Configuration

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

The txtai agent framework is built with Transformers Agents and additional options can be directly 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.