Update llm_providers.py

llm_providers.py

@@ -1,133 +1,128 @@
-from langchain_anthropic import ChatAnthropic
-from langchain_openai import ChatOpenAI
-from langchain_ollama import ChatOllama
-from langchain_core.language_models.base import BaseLanguageModel
-from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
-from typing import Optional, Dict, List, Any
-import os
-import requests
-import json
-from dotenv import load_dotenv
-from dataclasses import dataclass
-
-
-load_dotenv()
-
-
-@dataclass
-class GeminiResponse:
-    content: str
-
-
-class GeminiProvider:
-    def __init__(self, api_key: str):
-        self.api_key = api_key
-        self.base_url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent"
-        
-    def chat(self, messages: List[Dict[str, Any]]) -> GeminiResponse:
-        # Convert messages to Gemini format
-        gemini_messages = []
-        for msg in messages:
-            # Handle both dict and LangChain message objects
-            if isinstance(msg, BaseMessage):
-                role = "user" if isinstance(msg, HumanMessage) else "model"
-                content = msg.content
-            else:
-                role = "user" if msg["role"] == "human" else "model"
-                content = msg["content"]
-                
-            gemini_messages.append({
-                "role": role,
-                "parts": [{"text": content}]
-            })
-        
-        # Prepare the request
-        headers = {
-            "Content-Type": "application/json"
-        }
-        
-        params = {
-            "key": self.api_key
-        }
-        
-        data = {
-            "contents": gemini_messages,
-            "generationConfig": {
-                "temperature": 0.7,
-                "topP": 0.8,
-                "topK": 40,
-                "maxOutputTokens": 2048,
-            }
-        }
-        
-        try:
-            response = requests.post(
-                self.base_url,
-                headers=headers,
-                params=params,
-                json=data,
-                verify='C:\\ProgramData\\NetFree\\CA\\netfree-ca-bundle-curl.crt'
-            )
-            response.raise_for_status()
-            
-            result = response.json()
-            if "candidates" in result and len(result["candidates"]) > 0:
-                return GeminiResponse(content=result["candidates"][0]["content"]["parts"][0]["text"])
-            else:
-                raise Exception("No response generated")
-                
-        except Exception as e:
-            raise Exception(f"Error calling Gemini API: {str(e)}")
-
-    def invoke(self, messages: List[BaseMessage], **kwargs) -> GeminiResponse:
-        return self.chat(messages)
-
-    def generate(self, prompts, **kwargs) -> GeminiResponse:
-        if isinstance(prompts, str):
-            return self.invoke([HumanMessage(content=prompts)])
-        elif isinstance(prompts, list):
-            return self.invoke([HumanMessage(content=prompts[0])])
-        raise ValueError("Unsupported prompt format")
-
-class LLMProvider:
-    def __init__(self):
-        self.providers: Dict[str, Any] = {}
-        self._setup_providers()
-
-    def _setup_providers(self):
-        os.environ['REQUESTS_CA_BUNDLE'] = 'C:\\ProgramData\\NetFree\\CA\\netfree-ca-bundle-curl.crt'
-        
-           # Google Gemini
-        if google_key := os.getenv('GOOGLE_API_KEY'):
-            self.providers['Gemini'] = GeminiProvider(api_key=google_key)
-
-        
-        # Anthropic
-        if anthropic_key := os.getenv('ANTHROPIC_API_KEY'):
-            self.providers['Claude'] = ChatAnthropic(
-                api_key=anthropic_key,
-                model_name="claude-3-5-sonnet-20241022",
-                
-            )
-
-        # OpenAI
-        if openai_key := os.getenv('OPENAI_API_KEY'):
-            self.providers['ChatGPT'] = ChatOpenAI(
-                api_key=openai_key,
-                model_name="gpt-4o-2024-11-20"
-            )
-
-     
-        # Ollama (local)
-        try:
-            self.providers['Ollama-dictalm2.0'] = ChatOllama(model="dictaLM")
-        except Exception:
-            pass  # Ollama not available
-
-    def get_available_providers(self) -> list[str]:
-        """Return list of available provider names"""
-        return list(self.providers.keys())
-
-    def get_provider(self, name: str) -> Optional[Any]:
-        """Get LLM provider by name"""
-        return self.providers.get(name)
+from langchain_anthropic import ChatAnthropic
+from langchain_openai import ChatOpenAI
+from langchain_ollama import ChatOllama
+from langchain_core.language_models.base import BaseLanguageModel
+from langchain_core.messages import BaseMessage, HumanMessage, AIMessage
+from typing import Optional, Dict, List, Any
+import os
+import requests
+import json
+from dotenv import load_dotenv
+from dataclasses import dataclass
+
+
+load_dotenv()
+
+
+@dataclass
+class GeminiResponse:
+    content: str
+
+
+class GeminiProvider:
+    def __init__(self, api_key: str):
+        self.api_key = api_key
+        self.base_url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash:generateContent"
+        
+    def chat(self, messages: List[Dict[str, Any]]) -> GeminiResponse:
+        # Convert messages to Gemini format
+        gemini_messages = []
+        for msg in messages:
+            # Handle both dict and LangChain message objects
+            if isinstance(msg, BaseMessage):
+                role = "user" if isinstance(msg, HumanMessage) else "model"
+                content = msg.content
+            else:
+                role = "user" if msg["role"] == "human" else "model"
+                content = msg["content"]
+                
+            gemini_messages.append({
+                "role": role,
+                "parts": [{"text": content}]
+            })
+        
+        # Prepare the request
+        headers = {
+            "Content-Type": "application/json"
+        }
+        
+        params = {
+            "key": self.api_key
+        }
+        
+        data = {
+            "contents": gemini_messages,
+            "generationConfig": {
+                "temperature": 0.7,
+                "topP": 0.8,
+                "topK": 40,
+                "maxOutputTokens": 2048,
+            }
+        }
+        
+        try:
+            response = requests.post(
+                self.base_url,
+                headers=headers,
+                params=params,
+                json=data,
+                
+            )
+            response.raise_for_status()
+            
+            result = response.json()
+            if "candidates" in result and len(result["candidates"]) > 0:
+                return GeminiResponse(content=result["candidates"][0]["content"]["parts"][0]["text"])
+            else:
+                raise Exception("No response generated")
+                
+        except Exception as e:
+            raise Exception(f"Error calling Gemini API: {str(e)}")
+
+    def invoke(self, messages: List[BaseMessage], **kwargs) -> GeminiResponse:
+        return self.chat(messages)
+
+    def generate(self, prompts, **kwargs) -> GeminiResponse:
+        if isinstance(prompts, str):
+            return self.invoke([HumanMessage(content=prompts)])
+        elif isinstance(prompts, list):
+            return self.invoke([HumanMessage(content=prompts[0])])
+        raise ValueError("Unsupported prompt format")
+
+class LLMProvider:
+    def __init__(self):
+        self.providers: Dict[str, Any] = {}
+        self._setup_providers()
+
+    def _setup_providers(self):
+        
+           # Google Gemini
+        if google_key := os.getenv('GOOGLE_API_KEY'):
+            self.providers['Gemini'] = GeminiProvider(api_key=google_key)
+
+        
+        # Anthropic
+        if anthropic_key := os.getenv('ANTHROPIC_API_KEY'):
+            self.providers['Claude'] = ChatAnthropic(
+                api_key=anthropic_key,
+                model_name="claude-3-5-sonnet-20241022",
+                
+            )
+
+        # OpenAI
+        if openai_key := os.getenv('OPENAI_API_KEY'):
+            self.providers['ChatGPT'] = ChatOpenAI(
+                api_key=openai_key,
+                model_name="gpt-4o-2024-11-20"
+            )
+
+     
+       
+
+    def get_available_providers(self) -> list[str]:
+        """Return list of available provider names"""
+        return list(self.providers.keys())
+
+    def get_provider(self, name: str) -> Optional[Any]:
+        """Get LLM provider by name"""
+        return self.providers.get(name)