RAG-Kurzanleitung für Python

Auf dieser Seite erfahren Sie, wie Sie mit dem Vertex AI SDK Aufgaben der Vertex AI RAG Engine ausführen.

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

Google Cloud Console 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 Console ein, um die Vertex AI RAG Engine auszuführen.

Python 

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-004".
embedding_model_config = rag.EmbeddingModelConfig(
  publisher_model="publishers/google/models/text-embedding-004"
)

backend_config = rag.RagVectorDbConfig(rag_embedding_model_config=embedding_model_config)

rag_corpus = rag.create_corpus(
    display_name=display_name,
    backend_config=backend_config,
)

# List the rag corpus you just created
rag_corpus = rag.list_corpora()

# Import Files to the RagCorpus
# choose a corpus to import files to, you can use rag_corpus.name for just created corpus
# or use the name in list_corpora()
corpus_name = rag_corpus.name

transformation_config = rag.TransformationConfig(
      chunking_config=rag.ChunkingConfig(
          chunk_size=512,
          chunk_overlap=100,
      ),
  )

rag.import_files(
    corpus_name,
    paths,
    transformation_config=transformation_config, # Optional
    max_embedding_requests_per_min=1000,  # Optional
)

# Alternatively, you can use async import
response = await rag.import_files_async(
  corpus_name,
  paths,
  transformation_config=transformation_config, # Optional
  max_embedding_requests_per_min=1000,  # Optional
)
result = await response.result()
print(result)

# List the files in the rag corpus
rag.list_files(corpus_name)

# 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=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=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-1.5-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