Upload 65 files

src/apis/routers/rag_agent_template.py

@@ -297,3 +297,36 @@ async def update_chatbot(chatbot_id: str, update_data: ChatbotUpdateRequest):
             status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
             content={"error": f"Failed to update chatbot: {str(e)}"},
         )
+
+
+@router.delete("/chatbots/{chatbot_id}")
+async def delete_chatbot(chatbot_id: str):
+    try:
+        existing_chatbot = await bot_crud.find_by_id(chatbot_id)
+
+        if not existing_chatbot:
+            return JSONResponse(
+                status_code=status.HTTP_404_NOT_FOUND,
+                content={"error": f"Chatbot with ID {chatbot_id} not found"},
+            )
+
+        deleted = await bot_crud.delete({"_id": ObjectId(chatbot_id)})
+
+        if not deleted:
+            return JSONResponse(
+                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+                content={"error": "Failed to delete chatbot"},
+            )
+
+        logger.info(f"Deleted chatbot with ID: {chatbot_id}")
+        return JSONResponse(
+            status_code=status.HTTP_200_OK,
+            content={"message": "Chatbot deleted successfully"},
+        )
+
+    except Exception as e:
+        logger.error(f"Error deleting chatbot: {str(e)}")
+        return JSONResponse(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            content={"error": f"Failed to delete chatbot: {str(e)}"},
+        )

src/config/llm.py

@@ -1,5 +1,6 @@
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings
+from langchain_openai import ChatOpenAI
 
 llm_2_0 = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=1, max_retries=2)
 
@@ -7,17 +8,21 @@ llm_2_5_flash_preview = ChatGoogleGenerativeAI(
     model="gemini-2.5-flash-preview-05-20", temperature=1, max_retries=2
 )
 
+llm_4o_mini = ChatOpenAI(model="gpt-4o-mini")
+llm_4o = ChatOpenAI(model="gpt-4o")
+
 embeddings = GoogleGenerativeAIEmbeddings(model="models/text-embedding-004")
 
+
 # Add this function for dynamic model selection
 def get_llm(model_name: str, **kwargs):
     if model_name == "gemini-2.0-flash":
         return ChatGoogleGenerativeAI(
             model="gemini-2.0-flash", temperature=0.1, max_retries=2, **kwargs
         )
-    elif model_name == "gemini-2.5-flash-preview-05-20":
-        return ChatGoogleGenerativeAI(
-            model="gemini-2.5-flash-preview-05-20", temperature=0.1, max_retries=2, **kwargs
-        )
+    elif model_name == "gpt-4o-mini":
+        return ChatOpenAI(model="gpt-4o-mini", **kwargs)
+    elif model_name == "gpt-4o":
+        return ChatOpenAI(model="gpt-4o", **kwargs)
     else:
-        raise ValueError(f"Unsupported model: {model_name}")
+        raise ValueError(f"Unknown model: {model_name}")