
Trino
Fast distributed SQL query engine for big data analytics.

An open-source, AI vector database designed to store and index data objects and their vector embeddings, enabling advanced semantic search capabilities.

Weaviate is an open-source, AI-native vector database designed to store and index both data objects and their vector embeddings. This architecture enables advanced semantic search capabilities by comparing the meaning encoded in vectors rather than relying solely on keyword matching. Key capabilities include semantic and hybrid search, Retrieval Augmented Generation (RAG), and agent-driven workflows. It offers language agnostic SDKs (Python, Go, TypeScript, JavaScript) and connects to GraphQL or REST APIs. Weaviate supports seamless integration of ML models, or the use of a built-in embedding service. Its architecture adapts to any workload, scaling seamlessly while optimizing costs. It can be deployed securely in the cloud or on-premise, meeting enterprise requirements like RBAC, SOC 2, and HIPAA. Weaviate simplifies AI application development by providing AI-first features under one roof, avoiding complex data pipelines and custom code.
Weaviate is an open-source, AI-native vector database designed to store and index both data objects and their vector embeddings.
Explore all tools that specialize in store vector embeddings. This domain focus ensures Weaviate delivers optimized results for this specific requirement.
Explore all tools that specialize in semantic search. This domain focus ensures Weaviate delivers optimized results for this specific requirement.
Combines vector and keyword search for improved retrieval accuracy, allowing for more relevant results even when the query terms don’t exactly match the stored data.
Supports Retrieval Augmented Generation (RAG) workflows, where vector search is used to retrieve context that enhances the output of generative models, making it easier to generate accurate, context-aware responses.
Provides a flexible API and integration with modern AI models making Weaviate suitable for powering applications that rely on intelligent agents. These agents can leverage semantic insights to make decisions or trigger actions based on the data stored in Weaviate.
Efficiently indexes and searches billions of vectors, ensuring fast and accurate retrieval of relevant data.
Supports multi-tenancy, allowing multiple users or organizations to share a single Weaviate instance while maintaining data isolation and security.
Offers SDKs for Python, Go, TypeScript, and JavaScript, along with GraphQL and REST APIs, providing flexibility for developers.
Spin up a Weaviate cluster using Weaviate Cloud or self-managed deployment options (Docker, Kubernetes).
Define the data schema (classes and properties) that reflects your data structure.
Configure vectorization modules to generate embeddings from your data (e.g., OpenAI, Cohere, Hugging Face).
Import data into Weaviate, either using client libraries or direct API calls.
Create queries using near_vector, near_text, or hybrid search to retrieve relevant data based on semantic similarity.
Implement RAG workflows to augment generative models with context retrieved from Weaviate.
Build AI agents that leverage semantic insights to make decisions and trigger actions.
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