Artificial intelligence

Building an AI-Powered Movie Recommendation System using RAG, MongoDB, Ollama, and Gemini 

In today’s AI-driven world, building intelligent applications requires more than just large language models. To deliver accurate and context-aware responses, modern systems combine data retrieval with generation — a technique known as Retrieval-Augmented Generation (RAG). 

In this blog, we will explore how to build a Movie Recommendation Chat System using: 

• MongoDB Atlas Vector Search 
• Ollama (Embeddings) 
• Google Gemini (LLM) 
• Streamlit (UI) 

The same can be replicated across any Database, LLM you like!
 

What is RAG? 

Retrieval-Augmented Generation (RAG) is a hybrid approach where: 

• Retriever fetches relevant data from a database 
• Generator (LLM) uses that data to create meaningful responses 

This ensures: 

• More accurate answers 
• Reduced hallucinations 
• Better explainability 

🏗️ System Architecture Overview 

The system consists of three main components: 

0. Pre-Read:

To follow along you will need

• Active Mongo DB Atlas account 
• Ollama (with the model nomic-embed-text)
• A Gemini API key / Service Account Json

1. Generating Embeddings 

We convert movie plot descriptions into numerical vectors using Ollama: 

emb = ollama.embeddings(model="nomic-embed-text", prompt=doc["fullplot"])["embedding"] 

These embeddings capture the semantic meaning of text, enabling similarity-based search. 

2. Creating a Vector Search Index 

MongoDB enables efficient similarity search using vector indexing: 

     { 

       "type": "vector", 

       "path": "embedding", 

       "numDimensions": dimensions, 

       "similarity": "cosine" 

     } 

This allows us to quickly find movies with similar plots. 

3. Building the Chat Application 

Using Streamlit, we create an interactive chat interface: 

• User enters a query 
• Query is converted into an embedding 
• MongoDB retrieves similar movie plots 
• Gemini generates the final answer 

Retrieval + Generation Flow 

1.  User asks: 

"Suggest a movie about space and emotions" 

2. System: 

• Converts query into embedding 
• Retrieves top similar plots 

3. LLM (Gemini): 

• Understands context 
• Generates explanation 

4. UI: 

• Displays recommendations with reasoning 

Why This Approach Works 

Context-Aware Responses 

The system uses real movie data instead of relying only on the model. 

Scalable Architecture 

Each component (DB, embeddings, LLM) can scale independently. 

Flexible Design 

You can swap: 

• Gemini → OpenAI 
• MongoDB → FAISS 
• Ollama → HuggingFace 

Real-World Applications 

This architecture is not limited to movies. It can be used for: 

• Enterprise knowledge assistants 
• Document search systems 
• Customer support chatbots 
• Internal copilots 

Key Takeaways 

• Vector embeddings are crucial for semantic understanding 
• RAG improves accuracy and reduces hallucination 
• MongoDB simplifies vector search infrastructure 
• Combining retrieval + LLM is the future of AI applications 

Conclusion 

By combining MongoDB, Ollama, and Gemini, we can build a powerful and scalable AI system that delivers intelligent recommendations.  As AI continues to evolve, RAG-based architectures will become the foundation of next-generation applications — especially in enterprise automation and copilots.