RAG-chatbot-Gemini-API-ollama

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✅ 1) Clone your repo

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echo "📥 Cloning repo..." git clone https://github.com/BhagyeshPatil2004/RAG-chatbot-Gemini-API-ollama.git cd RAG-chatbot-Gemini-API-ollama

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✅ 2) Create .env placeholder

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echo "🔑 Creating .env..." echo 'GEMINI_API_KEY="YOUR_GEMINI_API_KEY"' > .env

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✅ 3) Create .gitignore

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"🗂️ Creating .gitignore..." cat < .gitignore

Python

pycache/ *.pyc .venv/

Env

.env

VSCode

.vscode/

Chroma DB

chroma_db/ EOL

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✅ 4) Write README.md

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"📝 Writing README.md..." cat < README.md

RAG-chatbot-Gemini-API-ollama 🤖

This is a simple Retrieval-Augmented Generation (RAG) chatbot built using:

• LangChain
• Ollama
• ChromaDB

It uses the original codebasics FAQ dataset from this video.
Note: I did some changes in the code only — the dataset remains the same.

---

⚙️ Features

✅ Loads CSV data of FAQs
✅ Creates embeddings with Ollama (`mxbai-embed-large`)
✅ Stores embeddings locally in ChromaDB
✅ Uses Gemini API (or your local LLM) to generate answers
✅ Runs a simple CLI chat loop

---

📚 Dataset

• `codebasics_faqs.csv` is sourced from the codebasics YouTube channel
• No modifications to dataset — only code improved to fit this RAG structure.

---

🚀 How to Run

1️⃣ Clone the Repo

``` git clone https://github.com/BhagyeshPatil2004/RAG-chatbot-Gemini-API-ollama.git cd RAG-chatbot-Gemini-API-ollama ```

2️⃣ Setup Python Environment

``` python -m venv .venv

Windows

.venv\Scripts\activate

Linux/Mac

source .venv/bin/activate

pip install -r requirements.txt ```

3️⃣ Add your Gemini API key

Edit `.env` and replace with your real API key.

4️⃣ Make sure Ollama is running

``` ollama pull mxbai-embed-large ```

5️⃣ Run the chatbot

``` python "AI Agent 1/agent.py" ```

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✅ Notes

• Ollama must be running for embeddings.

• Gemini API generates answers.

• Easily swap a bigger dataset for your major project.

---

👨‍💻 Author

😎 Bhagyesh Patil
📌 Repo: RAG-chatbot-Gemini-API-ollama

---

EOL

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✅ 5) Create requirements.txt

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"📦 Creating requirements.txt..."

langchain

langchain_community

langchain_ollama

langchain_chroma

ollama

python-dotenv

EOL

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✅ 6) Setup Python venv + install

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"🐍 Creating Python venv..."

python -m venv .venv

"🐍 Activating venv & installing..."

Detect OS to activate venv properly

if [[ "$OSTYPE" == "msys" || "$OSTYPE" == "win32" ]]; then .venv\Scripts\activate && pip install -r requirements.txt

else source .venv/bin/activate && pip install -r requirements.txt

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✅ 7) Pull Ollama embedding model

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"🧠 Pulling Ollama embedding model..." ollama pull mxbai-embed-large

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✅ Done!

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"" "1️⃣ Add your real Gemini API key in .env"

"2️⃣ Make sure Ollama is running"

"3️⃣ Run your bot: python "AI Agent 1/agent.py""

""

"🔥 Happy!"