How does LlamaIndex handle text embeddings?

LlamaIndex handles text embeddings by integrating with external embedding models to convert text into numerical representations (vectors) that capture semantic meaning. Instead of providing its own embedding algorithms, LlamaIndex acts as a framework that connects to existing embedding providers like OpenAI, Hugging Face, or sentence-transformers. For example, you might use OpenAI’s text-embedding-ada-002 model via their API or a local model like all-MiniLM-L6-v2 from Hugging Face. This approach lets developers choose the best embedding model for their needs without being locked into a specific technology. LlamaIndex simplifies the process by offering pre-built integrations—you configure the embedding model once, and it automatically processes text during data ingestion and querying.

When you load data into LlamaIndex, it splits documents into manageable chunks (e.g., paragraphs or sections) and passes each chunk to the selected embedding model. The generated vectors are stored alongside the original text in a structured index, often paired with a vector database like FAISS, Pinecone, or Chroma for efficient similarity searches. For instance, if you index a research paper, LlamaIndex might split it into sections, embed each section into a vector, and store those vectors. During queries, your search input is also converted into a vector using the same model, and the system retrieves the most semantically similar text chunks based on vector proximity. This enables tasks like finding relevant answers in a knowledge base or matching user queries to stored content.

Developers can customize LlamaIndex’s embedding workflow in several ways. First, you can adjust chunk size and overlap to balance context retention and computational efficiency—for example, splitting a blog post into 512-token chunks with 20% overlap. Second, you can swap embedding models depending on performance requirements: a local model reduces API costs but might sacrifice accuracy, while a cloud-based model offers higher quality at increased latency. Third, LlamaIndex supports hybrid approaches, letting you combine embeddings with keyword-based retrieval for improved results. For instance, a medical app might use a domain-specific embedding model fine-tuned on clinical text alongside traditional keyword matching to handle technical jargon. By decoupling embeddings from the core indexing logic, LlamaIndex provides flexibility while handling the infrastructure needed to connect embeddings to downstream tasks like retrieval-augmented generation (RAG).