llama-index-tools-vectara-query 0.1.0

llama-index tools vectara query integration

Vectara Query Tool

This tool connects to a Vectara corpus and allows agents to make semantic search or retrieval augmented generation (RAG) queries.

Usage

This tool has a more extensive example usage documented in a Jupyter notebok here

To use this tool, you'll need the following information in your environment:

• VECTARA_CUSTOMER_ID: The customer id for your Vectara account. If you don't have an account, you can create one here.
• VECTARA_CORPUS_ID: The corpus id for the Vectara corpus that you want your tool to search for information. If you need help creating a corpus with your data, follow this Quick Start guide.
• VECTARA_API_KEY: An API key that can perform queries on this corpus.

Here's an example usage of the VectaraQueryToolSpec.

from llama_index.tools.vectara_query import VectaraQueryToolSpec
from llama_index.agent.openai import OpenAIAgent

# Connecting to a Vectara corpus about Electric Vehicles
tool_spec = VectaraQueryToolSpec()

agent = OpenAIAgent.from_tools(tool_spec.to_tool_list())

agent.chat("What are the different types of electric vehicles?")

The available tools are:

semantic_search: A tool that accepts a query and uses semantic search to obtain the top search results.

rag_query: A tool that accepts a query and uses RAG to obtain a generative response grounded in the search results.