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02565cd58d
* fix(langfuse.py): prevent double logging requester metadata Fixes https://github.com/BerriAI/litellm/issues/5935 * build(model_prices_and_context_window.json): add mistral pixtral cost tracking Closes https://github.com/BerriAI/litellm/issues/5837 * handle streaming for azure ai studio error * [Perf Proxy] parallel request limiter - use one cache update call (#5932) * fix parallel request limiter - use one cache update call * ci/cd run again * run ci/cd again * use docker username password * fix config.yml * fix config * fix config * fix config.yml * ci/cd run again * use correct typing for batch set cache * fix async_set_cache_pipeline * fix only check user id tpm / rpm limits when limits set * fix test_openai_azure_embedding_with_oidc_and_cf * fix(groq/chat/transformation.py): Fixes https://github.com/BerriAI/litellm/issues/5839 * feat(anthropic/chat.py): return 'retry-after' headers from anthropic Fixes https://github.com/BerriAI/litellm/issues/4387 * feat: raise validation error if message has tool calls without passing `tools` param for anthropic/bedrock Closes https://github.com/BerriAI/litellm/issues/5747 * [Feature]#5940, add max_workers parameter for the batch_completion (#5947) * handle streaming for azure ai studio error * bump: version 1.48.2 → 1.48.3 * docs(data_security.md): add legal/compliance faq's Make it easier for companies to use litellm * docs: resolve imports * [Feature]#5940, add max_workers parameter for the batch_completion method --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: Krrish Dholakia <krrishdholakia@gmail.com> Co-authored-by: josearangos <josearangos@Joses-MacBook-Pro.local> * fix(converse_transformation.py): fix default message value * fix(utils.py): fix get_model_info to handle finetuned models Fixes issue for standard logging payloads, where model_map_value was null for finetuned openai models * fix(litellm_pre_call_utils.py): add debug statement for data sent after updating with team/key callbacks * fix: fix linting errors * fix(anthropic/chat/handler.py): fix cache creation input tokens * fix(exception_mapping_utils.py): fix missing imports * fix(anthropic/chat/handler.py): fix usage block translation * test: fix test * test: fix tests * style(types/utils.py): trigger new build * test: fix test --------- Co-authored-by: Ishaan Jaff <ishaanjaffer0324@gmail.com> Co-authored-by: Jose Alberto Arango Sanchez <jose.arangos@udea.edu.co> Co-authored-by: josearangos <josearangos@Joses-MacBook-Pro.local>
70 lines
2.2 KiB
Python
70 lines
2.2 KiB
Python
# What is this?
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## Unit testing for the 'get_model_info()' function
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import os
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import sys
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import traceback
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import pytest
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import litellm
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from litellm import get_model_info
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def test_get_model_info_simple_model_name():
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"""
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tests if model name given, and model exists in model info - the object is returned
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"""
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model = "claude-3-opus-20240229"
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litellm.get_model_info(model)
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def test_get_model_info_custom_llm_with_model_name():
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"""
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Tests if {custom_llm_provider}/{model_name} name given, and model exists in model info, the object is returned
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"""
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model = "anthropic/claude-3-opus-20240229"
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litellm.get_model_info(model)
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def test_get_model_info_custom_llm_with_same_name_vllm():
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"""
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Tests if {custom_llm_provider}/{model_name} name given, and model exists in model info, the object is returned
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"""
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model = "command-r-plus"
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provider = "openai" # vllm is openai-compatible
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try:
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litellm.get_model_info(model, custom_llm_provider=provider)
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pytest.fail("Expected get model info to fail for an unmapped model/provider")
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except Exception:
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pass
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def test_get_model_info_shows_correct_supports_vision():
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info = litellm.get_model_info("gemini/gemini-1.5-flash")
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print("info", info)
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assert info["supports_vision"] is True
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def test_get_model_info_shows_assistant_prefill():
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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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info = litellm.get_model_info("deepseek/deepseek-chat")
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print("info", info)
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assert info.get("supports_assistant_prefill") is True
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def test_get_model_info_shows_supports_prompt_caching():
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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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info = litellm.get_model_info("deepseek/deepseek-chat")
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print("info", info)
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assert info.get("supports_prompt_caching") is True
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def test_get_model_info_finetuned_models():
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info = litellm.get_model_info("ft:gpt-3.5-turbo:my-org:custom_suffix:id")
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print("info", info)
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assert info["input_cost_per_token"] == 0.000003
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