Panduan memulai RAG untuk Python

Halaman ini menunjukkan cara menggunakan Vertex AI SDK untuk menjalankan tugas Vertex AI RAG Engine.

Anda juga dapat mengikutinya menggunakan notebook Pengantar Mesin RAG Vertex AI ini.

Menyiapkan konsol Google Cloud

Untuk menggunakan Mesin RAG Vertex AI, lakukan hal berikut:

  1. Instal Vertex AI SDK untuk Python.

  2. Jalankan perintah ini di konsol Google Cloud untuk menyiapkan project Anda.

    gcloud config set {project}

  3. Jalankan perintah ini untuk mengizinkan login Anda.

    gcloud auth application-default login

Menjalankan Vertex AI RAG Engine

Salin dan tempel kode contoh ini ke konsol Google Cloud untuk menjalankan Vertex AI RAG Engine.

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
# ...

Langkah berikutnya