feat: model registry and capabilities for multi-provider support (#2158)

* feat: Implement model registry and capabilities for multi-provider support

- Added ModelRegistry to manage available models and their capabilities.
- Introduced ModelProvider enum for different LLM providers.
- Created ModelCapabilities dataclass to define model features.
- Implemented methods to load models based on API keys and settings.
- Added utility functions for model management in model_utils.py.
- Updated settings.py to include provider-specific API keys.
- Refactored LLM classes (Anthropic, OpenAI, Google, etc.) to utilize new model registry.
- Enhanced utility functions to handle token limits and model validation.
- Improved code structure and logging for better maintainability.

* feat: Add model selection feature with API integration and UI component

* feat: Add model selection and default model functionality in agent management

* test: Update assertions and formatting in stream processing tests

* refactor(llm): Standardize model identifier to model_id

* fix tests

---------

Co-authored-by: Alex <a@tushynski.me>

application/llm/llm_creator.py

@@ -1,13 +1,17 @@
-from application.llm.groq import GroqLLM
-from application.llm.openai import OpenAILLM, AzureOpenAILLM
-from application.llm.sagemaker import SagemakerAPILLM
-from application.llm.huggingface import HuggingFaceLLM
-from application.llm.llama_cpp import LlamaCpp
+import logging
+
 from application.llm.anthropic import AnthropicLLM
 from application.llm.docsgpt_provider import DocsGPTAPILLM
-from application.llm.premai import PremAILLM
 from application.llm.google_ai import GoogleLLM
+from application.llm.groq import GroqLLM
+from application.llm.huggingface import HuggingFaceLLM
+from application.llm.llama_cpp import LlamaCpp
 from application.llm.novita import NovitaLLM
+from application.llm.openai import AzureOpenAILLM, OpenAILLM
+from application.llm.premai import PremAILLM
+from application.llm.sagemaker import SagemakerAPILLM
+
+logger = logging.getLogger(__name__)
 
 
 class LLMCreator:
@@ -26,10 +30,26 @@ class LLMCreator:
     }
 
     @classmethod
-    def create_llm(cls, type, api_key, user_api_key, decoded_token, *args, **kwargs):
+    def create_llm(
+        cls, type, api_key, user_api_key, decoded_token, model_id=None, *args, **kwargs
+    ):
+        from application.core.model_utils import get_base_url_for_model
+
         llm_class = cls.llms.get(type.lower())
         if not llm_class:
             raise ValueError(f"No LLM class found for type {type}")
+
+        # Extract base_url from model configuration if model_id is provided
+        base_url = None
+        if model_id:
+            base_url = get_base_url_for_model(model_id)
+
         return llm_class(
-            api_key, user_api_key, decoded_token=decoded_token, *args, **kwargs
+            api_key,
+            user_api_key,
+            decoded_token=decoded_token,
+            model_id=model_id,
+            base_url=base_url,
+            *args,
+            **kwargs,
         )