https://github.com/eziodevio/ai-knowledge-bot
his is my own custom-built offline AI bot that lets you chat with PDFs and web pages using **local embeddings** and **local LLMs** like LLaMA 3. I built it step by step using LangChain, FAISS, HuggingFace, and Ollama — without relying on OpenAI or DeepSeek APIs anymore (they just kept failing or costing too much)
https://github.com/eziodevio/ai-knowledge-bot
ai-chatbot chat-with-pdf chat-with-webpage document-summarization eziodevio faiss huggingface-embeddings langchain llama3 local-llm offline-ai ollama rag streamlit vectorstore
Last synced: 4 months ago
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his is my own custom-built offline AI bot that lets you chat with PDFs and web pages using **local embeddings** and **local LLMs** like LLaMA 3. I built it step by step using LangChain, FAISS, HuggingFace, and Ollama — without relying on OpenAI or DeepSeek APIs anymore (they just kept failing or costing too much)
- Host: GitHub
- URL: https://github.com/eziodevio/ai-knowledge-bot
- Owner: EzioDEVio
- Created: 2025-06-11T02:45:09.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-11T05:18:31.000Z (4 months ago)
- Last Synced: 2025-06-11T06:24:57.701Z (4 months ago)
- Topics: ai-chatbot, chat-with-pdf, chat-with-webpage, document-summarization, eziodevio, faiss, huggingface-embeddings, langchain, llama3, local-llm, offline-ai, ollama, rag, streamlit, vectorstore
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧠 AI Knowledge Bot
This is my own custom-built offline AI bot that lets you chat with PDFs and web pages using **local embeddings** and **local LLMs** like LLaMA 3.
I built it step by step using LangChain, FAISS, HuggingFace, and Ollama — without relying on OpenAI or DeepSeek APIs anymore (they just kept failing or costing too much).
---
## 🚀 Features
- 📄 Chat with uploaded PDF files
- 🌍 Ask questions about a webpage URL
- 🧠 Uses local HuggingFace embeddings (`all-MiniLM-L6-v2`)
- 🦙 Powered by Ollama + LLaMA 3 (fully offline LLM)
- 🗃️ Built-in FAISS vectorstore
- 🧾 PDF inline preview
- 🧮 Built-in calculator + summarizer tools (via LangChain agents)
- 🧠 Page citation support (know where each answer came from)
- 📜 Chat history viewer with download button (JSON)
- 🎛️ Simple Streamlit UI with dark/light mode toggle
- 👨💻 Footer credit: *Developed by EzioDEVio*
---
## 📦 Tech Stack
- `langchain`, `langchain-community`
- `sentence-transformers` for local embeddings
- `ollama` for local LLMs (`llama3`)
- `PyPDF2` for PDF parsing
- `FAISS` for vector indexing
- `Streamlit` for frontend
---
## 🛠 Setup Guide
### 1. Clone this repo
```bash
git clone https://github.com/EzioDEVio/ai-knowledge-bot.git
cd ai-knowledge-bot
````
---
### 2. Create and activate virtualenv (optional but recommended)
```bash
python -m venv venv
.\venv\Scripts\activate # Windows for Mac is different
```
---
### 3. Install dependencies
```bash
pip install -r requirements.txt
```
Make sure `sentence-transformers` is installed — needed for local embeddings.
---
### 4. Install Ollama (for local LLM)
Download and install from:
👉 [https://ollama.com/download](https://ollama.com/download)
After installation, verify:
```bash
ollama --version
```
Then pull and run the model:
```bash
ollama run llama3
```
> This will download the LLaMA 3 model (approx. 4–8GB). You can also try `mistral`, `codellama`, etc.
---
### 5. Run the app
```bash
streamlit run app.py
```
The app will open at:
```
http://localhost:8501
```
---
## 📁 Folder Structure
```
ai-knowledge-bot/
├── app.py # Main Streamlit UI
├── backend/
│ ├── pdf_loader.py # PDF text extraction
│ ├── web_loader.py # Webpage scraper
│ ├── vector_store.py # Embedding + FAISS
│ └── qa_chain.py # LLM QA logic (Ollama + tools)
├── .env # Not used anymore (was for API keys)
├── requirements.txt
└── README.md
```
---
## ✅ Working Setup Summary
| Component | Mode |
| ---------------- | ------------------------------------ |
| Embeddings | Local (`HuggingFace`) |
| Vectorstore | Local (`FAISS`) |
| LLM Response | Local (`Ollama` + `llama3`) |
| Internet Needed? | ❌ Only for first-time model download |
---
## ⚠️ Why I Avoided OpenAI / DeepSeek
* **OpenAI** failed with `RateLimitError` and quota issues unless I added billing.
* **DeepSeek** embedding endpoints didn’t work — only chat models supported.
So I switched to:
* 🔁 Local `HuggingFaceEmbeddings` for vectorization
* 🦙 `ChatOllama` for full offline AI answers
---
## ✅ Now Completed Features
* ✅ PDF upload + preview
* ✅ URL content QA
* ✅ Chat history with page citations
* ✅ Calculator + summarizer tools
* ✅ Footer attribution
* ✅ JSON export
* ✅ 100% offline functionality
---
## 🐳 Run with Docker (Secure Production Mode)
Build and run the app securely using a **multi-stage Dockerfile**:
1. Build the container
```bash
docker build -t ai-knowledge-bot .
```
2. Run the container
Make sure Ollama is running on the host, open up a powershell or in different terminal then:
```
docker run -p 8501:8501 \
--add-host=host.docker.internal:host-gateway \
ai-knowledge-bot
```
---
## 🔐 Dockerfile Security Highlights
✅ Multi-stage build (separates dependencies from runtime)
✅ Minimal base (python:3.10-slim)
✅ Non-root appuser by default
✅ .env, venv, logs excluded via .dockerignore
✅ Exposes only necessary port (8501)
✅ Automatically starts Streamlit app
---
## 💬 License
MIT — feel free to fork, use, or improve it.
---
## 🔥 Built by EzioDEVio | 🇮🇶 | 🧠
From concept to offline AI — all step by step.
---