Build, test, and deploy a Langchain chatbot on Reasoning Engine

This sample demonstrates how to build, test, and deploy a Langchain chatbot on Reasoning Engine.

Code sample

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

from typing import List

import vertexai
from vertexai.preview import reasoning_engines

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# staging_bucket = "gs://YOUR_BUCKET_NAME"

vertexai.init(
    project=PROJECT_ID, location="us-central1", staging_bucket=staging_bucket
)

class LangchainApp:
    def __init__(self, project: str, location: str) -> None:
        self.project_id = project
        self.location = location

    def set_up(self) -> None:
        from langchain_core.prompts import ChatPromptTemplate
        from langchain_google_vertexai import ChatVertexAI

        system = (
            "You are a helpful assistant that answers questions "
            "about Google Cloud."
        )
        human = "{text}"
        prompt = ChatPromptTemplate.from_messages(
            [("system", system), ("human", human)]
        )
        chat = ChatVertexAI(project=self.project_id, location=self.location)
        self.chain = prompt | chat

    def query(self, question: str) -> Union[str, List[Union[str, Dict]]]:
        """Query the application.
        Args:
            question: The user prompt.
        Returns:
            str: The LLM response.
        """
        return self.chain.invoke({"text": question}).content

# Locally test
app = LangchainApp(project=PROJECT_ID, location="us-central1")
app.set_up()
print(app.query("What is Vertex AI?"))

# Create a remote app with Reasoning Engine
# Deployment of the app should take a few minutes to complete.
reasoning_engine = reasoning_engines.ReasoningEngine.create(
    LangchainApp(project=PROJECT_ID, location="us-central1"),
    requirements=[
        "google-cloud-aiplatform[langchain,reasoningengine]",
        "cloudpickle==3.0.0",
        "pydantic==2.7.4",
    ],
    display_name="Demo LangChain App",
    description="This is a simple LangChain app.",
    # sys_version="3.10",  # Optional
    extra_packages=[],
)
# Example response:
# Model_name will become a required arg for VertexAIEmbeddings starting...
# ...
# Create ReasoningEngine backing LRO: projects/123456789/locations/us-central1/reasoningEngines/...
# ReasoningEngine created. Resource name: projects/123456789/locations/us-central1/reasoningEngines/...
# ...

What's next

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