Changed default LLM to 'gpt-4o-mini'

Only used if the model isn't specified in the config or on the command line

SSH/config.ini.TEMPLATE

@@ -30,6 +30,13 @@ server_version_string = OpenSSH_8.2p1 Ubuntu-4ubuntu0.3
 llm_provider = openai
 model_name = gpt-4o
 
+##### Azure OpenAI
+#llm_provider = azure 
+#azure_deployment = gpt-4o 
+#azure_api_version = 2025-01-01-preview
+#azure_endpoint = <your endpoint url>
+#model_name = gpt-4o
+
 ##### ollama llama3
 #llm_provider = ollama
 #model_name = llama3.3
@@ -46,6 +53,12 @@ model_name = gpt-4o
 #llm_provider = gemini
 #model_name = gemini-1.5-pro
 
+# Temperature controls randomness in LLM responses. Values usually range from 0.0 to 2.0.
+# Lower values (e.g., 0.2) make responses more focused and deterministic.
+# Higher values (e.g., 0.8) make responses more creative and variable.
+# Default is 0.2.
+temperature = 0.2
+
 # The maximum number of tokens to send to the LLM backend in a single 
 # request. This includes the message history for the session, so should
 # be fairly high. Not all models support large token counts, so be sure 

SSH/ssh_server.py

@@ -15,7 +15,7 @@ import datetime
 import uuid
 from base64 import b64encode
 from operator import itemgetter
-from langchain_openai import ChatOpenAI
+from langchain_openai import ChatOpenAI, AzureChatOpenAI
 from langchain_aws import ChatBedrock, ChatBedrockConverse
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_ollama import ChatOllama 
@@ -234,7 +234,7 @@ async def handle_client(process: asyncssh.SSHServerProcess, server: MySSHServer)
                     },
                         config=llm_config
                 )
-                if llm_response.content == "XXX-END-OF-SESSION-XXX":
+                if llm_response.content == "YYY-END-OF-SESSION-YYY":
                     await session_summary(process, llm_config, with_message_history, server)
                     process.exit(0)
                     return
@@ -308,24 +308,40 @@ def get_user_accounts() -> dict:
 def choose_llm(llm_provider: Optional[str] = None, model_name: Optional[str] = None):
     llm_provider_name = llm_provider or config['llm'].get("llm_provider", "openai")
     llm_provider_name = llm_provider_name.lower()
-    model_name = model_name or config['llm'].get("model_name", "gpt-3.5-turbo")
+    model_name = model_name or config['llm'].get("model_name", "gpt-4o-mini")
+    
+    # Get temperature parameter from config, default to 0.2 if not specified
+    temperature = config['llm'].getfloat("temperature", 0.2)
 
     if llm_provider_name == 'openai':
         llm_model = ChatOpenAI(
-            model=model_name
+            model=model_name,
+            temperature=temperature
+        )
+    elif llm_provider_name == 'azure':
+        llm_model = AzureChatOpenAI(
+            azure_deployment=config['llm'].get("azure_deployment"),
+            azure_endpoint=config['llm'].get("azure_endpoint"),
+            api_version=config['llm'].get("azure_api_version"),
+            model=config['llm'].get("model_name"),  # Ensure model_name is passed here
+            temperature=temperature
         )
     elif llm_provider_name == 'ollama':
-            llm_model = ChatOllama(
+        llm_model = ChatOllama(
-            model=model_name
+            model=model_name,
+            temperature=temperature
         )
     elif llm_provider_name == 'aws':
         llm_model = ChatBedrockConverse(
             model=model_name,
             region_name=config['llm'].get("aws_region", "us-east-1"),
-            credentials_profile_name=config['llm'].get("aws_credentials_profile", "default")        )
+            credentials_profile_name=config['llm'].get("aws_credentials_profile", "default"),
+            temperature=temperature
+        )
     elif llm_provider_name == 'gemini':
         llm_model = ChatGoogleGenerativeAI(
             model=model_name,
+            temperature=temperature
         )
     else:
         raise ValueError(f"Invalid LLM provider {llm_provider_name}.")
@@ -367,6 +383,7 @@ try:
     parser.add_argument('-m', '--model-name', type=str, help='The model name to use')
     parser.add_argument('-t', '--trimmer-max-tokens', type=int, help='The maximum number of tokens to send to the LLM backend in a single request')
     parser.add_argument('-s', '--system-prompt', type=str, help='System prompt for the LLM')
+    parser.add_argument('-r', '--temperature', type=float, help='Temperature parameter for controlling randomness in LLM responses (0.0-2.0)')
     parser.add_argument('-P', '--port', type=int, help='The port the SSH honeypot will listen on')
     parser.add_argument('-k', '--host-priv-key', type=str, help='The host key to use for the SSH server')
     parser.add_argument('-v', '--server-version-string', type=str, help='The server version string to send to clients')
@@ -391,7 +408,7 @@ try:
             # Use defaults when no config file found.
             config['honeypot'] = {'log_file': 'ssh_log.log', 'sensor_name': socket.gethostname()}
             config['ssh'] = {'port': '8022', 'host_priv_key': 'ssh_host_key', 'server_version_string': 'SSH-2.0-OpenSSH_8.2p1 Ubuntu-4ubuntu0.3'}
-            config['llm'] = {'llm_provider': 'openai', 'model_name': 'gpt-3.5-turbo', 'trimmer_max_tokens': '64000', 'system_prompt': ''}
+            config['llm'] = {'llm_provider': 'openai', 'model_name': 'gpt-3.5-turbo', 'trimmer_max_tokens': '64000', 'temperature': '0.7', 'system_prompt': ''}
             config['user_accounts'] = {}
 
     # Override config values with command line arguments if provided
@@ -403,6 +420,8 @@ try:
         config['llm']['trimmer_max_tokens'] = str(args.trimmer_max_tokens)
     if args.system_prompt:
         config['llm']['system_prompt'] = args.system_prompt
+    if args.temperature is not None:
+        config['llm']['temperature'] = str(args.temperature)
     if args.port:
         config['ssh']['port'] = str(args.port)
     if args.host_priv_key: