# ContextualAIQueryTool ## Description This tool is designed to integrate Contextual AI's enterprise-grade RAG agents with CrewAI. Run this tool to query existing Contextual AI RAG agents that have been pre-configured with documents and knowledge bases. ## Installation To incorporate this tool into your project, follow the installation instructions below: ```shell pip install 'crewai[tools]' contextual-client ``` **Note**: You'll need a Contextual AI API key. Sign up at [app.contextual.ai](https://app.contextual.ai) to get your free API key. ## Example Make sure you have already created a Contextual agent and ingested documents into the datastore before using this tool. ```python from crewai_tools import ContextualAIQueryTool # Initialize the tool tool = ContextualAIQueryTool(api_key="your_api_key_here") # Query the agent with IDs result = tool._run( query="What are the key findings in the financial report?", agent_id="your_agent_id_here", datastore_id="your_datastore_id_here" # Optional: for document readiness checking ) print(result) ``` The result will contain the generated answer to the user's query. ## Parameters **Initialization:** - `api_key`: Your Contextual AI API key **Query (_run method):** - `query`: The question or query to send to the agent - `agent_id`: ID of the existing Contextual AI agent to query (required) - `datastore_id`: Optional datastore ID for document readiness verification (if not provided, document status checking is disabled with a warning) ## Key Features - **Document Readiness Checking**: Automatically waits for documents to be processed before querying - **Grounded Responses**: Built-in grounding ensures factual, source-attributed answers ## Use Cases - Query pre-configured RAG agents with document collections - Access enterprise knowledge bases through user queries - Build specialized domain experts with access to curated documents For more detailed information about Contextual AI's capabilities, visit the [official documentation](https://docs.contextual.ai).