(fix) agentc tools usage

notebooks/flight_search_agent_langraph/data/airline_reviews_data.py

@@ -14,7 +14,7 @@
 from couchbase.options import ClusterOptions
 import dotenv
 import pandas as pd
-from langchain_couchbase.vectorstores import CouchbaseVectorStore
+from langchain_couchbase.vectorstores import CouchbaseSearchVectorStore
 from tqdm import tqdm
 
 # Import kagglehub only when needed to avoid import errors during indexing
@@ -153,7 +153,7 @@ def load_to_vector_store(
             review_texts = self.process_to_texts()
 
             # Setup vector store for the target collection
-            vector_store = CouchbaseVectorStore(
+            vector_store = CouchbaseSearchVectorStore(
                 cluster=cluster,
                 bucket_name=bucket_name,
                 scope_name=scope_name,

notebooks/flight_search_agent_langraph/data/queries.py

@@ -28,9 +28,9 @@
     # Query 2: Flight booking LAX to JFK for tomorrow, 2 passengers, business class
     """Flight Booking Confirmed!
 
-Booking ID: FL08061563CACD
+Booking ID: FL09251563CACD
 Route: LAX → JFK
-Departure Date: 2025-08-06
+Departure Date: 2025-09-25
 Passengers: 2
 Class: business
 Total Price: $1500.00
@@ -45,9 +45,9 @@
     # Query 3: Flight booking JFK to MIA for next week
     """Flight Booking Confirmed!
 
-Booking ID: FL08124E7B9C2A
+Booking ID: FL10014E7B9C2A
 Route: JFK → MIA
-Departure Date: 2025-08-12
+Departure Date: 2025-10-01
 Passengers: 1
 Class: economy
 Total Price: $250.00
@@ -63,42 +63,42 @@
     """Your Current Bookings (2 found):
 
 Booking 1:
-  Booking ID: FL08061563CACD
+  Booking ID: FL09251563CACD
   Route: LAX → JFK
-  Date: 2025-08-06
+  Date: 2025-09-25
   Passengers: 2
   Class: business
   Total: $1500.00
   Status: confirmed
-  Booked: 2025-08-05
+  Booked: 2025-09-24
 
 Booking 2:
-  Booking ID: FL08124E7B9C2A
+  Booking ID: FL10014E7B9C2A
   Route: JFK → MIA
-  Date: 2025-08-12
+  Date: 2025-10-01
   Passengers: 1
   Class: economy
   Total: $250.00
   Status: confirmed
-  Booked: 2025-08-05""",
+  Booked: 2025-09-24""",
 
     # Query 5: SpiceJet service quality reviews
-    """Found 5 relevant airline reviews for 'SpiceJet service':
+    """Found 5 relevant airline reviews for 'SpiceJet service quality':
 
 Review 1:
-Airline: SpiceJet. Title: "Service is impeccable". Review: ✅ Trip Verified | Much better than airbus models. Even the basic economy class has ambient lighting. Better personal air vents and better spotlights. Even overhead storage bins are good. Service is impeccable with proper care taken of guests...
+Airline: SpiceJet. Title: "Great travel experience". Review: ✅ Trip Verified |  Marvelous courteous crew who took good care of all passengers. They should be rewarded for the patience shown towards the unruly ones. Great travel experience.. Rating: 10.0/10. Reviewer: Ranjita Pandey. Date: 18th April 2024. Recommended: yes
 
 Review 2:
-Airline: SpiceJet. Title: "good service by the crew". Review: ✅ Trip Verified | I have had good service by the crew. It was amazing, the crew was very enthusiastic and warm welcome. It was one of the best services in my experience.. Rating: 10.0/10. Reviewer: K Mansour. Date: 10th August 2024. Recom...
+Airline: SpiceJet. Title: "good service by the crew". Review: ✅ Trip Verified | I have had good service by the crew. It was amazing, the crew was very enthusiastic and warm welcome. It was one of the best services in my experience.. Rating: 10.0/10. Reviewer: K Mansour. Date: 10th August 2024. Recommended: yes
 
 Review 3:
-Airline: SpiceJet. Title: "outstanding service I experienced". Review: Not Verified |  I wanted to take a moment to express my sincere thanks for the outstanding service I experienced on my recent flight from Pune to Delhi. SG-8937. From the moment I boarded, the warmth and friendliness of the air h...
+Airline: SpiceJet. Title: "delayed both ways by many hours". Review: Not Verified |  Flight was delayed both ways by many hours. Poor service for the same price as other airlines like IndiGo. No wifi or other amenities to compensate for terrible service.. Rating: 2.0/10. Reviewer: Somil Jain Jain. Date: 20th May 2022. Recommended: no
 
 Review 4:
-Airline: SpiceJet. Title: "efficient and warm onboard service". Review: ✅ Trip Verified |  New Delhi to Kolkata. Delighted with the prompt, efficient and warm onboard service provided by the crew. Appreciate their efforts towards customer centricity.. Rating: 10.0/10. Reviewer: Debashis Roy. Date: 2...
+Airline: SpiceJet. Title: "Excellent service". Review: ✅ Trip Verified |  Excellent service by the ground staff courteous beyond expectations always willing to help in the real sense and not lipservice i will recommend to all whom I know. Rating: 10.0/10. Reviewer: Ramanathan Ramchandra. Date: 1st November 2023. Recommended: yes
 
 Review 5:
-Airline: SpiceJet. Title: "Service is very good". Review: Service is very good,  I am impressed with Miss Renu  who gave the best services ever. Thanks to Renu who is very sweet by her nature as well as her service. Rating: 9.0/10. Reviewer: Sanjay Patnaik. Date: 21st September 2023. Recommended: ye...""",
+Airline: SpiceJet. Title: "hospitality service given". Review: ✅ Trip Verified |  Seats are comparable if compare to other budget airlines. Cabin crews are friendly and hospitality service given with smiles. Very happy and enjoy experience.. Rating: 8.0/10. Reviewer: A Bameen. Date: 20th March 2022. Recommended: yes""",
 ]
 
 # Create dictionary for backward compatibility

notebooks/flight_search_agent_langraph/main.py

@@ -24,9 +24,6 @@
 from couchbase.cluster import Cluster
 from couchbase.exceptions import KeyspaceNotFoundException
 from couchbase.options import ClusterOptions
-from langchain.agents import AgentExecutor, create_react_agent
-from langchain_core.prompts import PromptTemplate
-from langchain_core.tools import Tool
 from pydantic import SecretStr
 
 
@@ -70,6 +67,9 @@ def find_project_root():
 dotenv.load_dotenv(override=True)
 
 
+# Agent Catalog tool integration - tools will be imported from tools/ directory
+
+
 class FlightSearchState(agentc_langgraph.agent.State):
     """State for flight search conversations - single user system."""
 
@@ -93,223 +93,69 @@ def __init__(self, catalog: agentc.Catalog, span: agentc.Span, chat_model=None):
             chat_model=chat_model, catalog=catalog, span=span, prompt_name="flight_search_assistant"
         )
 
+    def _get_tool_names(self) -> set[str]:
+        """Get available tool names from agent metadata."""
+        if hasattr(self, 'tools') and self.tools:
+            return {tool.name for tool in self.tools}
+        return set()
+
+    def _extract_tool_results(self, messages):
+        """Extract tool results from messages for production display."""
+        # Get available tool names dynamically
+        tool_names = self._get_tool_names()
+
+        # Find the first successful ToolMessage (skip error results)
+        for message in messages:
+            if (hasattr(message, 'name') and
+                message.name in tool_names and
+                not message.content.startswith("Error:")):
+                return message.content
+
+        return None
+
     def _invoke(
         self,
         span: agentc.Span,
         state: FlightSearchState,
         config: langchain_core.runnables.RunnableConfig,
     ) -> FlightSearchState:
-        """Handle flight search conversation using ReActAgent."""
+        """Handle flight search conversation using proper Agent Catalog patterns."""
 
         # Initialize conversation if this is the first message
         if not state["messages"]:
             initial_msg = langchain_core.messages.HumanMessage(content=state["query"])
             state["messages"].append(initial_msg)
             logger.info(f"Flight Query: {state['query']}")
 
-        # Get prompt resource first - we'll need it for the ReAct agent
-        prompt_resource = self.catalog.find("prompt", name="flight_search_assistant")
-
-        # Get tools from Agent Catalog with simplified discovery
-        tools = []
-        tool_names = [
-            "lookup_flight_info",
-            "save_flight_booking", 
-            "retrieve_flight_bookings",
-            "search_airline_reviews",
-        ]
-
-        for tool_name in tool_names:
-            try:
-                # Find tool using Agent Catalog
-                catalog_tool = self.catalog.find("tool", name=tool_name)
-                if catalog_tool:
-                    logger.info(f"✅ Found tool: {tool_name}")
-                else:
-                    logger.error(f"❌ Tool not found: {tool_name}")
-                    continue
-
-            except Exception as e:
-                logger.error(f"❌ Failed to find tool {tool_name}: {e}")
-                continue
-
-            # Create wrapper function to handle proper parameter parsing
-            def create_tool_wrapper(original_tool, name):
-                """Create a wrapper for Agent Catalog tools with robust input handling."""
-
-                def wrapper_func(tool_input: str) -> str:
-                    """Wrapper function that handles input parsing and error handling."""
-                    try:
-                        logger.info(f"🔧 Tool {name} called with raw input: {repr(tool_input)}")
-
-                        # Robust input sanitization to handle ReAct format artifacts
-                        if isinstance(tool_input, str):
-                            # Remove ReAct format artifacts that get mixed into input
-                            clean_input = tool_input.strip()
-
-                            # Remove common ReAct artifacts
-                            artifacts_to_remove = [
-                                '\nObservation', 'Observation', '\nThought:', 'Thought:', 
-                                '\nAction:', 'Action:', '\nAction Input:', 'Action Input:',
-                                '\nFinal Answer:', 'Final Answer:'
-                            ]
-
-                            for artifact in artifacts_to_remove:
-                                if artifact in clean_input:
-                                    clean_input = clean_input.split(artifact)[0]
-
-                            # Clean up quotes and whitespace
-                            clean_input = clean_input.strip().strip("\"'").strip()
-                            # Normalize whitespace
-                            clean_input = " ".join(clean_input.split())
-
-                            tool_input = clean_input
-
-                        logger.info(f"🧹 Tool {name} cleaned input: {repr(tool_input)}")
-
-                        # Call appropriate tool with proper parameter handling
-                        if name == "lookup_flight_info":
-                            # Parse airport codes from input
-                            import re
-
-                            source = None
-                            dest = None
-
-                            # 1) Support key=value style inputs from ReAct (e.g., source_airport="JFK", destination_airport="LAX")
-                            try:
-                                m_src = re.search(r"source_airport\s*[:=]\s*\"?([A-Za-z]{3})\"?", tool_input, re.I)
-                                m_dst = re.search(r"destination_airport\s*[:=]\s*\"?([A-Za-z]{3})\"?", tool_input, re.I)
-                                if m_src and m_dst:
-                                    source = m_src.group(1).upper()
-                                    dest = m_dst.group(1).upper()
-                            except Exception:
-                                pass
-
-                            # 2) Fallback: comma separated codes (e.g., "JFK,LAX")
-                            if source is None or dest is None:
-                                if ',' in tool_input:
-                                    parts = tool_input.split(',')
-                                    if len(parts) >= 2:
-                                        source = parts[0].strip().upper()
-                                        dest = parts[1].strip().upper()
-
-                            # 3) Fallback: natural language (e.g., "JFK to LAX")
-                            if source is None or dest is None:
-                                words = tool_input.upper().split()
-                                airport_codes = [w for w in words if len(w) == 3 and w.isalpha()]
-                                if len(airport_codes) >= 2:
-                                    source, dest = airport_codes[0], airport_codes[1]
-
-                            if not source or not dest:
-                                return "Error: Please provide source and destination airports (e.g., JFK,LAX or JFK to LAX)"
-
-                            result = original_tool.func(source_airport=source, destination_airport=dest)
-
-                        elif name == "save_flight_booking":
-                            result = original_tool.func(booking_input=tool_input)
-
-                        elif name == "retrieve_flight_bookings":
-                            # Handle empty input for "all bookings"
-                            if not tool_input or tool_input.lower() in ["", "all", "none"]:
-                                result = original_tool.func(booking_query="")
-                            else:
-                                result = original_tool.func(booking_query=tool_input)
-
-                        elif name == "search_airline_reviews":
-                            if not tool_input:
-                                return "Error: Please provide a search query for airline reviews"
-                            result = original_tool.func(query=tool_input)
-
-                        else:
-                            # Generic fallback - pass as first positional argument
-                            result = original_tool.func(tool_input)
-
-                        logger.info(f"✅ Tool {name} executed successfully")
-                        return str(result) if result is not None else "No results found"
-
-                    except Exception as e:
-                        error_msg = f"Error in tool {name}: {str(e)}"
-                        logger.error(f"❌ {error_msg}")
-                        return error_msg
-
-                return wrapper_func
-
-            # Create LangChain tool with descriptive information
-            tool_descriptions = {
-                "lookup_flight_info": "Find available flights between airports. Input: 'JFK,LAX' or 'JFK to LAX'. Returns flight options with airlines and aircraft.",
-                "save_flight_booking": "Create a flight booking. Input: 'JFK,LAX,2025-12-25' or natural language. Handles passenger count and class automatically.",
-                "retrieve_flight_bookings": "View existing bookings. Input: empty string for all bookings, or 'JFK,LAX,2025-12-25' for specific booking.",
-                "search_airline_reviews": "Search airline customer reviews. Input: 'SpiceJet service' or 'food quality'. Returns passenger reviews and ratings."
-            }
-
-            langchain_tool = Tool(
-                name=tool_name,
-                description=tool_descriptions.get(tool_name, f"Tool for {tool_name.replace('_', ' ')}"),
-                func=create_tool_wrapper(catalog_tool, tool_name),
-            )
-            tools.append(langchain_tool)
-
-        # Use the Agent Catalog prompt content directly - get first result if it's a list
-        if isinstance(prompt_resource, list):
-            prompt_resource = prompt_resource[0]
-
-        # Safely get the content from the prompt resource
-        prompt_content = getattr(prompt_resource, "content", "")
-        if not prompt_content:
-            prompt_content = "You are a helpful flight search assistant. Use the available tools to help users with their flight queries."
-
-        # Inject current date into the prompt content
-        import datetime
-
-        current_date = datetime.date.today().strftime("%Y-%m-%d")
-        prompt_content = prompt_content.replace("{current_date}", current_date)
-
-        # Use the Agent Catalog prompt content directly - it already has ReAct format
-        react_prompt = PromptTemplate.from_template(str(prompt_content))
-
-        # Create ReAct agent with tools and prompt
-        agent = create_react_agent(self.chat_model, tools, react_prompt)
-
-        # Custom parsing error handler - force stopping on parsing errors
-        def handle_parsing_errors(error):
-            """Custom handler for parsing errors - force early termination."""
-            error_msg = str(error)
-            if "both a final answer and a parse-able action" in error_msg:
-                # Force early termination - return a reasonable response
-                return "Final Answer: I encountered a parsing error. Please reformulate your request."
-            elif "Missing 'Action:'" in error_msg:
-                return "I need to use the correct format with Action: and Action Input:"
+        # Use Agent Catalog's built-in create_react_agent method (like FastAPI example)
+        agent = self.create_react_agent(span)
+
+        # Execute the agent with proper Agent Catalog integration
+        response = agent.invoke(input=state, config=config)
+        logger.info(f"🔍FULL Agent response: {response}")
+
+        # Extract tool results instead of conversational responses for production display
+        if "messages" in response and response["messages"]:
+            # Find the first successful tool result
+            tool_content = self._extract_tool_results(response["messages"])
+            if tool_content:
+                # Use tool results for production display
+                assistant_msg = langchain_core.messages.AIMessage(content=tool_content)
+                state["messages"].append(assistant_msg)
+                logger.info(f"📊 Tool results: {tool_content}")
             else:
-                return f"Final Answer: I encountered an error processing your request. Please try again."
-
-        # Create agent executor - very strict: only 2 iterations max
-        agent_executor = AgentExecutor(
-            agent=agent,
-            tools=tools,
-            verbose=True,
-            handle_parsing_errors=handle_parsing_errors,
-            max_iterations=2,  # STRICT: 1 tool call + 1 Final Answer only
-            early_stopping_method="force",  # Force stop
-            return_intermediate_steps=True,
-        )
+                # Fallback to last AI message if no tool results
+                last_message = response["messages"][-1]
+                state["messages"].append(last_message)
+                logger.info(f"📊 Agent response: {last_message.content}...")
+        else:
+            # Fallback to output field
+            output_content = response.get("output", "No response generated")
+            assistant_msg = langchain_core.messages.AIMessage(content=output_content)
+            state["messages"].append(assistant_msg)
+            logger.info(f"📊 Agent output: {output_content}...")
 
-        # Execute the agent
-        response = agent_executor.invoke({"input": state["query"]})
-
-        # Extract tool outputs from intermediate_steps and store in search_results
-        if "intermediate_steps" in response and response["intermediate_steps"]:
-            tool_outputs = []
-            for step in response["intermediate_steps"]:
-                if isinstance(step, tuple) and len(step) >= 2:
-                    # step[0] is the action, step[1] is the tool output/observation
-                    tool_output = str(step[1])
-                    if tool_output and tool_output.strip():
-                        tool_outputs.append(tool_output)
-            state["search_results"] = tool_outputs
-
-        # Add response to conversation
-        assistant_msg = langchain_core.messages.AIMessage(content=response["output"])
-        state["messages"].append(assistant_msg)
+        # Mark as resolved
         state["resolved"] = True
 
         return state