Zainab4626/AIEngineeringWeek2V1

Textbook RAG Assistant (LangChain + Chroma + OpenAI)

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.

Live repo: https://github.com/zainabahmed4626-lab/AIEngineeringWeek2V1

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What this project demonstrates

• 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.

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Architecture (high level)

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]

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## 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)

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## 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

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## 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).

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Observations — What Worked, What Didn’t, What I’d Improve

What Worked The combination of metadata filtering + hybrid retrieval (BM25 + vector search) noticeably improved relevance, especially for definition‑heavy sections. Chunking at 500 characters produced more accurate retrieval for specific questions. The custom grounding prompt kept answers concise and reduced hallucinations.

What Didn’t Work Some textbook sections still produced noisy chunks due to headers, footers, and formatting artifacts. A few queries returned context that was technically related but not the most precise match.

What I’d Improve Clean the PDF text further before chunking to remove repeated structural elements. Experiment with stronger embedding models to improve semantic matching. Add lightweight evaluation automation to speed up iteration.

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Optional helper script

build_github_push_payload.py generates a JSON payload for GitHub uploads. It is not required to run the RAG notebook.