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Add agents, API, and comprehensive tests
Co-authored-by: MinecraftFuns <25814618+MinecraftFuns@users.noreply.github.com>
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‎python/langchain/.env.example‎

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# OpenAI Configuration
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OPENAI_API_KEY=your_openai_api_key_here
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OPENAI_MODEL=gpt-4o-mini
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OPENAI_TEMPERATURE=0.1
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# Path to Go application state directory
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PROMPTPIPE_STATE_DIR=/var/lib/promptpipe
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# Prompt files (relative to python/langchain directory)
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INTAKE_BOT_PROMPT_FILE=../../prompts/intake_bot_system.txt
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COORDINATOR_PROMPT_FILE=../../prompts/conversation_system_3bot.txt
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FEEDBACK_TRACKER_PROMPT_FILE=../../prompts/feedback_tracker_system.txt
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PROMPT_GENERATOR_PROMPT_FILE=../../prompts/prompt_generator_system.txt
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# Timeouts
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FEEDBACK_INITIAL_TIMEOUT=15m
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FEEDBACK_FOLLOWUP_DELAY=3h
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# Chat History
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CHAT_HISTORY_LIMIT=-1
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# API Configuration
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API_HOST=0.0.0.0
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API_PORT=8001
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# Debug Mode
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DEBUG=false

‎python/langchain/.gitignore‎

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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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# Virtual Environment
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.venv/
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venv/
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ENV/
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env/
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# IDE
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# Testing
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.pytest_cache/
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.coverage
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htmlcov/
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.tox/
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# Environment
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.env
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.env.local
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# Logs
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*.log
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# Database
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*.db
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*.db-journal
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*.db-wal
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*.db-shm
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# macOS
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.DS_Store

‎python/langchain/promptpipe_agent/agents/coordinator_agent.py‎

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"""Coordinator agent for routing conversations and managing overall flow."""
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import os
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from typing import Any, Optional
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_openai import ChatOpenAI
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from promptpipe_agent.config import settings
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from promptpipe_agent.models.schemas import ConversationHistory, MessageRole
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from promptpipe_agent.models.state_manager import StateManager
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from promptpipe_agent.tools.profile_save_tool import ProfileSaveTool
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from promptpipe_agent.tools.prompt_generator_tool import PromptGeneratorTool
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from promptpipe_agent.tools.scheduler_tool import SchedulerTool
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from promptpipe_agent.tools.state_transition_tool import StateTransitionTool
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class CoordinatorAgent:
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"""Coordinator agent for managing conversation flow and routing."""
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def __init__(
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self,
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state_manager: StateManager,
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llm: Optional[ChatOpenAI] = None,
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system_prompt_file: Optional[str] = None,
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):
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"""Initialize the coordinator agent.
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Args:
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state_manager: State manager for conversation state
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llm: Language model (if None, creates default)
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system_prompt_file: Path to system prompt file (if None, uses default)
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"""
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self.state_manager = state_manager
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# Initialize LLM
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if llm is None:
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self.llm = ChatOpenAI(
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model=settings.openai_model,
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temperature=settings.openai_temperature,
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api_key=settings.openai_api_key,
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)
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else:
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self.llm = llm
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# Load system prompt
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self.system_prompt_file = (
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system_prompt_file or settings.coordinator_prompt_file
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)
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self.system_prompt = self._load_system_prompt()
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# Initialize tools
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self.tools = self._create_tools()
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# Create agent
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self.agent = self._create_agent()
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def _load_system_prompt(self) -> str:
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"""Load the system prompt from file."""
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prompt_file = self.system_prompt_file
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if not os.path.isabs(prompt_file):
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# Make path relative to project root
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base_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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prompt_file = os.path.join(base_dir, prompt_file)
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if os.path.exists(prompt_file):
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with open(prompt_file, "r") as f:
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return f.read().strip()
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else:
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# Fallback prompt
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return (
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"You are a helpful coordinator assistant for a habit-building conversation system. "
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"You can help users with general questions, route them to specialized modules, "
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"and use tools to manage their experience."
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)
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def _create_tools(self) -> list:
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"""Create the tools available to the coordinator."""
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return [
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StateTransitionTool(state_manager=self.state_manager),
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ProfileSaveTool(state_manager=self.state_manager),
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SchedulerTool(state_manager=self.state_manager),
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PromptGeneratorTool(state_manager=self.state_manager),
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]
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def _create_agent(self) -> AgentExecutor:
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"""Create the LangChain agent executor."""
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", self.system_prompt),
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MessagesPlaceholder(variable_name="chat_history", optional=True),
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("human", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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agent = create_tool_calling_agent(self.llm, self.tools, prompt)
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return AgentExecutor(agent=agent, tools=self.tools, verbose=settings.debug)
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def process_message(
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self, participant_id: str, user_message: str
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) -> str:
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"""Process a user message and return the response.
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Args:
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participant_id: The participant ID
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user_message: The user's message
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Returns:
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The agent's response
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"""
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# Get conversation history
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history = self.state_manager.get_conversation_history(participant_id)
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# Convert history to LangChain format (limit if needed)
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chat_history = []
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messages = history.messages
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if settings.chat_history_limit > 0:
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messages = messages[-settings.chat_history_limit :]
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for msg in messages:
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if msg.role == MessageRole.USER:
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chat_history.append(("human", msg.content))
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elif msg.role == MessageRole.ASSISTANT:
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chat_history.append(("ai", msg.content))
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# Add user message to history
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self.state_manager.add_message(participant_id, MessageRole.USER.value, user_message)
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# Process message through agent
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try:
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result = self.agent.invoke(
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{
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"input": user_message,
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"chat_history": chat_history,
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}
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)
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response = result["output"]
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except Exception as e:
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response = f"I apologize, but I encountered an error: {str(e)}"
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# Add response to history
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self.state_manager.add_message(
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participant_id, MessageRole.ASSISTANT.value, response
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)
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return response

‎python/langchain/promptpipe_agent/agents/feedback_agent.py‎

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"""Feedback agent for tracking user feedback and updating profiles."""
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import os
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from typing import Optional
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from langchain.agents import AgentExecutor, create_tool_calling_agent
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_openai import ChatOpenAI
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from promptpipe_agent.config import settings
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from promptpipe_agent.models.schemas import MessageRole
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from promptpipe_agent.models.state_manager import StateManager
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from promptpipe_agent.tools.profile_save_tool import ProfileSaveTool
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from promptpipe_agent.tools.scheduler_tool import SchedulerTool
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from promptpipe_agent.tools.state_transition_tool import StateTransitionTool
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class FeedbackAgent:
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"""Feedback agent for tracking habit feedback and updating profiles."""
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def __init__(
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self,
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state_manager: StateManager,
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llm: Optional[ChatOpenAI] = None,
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system_prompt_file: Optional[str] = None,
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):
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"""Initialize the feedback agent.
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Args:
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state_manager: State manager for conversation state
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llm: Language model (if None, creates default)
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system_prompt_file: Path to system prompt file (if None, uses default)
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"""
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self.state_manager = state_manager
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# Initialize LLM
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if llm is None:
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self.llm = ChatOpenAI(
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model=settings.openai_model,
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temperature=settings.openai_temperature,
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api_key=settings.openai_api_key,
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)
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else:
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self.llm = llm
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# Load system prompt
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self.system_prompt_file = (
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system_prompt_file or settings.feedback_tracker_prompt_file
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)
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self.system_prompt = self._load_system_prompt()
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# Initialize tools
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self.tools = self._create_tools()
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# Create agent
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self.agent = self._create_agent()
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def _load_system_prompt(self) -> str:
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"""Load the system prompt from file."""
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prompt_file = self.system_prompt_file
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if not os.path.isabs(prompt_file):
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# Make path relative to project root
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base_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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prompt_file = os.path.join(base_dir, prompt_file)
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if os.path.exists(prompt_file):
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with open(prompt_file, "r") as f:
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return f.read().strip()
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else:
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# Fallback prompt
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return (
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"You are a feedback tracker that helps users reflect on their habit attempts. "
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"Ask about what worked, what didn't, and help them improve their approach."
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)
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def _create_tools(self) -> list:
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"""Create the tools available to the feedback agent."""
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return [
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StateTransitionTool(state_manager=self.state_manager),
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ProfileSaveTool(state_manager=self.state_manager),
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SchedulerTool(state_manager=self.state_manager),
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]
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def _create_agent(self) -> AgentExecutor:
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"""Create the LangChain agent executor."""
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prompt = ChatPromptTemplate.from_messages(
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[
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("system", self.system_prompt),
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MessagesPlaceholder(variable_name="chat_history", optional=True),
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("human", "{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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)
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agent = create_tool_calling_agent(self.llm, self.tools, prompt)
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return AgentExecutor(agent=agent, tools=self.tools, verbose=settings.debug)
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def process_message(
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self, participant_id: str, user_message: str
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) -> str:
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"""Process a user message and return the response.
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Args:
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participant_id: The participant ID
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user_message: The user's message
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Returns:
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The agent's response
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"""
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# Get conversation history
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history = self.state_manager.get_conversation_history(participant_id)
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# Get user profile for context
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profile = self.state_manager.get_user_profile(participant_id)
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profile_context = ""
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if profile:
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profile_context = f"\n\nCurrent Profile:\n{profile.to_context_string()}"
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# Convert history to LangChain format (limit if needed)
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chat_history = []
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messages = history.messages
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if settings.chat_history_limit > 0:
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messages = messages[-settings.chat_history_limit :]
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for msg in messages:
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if msg.role == MessageRole.USER:
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chat_history.append(("human", msg.content))
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elif msg.role == MessageRole.ASSISTANT:
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chat_history.append(("ai", msg.content))
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# Add user message to history
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self.state_manager.add_message(participant_id, MessageRole.USER.value, user_message)
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# Enhance input with profile context
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enhanced_input = user_message
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if profile_context:
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enhanced_input = f"{user_message}{profile_context}"
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# Process message through agent
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try:
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result = self.agent.invoke(
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{
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"input": enhanced_input,
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"chat_history": chat_history,
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}
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)
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response = result["output"]
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except Exception as e:
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response = f"I apologize, but I encountered an error: {str(e)}"
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# Add response to history
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self.state_manager.add_message(
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participant_id, MessageRole.ASSISTANT.value, response
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)
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return response

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