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/anthropic.py

@@ -1,30 +1,41 @@
-from application.llm.base import BaseLLM
+from anthropic import AI_PROMPT, Anthropic, HUMAN_PROMPT
+
 from application.core.settings import settings
+from application.llm.base import BaseLLM
 
 
 class AnthropicLLM(BaseLLM):
 
-    def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
-        from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
+    def __init__(self, api_key=None, user_api_key=None, base_url=None, *args, **kwargs):
 
         super().__init__(*args, **kwargs)
-        self.api_key = (
-            api_key or settings.ANTHROPIC_API_KEY
-        )  # If not provided, use a default from settings
+        self.api_key = api_key or settings.ANTHROPIC_API_KEY or settings.API_KEY
         self.user_api_key = user_api_key
-        self.anthropic = Anthropic(api_key=self.api_key)
+
+        # Use custom base_url if provided
+        if base_url:
+            self.anthropic = Anthropic(api_key=self.api_key, base_url=base_url)
+        else:
+            self.anthropic = Anthropic(api_key=self.api_key)
+
         self.HUMAN_PROMPT = HUMAN_PROMPT
         self.AI_PROMPT = AI_PROMPT
 
     def _raw_gen(
-        self, baseself, model, messages, stream=False, tools=None, max_tokens=300, **kwargs
+        self,
+        baseself,
+        model,
+        messages,
+        stream=False,
+        tools=None,
+        max_tokens=300,
+        **kwargs,
     ):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
         prompt = f"### Context \n {context} \n ### Question \n {user_question}"
         if stream:
             return self.gen_stream(model, prompt, stream, max_tokens, **kwargs)
-
         completion = self.anthropic.completions.create(
             model=model,
             max_tokens_to_sample=max_tokens,
@@ -34,7 +45,14 @@ class AnthropicLLM(BaseLLM):
         return completion.completion
 
     def _raw_gen_stream(
-        self, baseself, model, messages, stream=True, tools=None, max_tokens=300, **kwargs
+        self,
+        baseself,
+        model,
+        messages,
+        stream=True,
+        tools=None,
+        max_tokens=300,
+        **kwargs,
     ):
         context = messages[0]["content"]
         user_question = messages[-1]["content"]
@@ -50,5 +68,5 @@ class AnthropicLLM(BaseLLM):
             for completion in stream_response:
                 yield completion.completion
         finally:
-            if hasattr(stream_response, 'close'):
+            if hasattr(stream_response, "close"):
                 stream_response.close()