LlamaIndex is primarily designed for text-based data, but you can use it with non-textual data like audio or video by converting that data into a text-based format or embedding representations first. The core functionality of LlamaIndex revolves around indexing and querying text using language models, so non-text data requires preprocessing to fit into this framework. For example, audio files could be transcribed into text using automatic speech recognition (ASR), and video content could be analyzed to extract metadata, transcripts, or scene descriptions. These text-based outputs can then be indexed using LlamaIndex for search or retrieval tasks.
One practical approach involves combining LlamaIndex with specialized models that handle non-text data. For audio, you might use a tool like OpenAI’s Whisper to generate transcripts, then index those transcripts with LlamaIndex to enable semantic search. For video, you could extract keyframes, use computer vision models to generate textual descriptions of scenes, or analyze subtitles. These text representations become the input for LlamaIndex, allowing you to leverage its retrieval capabilities. Alternatively, you could use multimodal embedding models (like CLIP or audio-specific embeddings) to convert non-text data into vector representations, store those vectors in LlamaIndex’s vector store, and perform similarity searches. However, this requires custom pipelines to map embeddings back to the original media files for practical use.
The main limitation is that LlamaIndex doesn’t natively process raw audio or video—it depends on preprocessing steps. Developers must design workflows that convert non-text data into a compatible format (text or embeddings) before indexing. For instance, a podcast search tool might transcribe episodes to text, index them with LlamaIndex, and let users query topics via keywords or natural language. Similarly, a video platform could index scene descriptions to enable queries like “find clips with outdoor landscapes.” While this adds complexity, LlamaIndex’s flexibility makes it viable for hybrid systems combining domain-specific models with text-based retrieval.