remove extra adapters

src/llm/adapters.py

@@ -1,48 +1,24 @@
 """LLM client adapters for different providers."""
-
-from typing import Dict, Any, List, Optional, Union
-from abc import ABC, abstractmethod
 from dataclasses import dataclass
+from abc import ABC, abstractmethod
+from typing import Dict, Any, List, Optional, Union
 
-# LangChain imports
-from langchain_mistralai.chat_models import ChatMistralAI
+# from langchain_ollama import ChatOllama
 from langchain_openai.chat_models import ChatOpenAI
-from langchain_ollama import ChatOllama
+# from langchain_mistralai.chat_models import ChatMistralAI
+
 
-# Legacy client dependencies
+# Legacy dependencies
 from huggingface_hub import InferenceClient
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain_community.llms import HuggingFaceEndpoint
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from langchain_community.chat_models.huggingface import ChatHuggingFace
 
-# Configuration loader
-from ..config.loader import load_config
 
-# Load configuration once at module level
+from ..config.loader import load_config
 _config = load_config()
 
-
-# Legacy client factory functions (inlined from auditqa_old.reader)
-def _create_inf_provider_client():
-    """Create INF_PROVIDERS client."""
-    reader_config = _config.get("reader", {})
-    inf_config = reader_config.get("INF_PROVIDERS", {})
-    
-    api_key = inf_config.get("api_key")
-    if not api_key:
-        raise ValueError("INF_PROVIDERS api_key not found in configuration")
-    
-    provider = inf_config.get("provider")
-    if not provider:
-        raise ValueError("INF_PROVIDERS provider not found in configuration")
-
-    return InferenceClient(
-        provider=provider,
-        api_key=api_key,
-        bill_to="GIZ",
-    )
-
-
+# Legacy functions
 def _create_nvidia_client():
     """Create NVIDIA client."""
     reader_config = _config.get("reader", {})
@@ -138,31 +114,31 @@ class BaseLLMAdapter(ABC):
         pass
 
 
-class MistralAdapter(BaseLLMAdapter):
-    """Adapter for Mistral AI models."""
+# class MistralAdapter(BaseLLMAdapter):
+#     """Adapter for Mistral AI models."""
     
-    def __init__(self, config: Dict[str, Any]):
-        super().__init__(config)
-        self.model = ChatMistralAI(
-            model=config.get("model", "mistral-medium-latest")
-        )
+#     def __init__(self, config: Dict[str, Any]):
+#         super().__init__(config)
+#         self.model = ChatMistralAI(
+#             model=config.get("model", "mistral-medium-latest")
+#         )
     
-    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
-        """Generate response using Mistral."""
-        response = self.model.invoke(messages)
+#     def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
+#         """Generate response using Mistral."""
+#         response = self.model.invoke(messages)
         
-        return LLMResponse(
-            content=response.content,
-            model=self.config.get("model", "mistral-medium-latest"),
-            provider="mistral",
-            metadata={"usage": getattr(response, 'usage_metadata', {})}
-        )
+#         return LLMResponse(
+#             content=response.content,
+#             model=self.config.get("model", "mistral-medium-latest"),
+#             provider="mistral",
+#             metadata={"usage": getattr(response, 'usage_metadata', {})}
+#         )
     
-    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
-        """Generate streaming response using Mistral."""
-        for chunk in self.model.stream(messages):
-            if chunk.content:
-                yield chunk.content
+#     def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
+#         """Generate streaming response using Mistral."""
+#         for chunk in self.model.stream(messages):
+#             if chunk.content:
+#                 yield chunk.content
 
 
 class OpenAIAdapter(BaseLLMAdapter):
@@ -192,34 +168,34 @@ class OpenAIAdapter(BaseLLMAdapter):
                 yield chunk.content
 
 
-class OllamaAdapter(BaseLLMAdapter):
-    """Adapter for Ollama models."""
+# class OllamaAdapter(BaseLLMAdapter):
+#     """Adapter for Ollama models."""
     
-    def __init__(self, config: Dict[str, Any]):
-        super().__init__(config)
-        self.model = ChatOllama(
-            model=config.get("model", "mistral-small3.1:24b-instruct-2503-q8_0"),
-            base_url=config.get("base_url", "http://localhost:11434/"),
-            temperature=config.get("temperature", 0.8),
-            num_predict=config.get("num_predict", 256)
-        )
+#     def __init__(self, config: Dict[str, Any]):
+#         super().__init__(config)
+#         self.model = ChatOllama(
+#             model=config.get("model", "mistral-small3.1:24b-instruct-2503-q8_0"),
+#             base_url=config.get("base_url", "http://localhost:11434/"),
+#             temperature=config.get("temperature", 0.8),
+#             num_predict=config.get("num_predict", 256)
+#         )
     
-    def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
-        """Generate response using Ollama."""
-        response = self.model.invoke(messages)
+#     def generate(self, messages: List[Dict[str, str]], **kwargs) -> LLMResponse:
+#         """Generate response using Ollama."""
+#         response = self.model.invoke(messages)
         
-        return LLMResponse(
-            content=response.content,
-            model=self.config.get("model", "mistral-small3.1:24b-instruct-2503-q8_0"),
-            provider="ollama",
-            metadata={}
-        )
-    
-    def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
-        """Generate streaming response using Ollama."""
-        for chunk in self.model.stream(messages):
-            if chunk.content:
-                yield chunk.content
+#         return LLMResponse(
+#             content=response.content,
+#             model=self.config.get("model", "mistral-small3.1:24b-instruct-2503-q8_0"),
+#             provider="ollama",
+#             metadata={}
+#         )
+    
+#     def stream_generate(self, messages: List[Dict[str, str]], **kwargs):
+#         """Generate streaming response using Ollama."""
+#         for chunk in self.model.stream(messages):
+#             if chunk.content:
+#                 yield chunk.content
 
 
 class OpenRouterAdapter(BaseLLMAdapter):