## Introducing Word Embedding Vector Databases
Word embedding vector databases have gained popularity due to the rise of large language models. These databases store data using advanced machine learning techniques, allowing for fast similarity search. They are crucial for various AI applications like recommendation systems, picture recognition, and NLP.
The key to the effectiveness of vector databases lies in representing each data point as a multidimensional vector. Modern indexing techniques such as k-d trees and hashing enable quick retrieval of related vectors. This architecture revolutionizes big data analytics by providing scalable and efficient solutions for data-heavy industries.
## Meet Chroma: A Small, Free, Open-Source Vector Database
## Storing and Retrieving Word Embeddings with Chroma
Chroma allows the persistence of database sets to disk in Apache Parquet format when operating in memory. Storing word embeddings minimizes the time and resources required for their generation, enabling efficient retrieval later.
Additionally, each referenced string in Chroma can have extra metadata that describes the original document. While this step is optional, researchers often use fabricated metadata in tutorials. Chroma organizes related media into collections, with each collection consisting of documents (lists of strings), IDs (unique identifiers for the documents), and optional metadata. Chroma provides built-in word embedding models or allows the use of external models like OpenAI, PaLM, or Cohere for generating embeddings. Third-party APIs can be easily incorporated into Chroma, automating the generation and storage of embeddings.
By default, Chroma generates embeddings using the all-MiniLM-L6-v2 Sentence Transformers model. This model can produce embeddings for sentences and documents for various applications. Depending on the situation, the embedding function may require the local download of model files and running them on the PC.
Furthermore, Chroma allows querying metadata or IDs in the database, simplifying the search process based on the origin of the papers.
## Key Features of Chroma
– Easy to use, with clear documentation and testing.
– Consistent API across all environments (development, testing, and production).
– Rich functionality for searches, filters, and density estimation.
– Apache 2.0 Licensed Open Source Software.
To try Chroma, visit the [Try it here](https://www.trychroma.com/) and [Github page](https://github.com/chroma-core/chroma). Remember to join our ML SubReddit with 28k+ members, 40k+ Facebook Community, Discord Channel, and Email Newsletter to stay updated on the latest AI research news and exciting projects.