first langgraph commit

Author: hfhoffman1144

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
+
+LangGraph is available on [PyPI](https://pypi.org/), and you can install it with [pip](https://realpython.com/what-is-pip/). Open a terminal or command prompt, create a new virtual environment, and then run the following command to install LangGraph:
+
+```console
+(venv) $ python -m pip install langgraph
+```
+
+This command will install the latest version of LangGraph from PyPI onto your machine. To verify that the installation was successful, start a [Python REPL](https://realpython.com/python-repl/) and import LangGraph:
+
+```pycon
+>>> import langgraph
+```
+
+If the import runs without error, then you've successfully installed LangGraph. You'll also need a few more libraries for this tutorial:
+
+```console
+(venv) $ python -m pip install langchain-openai pydantic[email]
+```
+
+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>
+```
+
+Note that while LangGraph was made by the creators of LangChain, and the two libraries are highly compatible, it is possible to use LangGraph without LangChain. However, it's more common to use LangChain and LangGraph together, and you'll see throughout this tutorial how they compliment each other. 
+
+With that, you've installed all the dependencies you'll need for this tutorial, and you're ready to create your LangGraph email processor. You'll start by going through an overview of LangChain chains and exploring LangGraph's core concept - the state graph.

python-langgraph/chains/binary_questions.py

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+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate
+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_openai import ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate
+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 langchain_openai import ChatOpenAI
+from langchain_core.prompts import ChatPromptTemplate
+from pydantic import BaseModel, EmailStr, Field
+from datetime import date
+
+
+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: [email protected]
+    Hey Betsy,
+    Here's your invoice for $1000 for the cookies you ordered.
+    """,
+    # Email 2
+    """
+    From: [email protected]
+    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 [email protected].
+    """,
+]

python-langgraph/graphs/email_agent.py

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+import time
+import logging
+from langchain_core.messages import AIMessage
+from langchain_core.tools import tool
+from langgraph.prebuilt import ToolNode
+from langchain_openai import ChatOpenAI
+from langgraph.graph import StateGraph, MessagesState, START, END
+from chains.notice_extraction import NoticeEmailExtract
+from graphs.notice_extraction import NOTICE_EXTRACTION_GRAPH
+
+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": ["[email protected]", "[email protected]"],
+    }
+
+    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:
+    [email protected]
+    and send a wrong email notice back to the sender. The correct department is
+    [email protected].
+    If the email appears to be from a customer, forward to [email protected],
+    [email protected], and [email protected]. 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 [email protected].
+    For any other emails, please send a wrong email notification and try to
+    infer the correct department from one of [email protected],
+    [email protected],
+    [email protected], and [email protected].
+    """
+
+    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()