chore: initial snapshot for gitea/github upload
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@@ -0,0 +1,13 @@
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"""
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Vertex AI RAG Engine module.
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Handles RAG ingestion via Vertex AI RAG Engine API.
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"""
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from litellm.llms.vertex_ai.rag_engine.ingestion import VertexAIRAGIngestion
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from litellm.llms.vertex_ai.rag_engine.transformation import VertexAIRAGTransformation
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__all__ = [
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"VertexAIRAGIngestion",
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"VertexAIRAGTransformation",
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]
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"""
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Vertex AI RAG Engine Ingestion implementation.
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Uses:
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- litellm.files.acreate_file for uploading files to GCS
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- Vertex AI RAG Engine REST API for importing files into corpus (via httpx)
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Key differences from OpenAI:
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- Files must be uploaded to GCS first (via litellm.files.acreate_file)
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- Embedding is handled internally using text-embedding-005 by default
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- Chunking configured via unified chunking_strategy in ingest_options
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"""
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from __future__ import annotations
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import os
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from typing import TYPE_CHECKING, Any, List, Optional, Tuple
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from litellm import get_secret_str
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from litellm._logging import verbose_logger
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from litellm.llms.vertex_ai.rag_engine.transformation import VertexAIRAGTransformation
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from litellm.rag.ingestion.base_ingestion import BaseRAGIngestion
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if TYPE_CHECKING:
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from litellm import Router
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from litellm.types.rag import RAGIngestOptions
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def _get_str_or_none(value: Any) -> Optional[str]:
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"""Cast config value to Optional[str]."""
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return str(value) if value is not None else None
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def _get_int(value: Any, default: int) -> int:
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"""Cast config value to int with default."""
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if value is None:
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return default
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return int(value)
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class VertexAIRAGIngestion(BaseRAGIngestion):
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"""
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Vertex AI RAG Engine ingestion.
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Uses litellm.files.acreate_file for GCS upload, then imports into RAG corpus.
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Required config in vector_store:
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- vector_store_id: RAG corpus ID (required)
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Optional config in vector_store:
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- vertex_project: GCP project ID (uses env VERTEXAI_PROJECT if not set)
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- vertex_location: GCP region (default: us-central1)
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- vertex_credentials: Path to credentials JSON (uses ADC if not set)
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- wait_for_import: Wait for import to complete (default: True)
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- import_timeout: Timeout in seconds (default: 600)
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Chunking is configured via ingest_options["chunking_strategy"]:
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- chunk_size: Maximum size of chunks (default: 1000)
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- chunk_overlap: Overlap between chunks (default: 200)
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Authentication:
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- Uses Application Default Credentials (ADC)
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- Run: gcloud auth application-default login
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"""
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def __init__(
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self,
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ingest_options: "RAGIngestOptions",
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router: Optional["Router"] = None,
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):
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super().__init__(ingest_options=ingest_options, router=router)
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# Get corpus ID (required for Vertex AI)
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self.corpus_id = self.vector_store_config.get("vector_store_id")
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if not self.corpus_id:
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raise ValueError(
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"vector_store_id (corpus ID) is required for Vertex AI RAG ingestion. "
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"Please provide an existing RAG corpus ID."
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)
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# GCP config
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self.vertex_project = self.vector_store_config.get(
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"vertex_project"
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) or get_secret_str("VERTEXAI_PROJECT")
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self.vertex_location = (
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self.vector_store_config.get("vertex_location")
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or get_secret_str("VERTEXAI_LOCATION")
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or "us-central1"
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)
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self.vertex_credentials = self.vector_store_config.get("vertex_credentials")
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# GCS bucket for file uploads
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self.gcs_bucket = self.vector_store_config.get("gcs_bucket") or os.environ.get(
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"GCS_BUCKET_NAME"
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)
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if not self.gcs_bucket:
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raise ValueError(
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"gcs_bucket is required for Vertex AI RAG ingestion. "
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"Set via vector_store config or GCS_BUCKET_NAME env var."
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)
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# Import settings
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self.wait_for_import = self.vector_store_config.get("wait_for_import", True)
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self.import_timeout = _get_int(
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self.vector_store_config.get("import_timeout"), 600
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)
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# Validate required config
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if not self.vertex_project:
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raise ValueError(
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"vertex_project is required for Vertex AI RAG ingestion. "
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"Set via vector_store config or VERTEXAI_PROJECT env var."
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)
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def _get_corpus_name(self) -> str:
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"""Get full corpus resource name."""
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return f"projects/{self.vertex_project}/locations/{self.vertex_location}/ragCorpora/{self.corpus_id}"
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async def _upload_file_to_gcs(
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self,
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file_content: bytes,
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filename: str,
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content_type: str,
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) -> str:
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"""
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Upload file to GCS using litellm.files.acreate_file.
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Returns:
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GCS URI of the uploaded file (gs://bucket/path/file)
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"""
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import litellm
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# Set GCS_BUCKET_NAME env var for litellm.files.create_file
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# The handler uses this to determine where to upload
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original_bucket = os.environ.get("GCS_BUCKET_NAME")
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if self.gcs_bucket:
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os.environ["GCS_BUCKET_NAME"] = self.gcs_bucket
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try:
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# Create file tuple for litellm.files.acreate_file
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file_tuple = (filename, file_content, content_type)
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verbose_logger.debug(
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f"Uploading file to GCS via litellm.files.acreate_file: {filename} "
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f"(bucket: {self.gcs_bucket})"
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)
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# Upload to GCS using LiteLLM's file upload
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response = await litellm.acreate_file(
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file=file_tuple,
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purpose="assistants", # Purpose for file storage
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custom_llm_provider="vertex_ai",
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vertex_project=self.vertex_project,
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vertex_location=self.vertex_location,
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vertex_credentials=self.vertex_credentials,
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)
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# The response.id should be the GCS URI
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gcs_uri = response.id
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verbose_logger.info(f"Uploaded file to GCS: {gcs_uri}")
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return gcs_uri
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finally:
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# Restore original env var
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if original_bucket is not None:
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os.environ["GCS_BUCKET_NAME"] = original_bucket
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elif "GCS_BUCKET_NAME" in os.environ:
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del os.environ["GCS_BUCKET_NAME"]
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async def _import_file_to_corpus_via_sdk(
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self,
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gcs_uri: str,
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) -> None:
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"""
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Import file into RAG corpus using the Vertex AI SDK.
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The REST API endpoint for importRagFiles is not publicly available,
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so we use the Python SDK.
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"""
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try:
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from vertexai import init as vertexai_init
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from vertexai import rag # type: ignore[import-not-found]
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except ImportError:
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raise ImportError(
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"vertexai.rag module not found. Vertex AI RAG requires "
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"google-cloud-aiplatform>=1.60.0. Install with: "
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"pip install 'google-cloud-aiplatform>=1.60.0'"
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)
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# Initialize Vertex AI
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vertexai_init(project=self.vertex_project, location=self.vertex_location)
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# Get chunking config from ingest_options (unified interface)
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transformation_config = self._build_transformation_config()
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corpus_name = self._get_corpus_name()
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verbose_logger.debug(f"Importing {gcs_uri} into corpus {self.corpus_id}")
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if self.wait_for_import:
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# Synchronous import - wait for completion
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response = rag.import_files(
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corpus_name=corpus_name,
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paths=[gcs_uri],
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transformation_config=transformation_config,
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timeout=self.import_timeout,
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)
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verbose_logger.info(
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f"Import complete: {response.imported_rag_files_count} files imported"
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)
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else:
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# Async import - don't wait
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_ = rag.import_files_async(
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corpus_name=corpus_name,
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paths=[gcs_uri],
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transformation_config=transformation_config,
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)
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verbose_logger.info("Import started asynchronously")
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def _build_transformation_config(self) -> Any:
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"""
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Build Vertex AI TransformationConfig from unified chunking_strategy.
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Uses chunking_strategy from ingest_options (not vector_store).
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"""
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try:
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from vertexai import rag # type: ignore[import-not-found]
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except ImportError:
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raise ImportError(
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"vertexai.rag module not found. Vertex AI RAG requires "
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"google-cloud-aiplatform>=1.60.0. Install with: "
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"pip install 'google-cloud-aiplatform>=1.60.0'"
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)
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# Get chunking config from ingest_options using transformation class
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from typing import cast
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from litellm.types.rag import RAGChunkingStrategy
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transformation = VertexAIRAGTransformation()
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chunking_config = transformation.transform_chunking_strategy_to_vertex_format(
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cast(Optional[RAGChunkingStrategy], self.chunking_strategy)
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)
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chunk_size = chunking_config["chunking_config"]["chunk_size"]
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chunk_overlap = chunking_config["chunking_config"]["chunk_overlap"]
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return rag.TransformationConfig(
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chunking_config=rag.ChunkingConfig(
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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),
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)
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async def embed(
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self,
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chunks: List[str],
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) -> Optional[List[List[float]]]:
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"""
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Vertex AI handles embedding internally - skip this step.
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Returns:
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None (Vertex AI embeds when files are imported)
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"""
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return None
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async def store(
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self,
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file_content: Optional[bytes],
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filename: Optional[str],
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content_type: Optional[str],
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chunks: List[str],
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embeddings: Optional[List[List[float]]],
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) -> Tuple[Optional[str], Optional[str]]:
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"""
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Store content in Vertex AI RAG corpus.
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Vertex AI workflow:
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1. Upload file to GCS via litellm.files.acreate_file
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2. Import file into RAG corpus via SDK
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3. (Optional) Wait for import to complete
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Args:
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file_content: Raw file bytes
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filename: Name of the file
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content_type: MIME type
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chunks: Ignored - Vertex AI handles chunking
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embeddings: Ignored - Vertex AI handles embedding
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Returns:
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Tuple of (corpus_id, gcs_uri)
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"""
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if not file_content or not filename:
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verbose_logger.warning(
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"No file content or filename provided for Vertex AI ingestion"
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)
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return _get_str_or_none(self.corpus_id), None
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# Step 1: Upload file to GCS
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gcs_uri = await self._upload_file_to_gcs(
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file_content=file_content,
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filename=filename,
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content_type=content_type or "application/octet-stream",
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)
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# Step 2: Import file into RAG corpus
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try:
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await self._import_file_to_corpus_via_sdk(gcs_uri=gcs_uri)
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except Exception as e:
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verbose_logger.error(f"Failed to import file into RAG corpus: {e}")
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raise RuntimeError(f"Failed to import file into RAG corpus: {e}") from e
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return str(self.corpus_id), gcs_uri
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@@ -0,0 +1,153 @@
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"""
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Transformation utilities for Vertex AI RAG Engine.
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Handles transforming LiteLLM's unified formats to Vertex AI RAG Engine API format.
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"""
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from typing import Any, Dict, Optional
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from litellm._logging import verbose_logger
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from litellm.constants import DEFAULT_CHUNK_OVERLAP, DEFAULT_CHUNK_SIZE
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from litellm.llms.vertex_ai.common_utils import get_vertex_base_url
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from litellm.llms.vertex_ai.vertex_llm_base import VertexBase
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from litellm.types.rag import RAGChunkingStrategy
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class VertexAIRAGTransformation(VertexBase):
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"""
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Transformation class for Vertex AI RAG Engine API.
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Handles:
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- Converting unified chunking_strategy to Vertex AI format
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- Building import request payloads
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- Transforming responses
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"""
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def __init__(self):
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super().__init__()
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def get_import_rag_files_url(
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self,
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vertex_project: str,
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vertex_location: str,
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corpus_id: str,
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) -> str:
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"""
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Get the URL for importing RAG files.
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Note: The REST endpoint for importRagFiles may not be publicly available.
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Vertex AI RAG Engine primarily uses gRPC-based SDK.
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"""
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base_url = get_vertex_base_url(vertex_location)
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return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}/ragCorpora/{corpus_id}:importRagFiles"
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def get_retrieve_contexts_url(
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self,
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vertex_project: str,
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vertex_location: str,
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) -> str:
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"""Get the URL for retrieving contexts (search)."""
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base_url = get_vertex_base_url(vertex_location)
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return f"{base_url}/v1/projects/{vertex_project}/locations/{vertex_location}:retrieveContexts"
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def transform_chunking_strategy_to_vertex_format(
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self,
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chunking_strategy: Optional[RAGChunkingStrategy],
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) -> Dict[str, Any]:
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"""
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Transform LiteLLM's unified chunking_strategy to Vertex AI RAG format.
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LiteLLM format (RAGChunkingStrategy):
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{
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"chunk_size": 1000,
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"chunk_overlap": 200,
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"separators": ["\n\n", "\n", " ", ""]
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}
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Vertex AI RAG format (TransformationConfig):
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{
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"chunking_config": {
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"chunk_size": 1000,
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"chunk_overlap": 200
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}
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}
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Note: Vertex AI doesn't support custom separators in the same way,
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so we only transform chunk_size and chunk_overlap.
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"""
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if not chunking_strategy:
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return {
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"chunking_config": {
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"chunk_size": DEFAULT_CHUNK_SIZE,
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"chunk_overlap": DEFAULT_CHUNK_OVERLAP,
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}
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}
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chunk_size = chunking_strategy.get("chunk_size", DEFAULT_CHUNK_SIZE)
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chunk_overlap = chunking_strategy.get("chunk_overlap", DEFAULT_CHUNK_OVERLAP)
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# Log if separators are provided (not supported by Vertex AI)
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if chunking_strategy.get("separators"):
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verbose_logger.warning(
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"Vertex AI RAG Engine does not support custom separators. "
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"The 'separators' parameter will be ignored."
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)
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return {
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"chunking_config": {
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"chunk_size": chunk_size,
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"chunk_overlap": chunk_overlap,
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}
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}
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def build_import_rag_files_request(
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self,
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gcs_uri: str,
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chunking_strategy: Optional[RAGChunkingStrategy] = None,
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) -> Dict[str, Any]:
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"""
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Build the request payload for importing RAG files.
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Args:
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gcs_uri: GCS URI of the file to import (e.g., gs://bucket/path/file.txt)
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chunking_strategy: LiteLLM unified chunking config
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Returns:
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Request payload dict for importRagFiles API
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"""
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transformation_config = self.transform_chunking_strategy_to_vertex_format(
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chunking_strategy
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)
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return {
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"import_rag_files_config": {
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"gcs_source": {"uris": [gcs_uri]},
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"rag_file_transformation_config": transformation_config,
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}
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}
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def get_auth_headers(
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self,
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vertex_credentials: Optional[str] = None,
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vertex_project: Optional[str] = None,
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) -> Dict[str, str]:
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"""
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Get authentication headers for Vertex AI API calls.
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Uses the base class method to get credentials.
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"""
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credentials = self.get_vertex_ai_credentials(
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{"vertex_credentials": vertex_credentials}
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)
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project = vertex_project or self.get_vertex_ai_project({})
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access_token, _ = self._ensure_access_token(
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credentials=credentials,
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project_id=project,
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custom_llm_provider="vertex_ai",
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)
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return {
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"Authorization": f"Bearer {access_token}",
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"Content-Type": "application/json",
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}
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Reference in New Issue
Block a user