13-raptor.ipynb	raptor.py	-
14	Indexing - ColBERT	Watch	View	14-colbert.ipynb	colbert_model.py	-
15	Retrieval - CRAG	Watch	-	15-crag.ipynb	crag.py	Retrieval - CRAG
16	Generation - Self-RAG	Watch	-	16-self-rag.ipynb	self_rag.py	Generation - Self-RAG

Query Translation

RAG Fusion

Decomposition (Recursive)

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models
Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Step-Back Prompting

Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

HyDE

Precise Zero-Shot Dense Retrieval without Relevance Labels

Routing

Semantic Router

Query Construction

Langchain Self Query With Dates

Indexing

Multi-Representation Indexing

Dense X Retrieval: What Retrieval Granularity Should We Use?

RAPTOR

RAPTOR: Recursive Abstractive Processing for Tre-Organized Retrieval Building long context RAG with RAPTOR from scratch

ColBERT

Agentic RAG

Self-Reflective RAG with LangGraph

CRAG

Corrective Retrieval Augmented Generation

Self-RAG

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

Similar repos

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Everything you need to know to build your own RAG application

4,166

jxzhangjhu/Awesome-LLM-RAG

Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language…

1,358

HKUDS/LightRAG

[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation

39,813

RUC-NLPIR/FlashRAG

⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)

3,579

lizhe2004/Awesome-LLM-RAG-Application

the resources about the application based on LLM with RAG pattern

1,659

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Contributors

labdmitriy

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labdmitriy/llm-rag

README

LLM RAG

Configuration

Environment

Installation

Using pip

Using uv

RAG From Scratch

Original Sources

Video

Code

Table of Contents

Query Translation

RAG Fusion

Decomposition (Recursive)

Step-Back Prompting

HyDE

Routing

Query Construction

Indexing

Multi-Representation Indexing

RAPTOR

ColBERT

Agentic RAG

CRAG

Self-RAG

Contributors

Similar repos

Languages

labdmitriy/llm-rag

README

LLM RAG

Configuration

Environment

Installation

Using pip

Using uv

RAG From Scratch

Original Sources

Video

Code

Table of Contents

Query Translation

RAG Fusion

Decomposition (Recursive)

Step-Back Prompting

HyDE

Routing

Query Construction

Indexing

Multi-Representation Indexing

RAPTOR

ColBERT

Agentic RAG

CRAG

Self-RAG

Similar repos

Contributors

Languages

Using `pip`

Using `uv`

Using `pip`

Using `uv`