Index and search text with Zeppelin vector store and BM25 full-text indexing.
npx skills add https://github.com/zepdb/zeppelin --skill zeppelin-zepdb
Please help me install this Agent Skill. Skill: zeppelin Source: https://github.com/zepdb/zeppelin/tree/main Command: npx skills add https://github.com/zepdb/zeppelin --skill zeppelin-zepdb
Zeppelin provides a scalable way to store and search text using semantic embeddings and full-text indexing, enabling fast retrieval across large document collections.
Install Zeppelin, configure a storage backend, and begin embedding text to upsert vectors and run similarity or BM25 queries.
High-intent search queries and answers about installing and using this skill.
To store and search text with semantic embeddings, you index text using BM25 full-text search and generate embeddings to upsert vectors into namespaces, enabling fast retrieval and similarity queries across large document collections.
Yes, multi-modal queries are supported across vectors and text. You can query by semantic similarity using generated embeddings or perform keyword-based BM25 full-text search with tokenization, stemming, and multi-field ranking.
Yes, Zeppelin supports S3-compatible object storage for durable, scalable storage and easy deployment. You configure an S3-compatible storage backend to upsert vectors and manage namespaces for organized data.
The best way to organize large document collections for scalable text retrieval is through namespace creation and vector upsert. This approach structures data across S3-compatible storage for fast semantic and full-text search.
Yes, embedding generation is required before upserting vectors. You must generate semantic embeddings from your text data, then upsert those vectors into namespaces to enable similarity queries within the vector store.
keiretsu-labsManage S3-compatible object storage operations for self-hosted Garage clusters.
jcolanoStore and retrieve text as 384-dimensional semantic vectors for similarity search.
48Nauts-OperatorCompare embedding models and design pipelines for semantic search and RAG.
ionmidoriImplement similarity search with Pinecone and Google Generative AI embeddings.
