forked from phoenix-oss/llama-stack-mirror
330cc9d09d
# What does this PR do? See https://github.com/meta-llama/llama-stack/pull/1171 which is the original PR. Author: @zc277584121 feat: add [Milvus](https://milvus.io/) vectorDB note: I use the MilvusClient to implement it instead of AsyncMilvusClient, because when I tested AsyncMilvusClient, it would raise issues about evenloop, which I think AsyncMilvusClient SDK is not robust enough to be compatible with llama_stack framework. ## Test Plan have passed the unit test and ene2end test Here is my end2end test logs, including the client code, client log, server logs from inline and remote settings [test_end2end_logs.zip](https://github.com/user-attachments/files/18964391/test_end2end_logs.zip) --------- Signed-off-by: ChengZi <chen.zhang@zilliz.com> Co-authored-by: Cheney Zhang <chen.zhang@zilliz.com>
31 lines
782 B
Markdown
31 lines
782 B
Markdown
---
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orphan: true
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---
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# Milvus
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[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly within a Milvus database.
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That means you're not limited to storing vectors in memory or in a separate service.
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## Features
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- Easy to use
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- Fully integrated with Llama Stack
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## Usage
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To use Milvus in your Llama Stack project, follow these steps:
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1. Install the necessary dependencies.
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2. Configure your Llama Stack project to use Milvus.
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3. Start storing and querying vectors.
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## Installation
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You can install Milvus using pymilvus:
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```bash
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pip install pymilvus
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```
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## Documentation
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See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for more details about Milvus in general.
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