fix: handle max_completeion_tokens error for newer openai models

src/ragas/llms/base.py

@@ -594,6 +594,110 @@ def __init__(
         # Check if client is async-capable at initialization
         self.is_async = self._check_client_async()
 
+    def _map_provider_params(self) -> t.Dict[str, t.Any]:
+        """Route to provider-specific parameter mapping.
+
+        Each provider may have different parameter requirements:
+        - Google: Wraps parameters in generation_config and renames max_tokens
+        - OpenAI: Maps max_tokens to max_completion_tokens for o-series models
+        - Anthropic: No special handling required (pass-through)
+        - LiteLLM: No special handling required (routes internally, pass-through)
+        """
+        provider_lower = self.provider.lower()
+
+        if provider_lower == "google":
+            return self._map_google_params()
+        elif provider_lower == "openai":
+            return self._map_openai_params()
+        else:
+            # Anthropic, LiteLLM - pass through unchanged
+            return self.model_args.copy()
+
+    def _map_openai_params(self) -> t.Dict[str, t.Any]:
+        """Map max_tokens to max_completion_tokens for OpenAI reasoning models.
+
+        Reasoning models (o-series and gpt-5 series) require max_completion_tokens
+        instead of the deprecated max_tokens parameter when using Chat Completions API.
+
+        Legacy OpenAI models (gpt-4, gpt-4o, etc.) continue to use max_tokens unchanged.
+
+        Pattern-based matching for future-proof coverage:
+        - O-series: o1, o2, o3, o4, o5, ... (all reasoning versions)
+        - GPT-5 series: gpt-5, gpt-5-*, gpt-6, gpt-7, ... (all GPT-5+ models)
+        - Other: codex-mini
+        """
+        mapped_args = self.model_args.copy()
+
+        model_lower = self.model.lower()
+
+        # Pattern-based detection for reasoning models that require max_completion_tokens
+        # Uses prefix matching to cover current and future model variants
+        def is_reasoning_model(model_str: str) -> bool:
+            """Check if model is a reasoning model requiring max_completion_tokens."""
+            # O-series reasoning models (o1, o1-mini, o1-2024-12-17, o2, o3, o4, o5, o6, o7, o8, o9)
+            # Pattern: "o" followed by single digit 1-9, then optional "-" or end of string
+            # TODO: Update to support o10+ when OpenAI releases models beyond o9
+            if (
+                len(model_str) >= 2
+                and model_str[0] == "o"
+                and model_str[1] in "123456789"
+            ):
+                # Allow single digit o-series: o1, o2, ..., o9
+                if len(model_str) == 2 or model_str[2] in ("-", "_"):
+                    return True
+
+            # GPT-5 and newer generation models (gpt-5, gpt-5-*, gpt-6, gpt-7, ..., gpt-19)
+            # Pattern: "gpt-" followed by single or double digit >= 5, max 19
+            # TODO: Update to support gpt-20+ when OpenAI releases models beyond gpt-19
+            if model_str.startswith("gpt-"):
+                version_str = (
+                    model_str[4:].split("-")[0].split("_")[0]
+                )  # Get version number
+                try:
+                    version = int(version_str)
+                    if 5 <= version <= 19:
+                        return True
+                except ValueError:
+                    pass
+
+            # Other specific reasoning models
+            if model_str == "codex-mini":
+                return True
+
+            return False
+
+        requires_max_completion_tokens = is_reasoning_model(model_lower)
+
+        # If max_tokens is provided and model requires max_completion_tokens, map it
+        if requires_max_completion_tokens and "max_tokens" in mapped_args:
+            mapped_args["max_completion_tokens"] = mapped_args.pop("max_tokens")
+
+        return mapped_args
+
+    def _map_google_params(self) -> t.Dict[str, t.Any]:
+        """Map parameters for Google Gemini models.
+
+        Google models require parameters to be wrapped in a generation_config dict,
+        and max_tokens is renamed to max_output_tokens.
+        """
+        google_kwargs = {}
+        generation_config_keys = {"temperature", "max_tokens", "top_p", "top_k"}
+        generation_config = {}
+
+        for key, value in self.model_args.items():
+            if key in generation_config_keys:
+                if key == "max_tokens":
+                    generation_config["max_output_tokens"] = value
+                else:
+                    generation_config[key] = value
+            else:
+                google_kwargs[key] = value
+
+        if generation_config:
+            google_kwargs["generation_config"] = generation_config
+
+        return google_kwargs
+
     def _check_client_async(self) -> bool:
         """Determine if the client is async-capable."""
         try:
@@ -676,34 +780,22 @@ def generate(
                 self.agenerate(prompt, response_model)
             )
         else:
-            if self.provider.lower() == "google":
-                google_kwargs = {}
-                generation_config_keys = {"temperature", "max_tokens", "top_p", "top_k"}
-                generation_config = {}
-
-                for key, value in self.model_args.items():
-                    if key in generation_config_keys:
-                        if key == "max_tokens":
-                            generation_config["max_output_tokens"] = value
-                        else:
-                            generation_config[key] = value
-                    else:
-                        google_kwargs[key] = value
-
-                if generation_config:
-                    google_kwargs["generation_config"] = generation_config
+            # Map parameters based on provider requirements
+            provider_kwargs = self._map_provider_params()
 
+            if self.provider.lower() == "google":
                 result = self.client.create(
                     messages=messages,
                     response_model=response_model,
-                    **google_kwargs,
+                    **provider_kwargs,
                 )
             else:
+                # OpenAI, Anthropic, LiteLLM
                 result = self.client.chat.completions.create(
                     model=self.model,
                     messages=messages,
                     response_model=response_model,
-                    **self.model_args,
+                    **provider_kwargs,
                 )
 
         # Track the usage
@@ -732,34 +824,22 @@ async def agenerate(
                 "Cannot use agenerate() with a synchronous client. Use generate() instead."
             )
 
-        if self.provider.lower() == "google":
-            google_kwargs = {}
-            generation_config_keys = {"temperature", "max_tokens", "top_p", "top_k"}
-            generation_config = {}
-
-            for key, value in self.model_args.items():
-                if key in generation_config_keys:
-                    if key == "max_tokens":
-                        generation_config["max_output_tokens"] = value
-                    else:
-                        generation_config[key] = value
-                else:
-                    google_kwargs[key] = value
-
-            if generation_config:
-                google_kwargs["generation_config"] = generation_config
+        # Map parameters based on provider requirements
+        provider_kwargs = self._map_provider_params()
 
+        if self.provider.lower() == "google":
             result = await self.client.create(
                 messages=messages,
                 response_model=response_model,
-                **google_kwargs,
+                **provider_kwargs,
             )
         else:
+            # OpenAI, Anthropic, LiteLLM
             result = await self.client.chat.completions.create(
                 model=self.model,
                 messages=messages,
                 response_model=response_model,
-                **self.model_args,
+                **provider_kwargs,
             )
 
         # Track the usage