fix(agent, forge): Fix Pydantic v2 protected namespace model_ warnings (#7340)

Rename `model_*` fields to `llm_*`

forge/forge/components/README.md

@@ -116,7 +116,7 @@ You can set sensitive variables in the `.json` file as well but it's recommended
         "github_username": null
     },
     "ActionHistoryConfiguration": {
-        "model_name": "gpt-3.5-turbo",
+        "llm_name": "gpt-3.5-turbo",
         "max_tokens": 1024,
         "spacy_language_model": "en_core_web_sm"
     },
@@ -129,7 +129,7 @@ You can set sensitive variables in the `.json` file as well but it's recommended
         "duckduckgo_max_attempts": 3
     },
     "WebSeleniumConfiguration": {
-        "model_name": "gpt-3.5-turbo",
+        "llm_name": "gpt-3.5-turbo",
         "web_browser": "chrome",
         "headless": true,
         "user_agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36",

forge/forge/components/action_history/action_history.py

@@ -16,7 +16,7 @@ from .model import ActionResult, AnyProposal, Episode, EpisodicActionHistory
 
 
 class ActionHistoryConfiguration(BaseModel):
-    model_name: ModelName = OpenAIModelName.GPT3
+    llm_name: ModelName = OpenAIModelName.GPT3
     """Name of the llm model used to compress the history"""
     max_tokens: int = 1024
     """Maximum number of tokens to use up with generated history messages"""
@@ -97,7 +97,7 @@ class ActionHistoryComponent(
     async def after_execute(self, result: ActionResult) -> None:
         self.event_history.register_result(result)
         await self.event_history.handle_compression(
-            self.llm_provider, self.config.model_name, self.config.spacy_language_model
+            self.llm_provider, self.config.llm_name, self.config.spacy_language_model
         )
 
     @staticmethod

forge/forge/components/web/selenium.py

@@ -55,7 +55,7 @@ class BrowsingError(CommandExecutionError):
 
 
 class WebSeleniumConfiguration(BaseModel):
-    model_name: ModelName = OpenAIModelName.GPT3
+    llm_name: ModelName = OpenAIModelName.GPT3
     """Name of the llm model used to read websites"""
     web_browser: Literal["chrome", "firefox", "safari", "edge"] = "chrome"
     """Web browser used by Selenium"""
@@ -164,7 +164,7 @@ class WebSeleniumComponent(
             elif get_raw_content:
                 if (
                     output_tokens := self.llm_provider.count_tokens(
-                        text, self.config.model_name
+                        text, self.config.llm_name
                     )
                 ) > MAX_RAW_CONTENT_LENGTH:
                     oversize_factor = round(output_tokens / MAX_RAW_CONTENT_LENGTH, 1)
@@ -382,7 +382,7 @@ class WebSeleniumComponent(
                 text,
                 topics_of_interest=topics_of_interest,
                 llm_provider=self.llm_provider,
-                model_name=self.config.model_name,
+                model_name=self.config.llm_name,
                 spacy_model=self.config.browse_spacy_language_model,
             )
             return "\n".join(f"* {i}" for i in information)
@@ -391,7 +391,7 @@ class WebSeleniumComponent(
                 text,
                 question=question,
                 llm_provider=self.llm_provider,
-                model_name=self.config.model_name,
+                model_name=self.config.llm_name,
                 spacy_model=self.config.browse_spacy_language_model,
             )
             return result

forge/forge/llm/prompting/base.py

@@ -10,7 +10,7 @@ from .schema import ChatPrompt, LanguageModelClassification
 class PromptStrategy(abc.ABC):
     @property
     @abc.abstractmethod
-    def model_classification(self) -> LanguageModelClassification:
+    def llm_classification(self) -> LanguageModelClassification:
         ...
 
     @abc.abstractmethod

forge/forge/llm/providers/_openai_base.py

@@ -224,7 +224,7 @@ class BaseOpenAIChatProvider(
                         tool_calls=tool_calls or None,
                     ),
                     parsed_result=parsed_result,
-                    model_info=self.CHAT_MODELS[model_name],
+                    llm_info=self.CHAT_MODELS[model_name],
                     prompt_tokens_used=t_input,
                     completion_tokens_used=t_output,
                 )
@@ -457,7 +457,7 @@ class BaseOpenAIEmbeddingProvider(
 
         return EmbeddingModelResponse(
             embedding=embedding_parser(response.data[0].embedding),
-            model_info=self.EMBEDDING_MODELS[model_name],
+            llm_info=self.EMBEDDING_MODELS[model_name],
             prompt_tokens_used=response.usage.prompt_tokens,
         )
 

forge/forge/llm/providers/anthropic.py

@@ -309,7 +309,7 @@ class AnthropicProvider(BaseChatModelProvider[AnthropicModelName, AnthropicSetti
         return ChatModelResponse(
             response=assistant_msg,
             parsed_result=parsed_result,
-            model_info=ANTHROPIC_CHAT_MODELS[model_name],
+            llm_info=ANTHROPIC_CHAT_MODELS[model_name],
             prompt_tokens_used=t_input,
             completion_tokens_used=t_output,
         )

forge/forge/llm/providers/schema.py

@@ -186,7 +186,7 @@ class ModelResponse(BaseModel):
 
     prompt_tokens_used: int
     completion_tokens_used: int
-    model_info: ModelInfo
+    llm_info: ModelInfo
 
 
 class ModelProviderConfiguration(SystemConfiguration):