Store embeddings and metadata with vector and full-text search.
npx skills add https://github.com/gigagiova/hermes-agent --skill chroma-gigagiova
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/chroma Command: npx skills add https://github.com/gigagiova/hermes-agent --skill chroma-gigagiova
Open-source embedding database for AI applications to store embeddings and metadata, enabling fast search and filtering for memory and retrieval tasks.
Install chromadb, create a collection, and start indexing your documents for fast retrieval.
High-intent search queries and answers about installing and using this skill.
An open-source vector database stores embeddings and metadata to enable fast vector and full-text search. It provides a simple 4-function API for semantic search, document retrieval, and RAG workflows, scaling from notebooks to production.
You can store embeddings and metadata locally by installing an open-source embedding database like Chroma. After installation, create a collection and start indexing your documents to perform fast vector search and metadata filtering for retrieval.
Yes, the open-source embedding database supports vector and full-text search with metadata filtering. This allows you to narrow down document retrieval results in RAG applications based on specific metadata attributes attached to your stored embeddings.
Yes, this open-source embedding database is designed to scale from notebooks to production deployments. It maintains a simple 4-function API with minimal dependencies, ensuring compatibility with common ML tooling across both prototyping and real-world production environments.
No, you do not need heavy dependencies. This open-source embedding database operates with minimal dependencies and a simple 4-function API, ensuring easy adoption and seamless compatibility with common ML tooling for semantic search and memory applications.
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AlexiosBluffMaraStore and query embeddings with metadata filtering in a local vector database.
