Configuration

An agent takes two main arguments, an LLM and a list of tools.

The txtai agent framework is built with Transformers Agents. Additional options can be passed in the Agent constructor.

from datetime import datetime

from txtai import Agent

wikipedia = {
    "name": "wikipedia",
    "description": "Searches a Wikipedia database",
    "provider": "huggingface-hub",
    "container": "neuml/txtai-wikipedia"
}

arxiv = {
    "name": "arxiv",
    "description": "Searches a database of scientific papers",
    "provider": "huggingface-hub",
    "container": "neuml/txtai-arxiv"
}

def today() -> str:
    """
    Gets the current date and time

    Returns:
        current date and time
    """

    return datetime.today().isoformat()

agent = Agent(
    tools=[today, wikipedia, arxiv, "websearch"],
    llm="hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4",
)

llm

llm: string|llm instance

LLM path or LLM pipeline instance. See the LLM pipeline for more information.

tools

tools: list

List of tools to supply to the agent. Supports the following configurations.

function

A function tool takes the following dictionary fields.

Field	Description
name	name of the tool
description	tool description
target	target method / callable

A function or callable method can also be directly supplied in the tools list. In this case, the fields are inferred from the method documentation.

embeddings

Embeddings indexes have built-in support. Provide the following dictionary configuration to add an embeddings index as a tool.

Field	Description
name	embeddings index name
description	embeddings index description
**kwargs	Parameters to pass to embeddings.load

transformers

A Transformers tool instance can be provided. Additionally, the following strings load tools directly from Transformers.

Tool	Description
websearch	Runs a websearch using built-in Transformers Agent tool

method

method: reactjson|reactcode|code

Sets the agent method. Defaults to reactjson. Read more on this here.