Managing embeddings in LlamaIndex involves several steps, primarily centered around the creation, storage, and retrieval of document embeddings. First, you’ll need to utilize the right embedding model, which can be a pre-trained one or a custom model that suits your data. You typically start by selecting or importing an embedding generator, such as Hugging Face’s transformers or OpenAI’s models. Once you have your model ready, the next logical step is to transform your documents into embeddings. This is done by passing your text data through the model, which converts each document into a high-dimensional vector representation.
After generating the embeddings, storing them efficiently is key. LlamaIndex provides options for various storage systems, including databases like SQLite or vector databases like Pinecone or Weaviate. Depending on the size of your dataset and your access patterns, you might choose one over the other. For example, if your application needs to handle real-time queries with many users, a vector database may offer better performance. When storing the embeddings, make sure to associate them with metadata or unique identifiers that allow you to retrieve the original documents later.
Lastly, retrieval is where the magic happens. When you're building an application that needs to fetch relevant documents based on user queries, you'll implement a similarity search algorithm. LlamaIndex can help with this by providing functionality to compare the query's embedding with stored embeddings and return the closest matches. Techniques like cosine similarity or nearest neighbor algorithms can be directly applied here. Throughout this process, you’ll want to continually evaluate and refine your embedding strategy by testing with different models, tuning hyperparameters, and adjusting how you store and query the embeddings based on the performance you observe in your application.