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Use Milvus to create vector collections, ingest embeddings, and serve filtered similarity search for RAG and agent retrieval workloads.
| name | Scale agent retrieval workloads with Milvus |
| slug | scale-agent-retrieval-workloads-with-milvus |
| description | Use Milvus to create vector collections, ingest embeddings, and serve filtered similarity search for RAG and agent retrieval workloads. |
| github_stars | 44675 |
| verification | security_reviewed |
| source | https://github.com/milvus-io/milvus |
| author | Milvus |
| publisher_type | open_source_project |
| category | Data Extraction & Transformation |
| framework | Multi-Framework |
| tool_ecosystem | {"github_repo":"milvus-io/milvus","github_stars":44675} |
Use Milvus to create vector collections, ingest embeddings, and serve filtered similarity search for RAG and agent retrieval workloads.
Milvus, embedding model, agent or RAG application
Use the upstream install or setup path that matches your environment:
Requirements and caveats from upstream:
Basic usage or getting-started notes:
🧑💻 Written in Go and C++, Milvus implements hardware acceleration for CPU/GPU to achieve best-in-class vector search performance. Thanks to its [fully-distributed and K8s-native architecture](https://milvus.io/docs/o...
from pymilvus import MilvusClient
You can also try Milvus Lite for quickstart by installing pymilvus[milvus-lite]. To create a local vector database, simply instantiate a client with a local file name for persisting data:
Extracted from upstream docs: https://raw.githubusercontent.com/milvus-io/milvus/HEAD/README.md
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