Add openai.AsyncOpenAI and stream and function support

examples/logging/magentic_async_function_logging.py

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+import openai
+from magentic import AsyncStreamedStr, FunctionCall, prompt
+
+from log10.load import log10
+
+
+log10(openai)
+
+
+def add(x: int, y: int) -> int:
+ """Add together two numbers."""
+ return x + y
+
+
+@prompt("What is 1+1? Use tools", functions=[add])
+async def agent() -> AsyncStreamedStr:
+ ...
+
+
+# Define an async main function
+async def main():
+ response = await agent()
+ if isinstance(response, FunctionCall):
+ print(response)
+ else:
+ async for chunk in response:
+ print(chunk, end="", flush=True)
+
+
+# Running the main function using asyncio
+if __name__ == "__main__":
+ import asyncio
+
+ asyncio.run(main())

examples/logging/magentic_async_stream_logging.py

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+import asyncio
+
+import openai
+from magentic import AsyncStreamedStr, prompt
+
+from log10.load import log10, log10_session
+
+
+log10(openai)
+
+
+@prompt("Tell me a 200-word story about {topic}")
+async def tell_story(topic: str) -> AsyncStreamedStr:
+ ...
+
+
+async def main():
+ with log10_session(tags=["async_tag"]):
+ output = await tell_story("Europe.")
+ async for chunk in output:
+ print(chunk, end="", flush=True)
+
+
+asyncio.run(main())

examples/logging/magentic_function_logging.py

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+# taken from Magentic README
+# https://github.com/jackmpcollins/magentic/blob/2493419f2db3a3be58fb308d7df51a51bf1989c1/README.md#usage
+
+from typing import Literal
+
+import openai
+from magentic import FunctionCall, prompt
+
+from log10.load import log10
+
+
+log10(openai)
+
+
+def activate_oven(temperature: int, mode: Literal["broil", "bake", "roast"]) -> str:
+ """Turn the oven on with the provided settings."""
+ return f"Preheating to {temperature} F with mode {mode}"
+
+
+@prompt(
+ "Prepare the oven so I can make {food}",
+ functions=[activate_oven],
+)
+def configure_oven(food: str) -> FunctionCall[str]:
+ ...
+
+
+output = configure_oven("cookies!")
+# FunctionCall(<function activate_oven at 0x1105a6200>, temperature=350, mode='bake')
+print(output())
+# 'Preheating to 350 F with mode bake'

examples/logging/openai_async_logging.py

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+import asyncio
+
+import openai
+from openai import AsyncOpenAI
+
+from log10.load import log10
+
+
+log10(openai)
+
+client = AsyncOpenAI()
+
+
+async def main():
+ completion = await client.chat.completions.create(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Say this is a test"}],
+ )
+ print(completion.choices[0].message.content)
+
+
+asyncio.run(main())

examples/logging/openai_async_stream_logging.py

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+import asyncio
+
+import openai
+from openai import AsyncOpenAI
+
+from log10.load import log10
+
+
+log10(openai)
+
+client = AsyncOpenAI()
+
+
+async def main():
+ stream = await client.chat.completions.create(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Count to 50."}],
+ stream=True,
+ )
+ async for chunk in stream:
+ print(chunk.choices[0].delta.content or "", end="", flush=True)
+
+
+asyncio.run(main())

examples/logging/openai_tools.py

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+import json
+
+from log10.load import OpenAI
+
+
+client = OpenAI()
+
+
+# Example dummy function hard coded to return the same weather
+# In production, this could be your backend API or an external API
+def get_current_weather(location, unit="fahrenheit"):
+ """Get the current weather in a given location"""
+ if "tokyo" in location.lower():
+ return json.dumps({"location": "Tokyo", "temperature": "10", "unit": unit})
+ elif "san francisco" in location.lower():
+ return json.dumps({"location": "San Francisco", "temperature": "72", "unit": unit})
+ elif "paris" in location.lower():
+ return json.dumps({"location": "Paris", "temperature": "22", "unit": unit})
+ else:
+ return json.dumps({"location": location, "temperature": "unknown"})
+
+
+def run_conversation():
+ # Step 1: send the conversation and available functions to the model
+ messages = [
+ {
+ "role": "user",
+ "content": "What's the weather like in San Francisco, Tokyo, and Paris?",
+ }
+ ]
+ tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_current_weather",
+ "description": "Get the current weather in a given location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "The city and state, e.g. San Francisco, CA",
+ },
+ "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+ },
+ "required": ["location"],
+ },
+ },
+ }
+ ]
+ response = client.chat.completions.create(
+ model="gpt-3.5-turbo-0125",
+ messages=messages,
+ tools=tools,
+ tool_choice="auto", # auto is default, but we'll be explicit
+ )
+ response_message = response.choices[0].message
+ tool_calls = response_message.tool_calls
+ # Step 2: check if the model wanted to call a function
+ if tool_calls:
+ # Step 3: call the function
+ # Note: the JSON response may not always be valid; be sure to handle errors
+ available_functions = {
+ "get_current_weather": get_current_weather,
+ } # only one function in this example, but you can have multiple
+ messages.append(response_message) # extend conversation with assistant's reply
+ # Step 4: send the info for each function call and function response to the model
+ for tool_call in tool_calls:
+ function_name = tool_call.function.name
+ function_to_call = available_functions[function_name]
+ function_args = json.loads(tool_call.function.arguments)
+ function_response = function_to_call(
+ location=function_args.get("location"),
+ unit=function_args.get("unit"),
+ )
+ messages.append(
+ {
+ "tool_call_id": tool_call.id,
+ "role": "tool",
+ "name": function_name,
+ "content": function_response,
+ }
+ ) # extend conversation with function response
+ second_response = client.chat.completions.create(
+ model="gpt-3.5-turbo-0125",
+ messages=messages,
+ ) # get a new response from the model where it can see the function response
+ return second_response
+
+
+print(run_conversation())