description Weaviate Overview
Weaviate is an open-source, cloud-native vector database that allows you to store and search vector embeddings. It combines graph and vector search capabilities, enabling semantic search and knowledge graph applications. Weaviate integrates seamlessly with machine learning models and provides a flexible data model for representing complex relationships. It's ideal for applications involving AI, machine learning, and knowledge discovery.
balance Weaviate Pros & Cons
- Fast vector search performance
- Supports hybrid search queries
- Good documentation and API
- Flexible data schema
- High memory usage
- Complex setup process
- Requires tuning for scale
help Weaviate FAQ
Does Weaviate create embeddings, or only store them?
Weaviate can accept vectors generated by your own model or use configured integrations to create them during import and queries. Its module ecosystem supports providers and models from companies such as OpenAI, Cohere, and Hugging Face.
How does Weaviate hybrid search combine keywords and vectors?
Hybrid search combines BM25 keyword results with vector similarity results and fuses their scores. This helps when a query contains an exact product code or name but also benefits from semantic matching.
Can Weaviate run locally without Weaviate Cloud?
Yes. Weaviate is open-source and can be run locally with Docker or deployed into Kubernetes, while Weaviate Cloud provides a managed option.
How is Weaviate different from Pinecone or PostgreSQL with pgvector?
Weaviate is a dedicated vector database with schema management, filtering, hybrid search, and built-in model integrations. Pinecone is primarily managed, while pgvector keeps vectors beside relational data inside PostgreSQL and may be simpler when an application already depends heavily on SQL.
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