RAG-Kurzanleitung für Python

Auf dieser Seite erfahren Sie, wie Sie das Vertex AI SDK verwenden, um Aufgaben der Vertex AI RAG Engine auszuführen.

Sie können auch dieses Notebook Einführung in die Vertex AI RAG Engine verwenden.

Erforderliche Rollen

Grant roles to your user account. Run the following command once for each of the following IAM roles: roles/aiplatform.user 

gcloud projects add-iam-policy-binding PROJECT_ID --member="user:USER_IDENTIFIER" --role=ROLE

Replace the following:

  • PROJECT_ID: your project ID.
  • USER_IDENTIFIER: the identifier for your user account—for example, myemail@example.com.
  • ROLE: the IAM role that you grant to your user account.

Google Cloud -Konsole vorbereiten

So verwenden Sie die Vertex AI RAG Engine:

  1. Installieren Sie Vertex AI SDK für Python .

  2. Führen Sie diesen Befehl in der Google Cloud -Console aus, um Ihr Projekt einzurichten.

    gcloud config set {project}

  3. Führen Sie diesen Befehl aus, um Ihre Anmeldung zu autorisieren.

    gcloud auth application-default login

Vertex AI RAG Engine ausführen

Kopieren Sie diesen Beispielcode und fügen Sie ihn in die Google Cloud Konsole ein, um die Vertex AI-RAG-Engine auszuführen.

Python

Informationen zur Installation des Vertex AI SDK for Python finden Sie unter Vertex AI SDK for Python installieren. Weitere Informationen finden Sie in der Python-API-Referenzdokumentation. 

from vertexai import rag
from vertexai.generative_models import GenerativeModel, Tool
import vertexai

# Create a RAG Corpus, Import Files, and Generate a response

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# display_name = "test_corpus"
# paths = ["https://drive.google.com/file/d/123", "gs://my_bucket/my_files_dir"]  # Supports Google Cloud Storage and Google Drive Links

# Initialize Vertex AI API once per session
vertexai.init(project=PROJECT_ID, location="us-central1")

# Create RagCorpus
# Configure embedding model, for example "text-embedding-005".
embedding_model_config = rag.RagEmbeddingModelConfig(
    vertex_prediction_endpoint=rag.VertexPredictionEndpoint(
        publisher_model="publishers/google/models/text-embedding-005"
    )
)

rag_corpus = rag.create_corpus(
    display_name=display_name,
    backend_config=rag.RagVectorDbConfig(
        rag_embedding_model_config=embedding_model_config
    ),
)

# Import Files to the RagCorpus
rag.import_files(
    rag_corpus.name,
    paths,
    # Optional
    transformation_config=rag.TransformationConfig(
        chunking_config=rag.ChunkingConfig(
            chunk_size=512,
            chunk_overlap=100,
        ),
    ),
    max_embedding_requests_per_min=1000,  # Optional
)

# Direct context retrieval
rag_retrieval_config = rag.RagRetrievalConfig(
    top_k=3,  # Optional
    filter=rag.Filter(vector_distance_threshold=0.5),  # Optional
)
response = rag.retrieval_query(
    rag_resources=[
        rag.RagResource(
            rag_corpus=rag_corpus.name,
            # Optional: supply IDs from `rag.list_files()`.
            # rag_file_ids=["rag-file-1", "rag-file-2", ...],
        )
    ],
    text="What is RAG and why it is helpful?",
    rag_retrieval_config=rag_retrieval_config,
)
print(response)

# Enhance generation
# Create a RAG retrieval tool
rag_retrieval_tool = Tool.from_retrieval(
    retrieval=rag.Retrieval(
        source=rag.VertexRagStore(
            rag_resources=[
                rag.RagResource(
                    rag_corpus=rag_corpus.name,  # Currently only 1 corpus is allowed.
                    # Optional: supply IDs from `rag.list_files()`.
                    # rag_file_ids=["rag-file-1", "rag-file-2", ...],
                )
            ],
            rag_retrieval_config=rag_retrieval_config,
        ),
    )
)

# Create a Gemini model instance
rag_model = GenerativeModel(
    model_name="gemini-2.0-flash-001", tools=[rag_retrieval_tool]
)

# Generate response
response = rag_model.generate_content("What is RAG and why it is helpful?")
print(response.text)
# Example response:
#   RAG stands for Retrieval-Augmented Generation.
#   It's a technique used in AI to enhance the quality of responses
# ...

Nächste Schritte