From 72edf871034ee9458f316deaac92945151146f57 Mon Sep 17 00:00:00 2001 From: Fendy <40378371+septemberfd@users.noreply.github.com> Date: Tue, 4 Jun 2024 15:31:26 +0800 Subject: [PATCH] update partner link (#334) --- blog/en/introducing-milvus-lite.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/blog/en/introducing-milvus-lite.md b/blog/en/introducing-milvus-lite.md index 3cb5675..0d0b5d9 100644 --- a/blog/en/introducing-milvus-lite.md +++ b/blog/en/introducing-milvus-lite.md @@ -3,7 +3,7 @@ id: introducing-milvus-lite.md title: 'Introducing Milvus Lite: Start Building a GenAI Application in Seconds' author: Jiang Chen date: 2024-05-30 -cover: assets.zilliz.com/1_10011_283380bc9b.png +cover: assets.zilliz.com/introducing_Milvus_Lite_76ed4baf75.jpeg tag: News tags: Milvus, Vector Database, Open Source, Data science, Artificial Intelligence, GenAI developers, Retrieval Augmented Generation, RAG recommend: true @@ -48,7 +48,7 @@ For scalability, an AI application developed with Milvus Lite can easily transit ## Integration with AI Development Stack -In addition to introducing Milvus Lite to make vector search easy to start with, Milvus also integrates with many frameworks and providers of the AI development stack, including [LangChain](https://python.langchain.com/v0.2/docs/integrations/vectorstores/milvus/), [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/vector_stores/MilvusIndexDemo/), [Haystack](https://haystack.deepset.ai/integrations/milvus-document-store), [Voyage AI](https://milvus.io/docs/integrate_with_voyageai.md), [Ragas](https://milvus.io/docs/integrate_with_ragas.md), [Jina AI](https://milvus.io/docs/integrate_with_jina.md), [DSPy](https://dspy-docs.vercel.app/docs/deep-dive/retrieval_models_clients/MilvusRM), [BentoML](https://www.bentoml.com/blog/building-a-rag-app-with-bentocloud-and-milvus-lite), [WhyHow](https://chiajy.medium.com/70873c7576f1), [Relari AI](https://blog.relari.ai/case-study-using-synthetic-data-to-benchmark-rag-systems-be324904ace1), [Airbyte](https://docs.airbyte.com/integrations/destinations/milvus), [HuggingFace](https://milvus.io/docs/integrate_with_hugging-face.md) and [MemGPT](https://memgpt.readme.io/docs/storage#milvus). Thanks to their extensive tooling and services, these integrations simplify the development of AI applications with vector search capability. +In addition to introducing Milvus Lite to make vector search easy to start with, Milvus also integrates with many frameworks and providers of the AI development stack, including [LangChain](https://python.langchain.com/v0.2/docs/integrations/vectorstores/milvus/), [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/vector_stores/MilvusIndexDemo/), [Haystack](https://haystack.deepset.ai/integrations/milvus-document-store), [Voyage AI](https://blog.voyageai.com/2024/05/30/semantic-search-with-milvus-lite-and-voyage-ai/), [Ragas](https://milvus.io/docs/integrate_with_ragas.md), [Jina AI](https://jina.ai/news/implementing-a-chat-history-rag-with-jina-ai-and-milvus-lite/), [DSPy](https://dspy-docs.vercel.app/docs/deep-dive/retrieval_models_clients/MilvusRM), [BentoML](https://www.bentoml.com/blog/building-a-rag-app-with-bentocloud-and-milvus-lite), [WhyHow](https://chiajy.medium.com/70873c7576f1), [Relari AI](https://blog.relari.ai/case-study-using-synthetic-data-to-benchmark-rag-systems-be324904ace1), [Airbyte](https://docs.airbyte.com/integrations/destinations/milvus), [HuggingFace](https://milvus.io/docs/integrate_with_hugging-face.md) and [MemGPT](https://memgpt.readme.io/docs/storage#milvus). Thanks to their extensive tooling and services, these integrations simplify the development of AI applications with vector search capability. And this is just the beginning—many more exciting integrations are coming soon! Stay tuned!Â