15 different RAG implementation from Scratch

## Content Type

This issue is to create 15 different Retrieval-Augmented Generation (RAG) implementations from scratch using base libraries like:

* `litellm` for LLM interaction
* `lancedb` for vector storage and retrieval
* Embedding models from HuggingFace (e.g., `BAAI/bge-base-en-v1.5`, `sentence-transformers` family)

The goal is to recreate each RAG variant found in [repo](https://github.com/FareedKhan-dev/all-rag-techniques) using a minimal stack with readable, modifiable code, integrating AI Engineering Academy’s educational focus and data sources.

## Topic Area

* Retrieval-Augmented Generation (RAG)
* LLMOps
* Embedding Techniques
* Knowledge Graphs
* RLHF for Retrieval

## Target Knowledge Level

* [x] Beginner
* [x] Intermediate
* [x] Advanced

## Content Description

This initiative will rebuild 15 of the most educational and distinct RAG pipelines from the original “all-rag-techniques” repository into modular notebooks compatible with the [[AI Engineering Academy format](https://github.com/adithya-s-k/AI-Engineering.academy/blob/main/docs/RAG/01_Basic_RAG/basic_rag_scratch.ipynb)](https://github.com/adithya-s-k/AI-Engineering.academy/blob/main/docs/RAG/01_Basic_RAG/basic_rag_scratch.ipynb).

Each notebook should:

* Use `litellm` as the LLM client for generation.
* Use `lancedb` as the local vector database for embedding storage/retrieval.
* Use an open-source embedding model from HuggingFace.
* Leverage the shared dataset from [`[/archives/data](https://github.com/adithya-s-k/AI-Engineering.academy/tree/main/archives/data)`](https://github.com/adithya-s-k/AI-Engineering.academy/tree/main/archives/data), replacing PDF test documents with unified AI-related content.

Notebooks to create:

1. Simple RAG
2. Semantic Chunking
3. Chunk Size Selector
4. Context Enriched RAG
5. Contextual Chunk Headers
6. Document Augmentation RAG
7. Query Transformation (includes subquery + step-back prompting)
8. Reranker (LLM-based)
9. RSE (Relevant Segment Extraction)
10. Contextual Compression
11. Feedback Loop RAG
12. Adaptive RAG
13. Fusion RAG (BM25 + vectors)
14. Graph RAG (concept traversal using networkx)
15. Self-RAG (retrieval + decision via LLM)

add more if possible

Each notebook will include:

* An explanation block (Markdown)
* Inline commentary for each code block
* Evaluation code (either LLM-assisted or metric-based)
* Visualizations for similarity scores and retrieval steps (matplotlib/seaborn)

## Educational Rationale

Retrieval-Augmented Generation is a cornerstone of modern LLM applications. By rebuilding these techniques from scratch:

* Learners get a transparent view into retrieval pipelines.
* They learn to decouple from opaque frameworks like LangChain or LlamaIndex.
* It provides a foundation for deploying RAG in constrained environments.
* Each variant highlights different trade-offs (speed, interpretability, relevance, adaptability), which are crucial to master in AI product engineering.

By using open-source embedding models and a fast, local vector DB like `lancedb`, the content aligns with privacy-first and cost-effective AI engineering practices.

## Resources

* Original RAG repo: [https://github.com/FareedKhan-dev/all-rag-techniques](https://github.com/FareedKhan-dev/all-rag-techniques)
* Reference notebook format: [[Basic RAG from Scratch](https://github.com/adithya-s-k/AI-Engineering.academy/blob/main/docs/RAG/01_Basic_RAG/basic_rag_scratch.ipynb)](https://github.com/adithya-s-k/AI-Engineering.academy/blob/main/docs/RAG/01_Basic_RAG/basic_rag_scratch.ipynb)
* Data source: [[AI Engineering Academy Data](https://github.com/adithya-s-k/AI-Engineering.academy/tree/main/archives/data)](https://github.com/adithya-s-k/AI-Engineering.academy/tree/main/archives/data)
* HuggingFace Embedding Models: [https://huggingface.co/models](https://huggingface.co/models) (e.g., `BAAI/bge-base-en-v1.5`)
* Lancedb Docs: [https://lancedb.github.io/](https://lancedb.github.io)
* litellm Docs: [https://docs.litellm.ai/](https://docs.litellm.ai)