Leveraging UnDatas.io and DeepSeek to Analyze Tesla Gen Report 2: Intelligent Question Answering Unveiled

Welcome back to our series on leveraging cutting-edge technologies for in-depth data analysis! In this installment, we continue our exploration of using the UnDatas.io platform and the deepseek model to dissect the Tesla Gen report.

In the previous blog post, we meticulously walked you through the fundamental steps, from setting up these powerful tools to extracting valuable data from the report. It was all about laying a solid foundation. Now, the journey gets even more exciting as we’re set to dig deeper.

Building on the two example questions we posed regarding this report, we’ll showcase the real power of intelligent question answering that these state-of-the-art technologies bring to the table, unlocking insights that could reshape how we understand and utilize financial reports like the Tesla Gen one.

1. Setting Up UnDatas.io and Integrating with OpenAI’s deepseek

To commence using UnDatas.io for math test paper analysis, the first step involves installing the UnDatas.io Python API library.

pip install -U -q openai undatasio

To import an UnDataIO object, you need a token and an optional task name from the Undatasio platform.

from undatasio.undatasio import UnDatasIO

undatasio_obj = UnDatasIO(UNDATASIO_API_KEY, task_name='PdfParserDemo')

Additionally, the OpenAI Python SDK needs to be installed as it is utilized to call the deepseek model. Initializing the OpenAI object necessitates applying for an API key, ensuring secure and authorized access to the model.

from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("API_KEY"),
    base_url="https://api.deepseek.com"
)

2. Extracting Valuable Information from Tsla-gen-report

The show_version function of the generated UnDatas.io object provides valuable insights by displaying all version information and file lists associated with the current token’s task name. This aids in organizing and understanding the available resources.

version_data = undatasio_obj.show_version()
version_data.data

This blog’s example selects two pages of tsla-20241023-gen.pdf as the sample content, as shown in the figures above.

Moreover, the get_result_type method of the UnDatas.io object is a powerful tool for extracting diverse information from PDF files, such as text, images, tables, titles, and inline equation details.

result = undatasio_obj.get_result_type(
    type_info=['title', 'table', 'text', 'image', 'interline_equation'],
    file_name='tsla-20241023-gen_test02.pdf',
    version='v4'
)
print(result.data)

3. Deep Dive into Intelligent Questioning

Question 1: What new features were included in the Summer Release of Tesla’s vehicle software?

We’ve already seen how to extract data from the Tsla-gen-report using UnDatas.io. Now, let’s put the deepseek-chat model to work to answer this question with pinpoint accuracy. The key lies in crafting the right prompts. When we ask the model about the new features in the Summer Release, we set up the system and user prompts as follows:

query1 = "What new features were included in the Summer Release of Tesla's vehicle software?"

response = client.chat.completions.create(
        model="deepseek-chat",
        messages=[
            {"role": "system", "content": "You are a data analysis expert. Please extract information from the data provided by the user. Note that only the information asked by the user should be returned, and nothing else should be returned. Data: %s" % (result.data, )},
            {"role": "user", "content": query1},
        ],
        stream=False
    )

res_data = response.choices[0].message.content
res_data

Code running result:

Question 2: How did the Energy business perform in Q3 in terms of gross margin?

Let’s also try asking a question over another piece of the text.

query2 = 'How did the Energy business perform in Q3 in terms of gross margin?'

response = client.chat.completions.create(
        model="deepseek-chat",
        messages=[
            {"role": "system", "content": "You are a data analysis expert. Please extract information from the data provided by the user. Note that only the information asked by the user should be returned, and nothing else should be returned. Data: %s" % (result.data, )},
            {"role": "user", "content": query2},
        ],
        stream=False
    )

res_data = response.choices[0].message.content
res_data

Code running result:

If you need to view or try the examples of this blog, please click on the link of the notebook：Undatas.io’s examples

4. Summary

What we’ve demonstrated here is more than just answering a couple of questions. It’s about unlocking the potential of data that was previously locked away in PDFs and other unstructured formats. For financial professionals, this means being able to quickly query and understand critical aspects of a company’s performance without hours of manual data sifting. Institutions can leverage this technology to conduct rapid market analyses, competitor benchmarking, and strategic planning.

Imagine a scenario where an investment firm needs to assess Tesla’s recent developments before making a significant investment decision. Instead of poring over hundreds of pages of reports, they can use UnDatas.io and deepseek to ask targeted questions and get instant, accurate answers. This streamlines the research process, reduces human error, and ultimately leads to more informed and timely decisions.

With advancements in natural language processing and data extraction techniques, we can expect more sophisticated question-answering capabilities. Future iterations might handle more complex, multi-faceted questions, provide predictive insights based on historical data, and even offer real-time analysis as new reports are released.

We encourage our readers to experiment with different questions and report sections. Dive deeper into the Tsla-gen-report, tweak the prompts, and see what other hidden gems of information you can uncover. The world of intelligent data analysis is at your fingertips, and with these tools, the possibilities are truly endless.

Stay tuned for more in-depth explorations and practical applications of these remarkable technologies as we continue to push the boundaries of what’s possible in financial data analysis. Until next time!