Remove notebook_version folder from git tracking

notebook_version/README.md

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-<p align = "center" draggable=”false” ><img src="https://github.com/AI-Maker-Space/LLM-Dev-101/assets/37101144/d1343317-fa2f-41e1-8af1-1dbb18399719" 
-     width="200px"
-     height="auto"/>
-</p>
-
-## <h1 align="center" id="heading">Session 8: Evaluating RAG with Ragas</h1>
-
-| 🤓 Pre-work | 📰 Session Sheet | ⏺️ Recording     | 🖼️ Slides        | 👨‍💻 Repo         | 📝 Homework      | 📁 Feedback       |
-|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|:-----------------|
-| [Session 8: Pre-Work](https://www.notion.so/Session-8-RAG-Evaluation-and-Assessment-1c8cd547af3d81d08f7cf5521d0253bb?pvs=4#1c8cd547af3d816583d6c23183b6f87f) | [Session 8: RAG Evaluation and Assessment](https://www.notion.so/Session-8-RAG-Evaluation-and-Assessment-1c8cd547af3d81d08f7cf5521d0253bb) | Coming soon! | [Session 8 Slides](https://www.canva.com/design/DAGjadKGqcw/0Gff9K2EwbOb3lX14un3uw/edit?utm_content=DAGjadKGqcw&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) | You are here! | [Session 8: RAG Evaluation and Assessment](https://forms.gle/ujAQLqx2ZHMWTUH79) | [AIE6 Feedback 4/24](https://forms.gle/wA7p89e6svCgjtr58) |
-
-In today's assignment, we'll be creating Synthetic Data, and using it to benchmark (and improve) a LCEL RAG Chain.
-
-- 🤝 Breakout Room #1
-  1. Task 1: Installing Required Libraries
-  2. Task 2: Set Environment Variables
-  3. Task 3: Synthetic Dataset Generation for Evaluation using Ragas
-  4. Task 4: Evaluating our Pipeline with Ragas
-  5. Task 6: Making Adjustments and Re-Evaluating
-
-  The notebook Colab link is located [here](https://colab.research.google.com/drive/1-t4POIFJI-SWF1lmoBOPETZZqgWCTV4Y?usp=sharing)
-
-- 🤝 Breakout Room #2
-  1. Task 1: Building a ReAct Agent with Metal Price Tool
-  2. Task 2: Implementing the Agent Graph Structure
-  3. Task 3: Converting Agent Messages to Ragas Format
-  4. Task 4: Evaluating Agent Performance using Ragas Metrics
-     - Tool Call Accuracy
-     - Agent Goal Accuracy  
-     - Topic Adherence
-
-The notebook Colab link is located [here](https://colab.research.google.com/drive/1KQm7nA_zTaCyjaAeAacjqanMPv03um7T?usp=sharing)
-
-## Ship 🚢
-
-The completed notebook!
-
-<details>
-<summary>🚧 BONUS CHALLENGE 🚧 (OPTIONAL)</summary>
-
-> NOTE: Completing this challenge will provide full marks on the assignment, regardless of the completion of the notebook. You do not need to complete this in the notebook for full marks.
-
-##### **MINIMUM REQUIREMENTS**:
-
-1. Baseline `LangGraph RAG` Application using `NAIVE RETRIEVAL`
-2. Baseline Evaluation using `RAGAS METRICS`
-  - [Faithfulness](https://docs.ragas.io/en/stable/concepts/metrics/faithfulness.html)
-  - [Answer Relevancy](https://docs.ragas.io/en/stable/concepts/metrics/answer_relevance.html)
-  - [Context Precision](https://docs.ragas.io/en/stable/concepts/metrics/context_precision.html)
-  - [Context Recall](https://docs.ragas.io/en/stable/concepts/metrics/context_recall.html)
-  - [Answer Correctness](https://docs.ragas.io/en/stable/concepts/metrics/answer_correctness.html)
-3. Implement a `SEMANTIC CHUNKING STRATEGY`.
-4. Create an `LangGraph RAG` Application using `SEMANTIC CHUNKING` with `NAIVE RETRIEVAL`.
-5. Compare and contrast results.
-
-##### **SEMANTIC CHUNKING REQUIREMENTS**:
-
-Chunk semantically similar (based on designed threshold) sentences, and then paragraphs, greedily, up to a maximum chunk size. Minimum chunk size is a single sentence.
-
-Have fun!
-</details>
-
-### Deliverables
-
-- A short Loom of the notebook, and a 1min. walkthrough of the application in full
-
-## Share 🚀
-
-Make a social media post about your final application!
-
-### Deliverables
-
-- Make a post on any social media platform about what you built!
-
-Here's a template to get you started:
-
-```
-🚀 Exciting News! 🚀
-
-I am thrilled to announce that I have just built and shipped Synthetic Data Generation, benchmarking, and iteration with RAGAS & LangChain! 🎉🤖
-
-🔍 Three Key Takeaways:
-1️⃣ 
-2️⃣ 
-3️⃣ 
-
-Let's continue pushing the boundaries of what's possible in the world of AI and question-answering. Here's to many more innovations! 🚀
-Shout out to @AIMakerspace !
-
-#LangChain #QuestionAnswering #RetrievalAugmented #Innovation #AI #TechMilestone
-
-Feel free to reach out if you're curious or would like to collaborate on similar projects! 🤝🔥
-```

notebook_version/pyproject.toml

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-[project]
-name = "08-evaluating-rag-with-ragas"
-version = "0.1.0"
-description = "Add your description here"
-readme = "README.md"
-requires-python = ">=3.13"
-dependencies = [
-    "jupyter>=1.1.1",
-    "langchain-community==0.3.14",
-    "langchain-openai==0.2.14",
-    "langchain-qdrant>=0.2.0",
-    "langgraph==0.2.61",
-    "numpy>=2.2.2",
-    "unstructured>=0.14.8",
-    "arxiv>=1.4.0",
-]