llamaindex

To build a RAG application with LlamaIndex, you load documents from a directory, create a vector index, and configure a query engine. This Skill manages data ingestion, indexing, and querying using OpenAI LLMs and embeddings.

Yes, ChromaDB is supported as an external vector store for LlamaIndex document indexing. You can store and retrieve vector embeddings from ChromaDB to build context-aware question answering systems over your internal documents.

The best way to index internal documents for natural language search is creating a vector index. This Skill facilitates document loading and indexing, allowing users to ask natural language questions and receive accurate, context-aware answers from the LLM.

Yes, this LlamaIndex Skill supports creating various index types including Vector, Summary, and Keyword indexes. You can build query engines for question answering and summarization over your loaded documents.

You can load files from a local directory like './data' using the included file readers, then create a vector index. The Skill handles document loading and query engine creation to enable retrieval augmented generation over your data.

LlamaIndex provides a robust framework specifically for data ingestion, indexing, and querying. It manages data connectors and various index types, simplifying the development of sophisticated LLM applications with context-aware document search.