Open coffepowered opened 1 month ago
To effectively search over a knowledge base with documents requiring different kinds of embeddings, including multimodal ones, LlamaIndex offers a robust approach that aligns well with the options you're considering. Here's a concise overview tailored to your needs:
Multimodal Embeddings: LlamaIndex employs MultiModalEmbedding
for handling various content types, such as text, images, and code. For images, it uses CLIP embeddings, and for documents containing code or SQL, it leverages specialized embeddings that understand the structure and semantics of code, enhancing retrieval accuracy.
Separate Indexes for Each Modality: It maintains separate vector stores for different modalities within the MultiModalVectorStoreIndex
class. This setup allows for efficient handling and storage of embeddings specific to each modality, ensuring that each type of content is indexed and queried optimally.
Combination of Approaches: By combining multimodal embeddings with separate indexes for each modality, LlamaIndex provides a comprehensive solution for indexing and querying a diverse knowledge base. This hybrid approach ensures that all types of documents, whether they contain text, images, code, or SQL, are effectively processed and made searchable.
For implementing these strategies, consider leveraging vector space models for semantic representation, utilizing specialized embeddings for code and text, and integrating with language models for enhanced query processing. Customizable ingestion pipelines and efficient indexing mechanisms are also crucial for handling large and diverse datasets.
For more detailed guidance and examples, you might want to explore the source code and documentation provided by LlamaIndex, which offers insights into the implementation of these strategies.
To continue the conversation, mention @dosu.
Basically it's 2. Create indexes for each type. The multimodal index is one specific use case of these, for images and text, but the general approach makes sense, since each modality requires a specific embedding model
the response from dosubot is useful, but nothing beats a human. Thank you @logan-markewich !
Question Validation
Question
Hello, I am reflecting on how to tackle the problem of searching over a knowledge base that could require different modalities for embeddings. To make this more concrete, my users are used to upload:
I recall llama_index has some approaches to images and tables, but is there a general preferred pattern to do this? In general, I see some options to handle this:
Do you see more pattern to apply? Which would be the "llama-index" way of approaching the problem, if any?