How do I handle mixed data types (e.g., text and images) in LlamaIndex?

Handling mixed data types like text and images in LlamaIndex involves structuring your data pipeline to process each modality separately and then combining them into a unified index for retrieval. LlamaIndex provides tools to manage multimodal data by allowing you to define custom processing logic for different data types while maintaining a consistent interface for querying. The key steps include data ingestion, transformation into embeddings, and indexing strategies that accommodate both text and images.

First, you’ll need to preprocess each data type using appropriate models. For text, this typically involves splitting documents into chunks and generating text embeddings using models like OpenAI’s text-embedding-ada-002 or open-source alternatives like BERT. For images, you’ll use vision models such as CLIP or ResNet to generate embeddings that capture visual features. LlamaIndex supports custom “readers” and “node parsers” to handle this. For example, you might use the SimpleDirectoryReader to load files from a folder, then route images to a dedicated image processor (e.g., extracting CLIP embeddings) while sending text through a separate text embedding pipeline. Each data type is converted into a vector representation, which is stored in a format LlamaIndex can index.

Next, you’ll structure the index to handle both modalities. One approach is to create separate indices for text and images, then use a “router” (like LlamaIndex’s RouterQueryEngine) to direct queries to the relevant index based on the input type. Alternatively, you can build a unified index where each node contains both text and image embeddings. For example, a MultiModalNode might store a text embedding, an image embedding, and metadata linking to the original files. During queries, a hybrid retrieval strategy could compare the query embedding (generated from a text prompt or image input) against both text and image vectors, then combine results using techniques like weighted scoring.

Finally, query execution requires a retriever that can process multimodal inputs. If a user asks, “Find images of red cars with technical specifications,” the system might first retrieve relevant text documents about car specs using text embeddings, then fetch images tagged with “red car” using vision embeddings. LlamaIndex’s RetrieverQueryEngine can be extended to support this by invoking separate retrieval pipelines and merging results. Developers can also leverage existing integrations, such as using CLIP’s joint text-image embedding space to enable cross-modal searches (e.g., finding images based on a text query). Code examples might include defining custom Node classes, configuring pipelines with ServiceContext, and using VectorIndex to store embeddings. By explicitly separating data processing per modality and designing the index to support joint retrieval, you can effectively manage mixed data in LlamaIndex.