Zilliz and AWS are working to provide developers the tools to build modern AI-powered applications. With Zilliz Cloud on AWS, users can quickly scale their vector search storage capacity without the need to re-provision hardware. Running Zilliz Cloud on AWS simplifies integrating your application with other AWS AI and ML technologies.

 <a href="https://aws.amazon.com/marketplace/seller-profile?id=4922a541-e428-480d-8e32-db4ee9a7f46e" target="_blank" class="outlinedButton">Zilliz Offers on AWS</a>



#### Zilliz Cloud on AWS

By subscribing to Zilliz Cloud on the AWS Marketplace, users can take advantage of simplified procurement and consolidated billing to allow developers to spend more time building applications and less time managing vendors and contacts. Zilliz Cloud is available in the US West (Oregon) and US East (Virginia) AWS regions. Visit the AWS Marketplace today to subscribe.

<a href="https://aws.amazon.com/marketplace/pp/prodview-iqbidum7feuio?sr=0-1&ref_=beagle&applicationId=AWSMPContessa" target="_blank" class="outlinedButton">Subscribe on AWS Marketplace</a>

Don’t have an AWS account? Try Zilliz for free.

 <a href="https://cloud.zilliz.com/signup" target="_blank" class="outlinedButton">Try Zilliz on AWS for Free</a>

Zilliz partnership with AWS - How you can access Zilliz Cloud on AWS

Zilliz and GCP are working to provide developers with the tools to build modern AI-powered applications. For example, with Zilliz Cloud on GCP, users can quickly scale their vector search storage capacity without re-provision hardware. In addition, running Zilliz Cloud on GCP simplifies integrating your application with other GCP AI and ML technologies.

 <a href="https://cloud.google.com/find-a-partner/partner/zilliz" target="_blank" class="outlinedButton">Google Partner Directory</a>



#### Zilliz Cloud on Google Cloud

Built as a cloud-native vector database solution to power the most innovative search applications, Zilliz Cloud runs on Google Cloud to make it easier to integrate your existing Google technology stack and seamlessly scale your application. Zilliz Cloud currently runs on the US-West-1 Google Cloud region. 

 <a href="https://cloud.zilliz.com/signup" target="_blank" class="outlinedButton">Try Zilliz Cloud on Google Cloud for Free</a>



Zilliz partnership with Google Cloud - Access Zilliz Cloud on GCP

Google Cloud

NVIDIA and Zilliz are both active contributors to the Milvus open source vector database community and have worked closely to provide NVIDIA GPU support to Milvus and Zilliz. This collaborative NVIDIA and Zilliz are active contributors to the Milvus open-source vector database community and have worked closely to provide NVIDIA GPU support to Milvus and Zilliz. This collaborative development effort ensures customers realize 10x performance gains over the previous CPU-only version. In addition, NVIDIA and Zilliz deliver robust performance to their users through continued collaboration. development effort ensures that customers can realize 10x performance gains over the previous CPU-only version. Through continued collaboration, NVIDIA and Zilliz are delivering robust performance to their users.

<a href="https://zilliz.com/news/Milvus-2.3-Massively-Accelerates-AI-Powered-Applications-With-NVIDIA-GPU-Support" target="_blank" class="arrowLink">NVIDIA GPU Support Announcement</a>


#### NVIDIA Announces RAFT Library and GPU Support in Milvus

“Recommender systems use vector databases to store, index, search and retrieve massive datasets of unstructured data. A new important use-case of vector databases is large language models to retrieve domain-specific or proprietary facts that can be queried during text generation. We are introducing a new library, RAFT to accelerate indexing, loading the data, and retrieving a batch of neighbors for a single query. We are bringing the acceleration of RAFT to Meta's open-source FAISS AI Similarity Search, Milvus open source vector database used by over 1,000 organizations and Redis with over 4B docker pulls. Vector databases will be essential for organizations building proprietary large language models.”

**Jensen Huang, CEO, NVIDIA during <a href="https://www.youtube.com/clip/UgkxDb-hOu7zW3WIVyzHbU_kmfD00zvYajlN" class="blackLink">2023 GTC Keynote</a>**

Zilliz partnership with NVIDIA - NVIDIA GPU support to Milvus & Zilliz

NVIDIA

OpenAI provides learning models that convert unstructured data to embeddings and store them in a vector database such as Zilliz Cloud or the open-source Milvus. To help OpenAI users start working with vector data, they partnered with Zilliz to create the OpenAI Cookbook that provides instructions on generating embeddings and storing the data in Milvus.

<a href="https://github.com/openai/openai-cookbook/blob/main/examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb" target="_blank" class="arrowLink">OpenAI Cookbook for Milvus</a>

<a href="https://platform.openai.com/docs/guides/embeddings/limitations-risks" target="_blank" class="arrowLink">OpenAI Documentation for Vector Data</a>



#### Open Source Retrieval Plugins from OpenAI Integrate with Milvus and Zilliz

OpenAI has open sourced [retrieval plugins](https://openai.com/blog/chatgpt-plugins#retrieval) that allow developers to deploy their own plugin and use it with ChatGPT to create a custom knowledge base retrieval solution. The OpenAI embeddings can then be stored in a vector database like Milvus or Zilliz for indexing and searching documents.

 <a href="https://zilliz.com/blog/chatgpt-retrieval-plugin-zilliz-milvus" target="_blank" class="outlinedButton">Learn more</a>

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#### Build a Similarity Search with Zilliz Cloud and OpenAI

This step-by-step tutorial showcases [OpenAI’s Embedding API](https://platform.openai.com/docs/guides/embeddings) with the Zilliz vector database to search across book titles. Many existing book search solutions (such as those used by public libraries, for example) rely on keyword matching rather than a semantic understanding of what the title is actually about. Using a trained model to represent the input data is known as semantic search and can be expanded to a variety of different text-based use cases, including anomaly detection and document search.

 <a href="https://zilliz.com/doc/integrate_with_openai" target="_blank" class="outlinedButton">Try Tutorial</a>



Zilliz partnership with OpenAI - View OpenAI Cookbook for vector data

OpenAI

Hugging Face is on a mission to democratize good machine learning, one commit at a time. Hugging Face builds open-source resources to empower developers to easily integrate AI into their products and workflows. They believe that AI can be accessible, optimized, and responsible.

<a href="https://huggingface.co/models" target="_blank" class="arrowLink">Explore Model Library</a>



#### Build Question Answering with Hugging Face and Zilliz Cloud

In this step-by-step code tutorial, learn how to build a question-answering system using Zilliz Cloud as the vector database and Hugging Face as the embedding system. 

 <a href="https://zilliz.com/doc/integrate_with_hugging-face" target="_blank" class="outlinedButton">Try Tutorial</a>

Zilliz-Hugging Face partnership - Explore transformer data model repo

Hugging Face

Cohere trains massive language models and offers an API to simplify adding language processing to any system. Through training, this enables users to create huge models customized to their use case and trained on their data. Then, the users generate vectors from this data with a cohere model; it can be ingested into a vector database like Milvus or Zilliz Cloud to build cutting-edge applications and search solutions. 

<a href="https://docs.cohere.ai/docs/integrations" target="_blank" class="arrowLink">Cohere Integrations</a>




#### Build Question Answering Using Cohere and Zilliz Cloud

This hands-on tutorial illustrates creating a question-answering system based on the SQuAD dataset using Zilliz Cloud as the vector database and Cohere as the embedding system.

 <a href="https://zilliz.com/doc/integrate_with_cohere" target="_blank" class="outlinedButton">Try Tutorial</a>

Zilliz partnership with Co:here - Co:here model and integrations

Co:here

LangChain is an AI Agent tool that adds functionality to large language models (LLMs) like GPT. In addition, it includes functionality for token and context management. Together LangChain and Zilliz can be used to inject domain knowledge to build better and more performant LLM applications.

<a href="https://zilliz.com/doc/integrate_with_langchain" target="_blank" class="arrowLink">View Integration</a>

<a href="https://zilliz.com/doc/integrate_with_langchain" target="_blank" class="arrowLink">Getting Started with LangChain</a>

#### Memory for LLM Applications

Connecting external data sources to LLMs to give them memory is crucial to many LLM applications. In this session, LangChain’s [Harrison Chase](https://github.com/hwchase17) dives into retrieval and dissects the challenges of finding relevant information from a large corpus of data.

<a href="https://zilliz.com/event/memory-for-llm-applications-webinar" target="_blank" class="outlinedButton">Save Your Spot</a>

Zilliz partnership with LangChain - How you can integrate a vector database with LangChain

LangChain

LlamaIndex and Zilliz are partnering to help developers quickly create scalable LLM applications by facilitating intelligent prompting, routing queries, and injecting domain-specific data. LlamaIndex manages LLM interactions by taking data and building an index around it; then, it uses the index to help answer questions related to the data input. Together with a vector database like Milvus or Zilliz, these indexes are stored for faster retrieval and better scalability.

<a href="https://zilliz.com/doc/integrate_with_llama" target="_blank" class="arrowLink">View Integration</a>

<a href="https://zilliz.com/blog/getting-started-with-llamaindex" target="_blank" class="arrowLink">Getting Started with LlamaIndex</a>

#### Boost your LLM with Private Data using LlamaIndex

In this on-demand session, LlamaIndex co-founder and CEO Jerry Liu shares details on how he built LlamaIndex, the various tools LlamaIndex contains, and how you can get started enriching your LLM with private data. The session ends with an overview of how to use LlamaIndex with Milvus or Zilliz Cloud.

<a href="https://zilliz.com/event/boost-your-llm-with-private-data-using-llamaindex" target="_blank" class="outlinedButton">Watch Session Replay</a>

Zilliz partnership with LlamaIndex - How you can integrate a vector database with LlamaIndex

LlamaIndex

PyTorch Foundation is a neutral home for the deep learning community to collaborate on the open-source PyTorch framework and ecosystem. Leading contributors to the PyTorch open source project support the PyTorch Foundation. The Foundation leverages the resources members and contributors provide to enable community discussions and collaboration.

<a href="https://pytorch.org/vision/stable/index.html" target="_blank" class="arrowLink">Torchvision Documentation</a>



#### Build Image Search Solution with PyTorch and Zilliz Cloud

Create a simple image search example using PyTorch and Zilliz Cloud. The dataset we are searching through is the Impressionist-Classifier Dataset found on Kaggle. This example uses a 1 CU instance and the Torchvision pre-trained Resnet50 model for embeddings.

 <a href="https://zilliz.com/doc/integrate_with_pytorch" target="_blank" class="outlinedButton">Try Tutorial</a>


Partners

Zilliz partners with leading technology, solution and service providers to enable users to build powerful vector search applications.

All Partners