To integrate LlamaIndex with vector databases like FAISS or Milvus, you’ll use LlamaIndex’s built-in abstractions to connect your data to these storage systems. LlamaIndex simplifies the process by handling document ingestion, embedding generation, and indexing, while allowing you to plug in external vector databases for efficient similarity search. The core steps involve configuring LlamaIndex to use your chosen database, storing embeddings, and querying them.
First, install the necessary libraries. For FAISS, use llama-index and faiss-cpu (or GPU-enabled faiss). For Milvus, add pymilvus to connect to the database. Next, load your data using LlamaIndex’s SimpleDirectoryReader or custom data connectors. Convert documents into embeddings using LlamaIndex’s default embedding models or a custom provider. For example, ServiceContext.from_defaults(embed_model="local") initializes a local embedding model. Then, create a VectorStoreIndex and pass your vector database client (FAISS or Milvus) to it. LlamaIndex’s VectorStoreIndex handles splitting text, generating embeddings, and storing them in the database.
For FAISS, after generating embeddings, you can save the index locally for reuse. With Milvus, ensure the database server is running and configure connection parameters like host and port. For example, initialize a Milvus client with MilvusVectorStore(host="localhost", port="19530") and pass it to VectorStoreIndex.from_documents(documents, vector_store=vector_store). When querying, LlamaIndex uses the vector database’s similarity search to retrieve relevant context. For instance, query_engine = index.as_query_engine() lets you ask questions, and the system retrieves embeddings from FAISS/Milvus to generate answers. Both databases scale differently: FAISS is ideal for smaller, single-node setups, while Milvus supports distributed architectures for large datasets.
A practical example with FAISS would involve creating an index, saving it to disk, and reloading it for queries. With Milvus, you’d define a collection schema and ensure data persistence. For instance, after inserting data into Milvus, you can reuse the collection name in subsequent sessions. LlamaIndex’s abstractions reduce boilerplate, but you’ll still need to manage database-specific configurations, like Milvus’s consistency settings or FAISS’s index type (e.g., IndexFlatL2). Always test retrieval accuracy and latency to optimize parameters like embedding dimensions or search thresholds for your use case.