Building an AI application with LlamaIndex

LlamaIndex is an open source library that helps build AI applications by integrating agents with various data sources, offering a modular approach for tasks like chatbots, document analysis and NLP. Here we will build a movie recommendation bot using LlamaIndex, where a user query is processed, relevant data is retrieved and recommendations are generated.

• Query Input: The user enters a query like “Show me some action movies.”
• Document Retrieval: The retriever searches indexed movie data (VectorStoreIndex) to find relevant results.
• Response Generation: The model combines the query and retrieved data to generate recommendations.

Step 1: Installing Required Packages

• llama-index: Used for indexing and querying large datasets.
• transformers: Provides access to pre-trained models.
• torch: Deep learning framework for running models.
• accelerate, bitsandbytes: Optimize performance and memory for large models.

Step 2: Importing Required Libraries

Step 3: Setting Up Models and Embeddings

• Use a Hugging Face embedding model (all-MiniLM-L6-v2) to convert movie data into vector representations.
• Set up a language model (TinyLlama-1.1B-Chat-v1.0) for generating responses to user queries.
• Configure parameters like tokenization, context window and token limits for efficient performance.

Step 4 Loading Data from CSV

• Load the dataset and convert each row into Document objects for indexing.
• Extract fields like ID, title, genre and rating from each row.
• Create Document objects using this data for further processing.

You can download dataset from here.

Step 5: Document Creation and Indexing

• Build a VectorStoreIndex from the movie documents to enable efficient search and retrieval.
• Create a query engine to interact with the index and handle user queries.

Step 6: Querying and Displaying Recommendations

• The user provides a movie genre as input through the loop.
• A structured prompt is created to guide the model to return movie titles with ratings in a specific format.
• The query is passed to the query engine, which retrieves relevant documents and generates recommendations.
• The response is printed directly using response.response, ensuring clean and readable output.

Output:

You can download source code from here.

Applications

• LlamaIndex can recommend products, movies or content by matching user preferences with indexed data.
• Helps build search systems for large datasets like company documents or FAQs using natural language queries.
• Enables chatbots and virtual assistants to understand user queries and provide intelligent responses using indexed knowledge.
• Can be used to analyze reviews or social media data to understand public sentiment about products or services.
• Supports retrieval across multiple languages, making it useful for global applications.