mirror of https://github.com/aliasrobotics/cai.git
generalice tool calls, remove ctf from arguments
This commit is contained in:
luijait
parent
05b07d7fd4
commit
33dff00caa
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@ -809,154 +809,48 @@ class OpenAIChatCompletionsModel(Model):
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# Try to extract a JSON object from the content
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json_start = ollama_full_content.find('{')
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json_end = ollama_full_content.rfind('}') + 1
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logger.debug(f"Ollama content length: {len(ollama_full_content)}, JSON start: {json_start}, JSON end: {json_end}")
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if json_start >= 0 and json_end > json_start:
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json_str = ollama_full_content[json_start:json_end]
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logger.debug(f"Extracted potential JSON: {json_str[:100]}...")
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# Special check for generic_linux_command format
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if "generic_linux_command" in json_str and "arguments" in json_str:
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logger.debug("Detected generic_linux_command pattern - using special handling")
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# Try regex pattern matching to handle potentially malformed JSON
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cmd_pattern = re.search(r'"command"\s*:\s*"([^"]+)"', json_str)
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args_pattern = re.search(r'"args"\s*:\s*"([^"]*)"', json_str)
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ctf_pattern = re.search(r'"ctf"\s*:\s*"([^"]*)"', json_str)
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if cmd_pattern:
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# Create a tool call specifically for generic_linux_command
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command = cmd_pattern.group(1)
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args = args_pattern.group(1) if args_pattern else ""
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ctf = ctf_pattern.group(1) if ctf_pattern else "<CTF_ENV>"
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tool_call_id = f"call_{hashlib.md5(('generic_linux_command' + str(time.time())).encode()).hexdigest()[:8]}"
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# Create a proper arguments object
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command_args = {
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"command": command,
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"args": args,
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"ctf": ctf,
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"async_mode": False,
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"session_id": ""
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}
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arguments_str = json.dumps(command_args)
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logger.debug(f"Created generic_linux_command with args: {arguments_str}")
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# Add it to our function_calls state
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state.function_calls[0] = ResponseFunctionToolCall(
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id=FAKE_RESPONSES_ID,
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arguments=arguments_str,
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name="generic_linux_command",
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type="function_call",
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call_id=tool_call_id,
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)
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# Display the tool call in CLI
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from cai.util import cli_print_agent_messages
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try:
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# Create a message-like object to display the function call
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tool_msg = type('ToolCallWrapper', (), {
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'content': None,
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'tool_calls': [
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type('ToolCallDetail', (), {
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'function': type('FunctionDetail', (), {
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'name': "generic_linux_command",
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'arguments': arguments_str
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}),
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'id': tool_call_id,
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'type': 'function'
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})
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]
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})
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# Print the tool call using the CLI utility
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cli_print_agent_messages(
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agent_name=getattr(self, 'agent_name', 'Agent'),
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message=tool_msg,
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counter=getattr(self, 'interaction_counter', 0),
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model=str(self.model),
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debug=False,
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interaction_input_tokens=estimated_input_tokens,
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interaction_output_tokens=estimated_output_tokens,
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interaction_reasoning_tokens=0,
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total_input_tokens=getattr(self, 'total_input_tokens', 0) + estimated_input_tokens,
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total_output_tokens=getattr(self, 'total_output_tokens', 0) + estimated_output_tokens,
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total_reasoning_tokens=0,
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interaction_cost=None,
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total_cost=None
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)
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except Exception as e:
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logger.error(f"Error displaying tool call in CLI: {e}")
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# Add to message history
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tool_call_msg = {
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"role": "assistant",
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"content": None,
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"tool_calls": [
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{
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": "generic_linux_command",
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"arguments": arguments_str
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}
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}
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]
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}
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streamed_tool_calls.append(tool_call_msg)
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add_to_message_history(tool_call_msg)
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logger.debug(f"Added generic_linux_command with args: {arguments_str}")
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# Standard JSON parsing for other function calls
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json_str = ollama_full_content[json_start:json_end]
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# Try to parse the JSON
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parsed = json.loads(json_str)
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# Check if it looks like a function call
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if ('name' in parsed and 'arguments' in parsed and
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isinstance(parsed['arguments'], dict)):
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if ('name' in parsed and 'arguments' in parsed):
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logger.debug(f"Found valid function call in Ollama output: {json_str}")
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# Create a tool call from the JSON object
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# Create a tool call ID
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tool_call_id = f"call_{hashlib.md5((parsed['name'] + str(time.time())).encode()).hexdigest()[:8]}"
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# Add it to our function_calls state
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state.function_calls[0] = ResponseFunctionToolCall(
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id=FAKE_RESPONSES_ID,
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arguments=json.dumps(parsed['arguments']),
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name=parsed['name'],
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type="function_call",
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call_id=tool_call_id,
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)
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# Ensure arguments is a valid JSON string
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arguments_str = ""
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if isinstance(parsed['arguments'], dict):
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# Remove 'ctf' field if it exists
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if 'ctf' in parsed['arguments']:
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del parsed['arguments']['ctf']
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arguments_str = json.dumps(parsed['arguments'])
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elif isinstance(parsed['arguments'], str):
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# If it's already a string, check if it's valid JSON
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try:
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# Try parsing to validate
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json.loads(parsed['arguments'])
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arguments_str = parsed['arguments']
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# Try parsing to validate and remove 'ctf' if present
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args_dict = json.loads(parsed['arguments'])
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if isinstance(args_dict, dict) and 'ctf' in args_dict:
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del args_dict['ctf']
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arguments_str = json.dumps(args_dict)
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except:
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# If not valid JSON, encode it as a JSON string
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arguments_str = json.dumps(parsed['arguments'])
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else:
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# For any other type, convert to string and then JSON
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arguments_str = json.dumps(str(parsed['arguments']))
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logger.debug(f"Final arguments string: {arguments_str}")
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# Update with the properly formatted arguments
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state.function_calls[0].arguments = arguments_str
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# Log the tool call for development purposes
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logger.debug(f"Adding tool call: {parsed['name']} with arguments: {arguments_str}")
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arguments_str = json.dumps(str(parsed['arguments']))
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# Add it to our function_calls state
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state.function_calls[0] = ResponseFunctionToolCall(
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id=FAKE_RESPONSES_ID,
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arguments=arguments_str,
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name=parsed['name'],
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type="function_call",
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call_id=tool_call_id,
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)
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# Display the tool call in CLI
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from cai.util import cli_print_agent_messages
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@ -1015,106 +909,9 @@ class OpenAIChatCompletionsModel(Model):
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streamed_tool_calls.append(tool_call_msg)
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add_to_message_history(tool_call_msg)
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logger.debug(f"Added function call: {parsed['name']} with args: {json.dumps(parsed['arguments'])}")
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logger.debug(f"Added function call: {parsed['name']} with args: {arguments_str}")
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except Exception as e:
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logger.debug(f"Failed to parse potential Ollama function call: {e}")
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# Even if JSON parsing fails, try to extract generic_linux_command with regex
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if "generic_linux_command" in ollama_full_content:
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logger.debug("JSON parsing failed but detected generic_linux_command - trying regex fallback")
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try:
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# Use regex to extract command components even from malformed output
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cmd_pattern = re.search(r'"command"\s*:\s*"([^"]+)"', ollama_full_content)
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args_pattern = re.search(r'"args"\s*:\s*"([^"]*)"', ollama_full_content)
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ctf_pattern = re.search(r'"ctf"\s*:\s*"([^"]*)"', ollama_full_content)
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if cmd_pattern:
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# Create a tool call specifically for generic_linux_command
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command = cmd_pattern.group(1)
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args = args_pattern.group(1) if args_pattern else ""
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ctf = ctf_pattern.group(1) if ctf_pattern else "<CTF_ENV>"
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tool_call_id = f"call_{hashlib.md5(('generic_linux_command' + str(time.time())).encode()).hexdigest()[:8]}"
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# Create a proper arguments object
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command_args = {
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"command": command,
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"args": args,
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"ctf": ctf,
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"async_mode": False,
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"session_id": ""
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}
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arguments_str = json.dumps(command_args)
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logger.debug(f"Fallback created generic_linux_command with args: {arguments_str}")
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# Add it to our function_calls state
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state.function_calls[0] = ResponseFunctionToolCall(
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id=FAKE_RESPONSES_ID,
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arguments=arguments_str,
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name="generic_linux_command",
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type="function_call",
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call_id=tool_call_id,
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)
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# Display the tool call in CLI
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from cai.util import cli_print_agent_messages
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try:
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# Create a message-like object for display
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tool_msg = type('ToolCallWrapper', (), {
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'content': None,
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'tool_calls': [
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type('ToolCallDetail', (), {
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'function': type('FunctionDetail', (), {
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'name': "generic_linux_command",
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'arguments': arguments_str
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}),
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'id': tool_call_id,
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'type': 'function'
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})
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]
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})
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cli_print_agent_messages(
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agent_name=getattr(self, 'agent_name', 'Agent'),
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message=tool_msg,
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counter=getattr(self, 'interaction_counter', 0),
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model=str(self.model),
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debug=False,
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interaction_input_tokens=estimated_input_tokens,
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interaction_output_tokens=estimated_output_tokens,
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interaction_reasoning_tokens=0,
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total_input_tokens=getattr(self, 'total_input_tokens', 0) + estimated_input_tokens,
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total_output_tokens=getattr(self, 'total_output_tokens', 0) + estimated_output_tokens,
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total_reasoning_tokens=0,
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interaction_cost=None,
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total_cost=None
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)
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except Exception as e:
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logger.error(f"Error displaying fallback tool call in CLI: {e}")
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# Add to message history
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tool_call_msg = {
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"role": "assistant",
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"content": None,
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"tool_calls": [
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{
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"id": tool_call_id,
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"type": "function",
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"function": {
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"name": "generic_linux_command",
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"arguments": arguments_str
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}
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}
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]
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}
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streamed_tool_calls.append(tool_call_msg)
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add_to_message_history(tool_call_msg)
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logger.debug(f"Added fallback generic_linux_command")
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except Exception as regex_err:
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logger.error(f"Regex fallback also failed: {regex_err}")
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pass
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function_call_starting_index = 0
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if state.text_content_index_and_output:
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