Can I use LlamaIndex to store and search through embeddings?

Yes, you can use LlamaIndex to store and search through embeddings. LlamaIndex is designed to simplify working with large language models (LLMs) by providing tools to manage and query data efficiently. It handles embeddings by integrating with vector databases or built-in storage solutions, allowing you to index and retrieve information based on semantic similarity. This makes it practical for tasks like document search, question answering, or recommendation systems where understanding context is critical.

LlamaIndex automates the process of generating and storing embeddings. When you load data—such as text documents—LlamaIndex can split the content into manageable chunks, generate embeddings for each using models like OpenAI’s text-embedding-ada-002, and store them in a structured index. For example, you might use the VectorStoreIndex class to create an index from a directory of PDFs. During a search, LlamaIndex converts the query into an embedding and compares it against stored embeddings to find the most relevant results. This abstraction hides the complexity of managing vector math, letting developers focus on application logic. A simple code snippet might involve using SimpleDirectoryReader to load data and VectorStoreIndex.from_documents() to build the index, with embeddings generated automatically.

The framework also offers flexibility in how embeddings are stored and queried. You can plug in different vector databases like FAISS, Pinecone, or Chroma to scale beyond local memory limits. For instance, using Pinecone as the backend enables distributed storage and fast similarity searches across large datasets. Additionally, LlamaIndex supports custom embedding models, allowing teams to use open-source alternatives (e.g., SentenceTransformers) or fine-tuned models tailored to their domain. Advanced features like hybrid search—combining keyword-based and vector-based retrieval—are also possible, ensuring precise results. By handling the infrastructure for embeddings, LlamaIndex lets developers build semantic search systems without deep expertise in machine learning or vector databases.