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/groq.py

@@ -1,13 +1,18 @@
-from application.llm.base import BaseLLM
 from openai import OpenAI
 
+from application.core.settings import settings
+from application.llm.base import BaseLLM
+
 
 class GroqLLM(BaseLLM):
     def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
+
         super().__init__(*args, **kwargs)
-        self.client = OpenAI(api_key=api_key, base_url="https://api.groq.com/openai/v1")
-        self.api_key = api_key
+        self.api_key = api_key or settings.GROQ_API_KEY or settings.API_KEY
         self.user_api_key = user_api_key
+        self.client = OpenAI(
+            api_key=self.api_key, base_url="https://api.groq.com/openai/v1"
+        )
 
     def _raw_gen(self, baseself, model, messages, stream=False, tools=None, **kwargs):
         if tools: