promptweaver 0.1.16

PromptWeaver streamlines prompt development and management in Generative AI workflows

PromptWeaver is a Python library that streamlines prompt development and management in Generative AI workflows. It decouples prompts from Python scripts, enhancing portability, maintainability, and scalability for developers working with multiple LLMs or complex prompting workflows.

This project is currently under active development and may undergo significant changes. We welcome your feedback and contributions!

Features

• Separate prompts from code for better modularity.
• Supports multimodal input (text, images, audio, video).
• Integration with large language models such as Gemini.
• YAML-based prompt configuration with sample and default values.
• Extensible to support other LLM APIs.

Installation

PromptWeaver can be easily installed using pip:

pip install promptweaver

Note: PromptWeaver requires Python 3.8 or higher.

Usage

First, you need to start by creating a .yml.j2 PromptWeaver template.

You will find template examples in our promptweaver gallery.

name: Hello World
description: A quickstart prompt showcasing how to answer a simple user message using gemini.
model:
  model_name: gemini-1.5-flash-001
  generation_config:
    temperature: 0.3
    max_output_tokens: 250
  system_instruction: You are an AI model trained to answer questions. Be kind and objective in your answers.
# ---
variables:
  user_message:
    sample: Hi!
# ---
user:
  - text: {{ user_message }}

Now you can call one of the supported LLM clients using the promptweaver template.

from promptweaver.core.prompt_template import PromptConfig
from promptweaver.clients.gemini.gemini_client import GeminiClient

# Initialize the Gemini client
gemini_client = GeminiClient(project="project_id", location="project_location")

# Load the prompt configuration
example_prompt = PromptConfig.from_file_with_sample_values("samples/example.yml.j2")

# Generate content
generate_content = gemini_client.generate_content(example_prompt)
print(generate_content.text)

Function Calling

For adding function calling support to your LLM requests you need to parametrize the .generate_content() method with all your tools.

Note: Currently, tools can't be declared in a .yml.j2 PromptWeaver template.

Example

from promptweaver.core.prompt_template import PromptConfig
from promptweaver.clients.gemini.gemini_client import GeminiClient
from vertexai.generative_models import Tool, grounding

# Initialize the Gemini client
gemini_client = GeminiClient(project="your_project", location="your_location")

# Load the prompt configuration
prompt_variables = {
    'user_message': 'What\'s the weather like tomorrow in San Francisco? Should I bring a raincoat?'
}
example_prompt = PromptConfig.from_file("samples/01-hello-world-text.yml.j2", prompt_variables)

# Define your tools, such as: GoogleSearchRetrieval (grounding) or FunctionDeclaration (custom tools)
tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval())

# The generate_content() method takes **kwargs, supporting any additional keyword arguments from vertexai
generate_content = gemini_client.generate_content(example_prompt, tools=[tool])
print(generate_content.text)

Contributing

We welcome contributions! Please read our contributing guide for details on how to get started. The project can be found on GitHub.