Add Langfuse AutoGen Tracing to example

kedro-agentic-workflows/conf/base/genai-config.yml → kedro-agentic-workflows/conf/langfuse/genai-config.yml

@@ -44,20 +44,13 @@ intent_prompt_langfuse:
   sync_policy: local      # local|remote|strict
   mode: langchain         # langchain|sdk
 
-# intent_prompt_opik:
-#   type: kedro_datasets_experimental.opik.OpikPromptDataset
-#   filepath: data/intent_detection/prompts/intent_prompt_opik.json
-#   prompt_name: "intent-classifier"
-#   prompt_type: "chat"
-#   credentials: opik_credentials
-
 # --- Tracing ---
 intent_tracer_langfuse:
   type: kedro_datasets_experimental.langfuse.LangfuseTraceDataset
   credentials: langfuse_credentials
-  mode: langchain    # langchain | openai | sdk
+  mode: langchain    # langchain | openai | sdk | autogen
 
-# intent_tracer_opik:
-#  type: kedro_datasets_experimental.opik.OpikTraceDataset
-#  credentials: opik_credentials
-#  mode: openai    # langchain | openai | sdk
+autogen_tracer_langfuse:
+  type: kedro_datasets_experimental.langfuse.LangfuseTraceDataset
+  credentials: langfuse_credentials
+  mode: autogen

kedro-agentic-workflows/conf/opik/genai-config.yml

@@ -0,0 +1,54 @@
+# --- LLMs setup ---
+llm:
+  type: langchain.ChatOpenAIDataset
+  kwargs:
+    model: "gpt-4o"
+    temperature: 0.0
+  credentials: openai
+
+llm_openai:
+  type: kedro_agentic_workflows.datasets.openai_model_dataset.OpenAIModelDataset
+  model_name: "gpt-4o"
+  model_settings:
+    temperature: 0.0
+  credentials: openai
+
+llm_autogen:
+  type: kedro_agentic_workflows.datasets.autogen_model_client.OpenAIChatCompletionClientDataset
+  kwargs:
+    model: "gpt-4o"
+    temperature: 0.0
+  credentials: openai
+
+# --- Prompts ---
+tool_prompt:
+  type: kedro_datasets_experimental.langchain.LangChainPromptDataset
+  filepath: data/response_generation/prompts/tool.txt
+  template: PromptTemplate
+  dataset:
+    type: text.TextDataset
+
+response_prompt:
+  type: kedro_datasets_experimental.langchain.LangChainPromptDataset
+  filepath: data/response_generation/prompts/response.yml
+  template: ChatPromptTemplate
+  dataset:
+    type: yaml.YAMLDataset
+
+intent_prompt_opik:
+  type: kedro_datasets_experimental.opik.OpikPromptDataset
+  filepath: data/intent_detection/prompts/intent_prompt_opik.json
+  prompt_name: "intent-classifier"
+  prompt_type: "chat"
+  credentials: opik_credentials
+
+# --- Tracing ---
+intent_tracer_opik:
+  type: kedro_datasets_experimental.opik.OpikTraceDataset
+  credentials: opik_credentials
+  mode: openai
+
+autogen_tracer_opik:
+  type: kedro_datasets_experimental.opik.OpikTraceDataset
+  credentials: opik_credentials
+  mode: autogen

kedro-agentic-workflows/requirements-langfuse.txt

@@ -0,0 +1,12 @@
+autogen-agentchat
+autogen-ext[openai]
+ipython>=8.10
+jupyterlab>=3.0
+notebook
+kedro>=1.2.0
+kedro-datasets[pandas-sqltabledataset,pandas-sqlquerydataset,yaml-yamldataset,langchain-chatopenaidataset,langfuse,langfuse-langfusetracedataset-autogen]~=9.0.0
+kedro-viz>=12.3.0
+langgraph~=1.0.0
+scikit-learn~=1.7.2
+questionary~=2.1.1
+openai-agents

kedro-agentic-workflows/requirements.txt → kedro-agentic-workflows/requirements-opik.txt

@@ -4,7 +4,7 @@ ipython>=8.10
 jupyterlab>=3.0
 notebook
 kedro>=1.2.0
-kedro-datasets[pandas-sqltabledataset,pandas-sqlquerydataset,yaml-yamldataset,opik,langchain-chatopenaidataset, langfuse]~=9.0.0
+kedro-datasets[pandas-sqltabledataset,pandas-sqlquerydataset,yaml-yamldataset,langchain-chatopenaidataset,opik]~=9.0.0
 kedro-viz>=12.3.0
 langgraph~=1.0.0
 scikit-learn~=1.7.2

kedro-agentic-workflows/src/kedro_agentic_workflows/pipelines/response_generation_autogen/nodes.py

@@ -16,6 +16,7 @@ def generate_response(
     intent_detection_result: dict,
     user_context: dict,
     session_config: dict,
+    tracer,
 ) -> dict:
     """
     Run the ResponseGenerationAgent to produce a final answer.
@@ -30,17 +31,37 @@ def generate_response(
         result = {"messages": [AIMessage(content=message)]}
 
     else:
-        agent = ResponseGenerationAgentAutogen(context=response_generation_context)
-        agent.compile()
+        # Wrap agent execution in a span for tracing
+        with tracer.start_as_current_span("response_generation") as span:
+            # Log input context to the span
+            span.set_attribute("intent", intent_detection_result["intent"])
+            span.set_attribute(
+                "intent_reason", intent_detection_result.get("reason", "")
+            )
+            span.set_attribute(
+                "user_id",
+                user_context.get("profile", {}).get("user_id", "unknown"),
+            )
 
-        context = {
-            "messages": [],
-            "intent": intent_detection_result["intent"],
-            "intent_generator_summary": intent_detection_result["reason"],
-            "user_context": user_context,
-        }
+            agent = ResponseGenerationAgentAutogen(context=response_generation_context)
+            agent.compile()
 
-        result = agent.invoke(context, session_config)
+            context = {
+                "messages": [],
+                "intent": intent_detection_result["intent"],
+                "intent_generator_summary": intent_detection_result["reason"],
+                "user_context": user_context,
+            }
+
+            result = agent.invoke(context, session_config)
+
+            # Log output to the span
+            if result.get("messages"):
+                span.set_attribute(
+                    "response", result["messages"][-1].content[:500]
+                )  # Truncate for safety
+            span.set_attribute("claim_created", result.get("claim_created", False))
+            span.set_attribute("escalated", result.get("escalated", False))
 
     for m in result["messages"]:
         try:

kedro-agentic-workflows/src/kedro_agentic_workflows/pipelines/response_generation_autogen/pipeline.py

@@ -1,9 +1,6 @@
 from kedro.pipeline import Pipeline, node, pipeline, llm_context_node, tool
 
-from .nodes import (
-    generate_response,
-    log_response_and_end_session,
-)
+from .nodes import generate_response, log_response_and_end_session
 from .tools import build_lookup_docs, build_get_user_claims, build_create_claim
 
 
@@ -23,11 +20,11 @@ def create_pipeline(**kwargs) -> Pipeline:
             ),
             node(
                 func=generate_response,
-                inputs=[
-                    "response_generation_context",
+                inputs=["response_generation_context",
                     "intent_detection_result",
                     "user_context",
                     "session_config",
+                    "autogen_tracer_langfuse"
                 ],
                 outputs="final_response",
                 name="generate_response_node",
@@ -39,4 +36,4 @@ def create_pipeline(**kwargs) -> Pipeline:
                 name="end_session_node",
             ),
         ]
-    )
+    )