# What data should you store in Zilliz for agentic RAG?

Source: https://zilliz.com/ai-faq/what-data-should-you-store-in-zilliz-for-agentic-rag
Author: zilliz

Store embeddings, metadata, and document references in Zilliz Cloud; store raw documents and large files externally to minimize storage costs.

**Zilliz Cloud storage:**

**1. Vector embeddings:** Dense embeddings (768–1536 dims) from OpenAI, Anthropic, or open-source models. This is the core retrieval data.

**2. Metadata fields:**
 - Document ID, source, ingestion timestamp
 - Document type (contract, invoice, email, code)
 - Author, department, classification level (security sensitive)
 - Date ranges, version numbers
 - Agent accessibility flags ("compliance_agent=true")

**3. Sparse embeddings:** BM25/keyword vectors for hybrid search. Store alongside dense embeddings.

**4. Summary tokens:** If documents are chunked, store chunk summaries (50–100 tokens) in a text field. Agents use summaries to decide relevance.

**External storage (S3, GCS):**

- Full document text
- Images, PDFs, binary files
- Large attachments
- Historical versions

Zilliz Cloud retrieves document IDs + metadata; agents fetch full content from external storage if needed.

**Data example:**
json
{
 "id": "doc_12345",
 "embedding": [0.23, -0.15, ...],
 "sparse_embedding": {"invoice": 0.8, "payment": 0.6},
 "metadata": {
 "source": "s3://bucket/invoices/INV-2025-00142",
 "doc_type": "invoice",
 "date": 1700000000,
 "agent_filter": ["finance", "audit"]
 },
 "summary": "Invoice for 500 units of SKU-X, $50k, Q3 2025"
}


Zilliz Cloud's flexible schema and managed storage keep costs low while agents access rich context.

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