first langgraph commit

python-langgraph/README.md

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+# LangGraph: Build Stateful AI Agents in Python
+
+This repo contains the source code for [LangGraph: Build Stateful AI Agents in Python](https://realpython.com/langgraph-build-stateful-ai-agents-in-python/)
+
+## Setup
+
+Create a new virtual environment, and run the following command to install LangGraph and the additional requirements for this project:
+
+```console
+(venv) $ python -m pip install -r requirements.txt
+```
+
+You'll use `langchain-openai` to interact with OpenAI LLMs, but keep in mind you can use any LLM provider you like with LangGraph and LangChain. You'll use [`pydantic`](https://realpython.com/python-pydantic/) to validate the information your agent parses from emails.
+
+Before moving forward, if you choose to use OpenAI, make sure you're signed up for an OpenAI account and you have a valid [API key](https://openai.com/api/). You'll need to set the following [environment variable](https://en.wikipedia.org/wiki/Environment_variable) before running any examples in this tutorial:
+
+```dotenv
+OPENAI_API_KEY=<YOUR-OPENAI-API-KEY>
+```
+
+## Usage
+
+TODO: A short note on how to run the project. You can tell them to go to the tutorial for more information.

python-langgraph/chains/binary_questions.py

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+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+
+class BinaryAnswer(BaseModel):
+    is_true: bool = Field(
+        description="""Whether the answer to the question is yes or no.
+        True if yes otherwise false."""
+    )
+
+
+binary_question_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            """
+            Answer this question as True for "yes" and False for "no".
+            No other answers are allowed:
+            {question}
+            """,
+        )
+    ]
+)
+
+binary_question_model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
+
+BINARY_QUESTION_CHAIN = (
+    binary_question_prompt
+    | binary_question_model.with_structured_output(BinaryAnswer)
+)

python-langgraph/chains/escalation_check.py

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+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, Field
+
+
+class EscalationCheck(BaseModel):
+    needs_escalation: bool = Field(
+        description="""Whether the notice requires escalation according
+        to specified criteria"""
+    )
+
+
+escalation_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            """
+            Determine whether the following notice received from a regulatory
+            body requires immediate escalation. Immediate escalation is
+            required when {escalation_criteria}.
+            Here's the notice message:
+            {message}
+            """,
+        )
+    ]
+)
+
+escalation_check_model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
+
+ESCALATION_CHECK_CHAIN = (
+    escalation_prompt
+    | escalation_check_model.with_structured_output(EscalationCheck)
+)

python-langgraph/chains/notice_extraction.py

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+from datetime import date
+
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from pydantic import BaseModel, EmailStr, Field
+
+
+class NoticeEmailExtract(BaseModel):
+    date_of_notice: date | None = Field(
+        default=None,
+        description="""The date of the notice (if any) reformatted
+        to match YYYY-mm-dd""",
+    )
+    entity_name: str | None = Field(
+        default=None,
+        description="""The name of the entity sending the notice (if present
+        in the message)""",
+    )
+    entity_phone: str | None = Field(
+        default=None,
+        description="""The phone number of the entity sending the notice
+        (if present in the message)""",
+    )
+    entity_email: EmailStr | None = Field(
+        default=None,
+        description="""The email of the entity sending the notice
+        (if present in the message)""",
+    )
+    project_id: int | None = Field(
+        default=None,
+        description="""The project ID (if present in the message) -
+        must be an integer""",
+    )
+    site_location: str | None = Field(
+        default=None,
+        description="""The site location of the project (if present
+        in the message)""",
+    )
+    violation_type: str | None = Field(
+        default=None,
+        description="""The type of violation (if present in the
+        message)""",
+    )
+    required_changes: str | None = Field(
+        default=None,
+        description="""The required changes specified by the entity
+        (if present in the message)""",
+    )
+    compliance_deadline: date | None = Field(
+        default=None,
+        description="""The date that the company must comply (if any)
+        reformatted to match YYYY-mm-dd""",
+    )
+    max_potential_fine: float | None = Field(
+        default=None,
+        description="""The maximum potential fine
+        (if any)""",
+    )
+
+
+info_parse_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            """
+            Parse the date of notice, sending entity name, sending entity
+            phone, sending entity email, project id, site location, violation
+            type, required changes, compliance deadline, and maximum potential
+            fine from the message. If any of the fields aren't present, don't
+            populate them. Try to cast dates into the YYYY-mm-dd format. Don't
+            populate fields if they're not present in the message.
+            Here's the notice message:
+            {message}
+            """,
+        )
+    ]
+)
+
+notice_parser_model = ChatOpenAI(model="gpt-4o-mini", temperature=0)
+
+NOTICE_PARSER_CHAIN = (
+    info_parse_prompt
+    | notice_parser_model.with_structured_output(NoticeEmailExtract)
+)

python-langgraph/example_emails.py

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+EMAILS = [
+    # Email 1
+    """
+    From: debby@stack.com
+    Hey Betsy,
+    Here's your invoice for $1000 for the cookies you ordered.
+    """,
+    # Email 2
+    """
+    From: tdavid@companyxyz.com
+    Hi Paul,
+    We have an issue with the HVAC system your team installed in
+    apartment 1235. We'd like to request maintenance or a refund.
+    Thanks,
+    Terrance
+    """,
+    # Email 3
+    """
+    Date: January 10, 2025
+    From: City of Los Angeles Building and Safety Department
+    To: West Coast Development, project 345678123 - Sunset Luxury
+    Condominiums
+    Location: Los Angeles, CA
+    Following an inspection of your site at 456 Sunset Boulevard, we have
+    identified the following building code violations:
+    Electrical Wiring: Exposed wiring was found in the underground parking
+    garage, posing a safety hazard. Fire Safety: Insufficient fire
+    extinguishers were available across multiple floors of the structure
+    under construction.
+    Structural Integrity: The temporary support beams in the eastern wing
+    do not meet the load-bearing standards specified in local building codes.
+    Required Corrective Actions:
+    Replace or properly secure exposed wiring to meet electrical safety
+    standards. Install additional fire extinguishers in compliance with
+    fire code requirements. Reinforce or replace temporary support beams
+    to ensure structural stability. Deadline for Compliance: Violations
+    must be addressed no later than February 5,
+    2025. Failure to comply may result in
+    a stop-work order and additional fines.
+    Contact: For questions or to schedule a re-inspection, please contact
+    the Building and Safety Department at
+    (555) 456-7890 or email inspections@lacity.gov.
+    """,
+]

python-langgraph/graphs/email_agent.py

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+import logging
+import time
+
+from chains.notice_extraction import NoticeEmailExtract
+from graphs.notice_extraction import NOTICE_EXTRACTION_GRAPH
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, MessagesState, StateGraph
+from langgraph.prebuilt import ToolNode
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+LOGGER = logging.getLogger(__name__)
+
+
+@tool
+def forward_email(email_message: str, send_to_email: str) -> bool:
+    """
+    Forward an email_message to the address of sent_to_email. Returns
+    true if the email was successful otherwise it wil return false. Note
+    that this tool only forwards the email to an internal department -
+    it does not reply to the sender.
+    """
+
+    LOGGER.info(f"Forwarding the email to {send_to_email}...")
+    time.sleep(2)
+    LOGGER.info("Email forwarded!")
+
+    return True
+
+
+@tool
+def send_wrong_email_notification_to_sender(
+    sender_email: str, correct_department: str
+):
+    """
+    Send an email back to the sender informing them that
+    they have the wrong address. The email should be sent
+    to the correct_department.
+    """
+
+    LOGGER.info(f"Sending wrong email notification to {sender_email}...")
+    time.sleep(2)
+    LOGGER.info("Email sent!")
+
+    return True
+
+
+@tool
+def extract_notice_data(
+    email: str, escalation_criteria: str
+) -> NoticeEmailExtract:
+    """
+    Extract structured fields from a regulatory notice.
+    This should be used when the email message comes from
+    a regulatory body or auditor regarding a property or
+    construction site that the company works on.
+    escalation_criteria is a description of which kinds of
+    notices require immediate escalation.
+    After calling this tool, you don't need to call any others.
+    """
+
+    LOGGER.info("Calling the email notice extraction graph...")
+
+    initial_state = {
+        "notice_message": email,
+        "notice_email_extract": None,
+        "critical_fields_missing": False,
+        "escalation_text_criteria": escalation_criteria,
+        "escalation_dollar_criteria": 100_000,
+        "requires_escalation": False,
+        "escalation_emails": ["brog@abc.com", "bigceo@company.com"],
+    }
+
+    results = NOTICE_EXTRACTION_GRAPH.invoke(initial_state)
+    return results["notice_email_extract"]
+
+
+@tool
+def determine_email_action(email: str) -> str:
+    """
+    Call to determine which action should be taken
+    for an email. Only use when the other tools don't seem
+    relevant for the email task. Do not call this tool if
+    you've already called extract_notice_data.
+    """
+
+    instructions = """
+    If the email appears to be an invoice of any kind or related to
+    billing, forward the email to the billing and invoices team:
+    billing@company.com
+    and send a wrong email notice back to the sender. The correct department is
+    billing@company.com.
+    If the email appears to be from a customer, forward to support@company.com,
+    cdetuma@company.com, and ctu@abc.com. Be sure to forward it to all three
+    emails listed.
+    Send a wrong email notice back to the
+    customer and let them know the correct department is support@company.com.
+    For any other emails, please send a wrong email notification and try to
+    infer the correct department from one of billing@company.com,
+    support@company.com,
+    humanresources@company.com, and it@company.com.
+    """
+
+    return instructions
+
+
+tools = [
+    determine_email_action,
+    forward_email,
+    send_wrong_email_notification_to_sender,
+    extract_notice_data,
+]
+tool_node = ToolNode(tools)
+
+EMAIL_AGENT_MODEL = ChatOpenAI(model="gpt-4o-mini", temperature=0).bind_tools(
+    tools
+)
+
+
+def call_agent_model_node(state: MessagesState) -> dict[str, list[AIMessage]]:
+    """Node to call the email agent model"""
+    messages = state["messages"]
+    response = EMAIL_AGENT_MODEL.invoke(messages)
+    return {"messages": [response]}
+
+
+def route_agent_graph_edge(state: MessagesState) -> str:
+    """Determine whether to call more tools or exit the graph"""
+    last_message = state["messages"][-1]
+    if last_message.tool_calls:
+        return "email_tools"
+    return END
+
+
+workflow = StateGraph(MessagesState)
+
+workflow.add_node("email_agent", call_agent_model_node)
+workflow.add_node("email_tools", tool_node)
+
+workflow.add_edge(START, "email_agent")
+workflow.add_conditional_edges(
+    "email_agent", route_agent_graph_edge, ["email_tools", END]
+)
+workflow.add_edge("email_tools", "email_agent")
+
+email_agent_graph = workflow.compile()