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fix ontology category, simulation context and reddit twhin-bert

This commit is contained in:
hduc-le 2026-06-01 03:42:42 +00:00
parent 09b1722da2
commit 84e2d2930f
5 changed files with 75 additions and 20 deletions
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8
backend/app/services/oasis_profile_generator.py
View File

@ -120,6 +120,8 @@ class OasisAgentProfile:
profile["profession"] = self.profession
if self.interested_topics:
profile["interested_topics"] = self.interested_topics
if self.entity_category:
profile["entity_category"] = self.entity_category
return profile
@ -929,7 +931,7 @@ Dựa vào toàn bộ thông tin trên, xác định thực thể này thuộc l
BƯỚC 2 — TẠO HỒ SƠ:
Tạo JSON với các trường sau, điều chỉnh theo kết quả phân loại:
1. entity_category: Kết quả phân loại ở Bước 1 — "individual" hoặc "organization"
1. entity_category: "individual"/"organization"
2. bio: Tiểu sử mạng xã hội ngắn gọn, tối đa 200 ký tự.
@ -1547,7 +1549,7 @@ QUAN TRỌNG:
"user_id": profile.user_id if profile.user_id is not None else idx,
"username": profile.user_name,
"name": profile.name,
"bio": profile.bio[:150] if profile.bio else f"{profile.name}", # Cap ở 150 ký tự
"bio": profile.bio if profile.bio else f"{profile.name}",
"persona": profile.persona or f"{profile.name} is a participant in social discussions.",
"karma": profile.karma if profile.karma else 1000,
"created_at": profile.created_at,
@ -1562,6 +1564,8 @@ QUAN TRỌNG:
item["profession"] = profile.profession
if profile.interested_topics:
item["interested_topics"] = profile.interested_topics
if profile.entity_category:
item["entity_category"] = profile.entity_category
data.append(item)

2
backend/app/services/simulation_ipc.py
View File

@ -137,7 +137,7 @@ class SimulationIPCClient:
TimeoutError: Lỗi quá thời gian chờ phản hồi
"""
command_id = str(uuid.uuid4())
command = s(
command = IPCCommand(
command_id=command_id,
command_type=command_type,
args=args

23
backend/app/services/zep_tools.py
View File

@ -438,9 +438,6 @@ class ZepToolsService:
if self._llm_client is None:
self._llm_client = LLMClient(
component="zep_tools",
metadata={
"phase": "zep_tools",
},
)
return self._llm_client
@ -1151,13 +1148,14 @@ Trả về danh sách các câu hỏi phụ dưới định dạng JSON."""
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.3
temperature=0.3,
metadata={"phase": "_generate_sub_queries"},
)
sub_queries = response.get("sub_queries", [])
# Ép kiểu để chắc chắn danh sách toàn kiểu string
return [str(sq) for sq in sub_queries[:max_queries]]
except Exception as e:
logger.warning(f"Failed to generate sub-queries: {str(e)}, using default sub-queries")
# Hạ cấp (fallback): Trả về các biến thể chung chung của câu hỏi ban đầu
@ -1672,9 +1670,10 @@ Hãy chọn tối đa {max_agents} Agent phù hợp nhất để phỏng vấn v
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.3
temperature=0.3,
metadata={"phase": "_select_agents_for_interview"},
)
selected_indices = response.get("selected_indices", [])[:max_agents]
reasoning = response.get("reasoning", "Auto selected based on relevance")
@ -1751,9 +1750,10 @@ Hãy tạo từ 3-5 câu hỏi phỏng vấn."""
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.5
temperature=0.5,
metadata={"phase": "_generate_interview_questions"},
)
return response.get("questions", [f"What is your opinion on {interview_requirement}?"])
except Exception as e:
@ -1832,7 +1832,8 @@ Hãy tạo bản tóm tắt phỏng vấn."""
{"role": "user", "content": user_prompt}
],
temperature=0.3,
max_tokens=4096
max_tokens=4096,
metadata={"phase": "_generate_interview_summary"},
)
return summary

6
backend/app/utils/llm_cost.py
View File

@ -24,7 +24,7 @@ MODEL_COSTS_PER_1M_TOKENS: Dict[str, Dict[str, float]] = {
"Qwen/Qwen3.5-27B": {"input": 0.5, "output": 3.0},
"Qwen/Qwen3.6-27B": {"input": 0.5, "output": 3.0},
"gemini-3-flash": {"input": 0.5, "output": 3.0},
"gemini-3.1-flash-lite": {"input": 0.25, "output": 1.5},
"Qwen/Qwen3.6-27B-FP8 ": {"input": 0.5, "output": 3.0},
}
@ -174,8 +174,10 @@ def record_llm_cost(
request_no = int(counter["requests"])
cumulative_cost = _quantize_8(counter["total_cost"])
phase = metadata.get("phase")
component_label = f"{component}.{phase}" if phase else component
line = (
f"[{timestamp}] [Request {request_no}] [{component}] "
f"[{timestamp}] [Request {request_no}] [{component_label}] "
f"Called model: {model_name}, "
f"input_tokens: {usage_dict['input_tokens']} | "
f"output_tokens: {usage_dict['output_tokens']} | "

56
backend/scripts/run_parallel_simulation.py
View File

@ -203,6 +203,9 @@ from llm_cost_patch import install_openai_cost_patch
try:
from camel.models import ModelFactory
from camel.types import ModelPlatformType
from camel.memories import ChatHistoryMemory, ScoreBasedContextCreator
from camel.utils import OpenAITokenCounter
from camel.types import ModelType
import oasis
from oasis import (
ActionType,
@ -211,6 +214,8 @@ try:
generate_twitter_agent_graph,
generate_reddit_agent_graph
)
from oasis.social_platform import Platform
from oasis.social_platform.channel import Channel
except ImportError as e:
print(f"Error: Missing dependency {e}")
print("Please install first: pip install oasis-ai camel-ai")
@ -1120,6 +1125,33 @@ def create_model(config: Dict[str, Any], use_boost: bool = False):
)
def configure_agent_memory_limits(agent_graph, token_limit=150000, message_window_size=25):
"""
Cấu hình giới hạn memory cho tất cả agent trong agent_graph.
OASIS tạo SocialAgent không truyền message_window_size hay token_limit
cho ChatAgent (CAMEL), khiến conversation history tăng vô hạn qua các round.
Khi context vượt quá giới hạn model (131072 tokens cho Qwen3.6-27B-FP8),
LLM API trả về BadRequestError và agent bị bỏ qua round đó.
Hàm này thay thế memory của mỗi agent bằng ChatHistoryMemory có:
- token_limit: giới hạn token context (ScoreBasedContextCreator sẽ cắt bớt
message cũ khi vượt, ưu tiên giữ message mới và system message)
- message_window_size: giới hạn số message giữ lại (tầng bảo vệ thứ hai)
ChatAgent.memory setter tự động gọi init_messages() để giữ system message.
"""
configured_count = 0
for agent_id, agent in agent_graph.get_agents():
token_counter = OpenAITokenCounter(ModelType.GPT_4O_MINI)
context_creator = ScoreBasedContextCreator(token_counter, token_limit=token_limit)
new_memory = ChatHistoryMemory(context_creator, window_size=message_window_size)
agent.memory = new_memory
configured_count += 1
return configured_count
def get_active_agents_for_round(
env,
config: Dict[str, Any],
@ -1277,6 +1309,10 @@ async def run_twitter_simulation(
available_actions=TWITTER_ACTIONS,
)
# Cấu hình giới hạn memory để tránh context overflow (131072 token limit)
mem_count = configure_agent_memory_limits(result.agent_graph)
log_info(f"Configured memory limits for {mem_count} agents")
# Xây dựng agent_names map — dùng cho log và enrich context
agent_names = get_agent_names_from_config(config)
for agent_id, agent in result.agent_graph.get_agents():
@ -1289,11 +1325,19 @@ async def run_twitter_simulation(
if os.path.exists(db_path):
os.remove(db_path)
twitter_channel = Channel()
twitter_platform = Platform(
db_path=db_path,
channel=twitter_channel,
recsys_type="reddit", # score-based, không load model embedding → nhanh hơn twhin-bert
refresh_rec_post_count=2,
max_rec_post_len=2,
following_post_count=3,
)
result.env = oasis.make(
agent_graph=result.agent_graph,
platform=oasis.DefaultPlatformType.TWITTER,
database_path=db_path,
semaphore=3, # Tối đa 3 LLM call đồng thời trong cùng 1 round
platform=twitter_platform,
semaphore=20,
)
await result.env.reset()
@ -1471,6 +1515,10 @@ async def run_reddit_simulation(
available_actions=REDDIT_ACTIONS,
)
# Cấu hình giới hạn memory để tránh context overflow (131072 token limit)
mem_count = configure_agent_memory_limits(result.agent_graph)
log_info(f"Configured memory limits for {mem_count} agents")
agent_names = get_agent_names_from_config(config)
for agent_id, agent in result.agent_graph.get_agents():
if agent_id not in agent_names:
@ -1484,7 +1532,7 @@ async def run_reddit_simulation(
agent_graph=result.agent_graph,
platform=oasis.DefaultPlatformType.REDDIT,
database_path=db_path,
semaphore=3,
semaphore=20,
)
await result.env.reset()