llama-index-skill

To augment LLM answers with custom data, you can use a retrieval-augmented generation workflow to ingest domain-specific documents, build embeddings, and store them in a vector-store index for fast contextual retrieval. This supplies precise information to the LLM during question answering.

Yes, you can load markdown files into a vector-store index. The data ingestion workflow converts diverse data formats like markdown, PDFs, and text into documents, generates embeddings, and stores them for retrieval-augmented responses.

Building a knowledge base for retrieval-augmented generation involves loading your documents, creating embeddings, and storing them in a vector-store index. This enables fast, relevant retrieval to augment research summaries or domain-specific chat with contextual data.

Yes, a vector-store is needed to query custom documents with LlamaIndex. The workflow builds embeddings from your ingested data and stores them in a vector-store index, which enables fast, relevant retrieval during question answering.

Yes, the data ingestion workflow supports PDFs and text files. It ingests diverse data formats including markdown, PDFs, and text, converting them into documents for indexing and retrieval-augmented generation.

Embeddings are used for domain-specific chat and research summaries to convert documents into searchable vectors stored in an index. This enables fast, relevant retrieval of precise contextual data to augment LLM answers.