ChatGPT + Wolfram Alpha: A Super Powerful Assistant

This article explores how to combine the natural language processing capabilities of ChatGPT with the computational power of Wolfram Alpha to create a significantly more robust and versatile assistant. We will demonstrate a basic implementation for integrating these two services and discuss the benefits and trade-offs of such an approach.

Fundamental Concepts / Prerequisites

To understand this article, you should have a basic understanding of:

• ChatGPT: A large language model capable of generating human-like text. Familiarity with its API and basic usage is helpful.
• Wolfram Alpha: A computational knowledge engine capable of answering factual queries and performing complex calculations. You'll need an API key to access its services.
• Python: A general-purpose programming language. The code example is written in Python.
• API Calls: Understanding how to make API requests using a library like `requests` in Python.

Core Implementation

The core idea is to first ask ChatGPT a question. Then, if ChatGPT determines that the question requires computational knowledge (e.g., solving an equation, getting real-time data), we send the question to Wolfram Alpha. Finally, we present the combined answer (ChatGPT's response and/or Wolfram Alpha's result) to the user.

import openai
import requests
import os

# Replace with your actual API keys
openai.api_key = os.getenv("OPENAI_API_KEY") # or set directly as "YOUR_OPENAI_API_KEY"
wolfram_alpha_app_id = os.getenv("WOLFRAM_ALPHA_APPID") # or set directly as "YOUR_WOLFRAM_ALPHA_APPID"

def ask_chatgpt(prompt):
  """
  Sends a prompt to ChatGPT and returns the response.
  """
  try:
    response = openai.Completion.create(
      engine="text-davinci-003",  # Or your preferred engine
      prompt=prompt,
      max_tokens=150,
      n=1,
      stop=None,
      temperature=0.7,
    )
    return response.choices[0].text.strip()
  except Exception as e:
    print(f"Error communicating with ChatGPT: {e}")
    return None


def ask_wolfram_alpha(query):
  """
  Sends a query to Wolfram Alpha and returns the result.
  """
  url = f"http://api.wolframalpha.com/v2/query?appid={wolfram_alpha_app_id}&input={query}&output=json"
  try:
    response = requests.get(url)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
    data = response.json()

    # Extract the relevant result from the Wolfram Alpha JSON response.  Handle errors gracefully
    if data['queryresult']['success']:
      for pod in data['queryresult']['pods']:
        if pod['primary']: #Use primary result pods. These provide more reliable answers.
          return pod['subpods'][0]['plaintext']
    else:
      return "Wolfram Alpha: No results found."
  except requests.exceptions.RequestException as e:
    print(f"Error communicating with Wolfram Alpha: {e}")
    return "Error connecting to Wolfram Alpha."
  except KeyError as e:
    print(f"Error parsing Wolfram Alpha response: {e}")
    return "Error parsing Wolfram Alpha response."
  except Exception as e:
    print(f"Unknown error during Wolfram Alpha execution: {e}")
    return "Wolfram Alpha Error."



def get_answer(question):
  """
  Combines ChatGPT and Wolfram Alpha to answer a question.
  """
  # Step 1: Ask ChatGPT if Wolfram Alpha is needed
  chatgpt_prompt = f"Answer the following question. If it involves calculations, data, or any knowledge Wolfram Alpha would be useful, please respond 'WOLFRAM_ALPHA_NEEDED'. Otherwise, give the answer directly:\n\n{question}\n\nAnswer:"
  chatgpt_response = ask_chatgpt(chatgpt_prompt)

  if chatgpt_response and "WOLFRAM_ALPHA_NEEDED" in chatgpt_response:
    # Step 2: Send the question to Wolfram Alpha
    wolfram_response = ask_wolfram_alpha(question)
    if wolfram_response:
      return f"Wolfram Alpha: {wolfram_response}"
    else:
      return "Wolfram Alpha could not answer the question."
  elif chatgpt_response:
    # Step 3: Return ChatGPT's direct answer
    return f"ChatGPT: {chatgpt_response}"
  else:
    return "Could not get an answer."


# Example usage
if __name__ == "__main__":
  question = "What is the square root of 256?"
  answer = get_answer(question)
  print(f"Question: {question}")
  print(f"Answer: {answer}")

  question = "Who is the president of the United States?"
  answer = get_answer(question)
  print(f"Question: {question}")
  print(f"Answer: {answer}")

  question = "What is the weather in New York City?"
  answer = get_answer(question)
  print(f"Question: {question}")
  print(f"Answer: {answer}")

Code Explanation

Let's break down the code step-by-step:

1. **Import Libraries:** The code starts by importing necessary libraries: `openai` for interacting with the ChatGPT API, `requests` for making HTTP requests to the Wolfram Alpha API, and `os` for accessing environment variables (API keys).

2. **API Key Initialization:** The code retrieves the OpenAI API key and the Wolfram Alpha App ID from environment variables (recommended for security). Make sure you set these environment variables before running the script. Alternatively, you can hardcode them (less secure).

3. **`ask_chatgpt(prompt)` function:** This function takes a text prompt as input, sends it to ChatGPT using the OpenAI API, and returns the generated text response. It also includes basic error handling for communication with the ChatGPT API.

4. **`ask_wolfram_alpha(query)` function:** This function takes a query as input, constructs the URL for the Wolfram Alpha API request, sends the request, and parses the JSON response to extract the relevant answer. It includes comprehensive error handling to manage network issues, invalid responses, and API errors. The function extracts the primary result pod to give a reliable answer.

5. **`get_answer(question)` function:** This is the main function that orchestrates the process. It first asks ChatGPT whether the question requires Wolfram Alpha. If ChatGPT indicates that Wolfram Alpha is needed ("WOLFRAM_ALPHA_NEEDED" in its response), the question is sent to Wolfram Alpha. Otherwise, ChatGPT's direct answer is returned. This function handles both API responses gracefully, presenting combined or individual results.

6. **Example Usage (`if __name__ == "__main__":`)**: This section demonstrates how to use the `get_answer` function with different types of questions, including numerical calculations and general knowledge queries.

Complexity Analysis

The complexity analysis is primarily determined by the API calls to ChatGPT and Wolfram Alpha.

• Time Complexity: The `ask_chatgpt` and `ask_wolfram_alpha` functions depend on the response time of the respective APIs. In the worst case, if both APIs are slow or unavailable, the function could time out, leading to a potentially long response time. However, the function executes with O(1) complexity as the API calls dominate the process. The main operations are API calls which are dependent on network latency and server-side processing.
• Space Complexity: The space complexity is determined by the size of the prompts and responses to/from the APIs. These are typically relatively small strings, leading to a low space footprint, approximately O(1).

Alternative Approaches

One alternative approach would be to use a different method for determining when to use Wolfram Alpha. Instead of relying on ChatGPT to tell us, we could use a rule-based system or a separate machine learning model trained to identify questions that require computational knowledge. For example, we could use regular expressions to detect mathematical symbols or keywords related to scientific data. This might be faster and more predictable than relying on ChatGPT's assessment, but it could also be less accurate and require more manual configuration.

Conclusion

This article demonstrated how to combine the power of ChatGPT and Wolfram Alpha to create a more capable assistant. By leveraging ChatGPT's natural language processing and Wolfram Alpha's computational knowledge, we can answer a wider range of questions with greater accuracy. While this implementation provides a foundation, more advanced techniques, like refining the prompt engineering to ChatGPT and improving error handling, can enhance the performance and robustness of the combined system further. By analyzing the outputs and results from various prompts, the system can be improved over time to produce higher quality and more accurate responses.