Chatbot Data Examples API: Understanding Context, Messages, and Examples

When building a chatbot, one of the most crucial aspects is determining how random the token selection is. This parameter, along with temperature, defines the model's creativity. The token limit also plays a significant role in retaining messages within the chat session history.

For this conversation, we set the max_output_tokens parameter to 250, which translates to approximately 60-80 words or 100 tokens. Each token roughly corresponds to four characters. The PaLM model accepts tokens as an input parameter that defines the size of the prompt and responses.

Since we are specializing in gravitational theory for our physics chatbot, both context and examples will have references to this topic. Notice how we defined the context and examples. The chat session history is comprised of pairs preceding the last pair. The model responds to the most recent message, which is the last pair in the list of messages.

A chat prompt's messages are a list of author-content pairs. Examples can be used to modify the way the model responds to specific questions. Context plays an important role in generating accurate responses and is optional but recommended for adding additional context to instruct the model on the key topic or theme of the conversation.

Customizing the behavior of our chat model is crucial, which is achieved by providing context that contains essential elements necessary for generating a meaningful and relevant response. This method captures the essence of building a chatbot.

Here's an example code snippet that demonstrates this concept:

```python

response = nd_message(msg, max_output_tokens=256, temperature=0.2)

output_text = 'Gravity is a force that attracts a body towards the centre of the earth or any other physical body having mass.'

```

In this scenario, you are playing the role of a physics teacher knowledgeable about gravitational theory.

To get the completion message, we can define a function that accepts the prompt as an input and returns the response generated by the model:

```python

def get_completion(msg):

ctx = 'My name is Jani.'

model = ChatModel(context=ctx, examples=exp)

return model.start_chat()

```

This code snippet initializes the object based on the pre-trained model optimized for chat-like conversations. The InputOutputTextPair class makes it easy to create pairs of questions and answers that provide examples to our chatbot.

In conclusion, understanding context, messages, and examples is vital in building a robust chatbot. By defining these parameters and using them effectively, we can generate accurate and relevant responses to user queries.

References:

* PaLM model documentation

* ChatModel class documentation

* InputOutputTextPair class documentation

Chatbot data examples api