The Vector Database Integration Hub

Use the ChatGPT Retrieval Plugin with Zilliz Cloud & Milvus for more accurate answers

Integrate Zilliz Cloud with LangChain to build LLM applications with semantic search, RAG, and context-aware retrieval using a flexible framework for orchestration, chains, and agents.

Build Ruby-based Retrieval-Augmented Generation applications with LangChainRB and Zilliz Cloud or Milvus Vector Database

Real-time ingestion of vectors from your Unstructured Data

Real-time data ingestion for your RAG applications with Kafka

Use the .NET SDK with Milvus or Zilliz Cloud

Use the JavaScript SDK with Milvus or Zilliz Cloud

Use the Ruby SDK with Milvus or Zilliz Cloud

Use the Python SDK to build Semantic Similarity Search with Zilliz Cloud

Monitor Zilliz Cloud metrics in real-time with Prometheus. Track vector database performance, system health, and usage patterns across your AI workloads.

Integrate Datadog with Zilliz Cloud to monitor, visualize, and optimize the performance of your vector database in AI-powered applications.

Integrate HoneyHive AI observability with Zilliz Cloud to trace, monitor, and optimize vector search performance in scalable RAG pipelines.

Build enhanced reasoning RAG

Build automated RAG Pipelines

Cohere and Zilliz Cloud integrate to power semantic search and question-answering systems, combining Cohere's multilingual embedding models and NLP capabilities with Zilliz Cloud's high-performance vector database for efficient similarity search across large-scale datasets.

Haystack and Zilliz Cloud integrate to build powerful Retrieval-Augmented Generation applications, combining Haystack's open-source Python framework for LLM-powered pipelines with Zilliz Cloud's high-performance vector database for efficient document storage, semantic search, and scalable retrieval.

FiftyOne and Zilliz Cloud integrate to power vision searches on computer vision datasets, combining FiftyOne's open-source toolkit for dataset curation and model evaluation with Zilliz Cloud's high-performance vector database for efficient visual similarity search, cross-modal retrieval, and multimodal embedding exploration.

DSPy and Zilliz Cloud integrate to build high-performance Retrieval-Augmented Generation pipelines, combining DSPy's programmatic framework for optimizing LLM prompts and weights with Zilliz Cloud's efficient vector search capabilities for scalable, automatically optimized RAG applications.

Voyage AI and Zilliz Cloud integrate to power semantic search and multimodal retrieval, combining Voyage AI's cutting-edge embedding models built on contrastive learning research with Zilliz Cloud's high-performance vector database for efficient similarity search across text and images.

Jina AI and Zilliz Cloud integrate to power semantic search and retrieval applications, combining Jina AI's cutting-edge embedding models and rerankers with Zilliz Cloud's high-performance vector database for efficient multilingual, multimodal, and code similarity search at scale.

Integrate Zilliz Cloud with n8n to build AI-powered workflows with semantic search, RAG, and agents using a visual no-code automation builder.

Integrate Zilliz Cloud with DocsGPT to build a documentation Q&A system that retrieves accurate, GPT-powered answers from your project docs. Use Milvus as the vector database backend to store and search document embeddings at scale, with minimal configuration and no vector infrastructure to manage.

vLLM and Zilliz Cloud integrate to build high-performance Retrieval-Augmented Generation systems, combining vLLM's optimized LLM inference with PagedAttention and up to 24x throughput improvement alongside Zilliz Cloud's scalable vector database for efficient context retrieval.

Langflow and Zilliz Cloud integrate to build production-ready RAG pipelines with visual workflow design and high-performance vector search, enabling teams to prototype, test, and deploy AI applications through a drag-and-drop interface backed by scalable vector storage.

Vanna and Zilliz Cloud integrate to power AI-driven SQL generation, combining Vanna's open-source Python RAG framework for natural language to SQL conversion with Zilliz Cloud's high-performance vector database for efficient retrieval of database schemas, documentation, and query history.