Manage an open-source embedding database for vector and full-text search.
npx skills add https://github.com/helix4u/hermes-agent-private --skill chroma-helix4u
Please help me install this Agent Skill. Skill: chroma Source: https://github.com/helix4u/hermes-agent-private/tree/main/skills/mlops/chroma Command: npx skills add https://github.com/helix4u/hermes-agent-private --skill chroma-helix4u
This Skill provides a robust, open-source embedding database for AI applications, enabling efficient storage and retrieval of vector data and associated metadata.
Use the chroma skill to create a new collection named 'my_documents' and add the provided text documents with their metadata.
High-intent search queries and answers about installing and using this skill.
To build RAG applications, you need an embedding database to store vector data and perform similarity searches. This skill manages an open-source vector database, facilitating the storage of embeddings and metadata for semantic search and document retrieval.
Filtering vector search results by metadata is a core feature of managing an embedding database. This skill supports metadata filtering, allowing you to efficiently narrow down semantic search results based on associated document attributes.
Yes, this embedding database integrates directly with LangChain and LlamaIndex. This compatibility allows you to seamlessly connect your vector storage and retrieval operations within those AI application frameworks.
You can use sentence-transformers for semantic search in a local development environment. This skill depends on sentence-transformers to generate embeddings and manages an open-source database supporting both local setups and production clusters.
Setting up a vector database for AI apps requires storing embeddings and associated metadata. This skill provides a robust, open-source embedding database that facilitates document retrieval and full-text search with options for local development.
Tommy-ywStore and query embeddings with metadata using chromadb and sentence-transformers.
eyadsibaiCompare RAG frameworks like LangChain and LlamaIndex for document QA.
cxnaiveStore and retrieve vector embeddings with metadata filtering for RAG applications.
brittaniebuffiecsuPerform vector similarity search with metadata filtering for RAG systems.
