Build a chatbot using Portkey's Prompt Templates
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 the 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}}
]{{chat_history}} - will be used in the next step
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 [].
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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