“Building a Knowledge Graph Chatbot: Avoiding External APIs and Utilizing Open-Source Language Models”

OpenAI has been making significant strides in the field of artificial intelligence, particularly with its large language models like ChatGPT. These models are designed to generate human-like text, making them a valuable tool for tasks such as chatbots, drafting emails, creating written content, and more. However, there is a new trend towards providing additional external context to LLM at query time, thereby eliminating the need to fine-tune the models with updated information.

In this post, we will demonstrate how to generate Python code using OpenAI’s ChatGPT. This Python program will be a small example of how to use the OpenAI API to execute a query.

import openai

openai.api_key = 'your-api-key'

inputQuestion = "What are the benefits of using Python for AI development?"

response = openai.Completion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": inputQuestion}
    ],
  max_tokens=4000,
  temperature=0.7
)

print(response.choices[0].message['content'])

Here, we are specifying the model to use (gpt-3.5-turbo), the user’s message, the maximum number of tokens to generate (4000), and the temperature which controls the randomness of the output (0.7 for a balance of randomness and determinism).

It’s worth noting that the model field can be changed to use other available models like gpt-4 or gpt-4-32k. When it comes to the max_tokens parameter, the higher the value, the longer the content. The temperature parameter can be modified for more accuracy. For instance, setting it to 0.2 will result in more deterministic output.

This code, when executed, will output a detailed response to the inputQuestion about the benefits of using Python for AI development.

In the context of creating a knowledge graph-based chatbot, this type of AI model can be invaluable. It can process and answer user queries based on the information available in the knowledge graph. However, developers should be cautious about using proprietary information with these models to avoid inadvertently sharing sensitive data.

By leveraging the power of OpenAI, developers can create robust, intelligent systems without needing to continually update their models with new information. It’s a promising development in the field of AI and one that holds exciting possibilities for the future.