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 RAG
This component leverages Vectara's Retrieval Augmented Generation (RAG) capabilities to search and summarize documents based on the provided input. For more information, see the Vectara documentation.
Parameters
Inputs
| Name | Type | Description |
|---|---|---|
| vectara_customer_id | String | Vectara customer ID |
| vectara_corpus_id | String | Vectara corpus ID |
| vectara_api_key | SecretString | Vectara API key |
| search_query | String | The query to receive an answer on |
| lexical_interpolation | Float | Hybrid search factor (0.005 to 0.1) |
| filter | String | Metadata filters to narrow the search |
| reranker | String | Reranker type (mmr, rerank_multilingual_v1, none) |
| reranker_k | Integer | Number of results to rerank (1 to 100) |
| diversity_bias | Float | Diversity bias for MMR reranker (0 to 1) |
| max_results | Integer | Maximum number of search results to summarize (1 to 100) |
| response_lang | String | Language code for the response (e.g., "eng", "auto") |
| prompt | String | Prompt name for summarization |
Outputs
| Name | Type | Description |
|---|---|---|
| answer | Message | Generated RAG response |
