llama-index-embeddings-google-genai
llama-index embeddings google genai integration
Decision gist · record as of 2026-08-14
Yes, if you are already using llama-index and need Google's embedding models. The low install friction, active maintenance, MIT license, and zero known vulnerabilities make it a straightforward choice. No if you don't use llama-index or prefer other embedding providers.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later (supports_current).
- Google GenAI API credentials must be configured.
- Low install friction with only 2 runtime dependencies (google-genai and llama-index-core).
License · maintenance · safety
MIT (permissive) — MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-05-19 (87 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 404,945 downloads/mo, #6,909 on PyPI
Alternatives
Verify before relying
pip install llama-index-embeddings-google-genai
from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
embed_model = GoogleGenAIEmbedding(model_name="gemini-embedding-2-preview")
embeddings = embed_model.get_text_embedding("Hello, world!")- Whether google-genai dependency requires API credentials or authentication setup beyond standard environment variables
- Performance characteristics and rate limits when used with Vertex AI vs. direct GenAI API
- Compatibility guarantees with specific versions of llama-index-core
What it is and what it does
This package wraps Google's embedding APIs (Gemini and Vertex AI) for use within the llama-index framework. It lets you generate text embeddings using Google's models without writing the integration layer yourself. The package handles both direct GenAI API calls and Vertex AI deployments, accepting configuration like project ID and location for the latter.
You instantiate an embedding model by specifying the model name (e.g., "gemini-embedding-2-preview") and optional Vertex AI credentials, then call get_text_embedding() to convert text into vector form. It's designed as a drop-in embedding provider for llama-index workflows that need Google's embedding models.
Use it for
- Building RAG pipelines with llama-index using Google's embedding models
- Generating embeddings for semantic search over document collections via Gemini or Vertex AI
- Integrating Google's embeddings into multi-step llama-index workflows without custom API glue code
- Using Vertex AI embeddings in enterprise GCP environments with project-scoped authentication
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already using llama-index and need Google's embedding models.
The low install friction, active maintenance, MIT license, and zero known vulnerabilities make it a straightforward choice. No if you don't use llama-index or prefer other embedding providers.
Install
llama-index-embeddings-google-genai on PyPI
Before you install
Low install friction with only 2 runtime dependencies (google-genai and llama-index-core). Maintenance status is active, with the latest release dated 2026-05-19.
Requires Python 3.10 or later (supports_current). Google GenAI API credentials must be configured.
License in practice
MIT license permits free use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install llama-index-embeddings-google-genai
from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
embed_model = GoogleGenAIEmbedding(model_name="gemini-embedding-2-preview")
embeddings = embed_model.get_text_embedding("Hello, world!")Verify before relying
- Whether google-genai dependency requires API credentials or authentication setup beyond standard environment variables
- Performance characteristics and rate limits when used with Vertex AI vs. direct GenAI API
- Compatibility guarantees with specific versions of llama-index-core
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-genaillama-index-core |
| Maintenance | Actively maintained 87 days since the last release |
| First released | |
| Downloads | 404,945 / month, #6,909 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: llama_index_embeddings_google_genai-0.5.1-py3-none-any.whl
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See also llama-index-embeddings-ollama · llama-index-embeddings-vertex · llama-index-llms-vertex · llama-index-llms-gemini · llama-index-embeddings-openai · llama-index-embeddings-bedrock · google-genai · llama-index-llms-google-genai · llama-index-embeddings-langchain · llama-index-embeddings-huggingface