TxAgent
Tips before using TxAgent:
Please click clear🗑️ (top-right) to remove previous context before submitting a new question.
Click retry🔄 (below message) to get multiple versions of the answer.
"""
css = """
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
.small-button button {
font-size: 12px !important;
padding: 4px 8px !important;
height: 6px !important;
width: 4px !important;
}
.gradio-accordion {
margin-top: 0px !important;
margin-bottom: 0px !important;
}
"""
chat_css = """
.gr-button { font-size: 20px !important; }
.gr-button svg { width: 32px !important; height: 32px !important; }
"""
def safe_load_embeddings(filepath: str) -> any:
"""Safely load embeddings with proper weights_only handling"""
try:
# First try with weights_only=True
return torch.load(filepath, weights_only=True)
except Exception as e:
logger.warning(f"Secure load failed, trying with weights_only=False: {str(e)}")
try:
# Fallback to unsafe load if needed
return torch.load(filepath, weights_only=False)
except Exception as e:
logger.error(f"Failed to load embeddings: {str(e)}")
return None
def patch_embedding_loading():
"""Monkey-patch the embedding loading functionality"""
try:
from txagent.toolrag import ToolRAGModel
original_load = ToolRAGModel.load_tool_desc_embedding
def patched_load(self, tooluniverse):
try:
if not os.path.exists(CONFIG["embedding_filename"]):
logger.error(f"Embedding file not found: {CONFIG['embedding_filename']}")
return False
self.tool_desc_embedding = safe_load_embeddings(CONFIG["embedding_filename"])
# Updated tool loading approach
if hasattr(tooluniverse, 'get_all_tools'):
tools = tooluniverse.get_all_tools()
elif hasattr(tooluniverse, 'tools'):
tools = tooluniverse.tools
else:
logger.error("No method found to access tools from ToolUniverse")
return False
current_count = len(tools)
embedding_count = len(self.tool_desc_embedding)
if current_count != embedding_count:
logger.warning(f"Tool count mismatch (tools: {current_count}, embeddings: {embedding_count})")
if current_count < embedding_count:
self.tool_desc_embedding = self.tool_desc_embedding[:current_count]
logger.info(f"Truncated embeddings to match {current_count} tools")
else:
last_embedding = self.tool_desc_embedding[-1]
padding = [last_embedding] * (current_count - embedding_count)
self.tool_desc_embedding = torch.cat([self.tool_desc_embedding] + padding)
logger.info(f"Padded embeddings to match {current_count} tools")
return True
except Exception as e:
logger.error(f"Failed to load embeddings: {str(e)}")
return False
ToolRAGModel.load_tool_desc_embedding = patched_load
logger.info("Successfully patched embedding loading")
except Exception as e:
logger.error(f"Failed to patch embedding loading: {str(e)}")
raise
def prepare_tool_files():
"""Ensure tool files exist and are populated"""
os.makedirs(os.path.join(current_dir, 'data'), exist_ok=True)
if not os.path.exists(CONFIG["tool_files"]["new_tool"]):
logger.info("Generating tool list using ToolUniverse...")
try:
tu = ToolUniverse()
if hasattr(tu, 'get_all_tools'):
tools = tu.get_all_tools()
elif hasattr(tu, 'tools'):
tools = tu.tools
else:
tools = []
logger.error("Could not access tools from ToolUniverse")
with open(CONFIG["tool_files"]["new_tool"], "w") as f:
json.dump(tools, f, indent=2)
logger.info(f"Saved {len(tools)} tools to {CONFIG['tool_files']['new_tool']}")
except Exception as e:
logger.error(f"Failed to prepare tool files: {str(e)}")
def create_agent():
"""Create and initialize the TxAgent"""
# Apply the embedding patch before creating the agent
patch_embedding_loading()
prepare_tool_files()
# Initialize the agent
try:
agent = TxAgent(
CONFIG["model_name"],
CONFIG["rag_model_name"],
tool_files_dict=CONFIG["tool_files"],
force_finish=True,
enable_checker=True,
step_rag_num=10,
seed=100,
additional_default_tools=['DirectResponse', 'RequireClarification']
)
agent.init_model()
return agent
except Exception as e:
logger.error(f"Failed to create agent: {str(e)}")
raise
def handle_chat_response(history, message, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
full_response = ""
for chunk in message:
if isinstance(chunk, dict):
full_response += chunk.get("content", "")
else:
full_response += str(chunk)
history.append({"role": "assistant", "content": full_response})
return history
def update_model_parameters(agent, enable_finish, enable_rag, enable_summary,
init_rag_num, step_rag_num, skip_last_k,
summary_mode, summary_skip_last_k, summary_context_length,
force_finish, seed):
"""Update model parameters"""
updated_params = agent.update_parameters(
enable_finish=enable_finish,
enable_rag=enable_rag,
enable_summary=enable_summary,
init_rag_num=init_rag_num,
step_rag_num=step_rag_num,
skip_last_k=skip_last_k,
summary_mode=summary_mode,
summary_skip_last_k=summary_skip_last_k,
summary_context_length=summary_context_length,
force_finish=force_finish,
seed=seed,
)
return updated_params
def update_seed(agent):
"""Update random seed"""
seed = random.randint(0, 10000)
updated_params = agent.update_parameters(seed=seed)
return updated_params
def handle_retry(agent, history, retry_data: gr.RetryData, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
"""Handle retry functionality"""
print("Updated seed:", update_seed(agent))
new_history = history[:retry_data.index]
previous_prompt = history[retry_data.index]['content']
print("previous_prompt", previous_prompt)
response = agent.run_gradio_chat(new_history + [{"role": "user", "content": previous_prompt}],
temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
yield from handle_chat_response(new_history, response, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
PASSWORD = "mypassword"
def check_password(input_password):
"""Check password for protected settings"""
if input_password == PASSWORD:
return gr.update(visible=True), ""
else:
return gr.update(visible=False), "Incorrect password, try again!"
def create_demo(agent):
"""Create the Gradio interface"""
default_temperature = 0.3
default_max_new_tokens = 1024
default_max_tokens = 81920
default_max_round = 30
question_examples = [
['Given a 50-year-old patient experiencing severe acute pain and considering the use of the newly approved medication, Journavx, how should the dosage be adjusted considering the presence of moderate hepatic impairment?'],
['Given a 50-year-old patient experiencing severe acute pain and considering the use of the newly approved medication, Journavx, how should the dosage be adjusted considering the presence of severe hepatic impairment?'],
['A 30-year-old patient is taking Prozac to treat their depression. They were recently diagnosed with WHIM syndrome and require a treatment for that condition as well. Is Xolremdi suitable for this patient, considering contraindications?'],
]
chatbot = gr.Chatbot(height=800, placeholder=PLACEHOLDER,
label='TxAgent', show_copy_button=True)
with gr.Blocks(css=css) as demo:
gr.Markdown(DESCRIPTION)
gr.Markdown(INTRO)
temperature_state = gr.State(value=default_temperature)
max_new_tokens_state = gr.State(value=default_max_new_tokens)
max_tokens_state = gr.State(value=default_max_tokens)
max_round_state = gr.State(value=default_max_round)
chatbot.retry(
lambda *args: handle_retry(agent, *args),
inputs=[chatbot, chatbot, temperature_state, max_new_tokens_state,
max_tokens_state, gr.Checkbox(value=False, render=False),
gr.State([]), max_round_state]
)
with gr.Row():
with gr.Column(scale=4):
msg = gr.Textbox(label="Input", placeholder="Type your question here...")
with gr.Column(scale=1):
submit_btn = gr.Button("Submit", variant="primary")
with gr.Row():
clear_btn = gr.ClearButton([msg, chatbot])
def respond(message, chat_history, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round):
response = agent.run_gradio_chat(
chat_history + [{"role": "user", "content": message}],
temperature,
max_new_tokens,
max_tokens,
multi_agent,
conversation,
max_round
)
return handle_chat_response(chat_history, response, temperature, max_new_tokens, max_tokens, multi_agent, conversation, max_round)
submit_btn.click(
respond,
inputs=[msg, chatbot, temperature_state, max_new_tokens_state,
max_tokens_state, gr.Checkbox(value=False, render=False),
gr.State([]), max_round_state],
outputs=[chatbot]
)
msg.submit(
respond,
inputs=[msg, chatbot, temperature_state, max_new_tokens_state,
max_tokens_state, gr.Checkbox(value=False, render=False),
gr.State([]), max_round_state],
outputs=[chatbot]
)
with gr.Accordion("Settings", open=False):
temperature_slider = gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=default_temperature,
label="Temperature"
)
max_new_tokens_slider = gr.Slider(
minimum=128,
maximum=4096,
step=1,
value=default_max_new_tokens,
label="Max new tokens"
)
max_tokens_slider = gr.Slider(
minimum=128,
maximum=32000,
step=1,
value=default_max_tokens,
label="Max tokens"
)
max_round_slider = gr.Slider(
minimum=0,
maximum=50,
step=1,
value=default_max_round,
label="Max round")
temperature_slider.change(
lambda x: x, inputs=temperature_slider, outputs=temperature_state)
max_new_tokens_slider.change(
lambda x: x, inputs=max_new_tokens_slider, outputs=max_new_tokens_state)
max_tokens_slider.change(
lambda x: x, inputs=max_tokens_slider, outputs=max_tokens_state)
max_round_slider.change(
lambda x: x, inputs=max_round_slider, outputs=max_round_state)
password_input = gr.Textbox(
label="Enter Password for More Settings", type="password")
incorrect_message = gr.Textbox(visible=False, interactive=False)
with gr.Accordion("⚙️ Advanced Settings", open=False, visible=False) as protected_accordion:
with gr.Row():
with gr.Column(scale=1):
with gr.Accordion("Model Settings", open=False):
model_name_input = gr.Textbox(
label="Enter model path", value=CONFIG["model_name"])
load_model_btn = gr.Button(value="Load Model")
load_model_btn.click(
agent.load_models,
inputs=model_name_input,
outputs=gr.Textbox(label="Status"))
with gr.Column(scale=1):
with gr.Accordion("Functional Parameters", open=False):
enable_finish = gr.Checkbox(label="Enable Finish", value=True)
enable_rag = gr.Checkbox(label="Enable RAG", value=True)
enable_summary = gr.Checkbox(label="Enable Summary", value=False)
init_rag_num = gr.Number(label="Initial RAG Num", value=0)
step_rag_num = gr.Number(label="Step RAG Num", value=10)
skip_last_k = gr.Number(label="Skip Last K", value=0)
summary_mode = gr.Textbox(label="Summary Mode", value='step')
summary_skip_last_k = gr.Number(label="Summary Skip Last K", value=0)
summary_context_length = gr.Number(label="Summary Context Length", value=None)
force_finish = gr.Checkbox(label="Force FinalAnswer", value=True)
seed = gr.Number(label="Seed", value=100)
submit_btn = gr.Button("Update Parameters")
updated_parameters_output = gr.JSON()
submit_btn.click(
lambda *args: update_model_parameters(agent, *args),
inputs=[enable_finish, enable_rag, enable_summary,
init_rag_num, step_rag_num, skip_last_k,
summary_mode, summary_skip_last_k,
summary_context_length, force_finish, seed],
outputs=updated_parameters_output
)
submit_button = gr.Button("Submit")
submit_button.click(
check_password,
inputs=password_input,
outputs=[protected_accordion, incorrect_message]
)
gr.Markdown(LICENSE)
return demo
def main():
"""Main function to run the application"""
try:
agent = create_agent()
demo = create_demo(agent)
demo.launch(share=True)
except Exception as e:
logger.error(f"Application failed to start: {str(e)}")
raise
if __name__ == "__main__":
main()