# embeddings insights

Rangoon Insights reporting and guides tagged embeddings.

Canonical: https://rangoon.ai/insights/tags/embeddings/

## Topics
- [Agent systems](https://rangoon.ai/insights/topics/agents/): Agent runtimes, handoffs, tools, and the boundaries that make them reviewable.
- [Governance](https://rangoon.ai/insights/topics/governance/): Policy, identity, authorization, approvals, and evidence for consequential work.
- [Infrastructure](https://rangoon.ai/insights/topics/infrastructure/): Model serving, sandboxes, tool services, and deployment boundaries.
- [Models](https://rangoon.ai/insights/topics/models/): Inference choices, local serving, compatibility, and operational tradeoffs.

## Stories
- [NVIDIA’s Llama-Embed-Nemotron-8B paper documented an instruction-aware multilingual retriever](https://rangoon.ai/insights/archive-nvidia-llama-embed-nemotron-8b-november-2025/)
  NVIDIA researchers published Llama-Embed-Nemotron-8B on November 10, 2025, describing an open-weights text embedding model for multilingual retrieval and related tasks.
- [Qdrant Cloud Inference put embedding generation, storage and indexing behind one API call](https://rangoon.ai/insights/archive-qdrant-cloud-inference-embedding-indexing-july-2025/)
  Qdrant launched Cloud Inference on July 15, 2025, combining model inference, vector storage and indexing in a managed retrieval workflow.
- [Jina’s v4 release combined text-image embeddings with dense and late-interaction outputs](https://rangoon.ai/insights/archive-jina-embeddings-v4-june-2025/)
  Jina released jina-embeddings-v4 on June 25, 2025, pairing unified text-image representations with single-vector and multi-vector retrieval output modes.
- [Voyage’s 3.5 embedding release put dimensions and quantization beside retrieval scores](https://rangoon.ai/insights/archive-voyage-3-5-embeddings-may-2025/)
  Voyage introduced voyage-3.5 and voyage-3.5-lite on May 20, 2025, coupling 32K-context embeddings with selectable dimensions and quantization for retrieval indexes.
- [EmbeddingGemma made embedding dimension a retrieval deployment control](https://rangoon.ai/insights/archive-google-embeddinggemma-on-device-september-2025/)
  Google introduced the 308M-parameter EmbeddingGemma on September 4, 2025, combining on-device embedding with configurable output dimensions for retrieval system design.


## More information

- [Documentation index](https://rangoon.ai/llms.txt)
- [AI access and policies](https://rangoon.ai/ai/)
- [Source repository](https://github.com/hypler-dev/rangoon)