Weaviate

What is Weaviate?

Weaviate is an open-source, cloud-native, vector-search engine designed to handle data and search queries efficiently. It leverages machine learning models to understand the relationships between data points and offers smart search functionalities based on context and relevance. Weaviate allows users to structure and connect data in a flexible manner, enabling complex searches across large datasets. With its focus on semantic similarity and contextual search, Weaviate is suitable for various applications such as natural language processing, chatbots, recommendation systems, and more. Overall, Weaviate stands out for its ability to provide intelligent search capabilities by organizing and retrieving data in a meaningful way.

Who created Weaviate?

Weaviate was launched on February 15, 2022. The founder of Weaviate is Bob van Luijt. The company details include being Copyright © 2024 Weaviate, B.V..

What is Weaviate used for?

• Building AI applications
• Creating ChatGPT services
• Handling NLP and generative AI tasks
• Automating generation of economic insights
• Powering natural language academic expert finder
• Hybrid search for tech talent
• Model serving and multi-tenant implementation
• Fast development of generative AI applications
• Enhancing intelligence-based bots with vector search
• AI-powered search engines
• Allowing customers to accurately categorize and search customer feedback
• Handling advanced NLP and generative AI tasks across millions of news articles per day in multiple languages
• Enabling businesses to build AI apps in minutes with developer tools and end-to-end functionality
• Powering natural language academic expert finders
• Facilitating fast development of generative AI applications
• Providing unmatched flexibility in schema definition to streamline storing unstructured data
• Enhancing RAG-based shopping assistants by building context for the generation phase
• Improving search accuracy and developer experience in various projects
• Acting as long-term memory for Conversational AI to store and retrieve data for deeper interactions
• Turbocharging talent hunt and pinpointing precise and related skills through hybrid search
• Building AI applications with features like keyword search, vector search, and document storage
• Developing RAG-based shopping assistant
• Handling advanced NLP and generative AI tasks
• Facilitating semantic search
• Creating AI-powered research and intelligence tools for pharma industry
• Providing hybrid search for talent hunting
• Enabling interaction with diverse data sources for business insights
• Storing and retrieving conversational data for future interactions to deepen relationships on LinkedIn
• Facilitating fast development of generative AI applications by removing the need for creating boilerplate code, setting up databases, and managing infrastructure
• Powering sophisticated use cases to handle thousands of queries simultaneously
• Integrating with intelligence-based bots for easy enhancement with vector search
• Enabling flexibility in starting and building GenAI applications
• Automating generation of deep economic insights in teaching environments
• Improving search accuracy and developer experience
• Handling advanced NLP and generative AI tasks across a large volume of news articles in multiple languages
• Providing an easy-to-set-up database for AI-powered research and intelligence tools
• Paving the way for AI platforms to interact with diverse data sources and provide businesses with unparalleled insights and capabilities
• Advanced NLP and generative AI tasks
• Building context for generation phase in shopping assistant applications
• Facilitating accurate categorization and search of customer feedback
• Prototype-friendly trial plan for AI projects
• Accurate and flexible vector database
• Efficient building and launching of ChatGPT services
• Hybrid search capabilities for fast talent pinpointing
• Development of AI-powered research and intelligence tools for regulated industries
• Advanced NLP and generative AI tasks across >4M news articles per day in 120 languages
• Prototype, iterate, and release Cognigy Knowledge AI product
• Unlock new potentials in AI with transformative and avant-garde semantic search solutions
• Building AI-powered research and intelligence tools for the highly-regulated pharma industry
• Developing a RAG-based shopping assistant
• Pioneering French legal research
• Managing multi-tenancy
• Building GenAI applications
• Model serving and multi-tenant implementation for vector search
• Enhancing AI Platform for effortless interaction with diverse data sources

Who is Weaviate for?

• Data Scientist
• Software Engineer
• Machine learning engineer
• Researcher
• Product Manager
• Data Analyst
• UX designer
• Content Strategist
• Business Analyst
• AI Developer
• System Architect
• Marketing Specialist
• Chatbot developer
• Recommendation system engineer

How to use Weaviate?

To use Weaviate, follow these step-by-step guidelines:

1. Installation: Begin by installing Weaviate on your system. You can install it via Docker, Kubernetes, or using the Helm Chart.

2. Initialization: Once installed, start the Weaviate server. You can initialize it using the command line interface.

3. Schema Setup: Define your schema by specifying the classes and properties of your data. You can create classes such as 'Person' with properties like 'name' and 'age'.

4. Data Ingestion: Import your data into Weaviate. You can do this through the RESTful API or by using the provided client libraries in various programming languages.

5. Exploration: Explore your data within Weaviate to understand its structure and relationships. Use GraphQL queries to retrieve specific information from your dataset.

6. Vector Search: Leverage Weaviate's vector search capabilities to perform similarity searches based on the embeddings of your data.

7. Schema Evolution: Modify your schema as needed to adapt to changes in your data requirements. You can add new classes, properties, or update existing ones.

8. Authentication and Authorization: Implement security measures by setting up authentication and authorization to control access to your Weaviate instance.

9. Integration: Integrate Weaviate with other tools and services to enhance its functionality and usability within your workflow.

10. Monitoring and Maintenance: Regularly monitor the performance of Weaviate and perform maintenance tasks to ensure its smooth operation.

By following these steps, you can effectively utilize Weaviate for your data management and retrieval needs.