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Add support for Z.AI GLM-4.7, latest flagship model with enhanced reasoning capabilities. Changes: - Add zai/glm-4.7 to model pricing with /bin/bash.60/M input, .20/M output - Add cached input pricing (/bin/bash.11/M) for GLM-4.7 - Add supports_reasoning flag to enable thinking parameter - Update ZAIChatConfig to support thinking parameter for models with reasoning - Update documentation with GLM-4.7 as latest flagship model - Add cached input column to pricing table (GLM-4.7 only) - Add tests for GLM-4.7 reasoning support and cost calculation - Update all examples to use GLM-4.7 Model specifications: - Context: 200K input, 128K output - Supports: reasoning, function calling, tool choice, prompt caching - Pricing: Same as GLM-4.6 with cache support See: https://docs.z.ai/guides/llm/glm-4.7
182 lines
5.3 KiB
Python
182 lines
5.3 KiB
Python
"""
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Tests for Z.AI (Zhipu AI) provider - GLM models
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"""
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import json
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import math
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import pytest
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import respx
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import litellm
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from litellm import completion
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from litellm.cost_calculator import cost_per_token
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@pytest.fixture
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def zai_response():
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"""Mock response from Z.AI API"""
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return {
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"id": "chatcmpl-zai-123",
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"object": "chat.completion",
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"created": 1677652288,
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"model": "glm-4.6",
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"choices": [
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{
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"index": 0,
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"message": {"role": "assistant", "content": "Hello! How can I help you today?"},
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"finish_reason": "stop",
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}
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],
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"usage": {"prompt_tokens": 10, "completion_tokens": 15, "total_tokens": 25},
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}
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def test_get_llm_provider_zai():
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"""Test that get_llm_provider correctly identifies zai provider"""
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from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
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model, provider, api_key, api_base = get_llm_provider("zai/glm-4.6")
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assert model == "glm-4.6"
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assert provider == "zai"
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assert api_base == "https://api.z.ai/api/paas/v4"
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def test_zai_in_provider_lists():
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"""Test that zai is registered in all necessary provider lists"""
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assert "zai" in litellm.openai_compatible_providers
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assert "zai" in litellm.provider_list
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def test_zai_models_in_model_cost():
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"""Test that ZAI models are in the model cost map"""
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import os
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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zai_models = [
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"zai/glm-4.7",
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"zai/glm-4.6",
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"zai/glm-4.5",
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"zai/glm-4.5v",
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"zai/glm-4.5-x",
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"zai/glm-4.5-air",
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"zai/glm-4.5-airx",
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"zai/glm-4-32b-0414-128k",
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"zai/glm-4.5-flash",
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]
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for model in zai_models:
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assert model in litellm.model_cost, f"Model {model} not found in model_cost"
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assert litellm.model_cost[model]["litellm_provider"] == "zai"
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def test_zai_glm46_cost_calculation():
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"""Test the cost calculation for glm-4.6"""
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import os
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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key = "zai/glm-4.6"
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info = litellm.model_cost[key]
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prompt_cost, completion_cost = cost_per_token(
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model="zai/glm-4.6",
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prompt_tokens=1000000, # 1M tokens
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completion_tokens=1000000,
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)
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# GLM-4.6: $0.6/M input, $2.2/M output
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assert math.isclose(prompt_cost, 0.6, rel_tol=1e-6)
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assert math.isclose(completion_cost, 2.2, rel_tol=1e-6)
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def test_zai_flash_model_is_free():
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"""Test that glm-4.5-flash has zero cost"""
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import os
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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key = "zai/glm-4.5-flash"
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info = litellm.model_cost[key]
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assert info["input_cost_per_token"] == 0
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assert info["output_cost_per_token"] == 0
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def test_glm47_supports_reasoning():
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"""Test that GLM-4.7 supports reasoning"""
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import os
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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key = "zai/glm-4.7"
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assert key in litellm.model_cost, f"Model {key} not found in model_cost"
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info = litellm.model_cost[key]
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assert info["supports_reasoning"] is True
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def test_glm47_cost_calculation():
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"""Test cost calculation for GLM-4.7"""
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import os
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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prompt_cost, completion_cost = cost_per_token(
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model="zai/glm-4.7",
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prompt_tokens=1000000, # 1M tokens
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completion_tokens=1000000,
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)
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# GLM-4.7: $0.6/M input, $2.2/M output (same as GLM-4.6)
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assert math.isclose(prompt_cost, 0.6, rel_tol=1e-6)
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assert math.isclose(completion_cost, 2.2, rel_tol=1e-6)
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@pytest.mark.asyncio
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async def test_zai_completion_call(respx_mock, zai_response, monkeypatch):
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"""Test completion call with zai provider using mocked response"""
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monkeypatch.setenv("ZAI_API_KEY", "test-api-key")
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litellm.disable_aiohttp_transport = True
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respx_mock.post("https://api.z.ai/api/paas/v4/chat/completions").respond(json=zai_response)
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response = await litellm.acompletion(
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model="zai/glm-4.6",
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messages=[{"role": "user", "content": "Hello"}],
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max_tokens=20,
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)
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assert response.choices[0].message.content == "Hello! How can I help you today?"
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assert response.usage.total_tokens == 25
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assert len(respx_mock.calls) == 1
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request = respx_mock.calls[0].request
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assert request.method == "POST"
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assert "api.z.ai" in str(request.url)
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assert "Authorization" in request.headers
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assert request.headers["Authorization"] == "Bearer test-api-key"
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def test_zai_sync_completion(respx_mock, zai_response, monkeypatch):
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"""Test synchronous completion call"""
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monkeypatch.setenv("ZAI_API_KEY", "test-api-key")
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litellm.disable_aiohttp_transport = True
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respx_mock.post("https://api.z.ai/api/paas/v4/chat/completions").respond(json=zai_response)
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response = completion(
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model="zai/glm-4.6",
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messages=[{"role": "user", "content": "Hello"}],
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max_tokens=20,
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)
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assert response.choices[0].message.content == "Hello! How can I help you today?"
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assert response.usage.total_tokens == 25
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