Introduction to LLM Streaming
Building a pattern around LLM streaming is an innovative approach to utilize Large Language Models (LLMs) more efficiently. In a previous article, the author explored building a DAX LLM using base API calls to stream data from Anthropic. This article delves into utilizing Pydantic AI to further productionize and expand on this pattern, incorporating tool calls for enhanced functionality.
What is Pydantic AI?
Pydantic AI is a newer LLM agent framework built by the team behind Pydantic. It is smaller in scope compared to other frameworks like LangChain but provides a robust bridge for creating LLM building blocks. The author, having used Pydantic in past FastAPI projects, appreciates its capabilities and sees potential in its application for LLM streaming.
Streaming with Pydantic AI
To illustrate the use of Pydantic AI for LLM streaming, the author provides an example of an async function designed to stream anthropic responses. This function, anthropic_stream_api_call, takes a list of chat inputs and yields an asynchronous generator of the anthropic response.
Example Function
async def anthropic_stream_api_call(chat_input_list: list) -> AsyncGenerator[str, None]:
"""Streams anthropic response.
Args:
chat_input_list (list): List of chat inputs to send to the API.
Yields:
AsyncGenerator[str, None]: Stream of anthropic response.
"""
# Build message list
message_input = build_anthropic_message_input(chat_input_list=chat_input_list)
# Setup and make api call.
client = AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
stream = await client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=4096,
temperature=0.2,
system=get_system_prompt(chat_input_list),
messages=message_input,
stream=True
)
async for event in stream:
if event.type in ['message_start', 'message_delta', 'message_stop', 'content_block_start', 'content_block_stop']:
pass
elif event.type == 'content_block_delta':
yield event.delta.text
else:
yield event.typeConclusion
The integration of Pydantic AI into LLM streaming patterns offers a promising approach to enhancing the production and functionality of these models. By leveraging async functions and generators, developers can create more efficient and scalable LLM applications. As the field of AI continues to evolve, frameworks like Pydantic AI will play a crucial role in shaping the future of LLM development.
FAQs
- What is LLM streaming?
LLM streaming refers to the process of continuously generating or processing data using Large Language Models. This can involve streaming inputs to the model and receiving outputs in real-time. - What is Pydantic AI?
Pydantic AI is a framework designed for building Large Language Model (LLM) applications. It provides tools and structures for creating scalable and efficient LLMs. - How does Pydantic AI enhance LLM streaming?
Pydantic AI enhances LLM streaming by offering a structured approach to building LLM applications, making it easier to integrate async functionality and handle real-time data processing. - What are the benefits of using async functions in LLM streaming?
Async functions in LLM streaming allow for non-blocking I/O operations, enabling the application to handle multiple tasks concurrently and improving overall performance and responsiveness.
