Log all responses

src/airflow_wingman/llm_client.py

@@ -75,6 +75,7 @@ class LLMClient:
 
             # If streaming, return the generator directly
             if stream:
+                logger.info(f"Using streaming response from {self.provider_name}")
                 return self.provider.get_streaming_content(response)
 
             # For non-streaming responses, handle tool calls if present
@@ -96,10 +97,18 @@ class LLMClient:
                     messages=messages, model=model, temperature=temperature, max_tokens=max_tokens, tool_results=tool_results, original_response=response
                 )
 
-                return {"content": self.provider.get_content(follow_up_response)}
+                content = self.provider.get_content(follow_up_response)
+                logger.info(f"Final content from {self.provider_name} with tool calls COMPLETE RESPONSE START >>>")
+                logger.info(content)
+                logger.info("<<< COMPLETE RESPONSE END")
+                return {"content": content}
             else:
                 logger.info("Response does not contain tool calls")
-                return {"content": self.provider.get_content(response)}
+                content = self.provider.get_content(response)
+                logger.info(f"Final content from {self.provider_name} without tool calls COMPLETE RESPONSE START >>>")
+                logger.info(content)
+                logger.info("<<< COMPLETE RESPONSE END")
+                return {"content": content}
 
         except Exception as e:
             error_msg = f"Error in {self.provider_name} API call: {str(e)}\n{traceback.format_exc()}"

src/airflow_wingman/providers/anthropic_provider.py

@@ -265,6 +265,7 @@ class AnthropicProvider(BaseLLMProvider):
         Returns:
             Generator yielding content chunks
         """
+        logger.info("Starting Anthropic streaming response processing")
 
         def generate():
             for chunk in response:

src/airflow_wingman/providers/openai_provider.py

@@ -213,6 +213,7 @@ class OpenAIProvider(BaseLLMProvider):
         Returns:
             Generator yielding content chunks
         """
+        logger.info("Starting OpenAI streaming response processing")
 
         def generate():
             for chunk in response:

src/airflow_wingman/views.py

@@ -1,5 +1,8 @@
 """Views for Airflow Wingman plugin."""
 
+import json
+import logging
+
 from flask import Response, request, session
 from flask.json import jsonify
 from flask_appbuilder import BaseView as AppBuilderBaseView, expose
@@ -10,6 +13,9 @@ from airflow_wingman.notes import INTERFACE_MESSAGES
 from airflow_wingman.prompt_engineering import prepare_messages
 from airflow_wingman.tools import list_airflow_tools
 
+# Create a properly namespaced logger for the Airflow plugin
+logger = logging.getLogger("airflow.plugins.wingman")
+
 
 class WingmanView(AppBuilderBaseView):
     """View for Airflow Wingman plugin."""
@@ -50,6 +56,11 @@ class WingmanView(AppBuilderBaseView):
             # Get base URL from models configuration based on provider
             base_url = MODELS.get(provider_name, {}).get("endpoint")
 
+            # Log the request parameters (excluding API key for security)
+            safe_data = {k: v for k, v in data.items() if k != "api_key"}
+            logger.info(f"Chat request: provider={provider_name}, model={data.get('model')}, stream={data.get('stream')}")
+            logger.info(f"Request parameters: {json.dumps(safe_data)[:200]}...")
+
             # Create a new client for this request with the appropriate provider
             client = LLMClient(provider_name=provider_name, api_key=data["api_key"], base_url=base_url)
 
@@ -96,24 +107,39 @@ class WingmanView(AppBuilderBaseView):
     def _handle_streaming_response(self, client: LLMClient, data: dict) -> Response:
         """Handle streaming response."""
         try:
-            # Get the streaming generator from the client
+            logger.info("Beginning streaming response")
             generator = client.chat_completion(messages=data["messages"], model=data["model"], temperature=data["temperature"], max_tokens=data["max_tokens"], stream=True)
 
             def stream_response():
+                complete_response = ""
+
                 # Send SSE format for each chunk
                 for chunk in generator:
                     if chunk:
                         yield f"data: {chunk}\n\n"
 
-                # Signal end of stream
+                # Log the complete assembled response at the end
+                logger.info("COMPLETE RESPONSE START >>>")
+                logger.info(complete_response)
+                logger.info("<<< COMPLETE RESPONSE END")
+
                 yield "data: [DONE]\n\n"
 
             return Response(stream_response(), mimetype="text/event-stream", headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
         except Exception as e:
-            # If streaming fails, return error
+            logger.error(f"Streaming error: {str(e)}")
             return jsonify({"error": str(e)}), 500
 
     def _handle_regular_response(self, client: LLMClient, data: dict) -> Response:
         """Handle regular response."""
+        try:
+            logger.info("Beginning regular (non-streaming) response")
             response = client.chat_completion(messages=data["messages"], model=data["model"], temperature=data["temperature"], max_tokens=data["max_tokens"], stream=False)
+            logger.info("COMPLETE RESPONSE START >>>")
+            logger.info(f"Response to frontend: {json.dumps(response)}")
+            logger.info("<<< COMPLETE RESPONSE END")
+
             return jsonify(response)
+        except Exception as e:
+            logger.error(f"Regular response error: {str(e)}")
+            return jsonify({"error": str(e)}), 500