RAG

RAG (Retrieval-Augmented Generation) components process a user query by retrieving relevant documents and generating a concise summary that addresses the user's question.

Vectara

Vectara performs RAG using a Vectara corpus, including document retrieval, reranking results, and summary generation.

Parameters:

• Vectara Customer ID: Customer ID.
• Vectara Corpus ID: Corpus ID.
• Vectara API Key: API key.
• Search Query: User query.
• Lexical Interpolation: How much to weigh lexical vs. embedding scores.
• Metadata Filters: Filters to narrow down the search documents and parts.
• Reranker Type: How to rerank the retrieved results.
• Number of Results to Rerank: Maximum reranked results.
• Diversity Bias: How much to diversify retrieved results (only for MMR reranker).
• Max Results to Summarize: Maximum search results to provide to summarizer.
• Response Language: The language code (use ISO 639-1 or 639-3 codes) of the summary.
• Prompt Name: The summarizer prompt.

For more information, consult the Vectara documentation