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import os
import tempfile
from autogen.agentchat import gather_usage_summary
from openai_server.backend_utils import structure_to_messages
from openai_server.agent_utils import get_ret_dict_and_handle_files
from openai_server.agent_prompting import get_full_system_prompt
from openai_server.autogen_utils import merge_group_chat_messages
from openai_server.autogen_utils import get_all_conversable_agents
def run_autogen_multi_agent(query=None,
visible_models=None,
stream_output=None,
max_new_tokens=None,
authorization=None,
chat_conversation=None,
text_context_list=None,
system_prompt=None,
image_file=None,
# autogen/agent specific parameters
agent_type=None,
agent_accuracy=None,
agent_chat_history=None,
agent_files=None,
autogen_stop_docker_executor=None,
autogen_run_code_in_docker=None,
autogen_max_consecutive_auto_reply=None,
autogen_max_turns=None,
autogen_timeout=None,
autogen_cache_seed=None,
agent_venv_dir=None,
agent_code_writer_system_message=None,
agent_system_site_packages=None,
autogen_code_restrictions_level=None,
autogen_silent_exchange=None,
agent_verbose=None) -> dict:
assert agent_type in ['autogen_multi_agent'], "Invalid agent_type: %s" % agent_type
# raise openai.BadRequestError("Testing Error Handling")
# raise ValueError("Testing Error Handling")
# handle parameters from chatAPI and OpenAI -> h2oGPT transcription versions
assert visible_models is not None, "No visible_models specified"
model = visible_models # transcribe early
if stream_output is None:
stream_output = False
assert max_new_tokens is not None, "No max_new_tokens specified"
# handle AutoGen specific parameters
if autogen_stop_docker_executor is None:
autogen_stop_docker_executor = False
if autogen_run_code_in_docker is None:
autogen_run_code_in_docker = False
if autogen_max_consecutive_auto_reply is None:
autogen_max_consecutive_auto_reply = 40
if autogen_max_turns is None:
autogen_max_turns = 40
if autogen_timeout is None:
autogen_timeout = 120
if agent_system_site_packages is None:
agent_system_site_packages = True
if autogen_code_restrictions_level is None:
autogen_code_restrictions_level = 2
if autogen_silent_exchange is None:
autogen_silent_exchange = True
if agent_verbose is None:
agent_verbose = False
if agent_verbose:
print("AutoGen using model=%s." % model, flush=True)
base_url = os.environ['H2OGPT_OPENAI_BASE_URL'] # must exist
api_key = os.environ['H2OGPT_OPENAI_API_KEY'] # must exist
agent_work_dir = tempfile.mkdtemp()
from openai_server.autogen_utils import get_code_executor
from openai_server.autogen_agents import (
get_human_proxy_agent,
get_main_group_chat_manager,
get_chat_agent,
get_code_group_chat_manager
)
# Create a code executor.
executor = get_code_executor(
autogen_run_code_in_docker=autogen_run_code_in_docker,
autogen_timeout=autogen_timeout,
agent_system_site_packages=agent_system_site_packages,
autogen_code_restrictions_level=autogen_code_restrictions_level,
agent_work_dir=agent_work_dir,
agent_venv_dir=agent_venv_dir,
)
# Prepare the system message for the code writer agent.
code_writer_system_prompt, internal_file_names, system_message_parts = \
get_full_system_prompt(agent_code_writer_system_message,
agent_system_site_packages, system_prompt,
base_url,
api_key, model, text_context_list, image_file,
agent_work_dir, query, autogen_timeout)
# Prepare the LLM config for the agents
extra_body = {
"agent_type": agent_type, # autogen_multi_agent
}
llm_config = {"config_list": [{"model": model,
"api_key": api_key,
"base_url": base_url,
"stream": stream_output,
"cache_seed": autogen_cache_seed,
'max_tokens': max_new_tokens,
"extra_body": extra_body,
}]}
human_proxy_agent = get_human_proxy_agent(
llm_config=llm_config,
autogen_max_consecutive_auto_reply=autogen_max_consecutive_auto_reply,
)
chat_agent = get_chat_agent(
llm_config=llm_config,
autogen_max_consecutive_auto_reply=1, # Always 1 turn for chat agent
)
code_group_chat_manager = get_code_group_chat_manager(
llm_config=llm_config,
code_writer_system_prompt=code_writer_system_prompt,
autogen_max_consecutive_auto_reply=autogen_max_consecutive_auto_reply,
max_round=40, # TODO: Define variable above
executor=executor,
)
main_group_chat_manager = get_main_group_chat_manager(
llm_config=llm_config,
prompt=query,
agents=[chat_agent, code_group_chat_manager],
max_round=40,
)
# apply chat history to human_proxy_agent and main_group_chat_manager
# TODO: check if working
if chat_conversation:
chat_messages = structure_to_messages(None, None, chat_conversation, None)
for message in chat_messages:
if message['role'] == 'assistant':
main_group_chat_manager.send(message['content'], human_proxy_agent, request_reply=False)
if message['role'] == 'user':
human_proxy_agent.send(message['content'], main_group_chat_manager, request_reply=False)
chat_result = human_proxy_agent.initiate_chat(
main_group_chat_manager,
message=query,
# summary_method="last_msg", # TODO: is summary really working for group chat? Doesnt include code group messages in it, why?
# summary_args=dict(summary_role="user"), # System by default, but in chat histort it comes last and drops user message in h2ogpt/convert_messages_to_structure method
max_turns=1,
)
# It seems chat_result.chat_history doesnt contain code group messages, so I'm manually merging them here. #TODO: research why so?
merged_group_chat_messages = merge_group_chat_messages(
code_group_chat_manager.groupchat.messages, main_group_chat_manager.groupchat.messages
)
chat_result.chat_history = merged_group_chat_messages
# Update summary after including group chats:
used_agents = list(set([msg['name'] for msg in chat_result.chat_history]))
# besides human_proxy_agent, check if there is only chat_agent and human_proxy_agent in the used_agents
if len(used_agents) == 2 and 'chat_agent' in used_agents:
# If it's only chat_agent and human_proxy_agent, then use last message as summary
summary = chat_result.chat_history[-1]['content']
else:
summarize_prompt = (
"* Given all the conversation and findings so far, try to answer first user instruction. "
"* Do not add any introductory phrases. "
"* After answering user instruction, now you can try to summarize the process. "
"* In your final summarization, if any key figures or plots were produced, "
"add inline markdown links to the files so they are rendered as images in the chat history. "
"Do not include them in code blocks, just directly inlined markdown like . "
"Only use the basename of the file, not the full path, "
"and the user will map the basename to a local copy of the file so rendering works normally. "
"* If you have already displayed some images in your answer to the user, you don't need to add them again in the summary. "
"* Do not try to answer the instruction yourself, just answer based on what is in chat history. "
)
summary_chat_history = [msg for msg in chat_result.chat_history]
for msg in summary_chat_history:
if msg['name'] == 'human_proxy_agent':
msg['role'] = 'user'
else:
msg['role'] = 'assistant'
summary = human_proxy_agent._reflection_with_llm(
prompt=summarize_prompt,
messages=chat_result.chat_history,
cache=None,
role="user"
)
# A little sumamry clean-up
summary = summary.replace("ENDOFTURN", " ").replace("<FINISHED_ALL_TASKS>", " ")
# Update chat_result with summary
chat_result.summary = summary
# Update final usage cost
all_conversable_agents = [human_proxy_agent] + get_all_conversable_agents(main_group_chat_manager)
chat_result.cost = gather_usage_summary(all_conversable_agents)
#### end
ret_dict = get_ret_dict_and_handle_files(chat_result,
None,
model,
agent_work_dir, agent_verbose, internal_file_names, authorization,
autogen_run_code_in_docker, autogen_stop_docker_executor, executor,
agent_venv_dir, agent_code_writer_system_message,
agent_system_site_packages,
system_message_parts,
autogen_code_restrictions_level, autogen_silent_exchange,
agent_accuracy)
return ret_dict
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