How do I use LlamaIndex to generate embeddings for text data?

To generate embeddings with LlamaIndex, you first need to set up the core components and choose an embedding model. LlamaIndex provides tools to integrate with embedding APIs like OpenAI’s text-embedding-ada-002 or open-source models from Hugging Face. Start by installing the library (pip install llama-index) and importing modules like ServiceContext, VectorStoreIndex, and SimpleDirectoryReader. Create a ServiceContext object to configure your embedding model—for example, using OpenAIEmbedding() for OpenAI’s API or HuggingFaceEmbedding() for local models. This context is passed to downstream processes to ensure consistency in how text is converted to vectors.

Next, prepare your data and build an index to generate embeddings automatically. Load text files using SimpleDirectoryReader to create Document objects, which represent chunks of text. For finer control, split these documents into Node objects (smaller text segments) using a text splitter. When you create a VectorStoreIndex (e.g., VectorStoreIndex(nodes, service_context=service_context)), LlamaIndex processes each node by sending its text to the embedding model specified in your ServiceContext. The resulting embeddings are stored in the index, enabling efficient similarity searches. For instance, indexing a set of research papers this way would create vector representations that capture semantic relationships between the documents.

If you need direct access to embeddings without building a full index, use the embedding model directly. Retrieve the embedding model from your ServiceContext (e.g., embed_model = service_context.embed_model) and call embed_model.get_text_embedding("your text here") to generate a vector. This is useful for tasks like comparing individual text snippets or populating a custom vector database. For example, embedding product descriptions separately allows you to compute similarities between them without indexing. Always ensure your embedding model’s token limits align with your text length—split long texts first to avoid truncation errors.