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* test: point CircleCI-only suites at models still in the cost map #42435 removed cost map entries past their deprecation date and #42437 added litellm_uisettings to the config-synced tables, but both only updated tests/test_litellm. The CircleCI-only suites (local_testing, llm_translation, logging_callback_tests, litellm_utils_tests, unit) kept using the removed models or the old table list and went red on main. Each test keeps its assertions and swaps the removed model for a current one with the same provider and capabilities. The fireworks tests pick a vision model from the cost map because #34941 set supports_vision false on minimax-m3, and the vertex image provider test injects the image model set because #42435 removed every vertex_ai-image-models entry. * test(vertex_ai): register the image model through add_known_models in the provider test
144 lines
4.4 KiB
Python
144 lines
4.4 KiB
Python
# What is this?
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## Test to make sure function call response always works with json.loads() -> no extra parsing required. Relevant issue - https://github.com/BerriAI/litellm/issues/2654
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import io
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import json
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import warnings
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from typing import List
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import pytest
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import litellm
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from litellm import completion
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# Just a stub to keep the sample code simple
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class Trade:
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def __init__(self, order: dict):
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self.order = order
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@staticmethod
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def buy(order: dict):
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return Trade(order)
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@staticmethod
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def sell(order: dict):
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return Trade(order)
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def trade(model_name: str) -> List[Trade]: # type: ignore
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def parse_order(order: dict) -> Trade:
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action = order["action"]
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if action == "buy":
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return Trade.buy(order)
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elif action == "sell":
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return Trade.sell(order)
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else:
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raise ValueError(f"Invalid action {action}")
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def parse_call(call) -> List[Trade]:
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arguments = json.loads(call.function.arguments)
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trades = [parse_order(order) for order in arguments["orders"]]
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return trades
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tool_spec = {
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"type": "function",
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"function": {
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"name": "trade",
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"description": "Execute orders to manage the portfolio. Orders will be executed immediately at the stated prices.",
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"parameters": {
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"type": "object",
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"properties": {
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"orders": {
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"type": "array",
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"items": {
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"type": "object",
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"properties": {
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"action": {"type": "string", "enum": ["buy", "sell"]},
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"asset": {"type": "string"},
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"amount": {
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"type": "number",
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"description": "Amount of asset to buy or sell.",
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},
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},
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"required": ["action", "asset", "amount"],
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},
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},
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},
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},
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},
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}
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try:
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response = completion(
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model_name,
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[
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{
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"role": "system",
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"content": """You are an expert asset manager, managing a portfolio.
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Always use the `trade` function. Make sure that you call it correctly. For example, the following is a valid call:
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```
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trade({
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"orders": [
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{"action": "buy", "asset": "BTC", "amount": 0.1},
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{"action": "sell", "asset": "ETH", "amount": 0.2}
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]
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})
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```
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If there are no trades to make, call `trade` with an empty array:
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```
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trade({ "orders": [] })
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```
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""",
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},
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{
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"role": "user",
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"content": """Manage the portfolio.
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Don't jabber.
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This is the current market data:
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```
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{market_data}
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```
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Your portfolio is as follows:
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```
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{portfolio}
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```
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""".replace(
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"{market_data}", "BTC: 64,000 USD\nETH: 3,500 USD"
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).replace(
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"{portfolio}", "USD: 1000, BTC: 0.1, ETH: 0.2"
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),
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},
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],
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tools=[tool_spec],
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tool_choice={
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"type": "function",
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"function": {"name": tool_spec["function"]["name"]}, # type: ignore
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},
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)
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calls = response.choices[0].message.tool_calls
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trades = [trade for call in calls for trade in parse_call(call)]
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return trades
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except litellm.InternalServerError:
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pass
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@pytest.mark.parametrize(
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"model", ["claude-haiku-4-5-20251001", "us.anthropic.claude-haiku-4-5-20251001-v1:0"]
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)
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@pytest.mark.flaky(retries=6, delay=10)
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def test_function_call_parsing(model):
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trades = trade(model)
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print([trade.order for trade in trades if trade is not None])
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