Kenneth Leung kennethleungty
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kennethleungty
/ langgraph_supervisor_conditional.py
Last active
December 6, 2025 08:01
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| def supervisor_conditional_node(state: SupervisorState) -> dict: | |
| messages = state["messages"] | |
| # Call supervisor with full conversation history | |
| response = supervisor_llm.invoke([SystemMessage(content=SUPERVISOR_PROMPT)] + messages) | |
| return {"messages": [AIMessage(content=response.content, name="supervisor")]} |
kennethleungty
/ langgraph_command.py
Created
December 6, 2025 07:25
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| def supervisor_command_node( | |
| state: SupervisorState, | |
| ) -> Command[Literal["transaction_history_agent", "property_profile_agent"]]: | |
| """Supervisor node using Command for routing + state updates. | |
| Uses structured output to: | |
| 1. Determine which agent to route to | |
| 2. Extract property_name from the query | |
| 3. Update state via Command.update | |
kennethleungty
/ langgraph_minimal_command.py
Created
December 6, 2025 07:20
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| def router(state): | |
| # Read user intent from the current graph state. | |
| user_intent = state.get("intent") | |
| # Decide which agent to call next based on simple logic. | |
| # This demonstrates how you choose a target node. | |
| if user_intent == "search": | |
| return Command( | |
| update={"task": "run_search"}, # Update part of the state | |
| goto="search_agent" # Explicitly route to this node |
kennethleungty
/ langgraph_routing_function.py
Created
December 5, 2025 15:28
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| def should_continue( | |
| state: SupervisorState, | |
| ) -> Literal["transaction_history_agent", "property_profile_agent", "end"]: | |
| """Extract routing decision from supervisor's message. | |
| Looks for agent names in the supervisor's latest message to determine routing. | |
| Returns "end" if supervisor provides a direct response (no routing needed). | |
| """ | |
| # Find latest supervisor message | |
| for msg in reversed(state["messages"]): | |
| if ( |
kennethleungty
/ langgraph_conditional_edge.py
Last active
December 6, 2025 14:50
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| graph.add_conditional_edges( | |
| source="supervisor", | |
| path=should_continue, | |
| path_map={ | |
| "transaction_history_agent": "transaction_history_agent", | |
| "property_profile_agent": "property_profile_agent", | |
| "end": END, | |
| }, | |
| ) |
kennethleungty
/ supervisor_prompt.py
Created
December 5, 2025 10:58
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| SUPERVISOR_PROMPT = """You are a real estate supervisor based in Singapore that routes queries to specialists or handles them directly. | |
| Available specialists: | |
| - transaction_history_agent - For sales history, market trends, past transactions | |
| - property_profile_agent - For property details, features, location info | |
| Your tasks: | |
| 1. Determine which agent to route to (or "none" if you should handle directly) | |
| 2. Extract any property name mentioned (e.g., "38 Oxley Road", "One Oxley Rise") | |
| 3. If no routing needed, provide a conversational response |
kennethleungty
/ langextract_output.json
Created
August 21, 2025 04:23
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| { | |
| "document_id": "doc_ec646730", | |
| "extractions": [ | |
| { | |
| "extraction_class": "exclusion", | |
| "extraction_text": "The insurance cover provided to You under this Policy is based on the information You have provided to Us.", | |
| "plain_english": "This policy is only valid if you honestly and completely tell us all the facts about your vehicle and driving history. If you don't, you may not be able to make a claim." | |
| }, | |
| { | |
| "extraction_class": "exclusion", |
kennethleungty
/ langextract_save.py
Last active
August 21, 2025 05:37
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| output_filename = "data/output/extraction_results.jsonl" | |
| lx.io.save_annotated_documents([result], | |
| output_name=output_filename, | |
| output_dir=".") |
kennethleungty
/ langextract_extract.py
Created
August 21, 2025 03:44
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| import langextract as lx | |
| # Run PDF processor on insurance policy PDF and get concatenated parsed text | |
| processor = PDFProcessor("data/input/driveshield_specimen_policy_value_plan.pdf") | |
| input_text = processor.get_all_text() | |
| # Run extraction | |
| result = lx.extract( | |
| text_or_documents=input_text, | |
| prompt_description=prompt, |
kennethleungty
/ langextract_prompt_2.py
Last active
August 21, 2025 03:39
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| import langextract as lx | |
| examples = [ | |
| lx.data.ExampleData( | |
| text="This policy does not cover damage caused by floods, earthquakes, or nuclear accidents.", | |
| extractions=[ | |
| lx.data.Extraction( | |
| extraction_class="exclusion", | |
| extraction_text="floods", | |
| attributes={ |
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