Features: Open-source, JSON payloads, filtering support.
Pros: Versatile, suitable for various data types.
Cons: Newer in the market, fewer integrations.
Use Cases: Neural network-based matching, faceted search.
- Chroma
Features: Open-source, feature-rich with queries and filtering.
Pros: Easy to use, suitable for LLM applications.
Cons: Limited to certain use cases.
Use Cases: LLM applications, knowledge management.
How Vector Databases Work
Data Ingestion: Importing data into the database, converting it into vector embeddings.
Indexing: Creating indices to enable efficient querying of vectors.
Querying: Using various distance metrics (e.g., cosine similarity, Euclidean distance) to find similar vectors.
Storage and Retrieval: Managing data across multiple nodes for scalability and performance.