README.md

---
title: RAG Implementation Notebook
emoji: 🔍
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false
---

# RAG Implementation Notebook

This space contains a Jupyter notebook demonstrating a Retrieval Augmented Generation (RAG) implementation using OpenAI's API and Hugging Face models.

## Features
- PDF document processing
- Text chunking and embedding
- Vector database implementation
- RAG pipeline with context-aware responses

## How to Use
1. Clone this repository
2. Install the requirements: `pip install -r requirements.txt`
3. Open the notebook: `jupyter notebook Pythonic_RAG_Assignment.ipynb`

## Requirements
See `requirements.txt` for the complete list of dependencies.

# 🧑‍💻 What is [AI Engineering](https://maven.com/aimakerspace/ai-eng-bootcamp)?

AI Engineering refers to the industry-relevant skills that data science and engineering teams need to successfully **build, deploy, operate, and improve Large Language Model (LLM) applications in production environments**.  

In practice, this requires understanding both prototyping and production deployments.

During the *prototyping* phase, Prompt Engineering, Retrieval Augmented Generation (RAG), Agents, and Fine-Tuning are all necessary tools to be able to understand and leverage. Prototyping includes:
1. Building RAG Applications
2. Building with Agent and Multi-Agent Frameworks
3. Fine-Tuning LLMs & Embedding Models
4. Deploying LLM Prototype Applications to Users

When *productionizing* LLM application prototypes, there are many important aspects ensuring helpful, harmless, honest, reliable, and scalable solutions for your customers or stakeholders. Productionizing includes:
1. Evaluating RAG and Agent Applications
2. Improving Search and Retrieval Pipelines for Production
3. Monitoring Production KPIs for LLM Applications
4. Setting up Inference Servers for LLMs and Embedding Models
5. Building LLM Applications with Scalable, Production-Grade Components

This bootcamp builds on our two previous courses, [LLM Engineering](https://maven.com/aimakerspace/llm-engineering) and [LLM Operations](https://maven.com/aimakerspace/llmops) 👇

- Large Language Model Engineering (LLM Engineering) refers to the emerging best-practices and tools for pretraining, post-training, and optimizing LLMs prior to production deployment.  Pre- and post-training techniques include unsupervised pretraining, supervised fine-tuning, alignment, model merging, distillation, quantization. and others.
    
- Large Language Model Ops (LLM Ops, or LLMOps (as from [WandB](https://docs.wandb.ai/guides/prompts) and [a16z](https://a16z.com/emerging-architectures-for-llm-applications/))) refers to the emerging best-practices, tooling, and improvement processes used to manage production LLM applications throughout the AI product lifecycle.  LLM Ops is a subset of Machine Learning Operations (MLOps) that focuses on LLM-specific infrastructure and ops capabilities required to build, deploy, monitor, and scale complex LLM applications in production environments.  _This term is being used much less in industry these days._

# 🏆 **Grading and Certification**

To become **AI-Makerspace Certified**, which will open you up to additional opportunities for full and part-time work within our community and network, you must:

1. Complete all project assignments.
2. Complete a project and present during Demo Day.
3. Receive at least an 85% total grade in the course.

If you do not complete all assignments, participate in Demo Day, or maintain a high-quality standard of work, you may still be eligible for a *certificate of completion* if you miss no more than 2 live sessions.

# 📚 About

This GitHub repository is your gateway to mastering the art of AI Engineering.  ***All assignments for the course will be released here for your building, shipping, and sharing adventures!***

# 🙏 Contributions

We believe in the power of collaboration. Contributions, ideas, and feedback are highly encouraged! Let's build the ultimate resource for AI Engineering together.

Please to reach out with any questions or suggestions. 

Happy coding! 🚀🚀🚀