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Weaviate

Open-source, cloud-native vector search engine and database designed to store, index, and search data based on semantic meaning via vector embeddings.

It supports both semantic vector search and knowledge graph functionality.

Key Components​

1. Core Engine​

  • Language: Written in Go, ensuring performance and scalability.
  • Functionality: Handles vector indexing, search, and CRUD operations.
  • Deployment: Supports Docker, Kubernetes, and Helm for flexible deployment options.

2. Modules​

  • Purpose: Extend Weaviate's capabilities with additional features.
  • Examples:
    • Text2Vec: Converts text into vector embeddings.
    • Image2Vec: Converts images into vector embeddings.
    • Hugging Face, OpenAI, Cohere: Integrate with external ML models for vectorization.

3. API Interfaces​

  • RESTful API: Allows interaction with Weaviate using standard HTTP methods.
  • GraphQL API: Provides a flexible query language for more complex data retrieval.

4. Schema Management  ​

  • Schema Definition: Users can define classes and properties to structure their data.
  • Flexibility: Supports dynamic schema updates and versioning.

5. Persistence Layer  ​

  • Storage: Utilizes a combination of in-memory and disk-based storage for efficiency.
  • Replication: Supports data replication for high availability and fault tolerance.

Integration with Qwen3, Graphiti, and Langraph​

  1. Vector Search & Knowledge Graph Integration: Weaviate excels at combining semantic vector search with graph-based data modeling. This dual capability aligns well with your need to store and retrieve data based on both meaning and relationships.
  2. Seamless Integration with Qwen3: While specific integrations with Qwen3 are not detailed, Weaviate's support for external model providers like OpenAI, Hugging Face, and Cohere suggests that integrating Qwen3 for generating embeddings is feasible.
  3. Compatibility with LangChain: Weaviate is a supported vector store in LangChain, allowing you to use it as a backend for storing and retrieving embeddings generated by language models.
  4. Modular Architecture: Weaviate's modular design allows you to extend its capabilities with additional functionalities, such as custom vectorizers or rerankers, which can enhance your application's performance.
  5. Multi-Tenancy Support: Weaviate supports multi-tenancy, enabling you to isolate data for different users or applications within the same instance, which is beneficial for scalable applications.

Minor Limitations​

  • Ecosystem maturity: Compared to some legacy databases or specialized graph DBs, Weaviate’s ecosystem is still growing. Sometimes advanced graph queries might be better handled by dedicated graph DBs.
  • Integration effort: Requires some engineering to glue components perfectly, especially with complex workflows involving multiple AI modules.