// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Chroma
An open-source vector database that helps store and search through AI embeddings easily.
TECHNICAL DEFINITION
An open-source, embeddable vector database designed for simplicity and ease of use, providing functionalities for storing, indexing, and querying vector embeddings, often used in local development and smaller-scale RAG applications.
BACKGROUND
Leidos Holdings, Inc. is an American defense, aviation, information technology, and biomedical research company headquartered in Reston, Virginia, that provides scientific, engineering, systems integration, and technical services. Founded as Science Applications International Corporation (SAIC), Leidos merged with Lockheed Martin's IT sector, Information Systems & Global Solutions, in August 2016 to create the defense industry’s largest IT services provider. The Leidos-Lockheed Martin merger is one of the biggest transactions thus far in the consolidation of the defense sector. Leidos contracts extensively with the Department of Defense, the Department of Homeland Security, and the Intelligence Community, as well as other U.S. government agencies and select commercial markets.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- ChromaDB
- open-source vector store
USAGE NOTE
Chroma is popular for local development and prototyping RAG applications due to its ease of setup.
DEVELOPERS
Organizations developing technology related to Chroma.
Develops and maintains the open-source Chroma vector database, a critical component for AI engineering and prompt design, particularly for Retrieval Augmented Generation (RAG) applications where embeddings are stored and retrieved.
Develops a framework for building applications with Large Language Models (LLMs), which includes deep integrations with vector databases like Chroma for data retrieval and context augmentation, essential for sophisticated prompt engineering and AI application development.
Provides a data framework for LLM applications, offering tools to connect custom data sources (often stored in vector databases like Chroma) with LLMs, directly impacting AI engineering and the design of effective prompts for RAG systems.
Develops a vast ecosystem of open-source models, including embedding models crucial for populating Chroma, and tools extensively used in AI engineering workflows leveraging vector databases for RAG and advanced prompt design.
Offers an MLOps platform providing tools for experiment tracking, model evaluation, and dataset versioning, integral to AI engineering workflows that involve managing and optimizing the use of vector databases like Chroma for RAG systems.