Suppress the three reportUnknownArgumentType diagnostics with reasons at
the untyped provider-params boundary, collapse the early return, and
assign extra_body via a TypedDict-annotated literal so the file's
basedpyright profile matches the merge base exactly. The user-supplied
extra_body merge is covered end-to-end through get_optional_params.
Address Greptile review comments and the strict lint budgets:
- read supports_adaptive_thinking from the model cost map instead of
substring-matching the model name, so aliases and newly onboarded
adaptive-only models need no code change
- add tencent/minimax-m3 to the pricing JSON (and backup), which also
fixes cost tracking for the model
- type the thinking/extra_body payloads with ReadOnly TypedDicts
- build the merged extra_body without rebinding or in-place mutation
Tencent chat completions route through the OpenAI SDK's
chat.completions.create(), which raises TypeError on unknown kwargs -
so a top-level 'thinking' optional param crashed every reasoning
request with a 500 before any HTTP call was made.
Nest the resolved thinking object in extra_body instead: the SDK merges
extra_body into the top-level JSON payload, so TokenHub still receives
the documented thinking field (type/budget_tokens) in the request body.
Also align the param mapping with TokenHub's documented behavior:
- reasoning_effort="none" now maps to thinking={"type": "disabled"}
instead of being dropped (deepseek-v4-* default to thinking enabled,
so dropping it never actually disabled thinking)
- MiniMax models only accept thinking.type "adaptive"/"disabled",
so "enabled" is coerced to "adaptive" instead of returning a 400
Refs: https://www.tencentcloud.com/document/product/1300/82345
* 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>
