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Function calling in assistants (#77)
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‎examples/async/assistants/function_calling‎

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../function_calling/assistants

‎examples/async/function_calling/assistants/stream_raw_tool_calls_processing.py‎

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#!/usr/bin/env python3
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from __future__ import annotations
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import asyncio
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from yandex_cloud_ml_sdk import AsyncYCloudML
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def create_tools(sdk: AsyncYCloudML):
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# it is imported inside only because yandex-cloud-ml-sdk does not require pydantic by default
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# pylint: disable=import-outside-toplevel
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from pydantic import BaseModel, Field
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calculator_tool = sdk.tools.function(
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name="calculator",
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description="A simple calculator that performs basic arithmetic operations.",
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# parameters could contain valid jsonschema with function parameters types and description
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parameters={
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"type": "object",
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"properties": {
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"expression": {
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"type": "string",
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"description": "The mathematical expression to evaluate (e.g., '2 + 3 * 4').",
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}
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},
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"required": ["expression"],
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}
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)
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class Weather(BaseModel):
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"""Getting the weather in specified location for specified date"""
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# It is important to describe all the arguments with a natural language
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location: str = Field(description="Name of the place for fetching weatcher at")
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date: str = Field(description="Date which a user interested in")
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weather_tool = sdk.tools.function(Weather)
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return [calculator_tool, weather_tool]
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def calculator(expression: str) -> str:
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print(f'calculator got {expression=}')
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return "-5"
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def weather(location: str, date: str) -> str:
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print(f"weather func got {location=} and {date=}")
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return "-10 celsius"
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def process_tool_calls(tool_calls) -> list[dict]:
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"""
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This function is an example how you could organize
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dispatching of function calls in general case
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"""
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function_map = {
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'calculator': calculator,
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'Weather': weather
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}
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result = []
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for tool_call in tool_calls:
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# only functions are available at the moment
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assert tool_call.function
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function = function_map[tool_call.function.name]
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answer = function(**tool_call.function.arguments) # type: ignore[operator]
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result.append({'name': tool_call.function.name, 'content': answer})
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return result
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async def run_on_thread(assistant, thread):
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run = await assistant.run_stream(thread)
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event = None
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async for event in run:
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print(
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'Stream event from a run - '
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f'status={event.status.name!r}, text={event.text!r}, tool_calls={event.tool_calls}'
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)
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if event.tool_calls:
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tool_results = process_tool_calls(event.tool_calls)
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await run.submit_tool_results(tool_results)
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# Just to show you it is the final event:
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assert event
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assert event.status.name == 'DONE'
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# Final event have text field you could use as a result
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return event
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async def main() -> None:
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sdk = AsyncYCloudML(folder_id='b1ghsjum2v37c2un8h64')
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sdk.setup_default_logging()
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model = sdk.models.completions('yandexgpt', model_version='rc')
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assistant = await sdk.assistants.create(
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model,
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tools=create_tools(sdk)
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)
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thread = await sdk.threads.create()
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try:
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for question in [
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"How much it would be 7 * 8?",
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"What is the weather like in Paris at 12 of March?"
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]:
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await thread.write(question)
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result = await run_on_thread(assistant, thread)
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print(f'Assistant response on question "{question}": {result.text}')
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print('All of the thread messages:')
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messages = reversed([message async for message in thread])
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for message in messages:
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print(f' {message.author.role}: {message.text}')
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finally:
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await thread.delete()
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await assistant.delete()
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if __name__ == '__main__':
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asyncio.run(main())

‎examples/async/function_calling/assistants/sync_raw_tool_calls_processing.py‎

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#!/usr/bin/env python3
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from __future__ import annotations
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5+
import asyncio
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from yandex_cloud_ml_sdk import AsyncYCloudML
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9+
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def create_tools(sdk: AsyncYCloudML):
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# it is imported inside only because yandex-cloud-ml-sdk does not require pydantic by default
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# pylint: disable=import-outside-toplevel
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from pydantic import BaseModel, Field
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calculator_tool = sdk.tools.function(
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name="calculator",
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description="A simple calculator that performs basic arithmetic operations.",
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# parameters could contain valid jsonschema with function parameters types and description
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parameters={
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"type": "object",
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"properties": {
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"expression": {
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"type": "string",
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"description": "The mathematical expression to evaluate (e.g., '2 + 3 * 4').",
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}
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},
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"required": ["expression"],
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}
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)
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class Weather(BaseModel):
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"""Getting the weather in specified location for specified date"""
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# It is important to describe all the arguments with a natural language
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location: str = Field(description="Name of the place for fetching weatcher at")
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date: str = Field(description="Date which a user interested in")
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weather_tool = sdk.tools.function(Weather)
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return [calculator_tool, weather_tool]
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def calculator(expression: str) -> str:
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print(f'calculator got {expression=}')
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return "-5"
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def weather(location: str, date: str) -> str:
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print(f"weather func got {location=} and {date=}")
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return "-10 celsius"
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def process_tool_calls(tool_calls) -> list[dict]:
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"""
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This function is an example how you could organize
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dispatching of function calls in general case
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"""
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function_map = {
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'calculator': calculator,
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'Weather': weather
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}
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result = []
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for tool_call in tool_calls:
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# only functions are available at the moment
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assert tool_call.function
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function = function_map[tool_call.function.name]
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answer = function(**tool_call.function.arguments) # type: ignore[operator]
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result.append({'name': tool_call.function.name, 'content': answer})
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return result
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async def run_on_thread(assistant, thread):
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run = await assistant.run(thread)
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result = await run
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if result.tool_calls:
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tool_results = process_tool_calls(result.tool_calls)
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await run.submit_tool_results(tool_results)
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# run starting to work again after submitting tool results
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# and we need to wait it's results again
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result = await run
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return result
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async def main() -> None:
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sdk = AsyncYCloudML(folder_id='b1ghsjum2v37c2un8h64')
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sdk.setup_default_logging()
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model = sdk.models.completions('yandexgpt', model_version='rc')
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assistant = await sdk.assistants.create(
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model,
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tools=create_tools(sdk)
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)
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thread = await sdk.threads.create()
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try:
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for question in [
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"How much it would be 7 * 8?",
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"What is the weather like in Paris at 12 of March?"
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]:
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await thread.write(question)
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result = await run_on_thread(assistant, thread)
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print(f'Assistant response on question "{question}": {result.text}')
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print('All of the thread messages:')
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messages = reversed([message async for message in thread])
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for message in messages:
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print(f' {message.author.role}: {message.text}')
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finally:
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await thread.delete()
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await assistant.delete()
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if __name__ == '__main__':
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asyncio.run(main())

‎examples/sync/assistants/function_calling‎

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../function_calling/assistants

‎examples/sync/function_calling/assistants/stream_raw_tool_calls_processing.py‎

Lines changed: 128 additions & 0 deletions
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#!/usr/bin/env python3
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from __future__ import annotations
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5+
from yandex_cloud_ml_sdk import YCloudML
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7+
8+
def create_tools(sdk: YCloudML):
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# it is imported inside only because yandex-cloud-ml-sdk does not require pydantic by default
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# pylint: disable=import-outside-toplevel
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from pydantic import BaseModel, Field
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13+
calculator_tool = sdk.tools.function(
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name="calculator",
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description="A simple calculator that performs basic arithmetic operations.",
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# parameters could contain valid jsonschema with function parameters types and description
17+
parameters={
18+
"type": "object",
19+
"properties": {
20+
"expression": {
21+
"type": "string",
22+
"description": "The mathematical expression to evaluate (e.g., '2 + 3 * 4').",
23+
}
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},
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"required": ["expression"],
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}
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)
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class Weather(BaseModel):
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"""Getting the weather in specified location for specified date"""
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32+
# It is important to describe all the arguments with a natural language
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location: str = Field(description="Name of the place for fetching weatcher at")
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date: str = Field(description="Date which a user interested in")
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weather_tool = sdk.tools.function(Weather)
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return [calculator_tool, weather_tool]
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def calculator(expression: str) -> str:
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print(f'calculator got {expression=}')
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return "-5"
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def weather(location: str, date: str) -> str:
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print(f"weather func got {location=} and {date=}")
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return "-10 celsius"
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def process_tool_calls(tool_calls) -> list[dict]:
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"""
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This function is an example how you could organize
54+
dispatching of function calls in general case
55+
"""
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function_map = {
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'calculator': calculator,
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'Weather': weather
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}
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result = []
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for tool_call in tool_calls:
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# only functions are available at the moment
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assert tool_call.function
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function = function_map[tool_call.function.name]
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answer = function(**tool_call.function.arguments) # type: ignore[operator]
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result.append({'name': tool_call.function.name, 'content': answer})
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return result
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def run_on_thread(assistant, thread):
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run = assistant.run_stream(thread)
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event = None
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for event in run:
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print(
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'Stream event from a run - '
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f'status={event.status.name!r}, text={event.text!r}, tool_calls={event.tool_calls}'
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)
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if event.tool_calls:
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tool_results = process_tool_calls(event.tool_calls)
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run.submit_tool_results(tool_results)
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# Just to show you it is the final event:
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assert event
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assert event.status.name == 'DONE'
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# Final event have text field you could use as a result
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return event
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def main() -> None:
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sdk = YCloudML(folder_id='b1ghsjum2v37c2un8h64')
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sdk.setup_default_logging()
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model = sdk.models.completions('yandexgpt', model_version='rc')
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assistant = sdk.assistants.create(
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model,
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tools=create_tools(sdk)
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)
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thread = sdk.threads.create()
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try:
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for question in [
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"How much it would be 7 * 8?",
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"What is the weather like in Paris at 12 of March?"
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]:
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thread.write(question)
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result = run_on_thread(assistant, thread)
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print(f'Assistant response on question "{question}": {result.text}')
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print('All of the thread messages:')
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messages = reversed(list(thread))
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for message in messages:
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print(f' {message.author.role}: {message.text}')
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finally:
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thread.delete()
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assistant.delete()
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if __name__ == '__main__':
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main()

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