Manage and query Pinecone vector databases with hybrid search.
npx skills add https://github.com/superfhp/lumi-agent --skill pinecone-superfhp
Please help me install this Agent Skill. Skill: pinecone Source: https://github.com/superfhp/lumi-agent/tree/main/optional-skills/mlops/pinecone Command: npx skills add https://github.com/superfhp/lumi-agent --skill pinecone-superfhp
This Skill streamlines the management and querying of Pinecone vector databases, reducing the complexity of AI application data storage and retrieval.
Initialize Pinecone with the appropriate API key and create an index with the required dimensions and metric.
High-intent search queries and answers about installing and using this skill.
Automate vector database management by using Python scripts to handle index creation, vector storage, and retrieval. This approach streamlines data storage and reduces the complexity of maintaining AI application infrastructure.
Yes, you can perform hybrid search combining dense and sparse vectors. This capability allows versatile application retrieval, supporting both semantic similarity and keyword-based matching within your queries.
The best way to store and retrieve vectors with metadata is by upserting and querying data directly through an automated client. This enables efficient search and supports filtering based on attached metadata fields.
Yes, this vector database setup supports auto-scaling for production use cases. It is designed with an emphasis on low latency, ensuring reliable retrieval performance as your AI application data grows.
Create a vector database index by initializing the client with an API key and specifying the required dimensions and metric. This dynamically provisions the database structure for efficient vector search.
zepdbIndex and search text with Zeppelin vector store and BM25 full-text indexing.
ckrono-progSearch Replit documentation using natural language queries.
RigohlCoordinate hybrid semantic and full-text search across multiple engines via FFI.
ccfDetail distance metrics and index types for vector similarity search across Pinecone, Qdrant, pgvector, and Weaviate.
