Chroma
Chroma is an open-source embedding database designed to simplify the development of AI applications by facilitating the storage, indexing, and querying of high-dimensional vector embeddings. It acts as a specialized backend for machine learning models, enabling capabilities like semantic search…
Key facts
- First appeared
- 2022
- Category
- database
- Problem solved
- Chroma was created to solve the problem of efficiently storing, indexing, and querying high-dimensional vector embeddings for AI applications. Prior to specialized vector databases, developers often relied on general-purpose databases with custom indexing solutions, which were inefficient for similarity search and lacked native vector operations, hindering the development of scalable semantic search and RAG systems.
- Platforms
- Linux, macOS, Windows, Docker, Cloud environments (AWS, GCP, Azure)
Related technologies
- Pinecone
- Weaviate
- Milvus
- Qdrant
- Zilliz Cloud
- Azure AI Search
- Amazon OpenSearch Service (with vector engine)
- Large Language Models (LLMs)
- Embedding models (e.g., Sentence-BERT, OpenAI embeddings)
- LangChain
- LlamaIndex
- Python data science libraries (e.g., NumPy, Pandas)
- Machine learning frameworks (e.g., PyTorch, TensorFlow)
- Docker
Notable users
- AI/ML Startups
- Research Projects in LLM space
- Individual Developers working with RAG
- Organizations integrating RAG into their applications (specific company names often proprietary but widely used in the ecosystem)