Project Introduction: Prompt Poet
Prompt Poet is a tool designed to make prompt creation easier for both technical and non-technical users. Employing a simplistic approach, it allows users to construct prompts effectively using minimal code. By integrating the flexibility of YAML and Jinja2, Prompt Poet enhances the interaction quality with AI models, focusing on important tasks rather than tedious string manipulations.
Quick Start
To get started with Prompt Poet, users can simply install it using pip:
pip install prompt-poet
Here's a basic example demonstrating how to set up a simple prompt:
import os
import getpass
from prompt_poet import Prompt
from langchain import ChatOpenAI
# Set your OpenAI API Key if needed
# os.environ["OPENAI_API_KEY"] = getpass.getpass()
raw_template = """
- name: system instructions
role: system
content: |
Your name is {{ character_name }} and you are meant to be helpful and never harmful to humans.
- name: user query
role: user
content: |
{{ username}}: {{ user_query }}
- name: response
role: user
content: |
{{ character_name }}:
"""
template_data = {
"character_name": "Character Assistant",
"username": "Jeff",
"user_query": "Can you help me with my homework?"
}
prompt = Prompt(
raw_template=raw_template,
template_data=template_data
)
model = ChatOpenAI(model="gpt-4o-mini")
response = model.invoke(prompt.messages)
Understanding Prompt Templates
Prompt Poet employs a combination of YAML and Jinja2 for its templates. These templates undergo two crucial stages:
- Rendering: Jinja2 processes the input data, executing logic control flow, validating and binding data to variables, and evaluating functions.
- Loading: After rendering, the result is a structured YAML file with repeated blocks or segments, each with attributes like Name, Content, and Role. These segments ensure clarity in interactions, with roles helping separate user and system inputs.
Example Templates and Operations
- Basic Q&A Bot: Start with a template defining system instructions, user queries, and responses.
- Interpolating Lists: Run through lists of elements such as chat messages, incorporating them into the template dynamically.
- Truncating Messages: Define a priority to manage which parts of messages should be truncated first if needed.
- Modality Adaptation: Change instructions depending on the user's current mode of interaction, like audio or text.
- Specific Query Targeting: Use conditional logic to provide context-specific responses, like including homework examples if queried.
- Whitespace Handling: Avoid unnecessary newlines in outputs through default whitespace stripping or using identifiers for spaces.
Composing Complex Prompts
Prompt Poet's template flexibility allows building complex prompts by combining different elements and making them reusable, assisting in scenarios such as A/B testing.
Detailed Features
- Tokenization and Truncation: Essential for managing cache and latency, ensuring optimized prompt operations.
- Templating Language: Extensibility of Jinja2 and YAML allows dynamic operations such as direct function calls within templates.
- Encoding Customization: Use default tokenization or define custom encoding functions for specialized needs.
- Advanced Caching Techniques: Optimize caching strategies using truncation algorithms to enhance processing speed and efficiency.
- Template Registry: Allows for organizing and managing template files separately from the core code, promoting modular coding practices.
Related Tools and Libraries
Prompt Poet shares its focus with similar tools like Priompt, dspy, and Microsoft’s Prompt Engine, which offer different takes on prompt creation, utilizing various technologies and methodologies for effective AI model interactions.
In conclusion, Prompt Poet stands out as a comprehensive toolkit for efficiently creating sophisticated prompts that enhance user interaction with AI systems, while saving time and effort on backend engineering tasks.