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Agent

agent

An agent automatically creates workflows to answer multi-faceted user requests. Agents iteratively prompt and/or interface with tools to step through a process and ultimately come to an answer for a request.

Agents excel at complex tasks where multiple tools and/or methods are required. They incorporate a level of randomness similar to different people working on the same task. When the request is simple and/or there is a rule-based process, other methods such as RAG and Workflows should be explored.

The following code snippet defines a basic agent.

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",
    max_iterations=10,
)

The agent above has access to two embeddings databases (Wikipedia and ArXiv) and the web. Given the user's input request, the agent decides the best tool to solve the task.

Example

The first example will solve a problem with multiple data points. See below.

agent("Which city has the highest population, Boston or New York?")

This requires looking up the population of each city before knowing how to answer the question. Multiple search requests are run to generate a final answer.

Agentic RAG

Standard retrieval augmented generation (RAG) runs a single vector search to obtain a context and builds a prompt with the context + input question. Agentic RAG is a more complex process that goes through multiple iterations. It can also utilize multiple databases to come to a final conclusion.

The example below aggregates information from multiple sources and builds a report on a topic.

researcher = """
You're an expert researcher looking to write a paper on {topic}.
Search for websites, scientific papers and Wikipedia related to the topic.
Write a report with summaries and references (with hyperlinks).
Write the text as Markdown.
"""

agent(researcher.format(topic="alien life"))

Agent Teams

Agents can also be tools. This enables the concept of building "Agent Teams" to solve problems. The previous example can be rewritten as a list of agents.

from txtai import Agent, LLM

llm = LLM("hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4")

websearcher = Agent(
    tools=["websearch"],
    llm=llm,
)

wikiman = Agent(
    tools=[{
        "name": "wikipedia",
        "description": "Searches a Wikipedia database",
        "provider": "huggingface-hub",
        "container": "neuml/txtai-wikipedia"
    }],
    llm=llm,
)

researcher = Agent(
    tools=[{
        "name": "arxiv",
        "description": "Searches a database of scientific papers",
        "provider": "huggingface-hub",
        "container": "neuml/txtai-arxiv"
    }],
    llm=llm,
)

agent = Agent(
    tools=[{
        "name": "websearcher",
        "description": "I run web searches, there is no answer a web search can't solve!",
        "target": websearcher
    }, {
        "name": "wikiman",
        "description": "Wikipedia has all the answers, I search Wikipedia and answer questions",
        "target": wikiman
    }, {
        "name": "researcher",
        "description": "I'm a science guy. I search arXiv to get all my answers.",
        "target": researcher
    }],
    llm=llm,
    max_iterations=10
)

This provides another level of intelligence to the process. Instead of just a single tool execution, each agent-tool combination has it's own reasoning engine.

agent("""
Work with your team and build a comprehensive report on fundamental
concepts about Signal Processing.
Write the output in Markdown.
""")

More examples

See the link below to learn more.

Notebook Description
What's new in txtai 8.0 Agents with txtai Open In Colab
Analyzing Hugging Face Posts with Graphs and Agents Explore a rich dataset with Graph Analysis and Agents Open In Colab
Granting autonomy to agents Agents that iteratively solve problems as they see fit Open In Colab