Membangun, menguji, dan men-deploy chatbot Langchain di Reasoning Engine

Contoh ini menunjukkan cara membangun, menguji, dan men-deploy chatbot Langchain di Reasoning Engine.

Mempelajari lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

• Agent Engine API

Contoh kode

Python

Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.

Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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/...
# ...

Langkah berikutnya

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