To create a financial analyst that can extract insights from Apple's financial reports, we will leverage the power of sub-agents. Here's how it works:

1. Install Required Packages: We start by installing the necessary packages, including the Anthropic Python client, a library to read PDF files, and Matplotlib to generate plots.

2. Define Sub-Agents: We define our sub-agents using the Anthropic client, with the smaller Clot 3 Hau models handling the individual quarterly reports.

3. Download Financial Reports: We download the financial earnings reports for each quarter of the 2023 fiscal year.

4. Generate Prompts for Sub-Agents: We use the larger Opus model to generate prompts for the sub-agents, instructing them to extract relevant information from the quarterly earnings reports.

5. Extract Information from Reports: We use the extract_information function to process each quarterly report, converting the PDF files to images and passing them through the Hau sub-agents with the generated prompts.

6. Combine Insights from Sub-Agents: The responses from the sub-agents, separated by XML tags, are collected and passed to the Opus super-agent to generate the final response.

7. Visualize the Insights: The super-agent provides a response that includes Python code using Matplotlib to generate a plot visualizing the changes in Apple's net sales over the quarters.

By leveraging the capabilities of the larger Opus model and the smaller Hau sub-agents, we can efficiently extract insights from complex financial reports and present them in a concise and visually appealing manner. This approach demonstrates the power of using a hierarchy of AI agents to tackle complex tasks.

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To create our financial analyst, we first need to download the relevant financial earning reports. We will be using the following steps:

1. **Download the PDF files**: We define a function `download_file` that takes a URL and a folder path, and downloads the PDF file to the specified folder.

2. **Convert PDF to Base64 PNG**: Since we want to leverage the vision capabilities of the Hau model, we need to convert the PDF files to a format that the model can process. We define a function `pdf_to_base64_png` that takes a PDF file, extracts each page as an image, and returns a list of Base64-encoded PNG images.

3. **Prepare the file paths**: We create a folder structure with an "images" folder and a "using_sub_agents" subfolder to store the downloaded files.

4. **Download the files**: We download the PDF files for the four quarters of the 2023 financial year and check for any issues during the download process.

With these steps, we have prepared the necessary data for our financial analyst to analyze the earning reports.

To create a financial analyst using Cloud 3 Hau, we first need to install the required packages, including the Anthropic Python client, a library to read PDF files, and Matplotlib for generating plots. We then import the necessary packages and set up the Anthropic API key.

Next, we define our sub-agents using the Anthropic client, with the sub-agents based on the smaller Cloud 3 Hau model. This allows us to assign simpler subtasks to these sub-agents, which can be more cost-effective.

To obtain the data for our analysis, we download the financial earnings reports for different quarters (Q1, Q2, Q3, and Q4) and convert the PDF files into a list of images using the PDF_to_base64_PNG function. This leverages the vision capabilities of Hau, which will be crucial for extracting information from the complex PDF documents.

We then create a function called generate_hu_prompt that uses the larger Opus model to generate a specific prompt for the sub-agents. This prompt instructs the sub-agents to extract relevant information from the earning reports they have access to.

The extract_information function is used to run the sub-agents on the individual earning report files, with each sub-agent extracting the required information from a single report. The results from the sub-agents are combined using XML tags to separate the information from each quarter.

Finally, we create a prompt for the larger Opus model, which acts as a super-agent. This super-agent takes the combined information from the sub-agents and generates a response to the original user question, including Python code using Matplotlib to visualize the results.

The output of this process includes a textual analysis of the changes in Apple's net sales and key contributors, as well as a generated plot that illustrates the quarterly trends.

This example demonstrates how you can leverage the capabilities of Cloud 3 Hau and Opus to build a custom AI framework for targeted information extraction and analysis, without the need for dedicated AI orchestration frameworks.

To create a comprehensive financial analysis, we will leverage the power of Opus, a larger language model, to combine the insights gathered from the individual sub-agents. The process involves the following steps:

1. Generate Prompts for Sub-Agents: Using the original user question, we will generate specific prompts for each sub-agent to extract relevant information from the quarterly earnings reports.

2. Extract Information from Quarterly Reports: We will run the sub-agents, each with access to a single quarterly report, to extract the required financial data and insights.

3. Combine Sub-Agent Outputs: The outputs from the sub-agents will be combined, with the help of XML tags, to create a structured response that separates the different sections of the analysis.

4. Utilize Opus to Generate the Final Report: The combined sub-agent outputs will be passed to the Opus model, which will generate the final response to the user's question, including a Python code snippet using Matplotlib to visualize the key insights.

By leveraging the specialized capabilities of the sub-agents and the comprehensive reasoning of Opus, we can create a detailed and insightful financial analysis that addresses the user's query effectively.

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The analysis of Apple's 2023 financial earnings reports reveals the following key insights:

- Net sales started decreasing from Q1 to Q3, but saw an upward trend in Q4 2023, likely due to the holiday season and new product releases.
- The key contributors to the changes in net sales across the quarters include:
  - Q1: Strong iPhone and Mac sales
  - Q2: Decline in iPhone and iPad sales, offset by growth in Services and Wearables
  - Q3: Continued decline in iPhone and iPad sales, partially offset by growth in Services and Wearables
  - Q4: Rebound in iPhone and iPad sales, along with continued growth in Services and Wearables

To visualize these trends, the following Python code using Matplotlib is provided:

```python
import matplotlib.pyplot as plt

# Quarterly net sales data
q1_sales = 117.2
q2_sales = 94.8
q3_sales = 81.8
q4_sales = 90.1

# Plot the quarterly net sales
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
sales = [q1_sales, q2_sales, q3_sales, q4_sales]

plt.figure(figsize=(8, 6))
plt.plot(quarters, sales)
plt.xlabel('Quarter')
plt.ylabel('Net Sales (in billions)')
plt.title('Apple Quarterly Net Sales Trend in 2023')
plt.grid(True)
plt.show()

This code generates a line plot that visualizes the quarterly net sales trend for Apple in the 2023 financial year. The plot clearly shows the decrease in net sales from Q1 to Q3, followed by the rebound in Q4.

In this example, we have demonstrated how to create a financial analyst using a combination of large language models (LLMs) and smaller sub-agents. We leveraged the advanced reasoning and vision analysis capabilities of the Opus model to generate prompts for the smaller Hau sub-agents, which were then used to extract relevant information from Apple's quarterly financial reports.

The key aspects of this approach include:

1. Prompt Engineering: We used the Opus model to generate specific prompts for the Hau sub-agents, ensuring that they focused on extracting the necessary information from the financial reports.

2. Parallel Processing: We executed the sub-agent tasks in parallel, allowing us to process the reports for all four quarters efficiently.

3. XML Tags: The use of XML tags in the prompts and responses enabled us to clearly separate the different sections of the output, making it easier to process and analyze the results.

4. Visualization: The final response included Python code using Matplotlib to generate a visual representation of the changes in Apple's net sales over the quarters, providing a clear and concise way to communicate the insights.

This example showcases the power of using a combination of large and small models to build complex AI applications, while also highlighting the importance of prompt engineering and the effective use of XML tags to structure the input and output data.