Tutorial: Answering Multihop Questions with Agents

• Level: Intermediate
• Time to complete: 10 minutes
• Nodes Used: Agent, PromptNode, InMemoryDocumentStore, FARMReader and ExtractiveQAPipeline
• Goal: After completing this tutorial, you will have learned how to use Agents to build a multi-hop question answering system with an ExtractiveQAPipeline as a tool
• Prerequisites: An OpenAI API Key

Overview

The Agent class uses a large language model (LLM) to make decisions and come up with the best next course of action. You can provide the Agent with a set of Tools that it can choose to use to reach a result. At each iteration, the agent will pick a tool from the ones available to it. Based on the result, the Agent has two options: It will either decide to select a tool again and do another iteration, or it will decide that it has reached a conclusion and return the final answer.

In this tutorial, we will provide the Agent with just one tool to answer questions: a commonly used Haystack component, the ExtractiveQAPipeline.

Preparing the Colab Environment

• Enable GPU Runtime in Colab
• Set logging level to INFO

Installing Haystack

To start, let’s install the latest release of Haystack with pip. In this tutorial, we’ll use a Hugging Face dataset that has already been prepared as Haystack Documents, so we will install datasets too:

%%bash

pip install --upgrade pip
pip install farm-haystack[colab]
pip install datasets

Enabling Telemetry

Knowing you’re using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See Telemetry for more details.

from haystack.telemetry import tutorial_running

tutorial_running(23)

Set the logging level to INFO:

import logging

logging.basicConfig(format="%(levelname)s - %(name)s -  %(message)s", level=logging.WARNING)
logging.getLogger("haystack").setLevel(logging.INFO)

Create an Extractive QA Pipeline

Now, we will introduce an ExtractiveQAPipeline as a Tool to our Agent. To do so, we’ll first write our documents about the presidents of the USA into a DocumentStore and then create our pipeline.

1) Write documents to the DocumentStore

from haystack.document_stores import InMemoryDocumentStore
from datasets import load_dataset

remote_dataset = load_dataset('Tuana/presidents', split="train")

document_store = InMemoryDocumentStore(use_bm25=True)
document_store.write_documents(remote_dataset)

2) Create an ExtractiveQAPipeline:

Let’s define our retriever and reader to to use in an ExtractiveQAPipeline:

from haystack.nodes import EmbeddingRetriever, FARMReader
from haystack.pipelines import ExtractiveQAPipeline

retriever = EmbeddingRetriever(document_store=document_store, embedding_model="sentence-transformers/multi-qa-mpnet-base-dot-v1", use_gpu=True)
document_store.update_embeddings(retriever=retriever)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=True)
presidents_qa = ExtractiveQAPipeline(reader=reader, retriever=retriever)

3) Let’s test the pipeline!

Now that you have an ExtractiveQAPipeline, go ahead and ask it a question about the presidents of the USA.

Below, we’re asking the question: “What year was the 1st president of the USA born?”

Notice how this is 2 questions in one. An extractive model will struggle to find the answer to this question unless the answer is phrased clearly in our documents. For example: “The first president of the USA was born in 1732”.

On the other hand, it does well with a question such as “Who was the 1st president of the USA?”

from haystack.utils import print_answers

#result = presidents_qa.run("Who was the 1st president of the USA?")
result = presidents_qa.run("What year was the 1st president of the USA born?")

print_answers(result, "minimum")

Create an Agent with the ExtractiveQAPipeline as a Tool

1) To create the Agent, we’ll make use of an Open AI model. So first, provide your Open AI key:

from getpass import getpass

api_key_prompt = "Enter OpenAI API key:" 
api_key = getpass(api_key_prompt)

2) Initialize the Agent

The Agent needs to determine the next best course of action at each iteration. It does this by using an LLM, and a prompt designed specially for this use case. Our Agent uses a PromptNode with the default “zero-shot-react” PromptTemplate .

Here, let’s define an Agent that uses the text-davinci-003 model by OpenAI.

from haystack.agents import Agent
from haystack.nodes import PromptNode

prompt_node = PromptNode(model_name_or_path="text-davinci-003", api_key=api_key, stop_words=["Observation:"])
agent = Agent(prompt_node=prompt_node)

3) Provide the Agent with a Tool

Next, let’s add our ExtractiveQAPipeline into the Agent’s arsenal. The Agent will then be able to use this pipeline when it decides it could be useful.

To do so, let’s define a tool and make sure to give it a description. The exact wording of your description matters a lot here. The agent uses it to understand in which cases it should pick this tool. If the agent fails to pick the right tool, adjusting the description might help.

from haystack.agents import Tool

search_tool = Tool(name="USA_Presidents_QA",pipeline_or_node=presidents_qa,
                  description="useful for when you need to answer questions related to the presidents of the USA.",
                  output_variable="answers"
              )
agent.add_tool(search_tool)

4) Ask a question!

result = agent.run("What year was the 1st president of the USA born?")

print(result["transcript"].split('---')[1])

Congratulations! 🎉 You’ve used an Agent that can use an extractive model iteratively, to arrive at a final answer to a multi-hop question!!!