How do I set up LlamaIndex in my Python environment?

To set up LlamaIndex in your Python environment, start by installing the package and verifying dependencies. Use pip install llama-index in your terminal or command line to install the core library. If you plan to integrate with external services like OpenAI, install additional dependencies with pip install "llama-index[openai]". Ensure your Python version is 3.10 or newer, as older versions may not be fully supported. For isolated development, consider creating a virtual environment using venv or conda to avoid dependency conflicts with other projects. This step-by-step approach ensures you have a clean foundation to work with LlamaIndex’s data indexing and retrieval tools.

Next, configure your environment variables and basic settings. If using cloud-based LLMs like OpenAI, set your API key as an environment variable. In Python, you can do this with import os; os.environ["OPENAI_API_KEY"] = "your-key-here". LlamaIndex requires data connectors to load documents—install specific connectors like llama-index-readers-file for PDFs or llama-index-readers-web for HTML pages. For example, SimpleDirectoryReader from llama_index.core lets you load local text files with documents = SimpleDirectoryReader("data").load_data(). This modular design allows you to tailor the setup to your data sources, whether local files, databases, or web content.

Finally, build a basic indexing and query pipeline. After loading data, create a vector store index using VectorStoreIndex.from_documents(documents). This generates embeddings (numeric representations of text) for semantic search. To query the index, initialize a query engine with query_engine = index.as_query_engine() and run response = query_engine.query("Your question"). For testing, try a simple dataset like a text file containing FAQs. If you encounter errors about missing tokenizers or embeddings, install packages like sentence-transformers for local embedding models. LlamaIndex’s flexibility allows swapping components like storage backends (e.g., Chroma, Pinecone) or LLM providers without rewriting your entire pipeline, making it adaptable to different project needs.