missing cost fixes

[dixitaniket]

No description provided.

[Copilot]

Pull request overview

This PR fixes cases where successful inferences were missing token-usage metadata (and therefore missing OpenGradient cost/settlement data), with a particular focus on xAI’s LangChain response shapes and streaming behavior.

Changes:

• Expand extract_usage() to normalize usage from both LangChain’s usage_metadata and raw provider payloads under response_metadata (e.g., token_usage / usage).
• Ensure xAI non-streaming requests don’t accidentally use a streaming-configured cached model, and ensure xAI streaming requests explicitly request usage when applicable.
• Add targeted regression tests covering xAI streaming/non-streaming and the new extract_usage() fallbacks.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.

File	Description
tests/test_opengradient_field.py	Adds regression tests ensuring xAI streaming/non-streaming paths propagate usage/cost behavior correctly.
tee_gateway/test/test_tee_core.py	Adds unit tests for llm_backend.extract_usage() fallback behavior when LangChain omits usage_metadata.
tee_gateway/llm_backend.py	Extends extract_usage() to support additional metadata shapes and normalize reasoning tokens.
tee_gateway/controllers/chat_controller.py	Adjusts xAI streaming/non-streaming invocation kwargs and uses extract_usage() for streaming usage accumulation; logs when usage is missing.

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