You can easily input your model prompt, adjust settings like model type and temperature, and instantly view outputs. Portkey’s robust versioning system ensures that you can experiment freely with your prompts, allowing for easy rollback. Try out new things without the fear of breaking your production. This seamless iteration process allows you to refine your chatbot’s performance until you’re satisfied.

Here’s the link to the collab notebook of the chatbot-

Portkey’s prompt templates offer a powerful solution for testing and building chatbots. You can easily input your model prompt, adjust settings like model type and temperature, and instantly view outputs. Portkey’s robust versioning system ensures that you can experiment freely with your prompts, allowing for easy rollback. Try out new things without the fear of breaking your production. This seamless iteration process allows you to refine your chatbot’s performance until you’re satisfied.

​
Setting Up Your Chatbot

Go to Portkey’s Prompts dashboard. Click on the Create button. You are now on Prompt Playground.

​
Step 1: Define Your System Prompt

Start by defining your system prompt. This sets the initial context and behavior for your chatbot. You can set this up in your Portkey’s Prompt Library using the JSON View

[
  {
    "content": "You're a helpful assistant.",
    "role": "system"
  },
  {{chat_history}}
]

​
Step 2: Create a Variable to Store Conversation History

In the Portkey UI, set the variable type: Look for two icons next to the variable name: “T” and ”{..}“. Click the ”{…}” icon to switch to JSON mode.

Initialize the variable: This array will store the conversation history, allowing your chatbot to maintain context. We can just initialize the variable with [].

As your chatbot interacts with users, it will append new messages to this array, building a comprehensive conversation history.

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Step 3: Implementing the Chatbot

Use Portkey’s API to generate responses based on your prompt template. Here’s a Python example::

from portkey_ai import Portkey
client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY"  # You can also set this as an environment variable
)

def generate_response(conversation_history):
    prompt_completion = client.prompts.completions.create(
        prompt_id="YOUR_PROMPT_ID",  # Replace with your actual prompt ID
        variables={
            "variable": conversation_history
        }
    )

    return prompt_completion.choices[0].message.content

# Example usage

conversation_history = [
    {
        "content": "Hello, how can I assist you today?",
        "role": "assistant"
    },
    {
        "content": "What's the weather like?",
        "role": "user"
    }
]

response = generate_response(conversation_history)

print(response)

​
Step 4: Append the Response

After generating a response, append it to your conversation history:

def append_response(conversation_history, response):

    conversation_history.append({

        "content": response,

        "role": "assistant"

    })

    return conversation_history

# Continuing from the previous example

conversation_history = append_response(conversation_history, response)

​
Step 5: Take User Input to Continue the Conversation

Implement a loop to continuously take user input and generate responses:

# Continue the conversation
while True:
    user_input = input("You: ")
    if user_input.lower() == 'exit':
        break

    conversation_history.append({
        "content": user_input,
        "role": "user"
    })

    response = generate_response(conversation_history)
    conversation_history = append_response(conversation_history, response)

    print("Bot:", response)
print("Conversation ended.")

​
Complete Example

Here’s a complete example that puts all these steps together:

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY"
)

def generate_response(conversation_history):
    prompt_completion = client.prompts.completions.create(
        prompt_id="YOUR_PROMPT_ID",
        variables={
            "variable": conversation_history
        }
    )

    return prompt_completion.choices[0].message.content

def append_response(conversation_history, response):
    conversation_history.append({
        "content": response,
        "role": "assistant"
    })

    return conversation_history

# Initial conversation

conversation_history = [
    {
        "content": "Hello, how can I assist you today?",
        "role": "assistant"
    }
]

# Generate and append response

response = generate_response(conversation_history)

conversation_history = append_response(conversation_history, response)

print("Bot:", response)

# Continue the conversation

while True:
    user_input = input("You: ")
    if user_input.lower() == 'exit':
        break

    conversation_history.append({
        "content": user_input,
        "role": "user"
    })

    response = generate_response(conversation_history)
    conversation_history = append_response(conversation_history, response)

    print("Bot:", response)

print("Conversation ended.")

​
Conclusion

Voilà! You’ve successfully set up your chatbot using Portkey’s prompt templates. Portkey enables you to experiment with various LLM providers. It acts as a definitive source of truth for your team, and it versions each snapshot of model parameters, allowing for easy rollback. Here’s a snapshot of the Prompt Management UI. To learn more about Prompt Management click here.

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