adding the README for the RAG + Distilled folder

Author: CaesarGhazi

Rag + Distilled Model/README.md

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+# Apollo 11 RAG & Distilled Model Evaluation
+
+This notebook demonstrates a **Retrieval-Augmented Generation (RAG)** system
+using data from the **Apollo 11 mission**.  
+It uses **LangChain**, **HuggingFace**, and **ChromaDB** to load, embed, and
+query textual data, then evaluates responses using a set of pre-defined prompts
+from a JSON file.
+
+---
+
+## Project Description
+
+The notebook `Apollo11_rag&distilled.ipynb` contains a structured RAG pipeline
+with four main parts:
+
+1. **Data Loading** – Reads Apollo 11 mission text data from a JSON file.
+2. **Database Creation** – Builds a local ChromaDB vector store for semantic
+search.  
+   > This step should be run **only once**, as it creates and saves the
+database locally.
+3. **Query & Generation** – Retrieves relevant context for a given question and
+uses a model to generate an answer.
+4. **Evaluation** – Tests the model’s responses using predefined data from the
+JSON file.
+
+---
+
+## Folder Structure
+
+```text
+ Rag + Distilled Model/
+├── Apollo11_rag&distilled.ipynb   ← Main Jupyter Notebook
+├── README.md                      ← Project documentation
+└── data/
+    ├── apollo11_docs.json     ← Apollo 11 text dataset and evaluation prompts
+    └── chroma_db/             ← Auto-created vector database folder
+                                  (It will appear after you run it)
+```
+
+---
+
+## Models Used
+
+* **LaMini-Flan-T5-248M**: It is a Local LLM and it is a distilled version of
+Google's Flan-T5, optimized for lightweight text generation tasks.
+Used here for reasoning, summarization, and RAG response generation.
+* **all-MiniLM-L6-v2**: It as an Embedding model and it is a compact sentence-transformer
+model used to convert text chunks into numerical vector embeddings for
+semantic search and retrieval.
+
+These two models make the project lightweight, fully local, and suitable for GPU
+or CPU execution.
+
+---
+
+## Notes
+
+* The ChromaDB folder (data/chroma_db/) is automatically generated when you first
+run the document loader.
+* You can safely delete it to rebuild embeddings later.
+* The notebook does not require an external .txt file — all content is inside
+the JSON.
+* The model automatically detects whether to use GPU (torch.cuda.is_available()).