Store and query embeddings locally with metadata filtering.
npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill chroma-monjyu1101
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/Monjyu1101/AiDiy2026/tree/main/backend_hermes/optional-skills/mlops/chroma Command: npx skills add https://github.com/Monjyu1101/AiDiy2026 --skill chroma-monjyu1101
Chroma provides an open-source, self-hosted embedding database that stores vectors and metadata, enabling fast, privacy-preserving retrieval with vector and text search.
Install chromadb, create a collection, add documents with metadata, and run a basic similarity search.
High-intent search queries and answers about installing and using this skill.
Store and query embeddings locally by creating a collection, adding documents with metadata, and running similarity searches. This self-hosted embedding database enables fast retrieval with vector and text search without external API dependencies.
Metadata filtering in a vector database narrows retrieval results by applying attribute conditions alongside vector similarity. You need it when building RAG pipelines or document retrieval systems requiring targeted searches across specific document categories or tags.
Yes, this self-hosted embedding database supports framework integrations with LangChain and LlamaIndex. You can connect it directly within your existing RAG pipelines to manage vector storage and retrieval locally.
Install the chromadb package, create a collection, add your documents with metadata, and execute a basic similarity search. This provides a local vector database ready for RAG pipeline integration and document retrieval.
Yes, this self-hosted vector database is suitable for notebooks, experiments, and light production deployments. It offers a simple four-function API for vector storage and retrieval while maintaining privacy-preserving local data control.
Yes, the semantic search tool supports both full-text search and vector retrieval capabilities. You can apply metadata filtering to both search methods to enable efficient AI retrieval across your stored documents.
SignmanalStore and query vector embeddings with metadata filtering for RAG applications.
ruskibeatsValidate retrieved graph edge evidence structure for LLM RAG context assembly.
floflo777Generate RAG project scaffolds for LangChain, LlamaIndex, and vector stores.
KarlinskySStore and search vector embeddings with metadata filtering via Python and JavaScript APIs.
