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fix: fix chat_max_tokens calculation method to show true input_max_tokens (#1241)
* fix: use real max_input_length * lint * Update rdagent/oai/backend/litellm.py Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * fix lint * lint * lint --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
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@@ -153,7 +153,7 @@ class DeprecBackend(APIBackend):
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self.gcr_endpoint_do_sample = LLM_SETTINGS.gcr_endpoint_do_sample
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self.gcr_endpoint_max_token = LLM_SETTINGS.gcr_endpoint_max_token
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if not os.environ.get("PYTHONHTTPSVERIFY", "") and hasattr(ssl, "_create_unverified_context"):
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ssl._create_default_https_context = ssl._create_unverified_context # noqa: SLF001
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ssl._create_default_https_context = ssl._create_unverified_context # type: ignore[assignment]
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self.chat_model_map = LLM_SETTINGS.chat_model_map
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self.chat_model = LLM_SETTINGS.chat_model
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self.encoder = None
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@@ -7,7 +7,7 @@ from litellm import (
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completion,
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completion_cost,
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embedding,
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get_max_tokens,
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get_model_info,
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supports_function_calling,
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supports_response_schema,
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token_counter,
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@@ -205,10 +205,19 @@ class LiteLLMAPIBackend(APIBackend):
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@property
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def chat_token_limit(self) -> int:
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"""Suggest an input token limit, ensuring enough space in the context window for the maximum output tokens."""
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try:
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max_tokens = get_max_tokens(LITELLM_SETTINGS.chat_model)
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if max_tokens is None:
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model_info = get_model_info(LITELLM_SETTINGS.chat_model)
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if model_info is None:
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return super().chat_token_limit
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return max_tokens
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max_input = model_info.get("max_input_tokens")
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max_output = model_info.get("max_output_tokens")
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if max_input is None or max_output is None:
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return super().chat_token_limit
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max_input_tokens = max_input - max_output
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return max_input_tokens
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except Exception as e:
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return super().chat_token_limit
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