feat(core): Support pass span id to threadpool

assets/schema/knowledge_management.sql

@@ -34,6 +34,7 @@ CREATE TABLE `knowledge_document` (
   `content` LONGTEXT NOT NULL COMMENT 'knowledge embedding sync result',
   `result` TEXT NULL COMMENT 'knowledge content',
   `vector_ids` LONGTEXT NULL COMMENT 'vector_ids',
+  `summary` LONGTEXT NULL COMMENT 'knowledge summary',
   `gmt_created` TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT 'created time',
   `gmt_modified` TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT 'update time',
   PRIMARY KEY (`id`),

pilot/scene/base_chat.py

@@ -145,6 +145,13 @@ class BaseChat(ABC):
         )
         self.current_message.tokens = 0
         if self.prompt_template.template:
+            metadata = {
+                "template_scene": self.prompt_template.template_scene,
+                "input_values": input_values,
+            }
+            with root_tracer.start_span(
+                "BaseChat.__call_base.prompt_template.format", metadata=metadata
+            ):
                 current_prompt = self.prompt_template.format(**input_values)
             self.current_message.add_system_message(current_prompt)
 

pilot/scene/chat_agent/chat.py

@@ -11,6 +11,7 @@ from pilot.common.string_utils import extract_content
 from .prompt import prompt
 from pilot.component import ComponentType
 from pilot.base_modules.agent.controller import ModuleAgent
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -51,6 +52,7 @@ class ChatAgent(BaseChat):
 
         self.api_call = ApiCall(plugin_generator=self.plugins_prompt_generator)
 
+    @trace()
     async def generate_input_values(self) -> Dict[str, str]:
         input_values = {
             "user_goal": self.current_user_input,
@@ -63,6 +65,9 @@ class ChatAgent(BaseChat):
 
     def stream_plugin_call(self, text):
         text = text.replace("\n", " ")
+        with root_tracer.start_span(
+            "ChatAgent.stream_plugin_call.api_call", metadata={"text": text}
+        ):
             return self.api_call.run(text)
 
     def __list_to_prompt_str(self, list: List) -> str:

pilot/scene/chat_dashboard/chat.py

@@ -13,6 +13,7 @@ from pilot.scene.chat_dashboard.data_preparation.report_schma import (
 from pilot.scene.chat_dashboard.prompt import prompt
 from pilot.scene.chat_dashboard.data_loader import DashboardDataLoader
 from pilot.utils.executor_utils import blocking_func_to_async
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -53,6 +54,7 @@ class ChatDashboard(BaseChat):
             data = f.read()
         return json.loads(data)
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         try:
             from pilot.summary.db_summary_client import DBSummaryClient

pilot/scene/chat_data/chat_excel/excel_analyze/chat.py

@@ -14,6 +14,7 @@ from pilot.scene.chat_data.chat_excel.excel_learning.chat import ExcelLearning
 from pilot.common.path_utils import has_path
 from pilot.configs.model_config import LLM_MODEL_CONFIG, KNOWLEDGE_UPLOAD_ROOT_PATH
 from pilot.base_modules.agent.common.schema import Status
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -62,6 +63,7 @@ class ChatExcel(BaseChat):
             # ]
         return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings))
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         input_values = {
             "user_input": self.current_user_input,
@@ -88,4 +90,9 @@ class ChatExcel(BaseChat):
 
     def stream_plugin_call(self, text):
         text = text.replace("\n", " ")
-        return self.api_call.run_display_sql(text, self.excel_reader.get_df_by_sql_ex)
+        with root_tracer.start_span(
+            "ChatExcel.stream_plugin_call.run_display_sql", metadata={"text": text}
+        ):
+            return self.api_call.run_display_sql(
+                text, self.excel_reader.get_df_by_sql_ex
+            )

pilot/scene/chat_data/chat_excel/excel_learning/chat.py

@@ -13,6 +13,7 @@ from pilot.scene.chat_data.chat_excel.excel_learning.prompt import prompt
 from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader
 from pilot.json_utils.utilities import DateTimeEncoder
 from pilot.utils.executor_utils import blocking_func_to_async
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -44,6 +45,7 @@ class ExcelLearning(BaseChat):
         if parent_mode:
             self.current_message.chat_mode = parent_mode.value()
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         # colunms, datas = self.excel_reader.get_sample_data()
         colunms, datas = await blocking_func_to_async(

pilot/scene/chat_db/auto_execute/chat.py

@@ -6,6 +6,7 @@ from pilot.common.sql_database import Database
 from pilot.configs.config import Config
 from pilot.scene.chat_db.auto_execute.prompt import prompt
 from pilot.utils.executor_utils import blocking_func_to_async
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -35,10 +36,13 @@ class ChatWithDbAutoExecute(BaseChat):
             raise ValueError(
                 f"{ChatScene.ChatWithDbExecute.value} mode should chose db!"
             )
-
+        with root_tracer.start_span(
+            "ChatWithDbAutoExecute.get_connect", metadata={"db_name": self.db_name}
+        ):
             self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name)
         self.top_k: int = 200
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         """
         generate input values
@@ -55,6 +59,7 @@ class ChatWithDbAutoExecute(BaseChat):
             #     query=self.current_user_input,
             #     topk=CFG.KNOWLEDGE_SEARCH_TOP_SIZE,
             # )
+            with root_tracer.start_span("ChatWithDbAutoExecute.get_db_summary"):
                 table_infos = await blocking_func_to_async(
                     self._executor,
                     client.get_db_summary,
@@ -80,4 +85,8 @@ class ChatWithDbAutoExecute(BaseChat):
 
     def do_action(self, prompt_response):
         print(f"do_action:{prompt_response}")
+        with root_tracer.start_span(
+            "ChatWithDbAutoExecute.do_action.run_sql",
+            metadata=prompt_response.to_dict(),
+        ):
             return self.database.run(prompt_response.sql)

pilot/scene/chat_db/auto_execute/out_parser.py

@@ -12,6 +12,9 @@ class SqlAction(NamedTuple):
     sql: str
     thoughts: Dict
 
+    def to_dict(self) -> Dict[str, Dict]:
+        return {"sql": self.sql, "thoughts": self.thoughts}
+
 
 logger = logging.getLogger(__name__)
 

pilot/scene/chat_db/professional_qa/chat.py

@@ -6,6 +6,7 @@ from pilot.common.sql_database import Database
 from pilot.configs.config import Config
 from pilot.scene.chat_db.professional_qa.prompt import prompt
 from pilot.utils.executor_utils import blocking_func_to_async
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -39,6 +40,7 @@ class ChatWithDbQA(BaseChat):
             else len(self.tables)
         )
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         table_info = ""
         dialect = "mysql"

pilot/scene/chat_execution/chat.py

@@ -6,6 +6,7 @@ from pilot.configs.config import Config
 from pilot.base_modules.agent.commands.command import execute_command
 from pilot.base_modules.agent import PluginPromptGenerator
 from .prompt import prompt
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -50,6 +51,7 @@ class ChatWithPlugin(BaseChat):
                     self.plugins_prompt_generator
                 )
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         input_values = {
             "input": self.current_user_input,

pilot/scene/chat_knowledge/inner_db_summary/chat.py

@@ -4,6 +4,7 @@ from pilot.scene.base import ChatScene
 from pilot.configs.config import Config
 
 from pilot.scene.chat_knowledge.inner_db_summary.prompt import prompt
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -31,6 +32,7 @@ class InnerChatDBSummary(BaseChat):
         self.db_input = db_select
         self.db_summary = db_summary
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         input_values = {
             "db_input": self.db_input,

pilot/scene/chat_knowledge/v1/chat.py

@@ -15,6 +15,7 @@ from pilot.configs.model_config import (
 from pilot.scene.chat_knowledge.v1.prompt import prompt
 from pilot.server.knowledge.service import KnowledgeService
 from pilot.utils.executor_utils import blocking_func_to_async
+from pilot.utils.tracer import root_tracer, trace
 
 CFG = Config()
 
@@ -92,6 +93,7 @@ class ChatKnowledge(BaseChat):
         """return reference"""
         return text + f"\n\n{self.parse_source_view(self.sources)}"
 
+    @trace()
     async def generate_input_values(self) -> Dict:
         if self.space_context:
             self.prompt_template.template_define = self.space_context["prompt"]["scene"]

pilot/utils/executor_utils.py

@@ -1,5 +1,6 @@
 from typing import Callable, Awaitable, Any
 import asyncio
+import contextvars
 from abc import ABC, abstractmethod
 from concurrent.futures import Executor, ThreadPoolExecutor
 from functools import partial
@@ -55,6 +56,12 @@ async def blocking_func_to_async(
     """
     if asyncio.iscoroutinefunction(func):
         raise ValueError(f"The function {func} is not blocking function")
+
+    # This function will be called within the new thread, capturing the current context
+    ctx = contextvars.copy_context()
+
+    def run_with_context():
+        return ctx.run(partial(func, *args, **kwargs))
+
     loop = asyncio.get_event_loop()
-    sync_function_noargs = partial(func, *args, **kwargs)
+    return await loop.run_in_executor(executor, run_with_context)
-    return await loop.run_in_executor(executor, sync_function_noargs)

pilot/utils/tracer/tracer_cli.py

@@ -303,8 +303,6 @@ def chat(
                 print(table.get_formatted_string(out_format=output, **out_kwargs))
         if sys_table:
             print(sys_table.get_formatted_string(out_format=output, **out_kwargs))
-    if hide_conv:
-        return
 
     if not found_trace_id:
         print(f"Can't found conversation with trace_id: {trace_id}")
@@ -315,9 +313,12 @@ def chat(
     trace_spans = [s for s in reversed(trace_spans)]
     hierarchy = _build_trace_hierarchy(trace_spans)
     if tree:
-        print("\nInvoke Trace Tree:\n")
+        print(f"\nInvoke Trace Tree(trace_id: {trace_id}):\n")
         _print_trace_hierarchy(hierarchy)
 
+    if hide_conv:
+        return
+
     trace_spans = _get_ordered_trace_from(hierarchy)
     table = PrettyTable(["Key", "Value Value"], title="Chat Trace Details")
     split_long_text = output == "text"