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* fix(token_counter): count OpenAI models newer than tiktoken's table with o200k_base Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): keep tiktoken's encoding for suffixed variants of models it knows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(token_counter): move the o200k provenance note into the assert message Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): strip openai/ and azure/ prefixes before picking the encoding Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(rust_bridge): azure/gpt-4o now resolves to o200k_base Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(token_counter): check the provider prefix through _fix_model_name to keep the type gate flat Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(token_counter): check o200k on the gpt-6 models tiktoken 0.14 still does not know * fix(token_counter): count Azure deployment names and OpenAI alias groups with their OpenAI tokenizer * fix(token_counter): count mixed, alias, and base-model deployments with every served model's tokenizer A group that serves OpenAI and non-OpenAI models now reserves at least each served model's count, model_group_alias names count as their target group, custom Azure deployment names count with their base model, and azure_ai/, chatgpt/ and similar prefixes resolve to the listed OpenAI model. * fix(token_counter): keep mixed groups on OpenAI tokenizers, cache served models, and count context checks, failed rows and stream fallback with the served model * fix(token-counter): keep mixed groups at their old reservation and count new OpenAI release names with o200k A group that serves an OpenAI model next to another vendor now counts its own name the way tiktoken reads it as written, plus its OpenAI models, so it reserves what main did instead of the lower o200k count base_model picks the tokenizer for estimated usage only when it names an OpenAI model, now also in the Cursor stream counter and the output-limit response Unlisted gpt-5+ and o-series names count with o200k_base, and wildcard routes run the context-window check with the matched OpenAI model * fix(token-counter): count an alias to a group no OpenAI model serves by the alias as written Keeps the count main gives such an alias, so its budget reservation and rate limit match main and no other vendor's tokenizer loads for it. Also sorts the import the main merge left unsorted. * test: type the new token-counting tests' parameters * fix(token-counter): count a release-named group no OpenAI model serves as main does, and snapshot the OpenAI model set before iterating it * fix(token-counter): read served models through the router's allowlisted deployments_named, and coordinate the registration race test * perf(token-counter): classify each distinct served model once when walking a model group Budgeted requests walk every deployment in their model group, since the router's cached copy is outside the router surface the proxy may use. Classifying each distinct served model once instead of once per deployment cuts a 200-deployment group from 2.3-4.5 ms to about 1.4 ms with the same result. --------- Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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| .. | ||
| _support | ||
| audio_tests | ||
| base_sdk_tests | ||
| benchmarks | ||
| code_coverage_tests | ||
| documentation_tests | ||
| e2e | ||
| e2e_harness | ||
| fixtures | ||
| harness_e2e | ||
| integration | ||
| rust-python-harness | ||
| test_litellm_rust | ||
| typing | ||
| unit | ||
| windows_tests | ||
| __init__.py | ||
| _fake_openai_endpoint_server.py | ||
| _flush_vcr_cache.py | ||
| _live_test_helpers.py | ||
| _master_key.py | ||
| _openai_record_replay_proxy.py | ||
| _process_helpers.py | ||
| _vcr_conftest_common.py | ||
| _vcr_redis_persister.py | ||
| _wait_helpers.py | ||
| _ws_vcr.py | ||
| AGENTS.md | ||
| capturing_transport.py | ||
| eval_swe_bench.py | ||
| fake_openai_endpoint.py | ||
| gettysburg.wav | ||
| large_text.py | ||
| openai_batch_completions.jsonl | ||
| pyrightconfig.json | ||
| README.MD | ||
| test_logging.conf | ||
| test_rust_python_harness.py | ||
| VCR_TESTING.md | ||
| white_100x100.png | ||
In total litellm runs 1000+ tests
[02/20/2025] Update:
To make it easier to contribute and map what behavior is tested,
we've started mapping the litellm directory in tests/unit
This folder can only run mock tests.