ask_vectara
Query Vectara's RAG system to retrieve search results and generate contextual responses using specified corpus keys and API parameters for accurate information extraction.
Instructions
Run a RAG query using Vectara, returning search results with a generated response.
Args:
query: str, The user query to run - required.
corpus_keys: list[str], List of Vectara corpus keys to use for the search - required. Please ask the user to provide one or more corpus keys.
api_key: str, The Vectara API key - required.
n_sentences_before: int, Number of sentences before the answer to include in the context - optional, default is 2.
n_sentences_after: int, Number of sentences after the answer to include in the context - optional, default is 2.
lexical_interpolation: float, The amount of lexical interpolation to use - optional, default is 0.005.
max_used_search_results: int, The maximum number of search results to use - optional, default is 10.
generation_preset_name: str, The name of the generation preset to use - optional, default is "vectara-summary-table-md-query-ext-jan-2025-gpt-4o".
response_language: str, The language of the response - optional, default is "eng".
Returns:
The response from Vectara, including the generated answer and the search results.Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | ||
| corpus_keys | No | ||
| generation_preset_name | No | vectara-summary-table-md-query-ext-jan-2025-gpt-4o | |
| lexical_interpolation | No | ||
| max_used_search_results | No | ||
| n_sentences_after | No | ||
| n_sentences_before | No | ||
| query | Yes | ||
| response_language | No | eng |