Enable Telemetry Tracing in Agent Comp and also add class name along with func name as span name for tracing

comps/agent/src/agent.py

@@ -30,6 +30,7 @@
     MessageObject,
     ThreadObject,
 )
+from comps.cores.telemetry.opea_telemetry import opea_telemetry, tracer
 
 logger = CustomLogger("comps-react-agent")
 logflag = os.getenv("LOGFLAG", False)
@@ -58,6 +59,7 @@ class AgentCompletionRequest(ChatCompletionRequest):
     host="0.0.0.0",
     port=args.port,
 )
+@opea_telemetry
 async def llm_generate(input: AgentCompletionRequest):
     if logflag:
         logger.info(input)
@@ -148,6 +150,7 @@ class CreateAssistant(CreateAssistantsRequest):
     host="0.0.0.0",
     port=args.port,
 )
+@opea_telemetry
 def create_assistants(input: CreateAssistant):
     # 1. initialize the agent
     print("@@@ Initializing agent with config: ", input.agent_config)
@@ -184,6 +187,7 @@ def create_assistants(input: CreateAssistant):
     host="0.0.0.0",
     port=args.port,
 )
+@opea_telemetry
 def create_threads(input: CreateThreadsRequest):
     # create a memory KV for the thread
     thread_inst, thread_id = instantiate_thread_memory()
@@ -205,6 +209,7 @@ def create_threads(input: CreateThreadsRequest):
     host="0.0.0.0",
     port=args.port,
 )
+@opea_telemetry
 def create_messages(thread_id, input: CreateMessagesRequest):
     with threads_global_kv as g_threads:
         thread_inst, _, _ = g_threads[thread_id]
@@ -249,6 +254,7 @@ def create_messages(thread_id, input: CreateMessagesRequest):
     host="0.0.0.0",
     port=args.port,
 )
+@opea_telemetry
 def create_run(thread_id, input: CreateRunResponse):
     with threads_global_kv as g_threads:
         thread_inst, _, status = g_threads[thread_id]
@@ -296,6 +302,7 @@ def create_run(thread_id, input: CreateRunResponse):
     host="0.0.0.0",
     port=args.port,
 )
+@opea_telemetry
 def cancel_run(thread_id):
     with threads_global_kv as g_threads:
         thread_inst, created_at, status = g_threads[thread_id]

comps/agent/src/integrations/strategy/base_agent.py

@@ -5,12 +5,15 @@
 
 from langgraph.checkpoint.memory import MemorySaver
 
+from comps.cores.telemetry.opea_telemetry import opea_telemetry, tracer
+
 from ..storage.persistence_redis import RedisPersistence
 from ..tools import get_tools_descriptions
 from ..utils import adapt_custom_prompt, setup_chat_model
 
 
 class BaseAgent:
+    @opea_telemetry
     def __init__(self, args, local_vars=None, **kwargs) -> None:
         self.llm = setup_chat_model(args)
         self.tools_descriptions = get_tools_descriptions(args.tools)
@@ -56,6 +59,7 @@ def execute(self, state: dict):
     def prepare_initial_state(self, query):
         raise NotImplementedError
 
+    @opea_telemetry
     async def stream_generator(self, query, config):
         initial_state = self.prepare_initial_state(query)
         try:
@@ -71,6 +75,7 @@ async def stream_generator(self, query, config):
         except Exception as e:
             yield str(e)
 
+    @opea_telemetry
     async def non_streaming_run(self, query, config):
         initial_state = self.prepare_initial_state(query)
         print("@@@ Initial State: ", initial_state)

comps/agent/src/integrations/strategy/planexec/planner.py

@@ -13,6 +13,8 @@
 from langgraph.graph.message import add_messages
 from pydantic import BaseModel, Field
 
+from comps.cores.telemetry.opea_telemetry import opea_telemetry, tracer
+
 from ...global_var import threads_global_kv
 from ...utils import has_multi_tool_inputs, tool_renderer
 from ..base_agent import BaseAgent
@@ -54,6 +56,7 @@ class PlanStepChecker:
         str: A decision for whether we should use this plan or not
     """
 
+    @opea_telemetry
     def __init__(self, llm, is_vllm=False):
         class grade(BaseModel):
             binary_score: str = Field(description="executable score 'yes' or 'no'")
@@ -66,9 +69,15 @@ class grade(BaseModel):
         output_parser = PydanticToolsParser(tools=[grade], first_tool_only=True)
         self.chain = plan_check_prompt | llm | output_parser
 
+    @opea_telemetry
+    def __llm_invoke__(self, state):
+        scored_result = self.chain.invoke(state)
+        return scored_result
+
+    @opea_telemetry
     def __call__(self, state):
         # print("---CALL PlanStepChecker---")
-        scored_result = self.chain.invoke(state)
+        scored_result = self.__llm_invoke__(state)
         score = scored_result.binary_score
         print(f"Task is {state['context']}, Score is {score}")
         if score.startswith("yes"):
@@ -79,6 +88,7 @@ def __call__(self, state):
 
 # Define workflow Node
 class Planner:
+    @opea_telemetry
     def __init__(self, llm, plan_checker=None, is_vllm=False):
         if is_vllm:
             llm = llm.bind_tools([Plan], tool_choice={"function": {"name": Plan.__name__}})
@@ -88,6 +98,12 @@ def __init__(self, llm, plan_checker=None, is_vllm=False):
         self.llm = planner_prompt | llm | output_parser
         self.plan_checker = plan_checker
 
+    @opea_telemetry
+    def __llm_invoke__(self, messages):
+        plan = self.llm.invoke(messages)
+        return plan
+
+    @opea_telemetry
     def __call__(self, state):
         print("---CALL Planner---")
         input = state["messages"][-1].content
@@ -96,7 +112,7 @@ def __call__(self, state):
         while not success:
             while not success:
                 try:
-                    plan = self.llm.invoke({"messages": [("user", state["messages"][-1].content)]})
+                    plan = self.__llm_invoke__({"messages": [("user", state["messages"][-1].content)]})
                     print("Generated plan: ", plan)
                     success = True
                 except OutputParserException as e:
@@ -116,6 +132,7 @@ def __call__(self, state):
 
 
 class Executor:
+    @opea_telemetry
     def __init__(self, llm, tools=[]):
         prompt = hwchase17_react_prompt
         if has_multi_tool_inputs(tools):
@@ -126,6 +143,7 @@ def __init__(self, llm, tools=[]):
             agent=agent_chain, tools=tools, handle_parsing_errors=True, max_iterations=50
         )
 
+    @opea_telemetry
     def __call__(self, state):
         print("---CALL Executor---")
         plan = state["plan"]
@@ -151,6 +169,7 @@ def __call__(self, state):
 
 
 class AnswerMaker:
+    @opea_telemetry
     def __init__(self, llm, is_vllm=False):
         if is_vllm:
             llm = llm.bind_tools([Response], tool_choice={"function": {"name": Response.__name__}})
@@ -159,13 +178,19 @@ def __init__(self, llm, is_vllm=False):
         output_parser = PydanticToolsParser(tools=[Response], first_tool_only=True)
         self.llm = answer_make_prompt | llm | output_parser
 
+    @opea_telemetry
+    def __llm_invoke__(self, state):
+        output = self.llm.invoke(state)
+        return output
+
+    @opea_telemetry
     def __call__(self, state):
         print("---CALL AnswerMaker---")
         success = False
         # sometime, LLM will not provide accurate steps per ask, try more than one time until success
         while not success:
             try:
-                output = self.llm.invoke(state)
+                output = self.__llm_invoke__(state)
                 print("Generated response: ", output.response)
                 success = True
             except OutputParserException as e:
@@ -183,6 +208,7 @@ class FinalAnswerChecker:
         str: A decision for whether we should use this plan or not
     """
 
+    @opea_telemetry
     def __init__(self, llm, is_vllm=False):
         class grade(BaseModel):
             binary_score: str = Field(description="executable score 'yes' or 'no'")
@@ -194,9 +220,15 @@ class grade(BaseModel):
         output_parser = PydanticToolsParser(tools=[grade], first_tool_only=True)
         self.chain = answer_check_prompt | llm | output_parser
 
+    @opea_telemetry
+    def __llm_invoke__(self, state):
+        output = self.chain.invoke(state)
+        return output
+
+    @opea_telemetry
     def __call__(self, state):
         print("---CALL FinalAnswerChecker---")
-        scored_result = self.chain.invoke(state)
+        scored_result = self.__llm_invoke__(state)
         score = scored_result.binary_score
         print(f"Answer is {state['response']}, Grade of good response is {score}")
         if score.startswith("yes"):
@@ -206,19 +238,26 @@ def __call__(self, state):
 
 
 class Replanner:
+    @opea_telemetry
     def __init__(self, llm, answer_checker=None):
         llm = llm.bind_tools([Plan])
         output_parser = PydanticToolsParser(tools=[Plan], first_tool_only=True)
         self.llm = replanner_prompt | llm | output_parser
         self.answer_checker = answer_checker
 
+    @opea_telemetry
+    def __llm_invoke__(self, state):
+        output = self.llm.invoke(state)
+        return output
+
+    @opea_telemetry
     def __call__(self, state):
         print("---CALL Replanner---")
         success = False
         # sometime, LLM will not provide accurate steps per ask, try more than one time until success
         while not success:
             try:
-                output = self.llm.invoke(state)
+                output = self.__llm_invoke__(state)
                 success = True
                 print("Replan: ", output)
             except OutputParserException as e:
@@ -230,6 +269,7 @@ def __call__(self, state):
 
 
 class PlanExecuteAgentWithLangGraph(BaseAgent):
+    @opea_telemetry
     def __init__(self, args, with_memory=False, **kwargs):
         super().__init__(args, local_vars=globals(), **kwargs)
 
@@ -260,6 +300,7 @@ def __init__(self, args, with_memory=False, **kwargs):
     def prepare_initial_state(self, query):
         return {"messages": [("user", query)]}
 
+    @opea_telemetry
     async def stream_generator(self, query, config, thread_id=None):
         initial_state = self.prepare_initial_state(query)
         if thread_id is not None:
@@ -282,6 +323,7 @@ async def stream_generator(self, query, config, thread_id=None):
             yield f"data: {repr(event)}\n\n"
         yield "data: [DONE]\n\n"
 
+    @opea_telemetry
     async def non_streaming_run(self, query, config):
         initial_state = self.prepare_initial_state(query)
         try: