promptbuilder 0.2.3

Library for building prompts for LLMs

Prompt Builder

Library for building prompts and agents with LLMs.

Installation

From PyPI:

pip install promptbuilder

From source:

git clone https://github.com/kapulkin/promptbuilder.git
cd promptbuilder
pip install -e .

Features

• Prompt templates with variables and content tags
• Structured output with TypeScript-like schema definition
• LLM client with native structured output support and caching option
• Integration with multiple LLM providers through aisuite
• Agents with routing based on tools
• Tools as agent for flexibility and scalability

Quick Start

Basic Prompt Usage

from promptbuilder.llm_client import LLMClient
from promptbuilder.prompt_builder import PromptBuilder

# Build prompt template
prompt_template = PromptBuilder() \
    .text("What is the capital of ").variable("country").text("?") \
    .build()

# Use with LLM
llm_client = LLMClient(model="your-model", api_key="your-api-key")
response = llm_client.from_text(
    prompt_template.render(country="France")
)
print(response)

Using Agents

from typing import List
from pydantic import BaseModel, Field
from promptbuilder.agent.agent import AgentRouter
from promptbuilder.agent.context import Context
from promptbuilder.agent.message import Message
from promptbuilder.llm_client import LLMClient

# Define tool arguments
class TodoItem(BaseModel):
    description: str = Field(..., description="Description of the todo item")

class AddTodoArgs(BaseModel):
    item: TodoItem = Field(..., description="Todo item to add")

# Create custom context
class TodoListContext(Context):
    todos: List[TodoItem] = []

# Create agent with tools
class TodoListAgent(AgentRouter[TodoListContext]):
    def __init__(self, llm_client: LLMClient, context: TodoListContext):
        super().__init__(llm_client=llm_client, context=context)
        
        # Register tools
        self.tool(
            description="Add a new todo item to the list",
            args_model=AddTodoArgs
        )(self.add_todo)
    
    async def add_todo(self, message: Message, args: AddTodoArgs, context: TodoListContext) -> str:
        context.todos.append(args.item)
        return f"Added todo item: {args.item.description}"

# Use the agent
async def main():
    llm_client = LLMClient(model="your-model", api_key="your-api-key")
    agent = TodoListAgent(llm_client=llm_client, context=TodoListContext())
    
    response = await agent(Message(role="user", content="Add a todo: Buy groceries"))
    print(response)

See the examples directory for more detailed examples, including a complete todo list manager.

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a pull request.