---
title: Rag
emoji: 🐢
colorFrom: gray
colorTo: indigo
sdk: gradio
sdk_version: 5.12.0
app_file: app.py
pinned: false
short_description: Just another rag but with Images 🖼️
---

# Just another RAG, dont bother much!!

## File Descriptions

### `z_document_reader.py`

Images are only useful to limited resource computer if it has a caption. So this file helps parse the wikipedia html strips it off the tags.

### `z_embedding.py`

Generates vector store.

### `z_generate.py`

Use LLM and prompting to find the relevant texts and images stored in the vector stores.

## Adding more data sources

Currently limited to wikipedia pages downloaded as HTML.

1. Place the html in the folder `_data`
2. Run command
    `python z_embedding.py`
3. Output will be two FAISS vectors stores in the folder `cache_vector...`

## Local Debug

Highly recommend VS Code, makes life easy.

1. Create Virtual environment with name `sb-rag` using the below command
    `python -m venv sb-rag`

2. Activate the environemnt (create new terminal VS Code to automatically do so)

3. Edit `.vscode/launch.json`. Fill in the environment variable `HF_SERVERLESS_API`.

4. Start VS Code debugger.

## References

1. UI Blocks Concepts: https://huggingface.co/learn/nlp-course/en/chapter9/7
2. UI Row-Column Arrangement: https://www.gradio.app/guides/controlling-layout
2. Show caption in image gallery: https://github.com/gradio-app/gradio/issues/3364
2. HF Implementation of basic: https://huggingface.co/learn/cookbook/en/advanced_rag
2. https://python.langchain.com/docs/integrations/vectorstores/faiss/


## Ideas 

1. Shows frames of design patterns: https://www.falkordb.com/blog/advanced-rag/
2. HF Implementation of basic: https://huggingface.co/learn/cookbook/en/advanced_rag
2. HF RAG Evaluation: https://huggingface.co/learn/cookbook/en/rag_evaluation
2. HF Implementation by someone: https://medium.aiplanet.com/advanced-rag-implementation-on-custom-data-using-hybrid-search-embed-caching-and-mistral-ai-ce78fdae4ef6
2. HF Agentic Rag: https://huggingface.co/learn/cookbook/en/agent_rag
2. Future read, tooning https://huggingface.co/blog/lucifertrj/finetune-embeddings
2. Opinion on instruct embeddings: https://huggingface.co/blog/Tonic/instruct-embeddings-and-advanced-rag
2. Another Implementation: https://huggingface.co/learn/cookbook/en/rag_zephyr_langchain
2. Another Opinion on ray: https://www.anyscale.com/blog/retrieval-augmented-generation-with-huggingface-transformers-and-ray
2. Ray Follow up: https://github.com/run-llama/ai-engineer-workshop/blob/main/presentation.pdf?__s=2il5g6hpfc4mtmydioir
2. llama Index rag implementation: https://docs.llamaindex.ai/en/latest/optimizing/production_rag/
2. Just some termino book: https://www.projectpro.io/article/advanced-rag-techniques/1063
2. Another Evaluation Guide: https://pub.towardsai.net/evaluating-rag-metrics-across-different-retrieval-methods-770aa01380c8
2. Oracle Garbage: https://blogs.oracle.com/ai-and-datascience/post/ai-health-mixtral-oracle-23ai-rag-langchain-streamlit