In today's data-driven world, effectively storing, retrieving, and analyzing unstructured data such as text, images, and videos is a core challenge. Traditional database systems often struggle to handle this type of data efficiently. It is against this backdrop that Weaviate has emerged as a powerful and highly scalable open-source vector database, fundamentally changing how we interact with data.
What is Weaviate?
Weaviate is an AI-Native database that uses Vector Embeddings as its core data structure. This means that whether you are storing text paragraphs, image features, or other types of unstructured data, Weaviate utilizes powerful machine learning models to transform this data into high-dimensional vectors. These vectors capture the semantic information of the data, allowing the database to understand "what" the data is and the "relationship" between data points.
Weaviate's Core Advantages
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Semantic Search
Unlike traditional full-text search, which relies on keyword matching, Weaviate implements Semantic Search. The database calculates the distance (usually cosine similarity) between the query vector and the stored data vectors to find the most relevant results in terms of meaning, even if the results do not contain the query keywords.
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AI-Native and Modular Design
Weaviate's design revolves around AI. Its modular architecture allows users to seamlessly integrate leading machine learning models (like Transformer models) or services (such as OpenAI, Cohere, etc.) for high-quality vector generation, adapting to various tasks and languages.
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Real-time Capability and Scalability
As a distributed system, Weaviate offers excellent horizontal scalability, handling massive datasets and high-concurrency requests. It provides near real-time data ingestion and querying, making it ideal for applications requiring rapid response times, such as recommendation systems.
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Hybrid Search
Weaviate supports Hybrid Search, which combines vector search with traditional BM25/full-text search. This blend ensures that query results maximize both semantic relevance and precise keyword matching, delivering a comprehensive retrieval experience.
Key Application Scenarios
Weaviate excels in critical areas:
- Knowledge Retrieval and Question Answering (RAG): Enhancing Large Language Models (LLMs) with up-to-date, external knowledge bases.
- Recommendation Systems: Providing personalized, context-aware content suggestions.
- Multimodal Search: Searching for images or videos using simple text descriptions.
Extending Intelligence: Introducing Velodb
While vector databases like Weaviate offer cutting-edge retrieval, the market increasingly demands a unified platform that can integrate both analytical and retrieval capabilities and connect to diverse data sources while offering exceptional, guaranteed real-time performance.
Velodb is engineered to meet exactly this need.
Velodb is a comprehensive database that supports both analytics and retrieval. It is purpose-built for speed and is a core differentiator that Velodb is designed to achieve real-time performance of less than 1 second. This sub-second latency is critical for applications that rely on immediate insights, such as real-time dashboards and highly responsive RAG systems.
To ensure the highest quality results, Velodb also fully supports Hybrid Search. This capability allows users to combine the power of semantic (vector) similarity search with precise keyword (full-text) search within a single query, significantly enhancing the accuracy and relevance of the retrieval process.
Furthermore, Velodb boasts excellent data source connectivity. It supports easy connection and integration with various external data ecosystems, enabling a single point of access:
- Lakehouse Architectures: It can seamlessly access and operate directly on data stored in Lakehouse systems like Delta Lake, Apache Hudi, or Apache Iceberg, performing analysis and retrieval without needing to move or duplicate data.
- Traditional Databases and Data Warehouses: Easily connecting to existing infrastructure like PostgreSQL, MySQL, Snowflake, and others.
Through its comprehensive design, subsecond real-time performance, Hybrid Search capability, and powerful integration, Velodb aims to provide enterprises with a unified data intelligence platform, significantly simplifying data infrastructure and accelerating the journey from raw data to actionable, intelligent insights.
