llamaindex

To build a retrieval-augmented generation pipeline for private enterprise data, you can use LlamaIndex to ingest documents into a vector index and route queries through multiple retrievers and query engines.

Data connectors are interfaces that ingest documents from various sources into a searchable index for LLMs. LlamaIndex provides over 300 data connectors to unify data ingestion, indexing, and querying across multi-modal datasets.

Yes, you can use OpenAI and Anthropic models with LlamaIndex query engines. The framework provides tool integrations for these platforms to support diverse LLM applications, chatbots, and agent tooling.

LlamaIndex does support multi-modal datasets and private data handling. It enables building scalable knowledge retrieval applications across private enterprise data and multi-modal inputs using vector indices.

The best way to index research corpora for question-answer workflows is using a framework like LlamaIndex to create vector indices from ingested documents. This allows scalable knowledge retrieval and targeted querying across your research data.