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Merged
merged 3 commits into from
Apr 4, 2025
Merged

Consolidate websocket #53

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210fadb
finish 1) steo start, 2) node_expanding, node created, 3) best_path_u…
TataKKKL Apr 4, 2025
cf8bca0
add 5) Browser Set Up, Account Reset, 6) Tree Update & Trajectory Upd…
TataKKKL Apr 4, 2025
bcb3ecc
wrap up the backend work
TataKKKL Apr 4, 2025
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154 changes: 143 additions & 11 deletions visual-tree-search-backend/app/api/lwats/agents_async/SearchAgents/lats_agent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,8 @@

openai_client = OpenAI()

## TODO: add best_path_update

class LATSAgent:
"""
Language-based Action Tree Search Agent implementation.
Expand Down Expand Up @@ -117,6 +119,13 @@ async def run(self, websocket=None) -> list[LATSNode]:
print_trajectory(best_node)

if websocket:
# trajectory_data = self._get_trajectory_data(best_node)
# await websocket.send_json({
# "type": "trajectory_update",
# "trajectory": trajectory_data,
# "timestamp": datetime.utcnow().isoformat()
# })
# TODO: use score instead of reward to determine success
await websocket.send_json({
"type": "search_complete",
"status": "success" if best_node.reward == 1 else "partial_success",
Expand Down Expand Up @@ -158,12 +167,19 @@ async def lats_search(self, websocket=None) -> LATSNode:
if websocket:
await websocket.send_json({
"type": "step_start",
"step": "selection",
"step": 1,
"step_name": "selection",
"iteration": i + 1,
"timestamp": datetime.utcnow().isoformat()
})

node = self.select_node(self.root_node)
if websocket:
await websocket.send_json({
"type": "node_selected",
"node_id": id(node),
"timestamp": datetime.utcnow().isoformat()
})

if node is None:
print("All paths lead to terminal nodes with reward 0. Ending search.")
Expand All @@ -177,7 +193,8 @@ async def lats_search(self, websocket=None) -> LATSNode:
if websocket:
await websocket.send_json({
"type": "step_start",
"step": "expansion",
"step": 2,
"step_name": "expansion",
"iteration": i + 1,
"timestamp": datetime.utcnow().isoformat()
})
Expand All @@ -202,28 +219,65 @@ async def lats_search(self, websocket=None) -> LATSNode:
print(f"{GREEN}Tree:{RESET}")
better_print(self.root_node)
print(f"")
tree_data = self._get_tree_data()
await websocket.send_json({
"type": "tree_update",
"tree": tree_data,
"timestamp": datetime.utcnow().isoformat()
})

# Step 3: Evaluation
print(f"")
print(f"{GREEN}Step 3: evaluation{RESET}")
if websocket:
await websocket.send_json({
"type": "step_start",
"step": 3,
"step_name": "evaluation",
"iteration": i + 1,
"timestamp": datetime.utcnow().isoformat()
})
await self.evaluate_node(node)

print(f"{GREEN}Tree:{RESET}")
better_print(self.root_node)
print(f"")
## send tree update, since evaluation is added to the tree
if websocket:
tree_data = self._get_tree_data()
await websocket.send_json({
"type": "tree_update",
"tree": tree_data,
"timestamp": datetime.utcnow().isoformat()
})


# Step 4: Simulation
print(f"{GREEN}Step 4: simulation{RESET}")
# # Find the child with the highest value
if websocket:
await websocket.send_json({
"type": "step_start",
"step": 4,
"step_name": "simulation",
"iteration": i + 1,
"timestamp": datetime.utcnow().isoformat()
})
## always = 1
reward, terminal_node = await self.simulate(max(node.children, key=lambda child: child.value), max_depth=self.config.max_depth, num_simulations=1)
reward, terminal_node = await self.simulate(max(node.children, key=lambda child: child.value), max_depth=self.config.max_depth, num_simulations=1, websocket=websocket)
terminal_nodes.append(terminal_node)

if reward == 1:
return terminal_node

# Step 5: Backpropagation
print(f"{GREEN}Step 5: backpropagation{RESET}")
if websocket:
await websocket.send_json({
"type": "step_start",
"step": 5,
"step_name": "backpropagation",
"timestamp": datetime.utcnow().isoformat()
})
self.backpropagate(terminal_node, reward)
print(f"{GREEN}Tree:{RESET}")
better_print(self.root_node)
Expand Down Expand Up @@ -335,7 +389,8 @@ async def evaluate_node(self, node: LATSNode) -> None:
child.value = score
child.reward = score

async def simulate(self, node: LATSNode, max_depth: int = 2, num_simulations=1) -> tuple[float, LATSNode]:
## TODO: make number of simulations configurable
async def simulate(self, node: LATSNode, max_depth: int = 2, num_simulations=1, websocket=None) -> tuple[float, LATSNode]:
"""
Perform a rollout simulation from a node.

Expand All @@ -351,13 +406,39 @@ async def simulate(self, node: LATSNode, max_depth: int = 2, num_simulations=1)
print_trajectory(node)
print("print the entire tree")
print_entire_tree(self.root_node)
return await self.rollout(node, max_depth=max_depth)
if websocket:
tree_data = self._get_tree_data()
await websocket.send_json({
"type": "tree_update",
"tree": tree_data,
"timestamp": datetime.utcnow().isoformat()
})
trajectory_data = self._get_trajectory_data(node)
await websocket.send_json({
"type": "trajectory_update",
"trajectory": trajectory_data,
"timestamp": datetime.utcnow().isoformat()
})
return await self.rollout(node, max_depth=max_depth, websocket=websocket)

async def send_completion_request(self, plan, depth, node, trajectory=[]):
async def send_completion_request(self, plan, depth, node, trajectory=[], websocket=None):
print("print the trajectory")
print_trajectory(node)
print("print the entire tree")
print_entire_tree(self.root_node)
if websocket:
# tree_data = self._get_tree_data()
# await websocket.send_json({
# "type": "tree_update",
# "tree": tree_data,
# "timestamp": datetime.utcnow().isoformat()
# })
trajectory_data = self._get_trajectory_data(node)
await websocket.send_json({
"type": "trajectory_update",
"trajectory": trajectory_data,
"timestamp": datetime.utcnow().isoformat()
})

if depth >= self.config.max_depth:
return trajectory, node
Expand Down Expand Up @@ -420,20 +501,20 @@ async def send_completion_request(self, plan, depth, node, trajectory=[]):
if goal_finished:
return trajectory, new_node

return await self.send_completion_request(plan, depth + 1, new_node, trajectory)
return await self.send_completion_request(plan, depth + 1, new_node, trajectory, websocket)

except Exception as e:
print(f"Attempt {attempt + 1} failed with error: {e}")
if attempt + 1 == retry_count:
print("Max retries reached. Skipping this step and retrying the whole request.")
# Retry the entire request from the same state
return await self.send_completion_request(plan, depth, node, trajectory)
return await self.send_completion_request(plan, depth, node, trajectory, websocket)

# If all retries and retries of retries fail, return the current trajectory and node
return trajectory, node


async def rollout(self, node: LATSNode, max_depth: int = 2)-> tuple[float, LATSNode]:
async def rollout(self, node: LATSNode, max_depth: int = 2, websocket=None)-> tuple[float, LATSNode]:
# Reset browser state
await self._reset_browser()
path = self.get_path_to_root(node)
Expand Down Expand Up @@ -467,11 +548,24 @@ async def rollout(self, node: LATSNode, max_depth: int = 2)-> tuple[float, LATSN
## call the prompt agent
print("current depth: ", len(path) - 1)
print("max depth: ", self.config.max_depth)
trajectory, node = await self.send_completion_request(self.goal, len(path) - 1, node=n, trajectory=trajectory)
trajectory, node = await self.send_completion_request(self.goal, len(path) - 1, node=n, trajectory=trajectory, websocket=websocket)
print("print the trajectory")
print_trajectory(node)
print("print the entire tree")
print_entire_tree(self.root_node)
if websocket:
# tree_data = self._get_tree_data()
# await websocket.send_json({
# "type": "tree_update",
# "tree": tree_data,
# "timestamp": datetime.utcnow().isoformat()
# })
trajectory_data = self._get_trajectory_data(node)
await websocket.send_json({
"type": "trajectory_update",
"trajectory": trajectory_data,
"timestamp": datetime.utcnow().isoformat()
})

page = await self.playwright_manager.get_page()
page_info = await extract_page_info(page, self.config.fullpage, self.config.log_folder)
Expand Down Expand Up @@ -769,8 +863,46 @@ def _get_tree_data(self):
"is_terminal": node.is_terminal,
"value": node.value,
"visits": node.visits,
"feedback": node.feedback,
"reward": node.reward
}
tree_data.append(node_data)

return tree_data

def _get_trajectory_data(self, terminal_node: LATSNode):
"""Get trajectory data in a format suitable for visualization

Args:
terminal_node: The leaf node to start the trajectory from

Returns:
list: List of node data dictionaries representing the trajectory
"""
trajectory_data = []
path = []

# Collect path from terminal to root
current = terminal_node
while current is not None:
path.append(current)
current = current.parent

# Process nodes in order from root to terminal
for level, node in enumerate(reversed(path)):
node_data = {
"id": id(node),
"level": level,
"action": node.action if node.action else "ROOT",
"description": node.natural_language_description,
"visits": node.visits,
"value": float(f"{node.value:.3f}") if hasattr(node, 'value') else None,
"reward": float(f"{node.reward:.3f}") if hasattr(node, 'reward') else None,
"is_terminal": node.is_terminal,
"feedback": node.feedback if hasattr(node, 'feedback') else None,
"is_root": not hasattr(node, 'parent') or node.parent is None,
"is_terminal_node": node == terminal_node
}
trajectory_data.append(node_data)

return trajectory_data
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