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RAG chatbot that answers questions about Alice in Wonderland, runs locally by default with Ollama and Llama 3, Streamlit interface with an optional hosted demo

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# 📚 Alice in Wonderland LLM

[![CI](https://github.com/fadyabirached/alice-llm/actions/workflows/ci.yml/badge.svg)](https://github.com/fadyabirached/alice-llm/actions/workflows/ci.yml)

This project is a **Retrieval-Augmented Generation (RAG) chatbot** built to answer questions about the book *Alice in Wonderland*. Its real, default design runs entirely locally using **Ollama** and the open-source **Llama 3** model, optimized to provide accurate, source-based answers while minimizing AI hallucinations. See [Tech Stack](#️-tech-stack) and [Live demo vs. local design](#-live-demo-vs-local-design) for the one deliberate exception.

You can interact with it four ways: from the **command line** in the notebook, through a **local Streamlit app**, in **Google Colab** with a public URL, or via the **live demo** below.

---

## 🌐 Live Demo

**[Try it here →](https://alice-llm.streamlit.app/)**

The live demo swaps the LLM backend from Ollama to Groq's hosted API, since free
hosting can't run a local Ollama server. **This is a deployment-only exception:
the project's real design is Ollama end-to-end**, unchanged everywhere else. See
[Live demo vs. local design](#-live-demo-vs-local-design) for exactly what that
means and why.

---

## 🚀 Key Features

- **High-Quality Embeddings:** Uses `mxbai-embed-large` for highly relevant search results.
- **Advanced Retrieval:** Implements **Maximal Marginal Relevance (MMR)** to fetch context chunks that are both relevant and diverse.
- **Grounded Generation:** The LLM never states a fact the retrieved text doesn't support. If a question's premise contradicts the book (e.g. gets who-did-what backwards), it corrects the premise using the context instead of refusing or hallucinating. It only refuses outright when the context truly has nothing relevant.
- **Interactive Web Demo:** A local Streamlit app (`app.py`), or the same demo from Colab via Ngrok/LocalTunnel.
- **Fully Local & Open-Source:** Runs on your machine with Ollama and open-source models, with no API keys required.
- **Tested Core Logic:** Chunking, prompt construction, and retriever configuration live in a small, unit-tested Python module (`src/rag.py`), not just notebook cells.
- **Colab Ready:** Easy setup and GPU access via a Google Colab notebook, if you'd rather not install anything locally.

---

## ⚙️ How It Works: The RAG Pipeline

### 1️⃣ Data Ingestion (Building the Knowledge Base)
The text of *Alice in Wonderland* is loaded, split into smaller, overlapping chunks, and converted into numerical embeddings using `mxbai-embed-large`. These embeddings are stored in a **FAISS vector store**, acting as a searchable library of the book's content.

```
[alice_in_wonderland.txt] -> Chunking -> Embedding -> [FAISS Vector Store]
```

### 2️⃣ Retrieval and Generation (Answering a Question)
When a user asks a question:

1. The query is embedded.
2. MMR searches the FAISS vector store for the most relevant and diverse chunks.
3. These chunks are added to a strict system prompt.
4. **Llama 3** generates a final answer based *only* on the provided context.

```
User Query -> Retrieval (MMR) -> [Relevant Chunks] -> Prompt + LLM -> Final Answer
```

---

## 🛠️ Tech Stack

- **Language:** Python 3.11
- **LLM Orchestration:** LangChain (`langchain`, `langchain-community`, `langchain-ollama`)
- **LLM Server (default/local):** Ollama
- **Generation Model (default/local):** Llama 3 (8B)
- **Embedding Model (default/local):** mxbai-embed-large
- **LLM Server (live demo only):** Groq's hosted API (`langchain-groq`), see below
- **Embedding Model (live demo only):** `sentence-transformers/all-MiniLM-L6-v2`, run locally on CPU (`langchain-huggingface`)
- **Vector Store:** FAISS (`faiss-cpu`)
- **Interface:** Streamlit
- **Testing / CI:** pytest, ruff, GitHub Actions
- **Environment:** Runs locally, in Google Colab, or deployed to Streamlit Community Cloud

---

## 🔀 Live demo vs. local design

The default, documented, real design of this project is **Ollama end-to-end**:
generation and embeddings both run locally, no API keys, no data leaves your
machine. That's what `LLM_BACKEND=ollama` (the default if unset) gives you,
locally or via the notebook.

Free hosting platforms can't run a local Ollama server, so the **live demo
only** sets `LLM_BACKEND=groq`, which swaps two things in `src/rag.py`
(`get_llm` / `get_embeddings`):

| | Local (default) | Live demo (`LLM_BACKEND=groq`) |
|---|---|---|
| Generation | Ollama, Llama 3 8B, on your machine | Groq's hosted API, Llama 3 8B (`llama-3.1-8b-instant`) |
| Embeddings | Ollama, `mxbai-embed-large`, on your machine | `sentence-transformers/all-MiniLM-L6-v2`, still local/CPU, no API key |
| Requires | Ollama installed + models pulled | `GROQ_API_KEY` (free, from [console.groq.com](https://console.groq.com)) |
| Data leaves your machine? | No | Only the prompt + retrieved chunks, to Groq, for generation |

Everything else (chunking, FAISS, MMR retrieval, the grounded prompt) is
identical in both modes; `backend` is a parameter on `build_qa_system()`, not
a fork of the codebase. Embeddings stay local even in the live demo because
Groq doesn't serve an embeddings API, so swapping to Groq only replaces the
generation half, not the whole pipeline.

---
<img width="1520" height="867" alt="image" src="https://github.com/user-attachments/assets/bd797140-b1ef-40c5-9a9e-f605084524e0" />

## 📁 Project Structure

```
alice-llm/
├── AliceWonderlandLLM.ipynb   # Interactive walkthrough / Colab entry point
├── app.py                     # Streamlit demo (imports src/rag.py); reads
│                               #   LLM_BACKEND to pick Ollama vs. Groq
├── alice_in_wonderland.txt    # Source text (the knowledge base)
├── src/
│   └── rag.py                 # Reusable, unit-tested RAG pipeline logic,
│                               #   incl. get_llm/get_embeddings backend swap
├── tests/
│   ├── test_rag.py             # Tests for pipeline logic that doesn't need
│   │                            #   a running Ollama server
│   └── test_backend_selection.py  # Tests for get_llm/get_embeddings
│                               #   (Ollama vs. Groq), mocked -- no real
│                               #   server, network call, or model download
├── requirements.txt            # Runtime deps: Ollama path + live-demo path
├── requirements-dev.txt        # + pytest, ruff (for local dev / CI)
└── .github/workflows/ci.yml    # Lints and runs the test suite on push/PR
```

`src/rag.py` factors out the chunking, prompt template, and retriever
configuration that both the notebook and `app.py` use, so that logic is
defined once, documented, and covered by tests, rather than duplicated
across notebook cells and an `app.py` written inline via `%%writefile`.

---

## 📖 Setup and Usage

### Option A: Run locally with Ollama (recommended)

**Prerequisites**

- Python 3.10+
- [Ollama](https://ollama.com/download) installed and running

**Steps**

1. **Clone the repository**
   ```bash
   git clone https://github.com/fadyabirached/alice-llm.git
   cd alice-llm
   ```

2. **Install Python dependencies**
   ```bash
   pip install -r requirements.txt
   ```

3. **Pull the required Ollama models**
   ```bash
   ollama pull llama3
   ollama pull mxbai-embed-large
   ```
   Make sure the Ollama server is running (`ollama serve`, or the desktop app) before continuing.

4. **Run the Streamlit demo**
   ```bash
   streamlit run app.py
   ```
   This opens the chatbot in your browser at `http://localhost:8501`.

Alternatively, open `AliceWonderlandLLM.ipynb` locally (e.g. with
   `jupyter lab`) and run the "Build the RAG Pipeline" and "Ask Questions"
   cells for a command-line-style Q&A loop.

### Option B: Run in Google Colab

1. Go to [Google Colab](https://colab.research.google.com/) and upload
   `AliceWonderlandLLM.ipynb`.
2. Upload `alice_in_wonderland.txt` via the file explorer panel.
3. Run the notebook cells in order:
   - **Cell 1:** Install libraries & Ollama.
   - **Cell 2:** Start and verify the Ollama server.
   - **Cell 3:** Download `llama3` and `mxbai-embed-large`.
   - **Cell 4:** Build the RAG pipeline.
   - **Cell 5 / 6:** Ask questions interactively, or run a single clean test query.
   - **Cell 7 (optional):** Launch the Streamlit demo with a public URL via ngrok.

### Option C: Deploy your own live demo (Streamlit Community Cloud)

This deploys `app.py` with `LLM_BACKEND=groq` (see
[Live demo vs. local design](#-live-demo-vs-local-design) above for what that
changes and why free hosting needs it).

1. Get a free Groq API key at [console.groq.com](https://console.groq.com) →
   API Keys.
2. Go to [share.streamlit.io](https://share.streamlit.io), sign in with
   GitHub, click **New app**.
3. Pick this repo, branch `main`, main file path `app.py`.
4. Under **Advanced settings → Secrets**, add:
   ```toml
   LLM_BACKEND = "groq"
   GROQ_API_KEY = "your-groq-api-key-here"
   ```
5. Click **Deploy**. First load takes a minute or two (downloading the
   `all-MiniLM-L6-v2` embedding model and building the FAISS index); after
   that it's cached for the life of the app instance.

No code changes needed. `requirements.txt` already includes the
`langchain-groq` / `langchain-huggingface` / `sentence-transformers`
dependencies this path needs, alongside the Ollama ones the local path uses.

---

## 🧪 Running the Tests

The test suite covers the pipeline logic that doesn't require a running
Ollama server or downloaded models (text chunking, prompt formatting,
retriever configuration). It is fast and runs entirely offline:

```bash
pip install -r requirements-dev.txt
pytest -v
ruff check .
```

Both are run automatically on every push/PR via GitHub Actions
(`.github/workflows/ci.yml`). Anything that requires a live Ollama server
(embedding, vector search end-to-end, generation) is intentionally left out
of CI and is meant to be exercised locally.

---

## 🔧 Customization

- **Different Book:** Replace `alice_in_wonderland.txt` and update the `file_path` passed to `src.rag.build_qa_system()`.
- **Different Model:** Change `llm_model` / `embedding_model` in `src.rag.build_qa_system()` (or `ChatOllama(model=...)` in the notebook) to any Ollama model you have pulled.
- **Different Backend:** Pass `backend="groq"` to `build_qa_system()`, or set the `LLM_BACKEND` env var before running `app.py`. See [Live demo vs. local design](#-live-demo-vs-local-design).
- **Retriever Tuning:** Adjust `k` and `fetch_k` via `src.rag.RetrieverConfig`.
- **Prompt Customization:** Edit `RAG_PROMPT_TEMPLATE` in `src/rag.py` for a different tone, style, or instructions.

---

## ⚖️ License

This project is licensed under the **MIT License**. See the `LICENSE` file for details.

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