# How do you version and update embeddings in agentic RAG?

Source: https://zilliz.com/ai-faq/how-do-you-version-and-update-embeddings-in-agentic-rag
Author: zilliz

Manage embedding versions in Zilliz Cloud by storing model metadata, versioning collections, and incrementally updating outdated embeddings.

**Versioning strategy:**

**1. Model metadata per embedding:**
 json
 {
 "embedding_model": "text-embedding-3-large",
 "model_version": "20250101",
 "embedding_dims": 1536,
 "generated_at": 1700000000
 }
 
 Store alongside vectors. Agents know which model generated each embedding.

**2. Collection naming:** Create collections by model version ("embeddings_v1", "embeddings_v2"). Agents can query specific versions or compare results.

**3. Gradual rollover:** When releasing a new embedding model:
 - Create new collection with new embeddings
 - Run both in parallel (dual-write)
 - Compare agent performance (relevance, loop count)
 - Migrate agents to new model over 1–2 weeks
 - Archive old collection after full migration

**4. Incremental updates:** Don't re-embed the entire database. Track:
 - Last embedding timestamp per document
 - If source document is newer, re-embed
 - Use background workers to incrementally update

**5. Semantic versioning:** When you update the embedding model, decide if the change is breaking (new dims), non-breaking (new model, same dims).

**Fallback strategy:** Keep previous embeddings available for agents that need stability. Newer agents use latest model.

For agentic RAG, embedding freshness is critical. Outdated embeddings (>30 days) degrade agent relevance by ~15%. Zilliz Cloud makes versioning seamless—manage your embedding lifecycle in one place.

**Related Resources:**
- [What is Zilliz Cloud](https://zilliz.com/what-is-zilliz-cloud)
- [Vector embeddings](https://zilliz.com/glossary/vector-embeddings)
- [Start free on Zilliz Cloud](https://cloud.zilliz.com/signup)