To integrate LlamaIndex with a vector database, you should start by understanding the basic function of each component. LlamaIndex is primarily used to index and manage data for retrieval purposes, while a vector database stores data points in a high-dimensional space, often used for similarity searches. The integration allows you to store, retrieve, and search through indexed documents efficiently by converting them into vector representations that the database can handle.
First, set up your vector database if you haven't done so already. Common options include Pinecone, Weaviate, and Milvus. Ensure that the database is running and you have the necessary access credentials. Once the database is ready, you can create a connection in your code to interact with it. For instance, if you are using Python, you might utilize an official library provided by your vector database. This connection will allow you to send and retrieve data between LlamaIndex and your vector database.
After establishing the connection, the next step is to configure LlamaIndex to normalize and embed the data appropriately before sending it to the vector database. You can achieve this by using a suitable embedding model to convert your documents into vector representations. Once the vectors are generated, you can store them in your vector database alongside any relevant metadata. When a query is made, retrieve the vector that matches the criteria, and use LlamaIndex’s functionality to handle the final retrieval or formatting of results. For example, if a user searches for similar items, the system can fetch vectors from the database and then retrieve the original documents based on their IDs or other identifiers stored with the vectors.