Panduan memulai RAG

Python

Untuk mempelajari cara menginstal atau mengupdate Vertex AI SDK untuk Python, lihat Menginstal Vertex AI SDK untuk Python. Untuk mengetahui informasi selengkapnya, lihat dokumentasi referensi API 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-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
# ...

curl

1. Buat korpus RAG.

     export LOCATION=LOCATION
     export PROJECT_ID=PROJECT_ID
     export CORPUS_DISPLAY_NAME=CORPUS_DISPLAY_NAME
   
     // CreateRagCorpus
     // Output: CreateRagCorpusOperationMetadata
     curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json" \
     https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/ragCorpora \
     -d '{
           "display_name" : "'"CORPUS_DISPLAY_NAME"'"
       }'
   

   Untuk mengetahui informasi selengkapnya, lihat Membuat contoh korpus RAG.

2. Mengimpor file RAG.

     // ImportRagFiles
     // Import a single Cloud Storage file or all files in a Cloud Storage bucket.
     // Input: LOCATION, PROJECT_ID, RAG_CORPUS_ID, GCS_URIS
     export RAG_CORPUS_ID=RAG_CORPUS_ID
     export GCS_URIS=GCS_URIS
     export CHUNK_SIZE=CHUNK_SIZE
     export CHUNK_OVERLAP=CHUNK_OVERLAP
     export EMBEDDING_MODEL_QPM_RATE=EMBEDDING_MODEL_QPM_RATE
   
     // Output: ImportRagFilesOperationMetadataNumber
     // Use ListRagFiles, or import_result_sink to get the correct rag_file_id.
     curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json" \
     https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/ragCorpora/RAG_CORPUS_ID/ragFiles:import \
     -d '{
       "import_rag_files_config": {
         "gcs_source": {
           "uris": "GCS_URIS"
         },
         "rag_file_chunking_config": {
           "chunk_size": CHUNK_SIZE,
           "chunk_overlap": CHUNK_OVERLAP
         },
         "max_embedding_requests_per_min": EMBEDDING_MODEL_QPM_RATE
       }
     }'
   

   Untuk mengetahui informasi selengkapnya, lihat Contoh mengimpor file RAG.

3. Jalankan kueri pengambilan RAG.

     export RAG_CORPUS_RESOURCE=RAG_CORPUS_RESOURCE
     export VECTOR_DISTANCE_THRESHOLD=VECTOR_DISTANCE_THRESHOLD
     export SIMILARITY_TOP_K=SIMILARITY_TOP_K
   
     {
     "vertex_rag_store": {
         "rag_resources": {
           "rag_corpus": "RAG_CORPUS_RESOURCE"
         },
         "vector_distance_threshold": VECTOR_DISTANCE_THRESHOLD
       },
       "query": {
       "text": TEXT
       "similarity_top_k": SIMILARITY_TOP_K
       }
     }
   
     curl -X POST \
         -H "Authorization: Bearer $(gcloud auth print-access-token)" \
         -H "Content-Type: application/json; charset=utf-8" \
         -d @request.json \
         "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION:retrieveContexts"
   

   Untuk mengetahui informasi selengkapnya, lihat RAG Engine API.

4. Buat konten.

   {
   "contents": {
     "role": "USER",
     "parts": {
       "text": "INPUT_PROMPT"
     }
   },
   "tools": {
     "retrieval": {
     "disable_attribution": false,
     "vertex_rag_store": {
       "rag_resources": {
         "rag_corpus": "RAG_CORPUS_RESOURCE"
       },
       "similarity_top_k": "SIMILARITY_TOP_K",
       "vector_distance_threshold": VECTOR_DISTANCE_THRESHOLD
     }
     }
   }
   }
   
   curl -X POST \
       -H "Authorization: Bearer $(gcloud auth print-access-token)" \
       -H "Content-Type: application/json; charset=utf-8" \
       -d @request.json \
       "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:GENERATION_METHOD"
   

   Untuk mengetahui informasi selengkapnya, lihat RAG Engine API.