To use LlamaIndex with pre-trained embeddings, you first need to install LlamaIndex and ensure you have the necessary libraries for working with embeddings, such as NumPy or TensorFlow, depending on your method of embedding. You would typically start by loading your pre-trained embeddings into your environment. For instance, if using word embeddings from GloVe or Word2Vec, you can load them into a dictionary or array where each word maps to its corresponding vector.
Once you have your embeddings loaded, the next step is creating an index with LlamaIndex. LlamaIndex is designed to efficiently manage and query large datasets of vector embeddings. You can initialize an index object and populate it with the vectors you've loaded. For example, if you have a batch of text data you want to index, you can convert the text into embeddings, using the pre-trained model, and then add these embeddings into LlamaIndex.
After indexing, you can query the indexed data using similarity search or other methods available in LlamaIndex. You can input a query text, convert it into its embedding using the same pre-trained model, and then use LlamaIndex to find similar embeddings based on cosine similarity or other metrics. This process allows you to efficiently find relevant data points similar to your query using the power of your pre-trained embeddings.