Building a RAG Pipeline with MongoDB: Vector Search for Personalized Movie Picks

This article discusses the creation of a movie recommendation system using a Retrieval-Augmented Generation (RAG) pipeline and MongoDB's vector search capabilities. The goal is to build a smart and efficient system that understands user preferences and provides personalized movie recommendations. By the end of the article, readers will have a functional system capable of answering specific queries, such as suggesting a good sci-fi movie about artificial intelligence or an animated film suitable for adults. A RAG pipeline combines the strengths of large language models (LLMs) and structured data retrieval, enhancing the accuracy and contextual relevance of responses. MongoDB is chosen for its open-source NoSQL database, which stores data in flexible, JSON-like documents, and its robust vector search capabilities that handle movie data embeddings efficiently. The project involves several steps: 1. Setting up the environment and loading a movie dataset from Hugging Face. 2. Modeling data with Pydantic to ensure consistency and type safety. 3. Generating embeddings for movie information using OpenAI's API. 4. Ingesting the processed data into a MongoDB database. 5. Creating a vector search index in MongoDB Atlas to enable similarity searches. 6. Implementing a vector search function to find relevant movies. 7. Handling user queries with an LLM, using the vector search results to generate contextually appropriate responses. The article provides detailed instructions for setting up the environment, including importing necessary libraries, loading the dataset, and configuring MongoDB. Data modeling with Pydantic involves defining a Movie class to ensure proper data validation and serialization. Embeddings are generated using OpenAI's "text-embedding-3-small" model, creating 1536-dimensional embeddings for semantic searches. The data is ingested into MongoDB, and a vector search index is created in MongoDB Atlas. This index uses cosine similarity to match user queries with movie descriptions. The vector search function includes a retry mechanism to handle transient issues and uses MongoDB's aggregation pipeline to retrieve relevant movies. User queries are processed by generating embeddings for the query, performing a vector search, and using OpenAI's GPT-3.5-turbo model to generate responses based on the search results. The article concludes by demonstrating how the system can recommend movies and provide reasons for the recommendations. The system's flexibility allows for future expansions, such as adding more data, fine-tuning embeddings, or implementing advanced recommendation algorithms. The complete code and resources are available on GitHub, and the dataset used is sourced from Kaggle, with public domain dedication. The project exemplifies the power of combining MongoDB and RAG pipelines to create personalized and efficient recommendation systems.