[Beta] Multi-modal ReAct Agent#

Open In Colab

In this tutorial we show you how to construct a multi-modal ReAct agent.

This is an agent that can take in both text and images as the input task definition, and go through chain-of-thought + tool use to try to solve the task.

This is implemented with our lower-level Agent API, allowing us to explicitly step through the ReAct loop to show you what’s happening in each step.

We show two use cases:

  1. RAG Agent: Given text/images, can query a RAG pipeline to lookup the answers. (given a screenshot from OpenAI Dev Day 2023)

  2. Web Agent: Given text/images, can query a web tool to lookup relevant information from the web (given a picture of shoes).

NOTE: This is explicitly a beta feature, the abstractions will likely change over time!

NOTE: This currently only works with GPT-4V.

Augment Image Analysis with a RAG Pipeline#

In this section we create a multimodal agent equipped with a RAG Tool.

Setup Data#

%pip install llama-index-llms-openai
%pip install llama-index-readers-web
%pip install llama-index-multi-modal-llms-openai
%pip install llama-index-tools-metaphor

# download images we'll use to run queries later
!wget "https://images.openai.com/blob/a2e49de2-ba5b-4869-9c2d-db3b4b5dcc19/new-models-and-developer-products-announced-at-devday.jpg?width=2000" -O other_images/openai/dev_day.png
!wget "https://drive.google.com/uc\?id\=1B4f5ZSIKN0zTTPPRlZ915Ceb3_uF9Zlq\&export\=download" -O other_images/adidas.png

--2024-01-02 20:25:25--  https://images.openai.com/blob/a2e49de2-ba5b-4869-9c2d-db3b4b5dcc19/new-models-and-developer-products-announced-at-devday.jpg?width=2000
Resolving images.openai.com (images.openai.com)... 2606:4700:4400::6812:28cd, 2606:4700:4400::ac40:9333, 172.64.147.51, ...
Connecting to images.openai.com (images.openai.com)|2606:4700:4400::6812:28cd|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 300894 (294K) [image/jpeg]
Saving to: ‘other_images/openai/dev_day.png’

other_images/openai 100%[===================>] 293.84K  --.-KB/s    in 0.02s   

2024-01-02 20:25:25 (13.8 MB/s) - ‘other_images/openai/dev_day.png’ saved [300894/300894]

from llama_index.readers.web import SimpleWebPageReader

url = "https://openai.com/blog/new-models-and-developer-products-announced-at-devday"
reader = SimpleWebPageReader(html_to_text=True)
documents = reader.load_data(urls=[url])

Setup Tools#

from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata

from llama_index.core import Settings

Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")

vector_index = VectorStoreIndex.from_documents(
    documents,
)

query_tool = QueryEngineTool(
    query_engine=vector_index.as_query_engine(),
    metadata=ToolMetadata(
        name=f"vector_tool",
        description=(
            "Useful to lookup new features announced by OpenAI"
            # "Useful to lookup any information regarding the image"
        ),
    ),
)

Setup Agent#

from llama_index.core.agent.react_multimodal.step import (
    MultimodalReActAgentWorker,
)
from llama_index.core.agent import AgentRunner
from llama_index.core.multi_modal_llms import MultiModalLLM
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core.agent import Task

mm_llm = OpenAIMultiModal(model="gpt-4-vision-preview", max_new_tokens=1000)

# Option 2: Initialize AgentRunner with OpenAIAgentWorker
react_step_engine = MultimodalReActAgentWorker.from_tools(
    [query_tool],
    # [],
    multi_modal_llm=mm_llm,
    verbose=True,
)
agent = AgentRunner(react_step_engine)

query_str = (
    "The photo shows some new features released by OpenAI. "
    "Can you pinpoint the features in the photo and give more details using relevant tools?"
)

from llama_index.core.schema import ImageDocument

# image document
image_document = ImageDocument(image_path="other_images/openai/dev_day.png")

task = agent.create_task(
    query_str,
    extra_state={"image_docs": [image_document]},
)

def execute_step(agent: AgentRunner, task: Task):
    step_output = agent.run_step(task.task_id)
    if step_output.is_last:
        response = agent.finalize_response(task.task_id)
        print(f"> Agent finished: {str(response)}")
        return response
    else:
        return None


def execute_steps(agent: AgentRunner, task: Task):
    response = execute_step(agent, task)
    while response is None:
        response = execute_step(agent, task)
    return response

# Run this and not the below if you just want to run everything at once.
# response = execute_steps(agent, task)

response = execute_step(agent, task)

Thought: I need to use a tool to help me identify the new features released by OpenAI as shown in the photo.
Action: vector_tool
Action Input: {'input': 'new features released by OpenAI'}
Observation: OpenAI has released several new features, including the GPT-4 Turbo model, the Assistants API, and multimodal capabilities. The GPT-4 Turbo model is more capable, cheaper, and supports a 128K context window. The Assistants API makes it easier for developers to build their own assistive AI apps with goals and the ability to call models and tools. The multimodal capabilities include vision, image creation (DALL·E 3), and text-to-speech (TTS). These new features are being rolled out to OpenAI customers starting at 1pm PT today.


response = execute_step(agent, task)

Thought: The observation provided information about the new features released by OpenAI, which I can now relate to the image provided.
Response: The photo shows a user interface with a section titled "Playground" and several options such as "GPT-4.0-turbo," "Code Interpreter," "Translate," and "Chat." Based on the observation from the tool, these features are part of the new releases by OpenAI. Specifically, "GPT-4.0-turbo" likely refers to the GPT-4 Turbo model, which is a more capable and cost-effective version of the language model with a larger context window. The "Code Interpreter" could be related to the Assistants API, which allows developers to build AI apps that can interpret and execute code. The "Translate" and "Chat" options might be part of the multimodal capabilities, with "Translate" possibly involving text-to-text language translation and "Chat" involving conversational AI capabilities. The multimodal capabilities also include vision and image creation, which could be represented in the Playground interface but are not visible in the provided section of the photo.
> Agent finished: The photo shows a user interface with a section titled "Playground" and several options such as "GPT-4.0-turbo," "Code Interpreter," "Translate," and "Chat." Based on the observation from the tool, these features are part of the new releases by OpenAI. Specifically, "GPT-4.0-turbo" likely refers to the GPT-4 Turbo model, which is a more capable and cost-effective version of the language model with a larger context window. The "Code Interpreter" could be related to the Assistants API, which allows developers to build AI apps that can interpret and execute code. The "Translate" and "Chat" options might be part of the multimodal capabilities, with "Translate" possibly involving text-to-text language translation and "Chat" involving conversational AI capabilities. The multimodal capabilities also include vision and image creation, which could be represented in the Playground interface but are not visible in the provided section of the photo.

print(str(response))

The photo shows a user interface with a section titled "Playground" and several options such as "GPT-4.0-turbo," "Code Interpreter," "Translate," and "Chat." Based on the observation from the tool, these features are part of the new releases by OpenAI. Specifically, "GPT-4.0-turbo" likely refers to the GPT-4 Turbo model, which is a more capable and cost-effective version of the language model with a larger context window. The "Code Interpreter" could be related to the Assistants API, which allows developers to build AI apps that can interpret and execute code. The "Translate" and "Chat" options might be part of the multimodal capabilities, with "Translate" possibly involving text-to-text language translation and "Chat" involving conversational AI capabilities. The multimodal capabilities also include vision and image creation, which could be represented in the Playground interface but are not visible in the provided section of the photo.