@arizeai/phoenix-mcp

--- description: Weaviate is an open source, AI-native vector database. --- # Weaviate <figure><img src="../.gitbook/assets/weaviate_logo.svg" alt=""><figcaption></figcaption></figure> **Website:** [weaviate.io](https://weaviate.io/) Phoenix can be used to trace and evaluate applications that use Weaviate as a vector database. ## Examples <table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Ingesting Data for Semantic Search</strong></td><td>This tutorial will show you how to embed a large volume of data, upload it to a vector database, run top K similarity searches against it, and monitor it in production using VectorFlow, Arize Phoenix, Weaviate and LlamaIndex.</td><td><a href="../.gitbook/assets/weaviate.jpg">weaviate.jpg</a></td><td><a href="https://arize.com/blog/ingesting-data-for-semantic-searches-in-a-production-ready-way/">https://arize.com/blog/ingesting-data-for-semantic-searches-in-a-production-ready-way/</a></td></tr><tr><td><strong>Instrumenting and Evaluating a Weaviate RAG Pipeline</strong></td><td>This tutorial serves as a great starting point to see how to manually instrument a RAG chatbot built on Weaviate, and visualize and evaluate the results in Phoenix.</td><td><a href="../.gitbook/assets/Tutorials.jpg">Tutorials.jpg</a></td><td><a href="https://github.com/Arize-ai/phoenix/blob/7c52b6695b87945bde61ab57b0c5ae4e2acafe16/tutorials/integrations/tracing_and_evals_weaviate.ipynb#L19">https://github.com/Arize-ai/phoenix/blob/7c52b6695b87945bde61ab57b0c5ae4e2acafe16/tutorials/integrations/tracing_and_evals_weaviate.ipynb#L19</a></td></tr></tbody></table>