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* feat(tencent): add Tencent TokenHub as a provider Tencent TokenHub is OpenAI- and Anthropic-compatible. This registers it as a new provider: TencentChatConfig routes /v1/chat/completions and gates the thinking/reasoning_effort params behind supports_reasoning, and TencentAnthropicMessagesConfig routes the Anthropic-compatible Messages API. Adds cost tracking, the deepseek-v4-pro/flash model entries, and provider endpoint support metadata. * test(tencent): add unit tests for Tencent TokenHub provider Covers TencentChatConfig (chat completions) and TencentAnthropicMessagesConfig (messages API) across transformation, param mapping, URL building, and header validation, plus get_optional_params routing. Tests mock supports_reasoning to stay independent of remote model cost data. * fix(tencent): correct max_output_tokens and reuse parent messages env validation Raise max_output_tokens/max_tokens for tencent/deepseek-v4-pro and tencent/deepseek-v4-flash from 8192 to 384000, matching Tencent TokenHub's published DeepSeek-V4 output limit; the 8192 value mirrored the native DeepSeek default and would have rejected valid larger requests before they reached Tencent Delegate validate_anthropic_messages_environment to the parent via super() so the Tencent messages endpoint keeps content-type and anthropic-beta header injection instead of dropping them, keeping only the TENCENT_API_KEY resolution overridden Add regression tests covering beta-header injection, the cost-calculator delegation, provider-info secret resolution, and validate_environment key handling * fix(tencent): normalize messages URL when TENCENT_API_BASE has chat completions suffix * fix(tencent): register tencent in models_by_provider The provider was added to the LlmProviders enum and cost map but not to the models_by_provider lookup, so test_models_by_provider (which asserts every litellm_provider present in the cost map is registered) failed once the tencent models were loaded. Add the tencent_models set, populate it from the cost map, and expose it under the tencent key, mirroring deepseek. * fix(tencent): import generic_cost_per_token from its canonical module Import generic_cost_per_token from litellm.litellm_core_utils.llm_cost_calc.utils instead of the top-level litellm.cost_calculator dispatcher, which imports the tencent cost module at load time. Removing the back-reference avoids the circular import and matches how deepseek and the other providers source the helper. --------- Co-authored-by: Felipe Rodrigues Gare Carnielli <felipe.gare@hotmail.com> Co-authored-by: Cursor Agent <cursoragent@cursor.com> |
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| .. | ||
| chat | ||
| messages | ||
| __init__.py | ||
| test_cost_calculator.py | ||