To update and retrain LlamaIndex with new data, you need to add the new information to your existing dataset and rebuild or extend the index. LlamaIndex structures data into indexes (like VectorStoreIndex) that rely on embeddings and retrieval logic. When new data arrives, you can either rebuild the index from scratch or incrementally insert new nodes (chunks of data) into the storage context. The process typically involves loading the new data, converting it into nodes, updating the index, and persisting the changes for future use. This ensures the index reflects the latest information for querying or retrieval tasks.
First, load your new data using LlamaIndex’s data connectors (e.g., SimpleDirectoryReader for files). Convert the raw data into Document objects, then split them into smaller nodes using a text splitter or node parser. For incremental updates, use index.insert_nodes() to add these nodes directly to an existing index. For example, if you have a VectorStoreIndex, you can create a SentenceSplitter to chunk the new documents into nodes and insert them into the index’s storage context. If rebuilding entirely, combine old and new data, reprocess all documents, and recreate the index. Tools like StorageContext help manage persisted data, allowing you to load existing indexes from disk before appending new nodes.
After updating the index, regenerate embeddings for the new nodes using your chosen embedding model (e.g., OpenAI’s text-embedding-ada-002). Persist the updated index using index.storage_context.persist(persist_dir="your_directory") to save the modified embeddings and metadata. If using a vector database like Chroma or Pinecone, ensure the new embeddings are added to the database. For performance, test retrieval accuracy with sample queries to verify the new data is integrated correctly. Note that LlamaIndex doesn’t “retrain” in the traditional ML sense—it rebuilds or extends the retrieval structure. If your use case requires fine-tuning the underlying LLM (e.g., GPT), that’s a separate process involving model training pipelines, not LlamaIndex itself. Focus on maintaining consistent preprocessing (chunking, metadata) to ensure seamless integration of new data.