mirror of
https://github.com/tiennm99/litellm.git
synced 2026-06-28 21:10:37 +00:00
Ishaan Jaff
319 lines
13 KiB
Python
319 lines
13 KiB
Python
"""
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Integration tests for Perplexity cost calculation and transformation.
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Tests the end-to-end functionality of Perplexity cost calculation
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including integration with the main LiteLLM cost calculator.
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"""
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import json
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import math
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import os
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import sys
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from unittest.mock import Mock, patch
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import pytest
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# Add the project root to Python path
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sys.path.insert(0, os.path.abspath("../../../.."))
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import litellm
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from litellm import ModelResponse
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from litellm.cost_calculator import completion_cost, cost_per_token
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from litellm.llms.perplexity.chat.transformation import PerplexityChatConfig
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from litellm.types.utils import PromptTokensDetailsWrapper, Usage
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from litellm.utils import get_model_info
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class TestPerplexityIntegration:
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"""Integration test suite for Perplexity functionality."""
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@pytest.fixture(autouse=True)
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def setup_model_cost_map(self):
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"""Set up the model cost map for testing."""
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# Ensure we use local model cost map for consistent testing
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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# Load the model cost map
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try:
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with open("model_prices_and_context_window.json", "r") as f:
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model_cost_map = json.load(f)
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litellm.model_cost = model_cost_map
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except FileNotFoundError:
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# Fallback to ensure we have the Perplexity model configuration
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litellm.model_cost = {
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"perplexity/sonar-deep-research": {
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"max_tokens": 128000,
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"max_input_tokens": 128000,
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"input_cost_per_token": 2e-06,
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"output_cost_per_token": 8e-06,
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"output_cost_per_reasoning_token": 3e-06,
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"citation_cost_per_token": 2e-06,
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"search_context_cost_per_query": {
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"search_context_size_low": 0.005,
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"search_context_size_medium": 0.005,
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"search_context_size_high": 0.005
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},
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"litellm_provider": "perplexity",
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"mode": "chat",
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"supports_reasoning": True,
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"supports_web_search": True,
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}
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}
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def test_end_to_end_cost_calculation_with_transformation(self):
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"""Test end-to-end cost calculation with response transformation."""
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# Create a Perplexity API response that includes citations and search queries
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config = PerplexityChatConfig()
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# Create a ModelResponse with basic usage (before transformation)
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model_response = ModelResponse()
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model_response.model = "sonar-deep-research"
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model_response.usage = Usage(
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prompt_tokens=100,
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completion_tokens=50,
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total_tokens=150,
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reasoning_tokens=10
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)
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# Simulate raw response from Perplexity API
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raw_response_dict = {
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"choices": [{"message": {"content": "Test response with citations"}}],
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"usage": {
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"prompt_tokens": 100,
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"completion_tokens": 50,
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"total_tokens": 150,
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"num_search_queries": 2
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},
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"citations": [
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"This is the first citation with important information about the topic",
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"Another citation providing additional context for the response"
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]
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}
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# Apply transformation to extract Perplexity-specific fields
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config._enhance_usage_with_perplexity_fields(model_response, raw_response_dict)
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# Now calculate the cost with the enhanced usage
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total_cost = completion_cost(completion_response=model_response, custom_llm_provider="perplexity")
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# Calculate expected cost
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citation_chars = sum(len(citation) for citation in raw_response_dict["citations"])
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citation_tokens = citation_chars // 4
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expected_prompt_cost = (100 * 2e-6) + (citation_tokens * 2e-6) # Input + citation
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expected_completion_cost = (50 * 8e-6) + (10 * 3e-6) + (2 / 1000 * 0.005) # Output + reasoning + search
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expected_total = expected_prompt_cost + expected_completion_cost
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assert math.isclose(total_cost, expected_total, rel_tol=1e-6)
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def test_cost_calculation_without_custom_fields(self):
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"""Test that cost calculation works normally when custom fields are absent."""
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# Create a standard response without Perplexity-specific fields
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model_response = ModelResponse()
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model_response.model = "sonar-deep-research"
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model_response.usage = Usage(
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prompt_tokens=100,
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completion_tokens=50,
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total_tokens=150
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)
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# Calculate cost without custom fields
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total_cost = completion_cost(completion_response=model_response, custom_llm_provider="perplexity")
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# Should only include basic input/output costs
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expected_cost = (100 * 2e-6) + (50 * 8e-6)
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assert math.isclose(total_cost, expected_cost, rel_tol=1e-6)
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def test_main_cost_calculator_integration(self):
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"""Test integration with the main LiteLLM cost calculator."""
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# Create usage with all Perplexity fields
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usage = Usage(
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prompt_tokens=200,
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completion_tokens=100,
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total_tokens=300,
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reasoning_tokens=25,
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prompt_tokens_details=PromptTokensDetailsWrapper(web_search_requests=3)
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)
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usage.citation_tokens = 40
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# Test main cost calculator
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prompt_cost, completion_cost_val = cost_per_token(
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model="sonar-deep-research",
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custom_llm_provider="perplexity",
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usage_object=usage
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)
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# Calculate expected costs
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expected_prompt_cost = (200 * 2e-6) + (40 * 2e-6) # Input + citation
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expected_completion_cost = (100 * 8e-6) + (25 * 3e-6) + (3 / 1000 * 0.005) # Output + reasoning + search
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assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6)
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assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6)
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def test_model_info_includes_custom_fields(self):
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"""Test that get_model_info returns the custom Perplexity cost fields."""
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model_info = get_model_info(model="sonar-deep-research", custom_llm_provider="perplexity")
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# Verify custom fields are included
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required_fields = [
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"citation_cost_per_token",
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"search_context_cost_per_query",
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"input_cost_per_token",
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"output_cost_per_token",
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"output_cost_per_reasoning_token"
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]
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for field in required_fields:
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assert field in model_info, f"Missing field: {field}"
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assert model_info[field] is not None, f"Null value for field: {field}"
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def test_various_citation_sizes(self):
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"""Test cost calculation with various citation sizes."""
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config = PerplexityChatConfig()
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test_cases = [
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# (citations, expected_approximate_tokens)
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(["Short"], 1),
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(["This is a medium-length citation with some content"], 12),
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(["Very short", "Another citation", "Third one with more text content"], 15),
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([""], 0), # Empty citation
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]
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for citations, expected_approx_tokens in test_cases:
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model_response = ModelResponse()
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model_response.model = "sonar-deep-research"
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model_response.usage = Usage(
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prompt_tokens=100,
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completion_tokens=50,
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total_tokens=150
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)
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raw_response_dict = {
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"usage": {"prompt_tokens": 100, "completion_tokens": 50, "total_tokens": 150},
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"citations": citations
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}
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config._enhance_usage_with_perplexity_fields(model_response, raw_response_dict)
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citation_tokens = getattr(model_response.usage, "citation_tokens", 0)
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# Allow for reasonable variance in token estimation
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if expected_approx_tokens == 0:
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assert citation_tokens == 0
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else:
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assert abs(citation_tokens - expected_approx_tokens) <= 5
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def test_cost_calculation_with_zero_values(self):
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"""Test cost calculation handles zero values for custom fields correctly."""
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usage = Usage(
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prompt_tokens=100,
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completion_tokens=50,
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total_tokens=150
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)
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# Set custom fields to zero
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usage.citation_tokens = 0
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usage.prompt_tokens_details = PromptTokensDetailsWrapper(web_search_requests=0)
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# Should not add any extra cost
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prompt_cost, completion_cost_val = cost_per_token(
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model="sonar-deep-research",
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custom_llm_provider="perplexity",
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usage_object=usage
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)
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expected_prompt_cost = 100 * 2e-6
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expected_completion_cost = 50 * 8e-6
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assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6)
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assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6)
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def test_high_volume_cost_calculation(self):
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"""Test cost calculation with high token and query counts."""
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usage = Usage(
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prompt_tokens=50000,
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completion_tokens=25000,
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total_tokens=75000,
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reasoning_tokens=10000
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)
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usage.citation_tokens = 5000
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usage.prompt_tokens_details = PromptTokensDetailsWrapper(web_search_requests=100)
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total_cost = completion_cost(
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completion_response=ModelResponse(usage=usage, model="sonar-deep-research"),
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custom_llm_provider="perplexity"
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)
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# Calculate expected cost
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expected_prompt_cost = (50000 * 2e-6) + (5000 * 2e-6) # $0.11
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expected_completion_cost = (25000 * 8e-6) + (10000 * 3e-6) + (100 / 1000 * 0.005) # $0.23
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expected_total = expected_prompt_cost + expected_completion_cost # $0.34
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assert math.isclose(total_cost, expected_total, rel_tol=1e-6)
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assert total_cost > 0.3 # Sanity check for high-volume scenario
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def test_transformation_preserves_existing_usage_fields(self):
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"""Test that transformation doesn't overwrite existing standard usage fields."""
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config = PerplexityChatConfig()
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model_response = ModelResponse()
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model_response.usage = Usage(
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prompt_tokens=100,
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completion_tokens=50,
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total_tokens=150,
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reasoning_tokens=20
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)
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# Store original values
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original_prompt_tokens = model_response.usage.prompt_tokens
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original_completion_tokens = model_response.usage.completion_tokens
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original_total_tokens = model_response.usage.total_tokens
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raw_response_dict = {
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"usage": {
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"prompt_tokens": 999, # Different from original
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"completion_tokens": 999, # Different from original
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"total_tokens": 999, # Different from original
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"num_search_queries": 3
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},
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"citations": ["Some citation"]
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}
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config._enhance_usage_with_perplexity_fields(model_response, raw_response_dict)
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# Original usage fields should be preserved
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assert model_response.usage.prompt_tokens == original_prompt_tokens
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assert model_response.usage.completion_tokens == original_completion_tokens
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assert model_response.usage.total_tokens == original_total_tokens
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# But custom fields should be added
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assert hasattr(model_response.usage, "prompt_tokens_details")
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assert hasattr(model_response.usage, "citation_tokens")
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assert model_response.usage.prompt_tokens_details.web_search_requests == 3
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@pytest.mark.parametrize("provider_name", ["perplexity", "PERPLEXITY", "Perplexity"])
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def test_case_insensitive_provider_matching(self, provider_name):
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"""Test that cost calculation works with different case variations of provider name."""
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usage = Usage(
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prompt_tokens=100,
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completion_tokens=50,
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total_tokens=150
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)
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usage.citation_tokens = 10
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usage.prompt_tokens_details = PromptTokensDetailsWrapper(web_search_requests=1)
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# Should work regardless of case
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prompt_cost, completion_cost_val = cost_per_token(
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model="sonar-deep-research",
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custom_llm_provider=provider_name.lower(), # Normalize to lowercase
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usage_object=usage
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)
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# Should calculate costs correctly
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expected_prompt_cost = (100 * 2e-6) + (10 * 2e-6)
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expected_completion_cost = (50 * 8e-6) + (1 / 1000 * 0.005)
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assert math.isclose(prompt_cost, expected_prompt_cost, rel_tol=1e-6)
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assert math.isclose(completion_cost_val, expected_completion_cost, rel_tol=1e-6) |