Building an AI application with LlamaIndex (original) (raw)

Last Updated : 14 Apr, 2026

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. Python `

!pip install --upgrade llama-index llama-index-embeddings-huggingface llama-index-llms-huggingface transformers torch accelerate bitsandbytes

`

**Step 2: Importing Required Libraries

Python `

from llama_index.core import Settings, VectorStoreIndex, Document from llama_index.embeddings.huggingface import HuggingFaceEmbedding from llama_index.llms.huggingface import HuggingFaceLLM import torch import pandas as pd import os

`

**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. Python `

Settings.embed_model = HuggingFaceEmbedding( model_name="sentence-transformers/all-MiniLM-L6-v2" )

Settings.llm = HuggingFaceLLM( model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0", tokenizer_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0", context_window=2048, max_new_tokens=256, device_map="auto", model_kwargs={"torch_dtype": torch.float32} )

`

Training

**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.

Python `

csv_file_path = "/content/movie_recommendations_with_names.csv"

df = pd.read_csv(csv_file_path)

`

**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. Python `

movies_data = [ Document( text=f"Title: {row['title']}, Genre: {row['genre']}, Rating: {row['rating']}", metadata={ "title": row['title'], "genre": row['genre'], "rating": row['rating'] } ) for _, row in df.iterrows() ] index = VectorStoreIndex.from_documents(movies_data) query_engine = index.as_query_engine()

`

**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. Python `

print("Welcome to MovieRecBot!") print("Type 'exit' to quit.\n")

while True: query_string = input("What type of movie are you in the mood for?\n> ")

if query_string.lower() == "exit":
    print("Thanks for using MovieRecBot!")
    break

prompt = f"""
Recommend top 5 movies for genre: {query_string}.
Only return:
Title - Rating

Do NOT return numbers alone.
"""

response = query_engine.query(prompt)

print("\nRecommended Movies:\n")
print(response.response)

`

**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.