Rewrite README for recruiter-facing presentation

README.md

@@ -1,18 +1,102 @@
-# AIEngineeringWeek2V1
+# Textbook RAG Assistant (LangChain + Chroma + OpenAI)
 
-LangChain RAG notebook: rag1.ipynb (Week 2 assignment).
+End-to-end **Retrieval-Augmented Generation (RAG)** system built for **Applied AI Engineering** workflows: ingest PDF textbooks, chunk + enrich metadata, embed into a vector database, evaluate retrieval quality, and ship a lightweight **Gradio** UI for interactive Q&A.
 
-Repository: https://github.com/zainabahmed4626-lab/AIEngineeringWeek2V1
+**Live repo:** https://github.com/zainabahmed4626-lab/AIEngineeringWeek2V1
 
-## Setup
+---
 
-- Create a `.env` file locally with your `OPENAI_API_KEY` (do not commit it).
-- Place `textbook_1.pdf` and `textbook_2.pdf` next to the notebook (not committed here).
+## What this project demonstrates (recruiter-friendly)
 
-## Run
+- **Production-shaped RAG**: not “call an LLM”—a full pipeline from documents → chunks → embeddings → retrieval → grounded answers.
+- **Grounding + safety posture**: answers are constrained to retrieved context with an explicit fallback when evidence is insufficient.
+- **Retrieval engineering**: hybrid retrieval (**vector similarity + BM25**) and **metadata-aware filtering** hooks (source / section / date).
+- **Evaluation discipline**: a small eval loop reporting retrieval / faithfulness / correctness style signals (assignment-oriented, extensible).
+- **Product thinking**: a simple **Gradio** interface for manual testing, plus debug panels to inspect retrieved chunks.
 
-Open `rag1.ipynb` and execute cells in order.
+---
 
-## Optional: GitHub helper
+## Architecture (high level)
 
-`build_github_push_payload.py` is a small local utility used to generate a JSON payload for GitHub file uploads. It is not required to run the notebook.
+```mermaid
+flowchart LR
+  PDFs[PDF textbooks] --> Load[LangChain PDF loader]
+  Load --> Chunk[RecursiveCharacterTextSplitter + metadata]
+  Chunk --> Embed[HuggingFace embeddings]
+  Embed --> VS[Chroma vector store]
+  VS --> RetV[Vector retriever]
+  Chunk --> RetB[BM25 retriever]
+  RetV --> Ens[Ensemble retriever]
+  RetB --> Ens
+  Ens --> QA[RetrievalQA (OpenAI chat)]
+  QA --> UI[Gradio UI]
+```
+
+---
+
+## Tech stack
+
+- **Orchestration**: LangChain (`RetrievalQA`, prompts, retrievers)
+- **Embeddings**: `sentence-transformers/all-MiniLM-L6-v2` (via LangChain community integrations)
+- **Vector DB**: Chroma (persistent store)
+- **Hybrid retrieval**: BM25 (`rank-bm25`) + dense vectors (`EnsembleRetriever`)
+- **LLM**: OpenAI chat model (`gpt-3.5-turbo`, temperature configurable)
+- **UI**: Gradio
+- **Notebook**: `rag1.ipynb` (single runnable artifact for the assignment)
+
+---
+
+## Key features implemented
+
+- **Step 1 — Load**: PDF ingestion + sanity prints (document count + preview)
+- **Step 2 — Chunk**: `RecursiveCharacterTextSplitter` with two chunking experiments + stats
+- **Step 3 — Embed + Store**: embeddings persisted to Chroma (`./chroma_db`, collection `textbook_rag`)
+- **Step 4 — Retrieval test**: `similarity_search` with annotated relevance notes
+- **Step 5 — RAG chain**: `RetrievalQA` + custom prompt + retriever tuning
+- **Step 6 — Evaluation**: mini eval set + structured reporting + aggregate scores
+- **Hybrid retrieval**: vector + BM25 ensemble for stronger keyword coverage
+- **Metadata enrichment**: `source`, `section`, `date` on chunks + optional filtered retrieval path
+- **UX polish**: greeting handling in the Gradio path for a friendlier chat experience
+
+---
+
+## Quickstart (local)
+
+### 1) Prerequisites
+
+- Python 3.10+ recommended (your notebook metadata may show newer; adjust if needed)
+- OpenAI API access
+
+### 2) Configure secrets locally
+
+Create a `.env` file in the project folder:
+
+```bash
+OPENAI_API_KEY=...your key...
+```
+
+This repo intentionally **does not** commit `.env`.
+
+### 3) Add your PDFs
+
+Place these next to `rag1.ipynb` (not committed here):
+
+- `textbook_1.pdf`
+- `textbook_2.pdf`
+
+### 4) Run the notebook
+
+Open `rag1.ipynb` and run cells **in order** (setup → load → chunk → embed → retrieval tests → RAG → eval → optional UI).
+
+---
+
+## Notes for hiring managers
+
+- This project highlights **applied ML systems engineering**: retrieval quality, prompt constraints, observability (chunk previews), and iterative evaluation—not just prompt writing.
+- The design is intentionally modular: you can swap embeddings, vector DB, rerankers, or swap the LLM provider without rewriting the whole pipeline.
+
+---
+
+## Optional helper script
+
+`build_github_push_payload.py` generates a JSON payload for GitHub uploads. It is **not** required to run the RAG notebook.