llama-index-embeddings-vertex
llama-index embeddings vertex integration
Decision gist · record as of 2026-08-14
Yes, if you are already using LlamaIndex and have access to Google Cloud with Vertex AI enabled. The package is actively maintained, has no known vulnerabilities, low install friction, and provides straightforward integration. Skip it if you don't use LlamaIndex or lack GCP infrastructure.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires valid Google Cloud credentials and a GCP project with Vertex AI enabled.
- Python 3.10 or later required.
- Low install friction with only two runtime dependencies (google-cloud-aiplatform and llama-index-core).
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions.
last release 2026-03-12 (155 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 81,613 downloads/mo, #14,212 on PyPI
Alternatives
Verify before relying
pip install llama-index-embeddings-vertex
from llama_index.embeddings.vertex import VertexTextEmbedding
from google.oauth2 import service_account
credentials = service_account.Credentials.from_service_account_file(
"path/to/service-account.json"
)
embedding = VertexTextEmbedding(
model_name="textembedding-gecko@003",
project="your-project-id",
location="your-region",
credentials=credentials,
)- Whether async support for multimodalembedding is planned or if this is a permanent limitation
- Performance characteristics and latency of the various gecko model versions
What it is and what it does
This package bridges LlamaIndex and Google Vertex AI's embedding models, letting you generate text and multimodal embeddings within a LlamaIndex workflow. It wraps Vertex AI's embedding APIs—including the textembedding-gecko family and multimodal models—and exposes them through LlamaIndex's standard embedding interface, handling credential management and model selection transparently.
You supply GCP credentials and a model name, and the package handles the API calls to Vertex AI. It supports both direct credential objects and service account parameters for flexibility. The main constraint is that multimodal embedding doesn't support async operations, though text embeddings do.
Use it for
- Build RAG pipelines in LlamaIndex using Vertex AI embeddings instead of third-party providers
- Generate multilingual embeddings with textembedding-gecko-multilingual for cross-language search
- Integrate multimodal embeddings for image and text understanding in LlamaIndex applications
- Migrate existing LlamaIndex applications to use Google Cloud's managed embedding service
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using LlamaIndex and have access to Google Cloud with Vertex AI enabled.
The package is actively maintained, has no known vulnerabilities, low install friction, and provides straightforward integration. Skip it if you don't use LlamaIndex or lack GCP infrastructure.
Install
llama-index-embeddings-vertex on PyPI
Before you install
Low install friction with only two runtime dependencies (google-cloud-aiplatform and llama-index-core). Actively maintained with recent releases.
Requires valid Google Cloud credentials and a GCP project with Vertex AI enabled. Python 3.10 or later required.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions.
Quickstart
pip install llama-index-embeddings-vertex
from llama_index.embeddings.vertex import VertexTextEmbedding
from google.oauth2 import service_account
credentials = service_account.Credentials.from_service_account_file(
"path/to/service-account.json"
)
embedding = VertexTextEmbedding(
model_name="textembedding-gecko@003",
project="your-project-id",
location="your-region",
credentials=credentials,
)Verify before relying
- Whether async support for multimodalembedding is planned or if this is a permanent limitation
- Performance characteristics and latency of the various gecko model versions
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesgoogle-cloud-aiplatformllama-index-core |
| Maintenance | Actively maintained 155 days since the last release |
| First released | |
| Downloads | 81,613 / month, #14,212 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_embeddings_vertex-0.5.0-py3-none-any.whl
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See also llama-index-embeddings-google-genai · llama-index-embeddings-ollama · llama-index-embeddings-openai · llama-index-embeddings-huggingface · llama-index-embeddings-langchain · llama-index-embeddings-bedrock · llama-index-embeddings-azure-openai · llama-index-llms-vertex · llama-index-vector-stores-qdrant · llama-index-vector-stores-milvus