Documentation Index#

Fetch the complete documentation index at: https://docs.crewai.com/llms.txt Use this file to discover all available pages before exploring further.

The MDXSearchTool is designed to search MDX files and return the most relevant results.

MDXSearchTool#

The MDXSearchTool is in continuous development. Features may be added or removed, and functionality could change unpredictably as we refine the tool.

Description#

The MDX Search Tool is a component of the crewai_tools package aimed at facilitating advanced markdown language extraction. It enables users to effectively search and extract relevant information from MD files using query-based searches. This tool is invaluable for data analysis, information management, and research tasks, streamlining the process of finding specific information within large document collections.

Installation#

Before using the MDX Search Tool, ensure the crewai_tools package is installed. If it is not, you can install it with the following command:

pip install 'crewai[tools]'

Usage Example#

To use the MDX Search Tool, you must first set up the necessary environment variables. Then, integrate the tool into your crewAI project to begin your market research. Below is a basic example of how to do this:

from crewai_tools import MDXSearchTool

# Initialize the tool to search any MDX content it learns about during execution
tool = MDXSearchTool()

# OR

# Initialize the tool with a specific MDX file path for an exclusive search within that document
tool = MDXSearchTool(mdx='path/to/your/document.mdx')

Parameters#

• mdx: Optional. Specifies the MDX file path for the search. It can be provided during initialization.

Customization of Model and Embeddings#

The tool defaults to using OpenAI for embeddings and summarization. For customization, utilize a configuration dictionary as shown below:

from chromadb.config import Settings

tool = MDXSearchTool(
    config={
        "embedding_model": {
            "provider": "openai",
            "config": {
                "model": "text-embedding-3-small",
                # "api_key": "sk-...",
            },
        },
        "vectordb": {
            "provider": "chromadb",  # or "qdrant"
            "config": {
                # "settings": Settings(persist_directory="/content/chroma", allow_reset=True, is_persistent=True),
                # from qdrant_client.models import VectorParams, Distance
                # "vectors_config": VectorParams(size=384, distance=Distance.COSINE),
            }
        },
    }
)