How do I customize the indexing pipeline in LlamaIndex?

Customizing the indexing pipeline in LlamaIndex involves modifying how your data is processed, structured, and stored for retrieval. The pipeline typically includes steps like loading data, splitting text into nodes, generating metadata, and storing nodes in a vector database. To tailor this process, you’ll work with LlamaIndex’s modular components, such as node parsers, metadata generators, and storage contexts. For example, you can adjust how documents are split into smaller chunks or define custom rules for enriching nodes with metadata.

A common starting point is customizing text splitting. LlamaIndex provides built-in node parsers like SimpleNodeParser or SentenceSplitter, which split documents based on fixed chunk sizes or sentence boundaries. To modify this, you could adjust parameters like chunk_size (e.g., 512 tokens) or chunk_overlap (e.g., 64 tokens) to balance context retention and granularity. If your data requires domain-specific splitting—like code files or markdown—you might create a custom parser by subclassing NodeParser and overriding the get_nodes_from_documents method. For instance, splitting code into function-level nodes instead of fixed-length chunks ensures more semantically meaningful units for retrieval.

Next, you can enhance nodes with custom metadata or transformations. The transformations parameter in ServiceContext allows you to chain preprocessing steps, such as filtering low-quality text or adding tags based on document content. For example, you might write a function that extracts key entities (e.g., dates, product names) from a node’s text and appends them as metadata. This metadata can later be used to filter search results. Additionally, you might integrate external tools—like a PDF table extractor or an image captioning model—into the pipeline by wrapping them in a custom MetadataExtractor class. After defining these components, pass them to the VectorStoreIndex during initialization to apply your custom logic.

Finally, you can customize the storage layer to fit your infrastructure. By default, LlamaIndex uses an in-memory vector store, but you can replace it with databases like Pinecone, FAISS, or Chroma. To do this, initialize your chosen vector store and pass it to VectorStoreIndex via StorageContext. For example, using PineconeVectorStore requires setting your API key and index name, then building the index with from_documents(documents, storage_context=storage_context). You can also combine custom parsers and storage—for instance, splitting medical reports into patient-specific nodes and storing them in a HIPAA-compliant database. By adjusting these components, you create a pipeline optimized for your data type, performance needs, and infrastructure constraints.