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Welcome back. In this lesson we introduce Weaviate — an open-source vector database optimized for semantic (vector) search, multimodal data, and hybrid ranking. Weaviate is available under the BSD-3-Clause license, which allows you to run, modify, and redistribute it — including in commercial projects. In addition to the self-hosted distribution, Weaviate Cloud Service (WCS) provides a managed, serverless option if you prefer a hosted experience with minimal operations. Core capabilities
Weaviate’s modular modules let you experiment with different embedding providers and models, preventing vendor lock‑in and enabling custom model integration.
How hybrid search works When you submit a query, Weaviate executes both a keyword-style (lexical) search and a vector (semantic) similarity search. It then merges results using a ranking fusion strategy to produce a single, ranked list that balances exact term matches with conceptual similarity. This hybrid approach is especially valuable when you need results that respect both precise terminology and broader meaning. Real-world usage Weaviate is commonly used to power semantic search and knowledge retrieval systems. For example:
  • Tech-directory or marketplace platforms use Weaviate to let users find conceptually similar tools even when the query terms don’t exactly match catalog entries.
  • Documentation assistants and internal knowledge bases combine keyword search for specific terms with vector search to find contextually relevant passages and related topics.
Deployment options Who is Weaviate for? Weaviate is a strong choice if you need:
  • An open-source, extensible vector database with hybrid search and multimodal capabilities.
  • Flexibility to self-host for full infrastructure and data control.
  • A managed cloud option (WCS) for quick deployment without heavy operational overhead.
  • Pluggable embedding modules so you can use hosted providers or integrate your own models.
The image is an infographic detailing the features and target users of Weaviate, an open-source vector database for semantic search, highlighting its core features, hybrid search pipeline, and deployment flexibility.
Getting started recommendations
  • Evaluate locally: Start with the open-source distribution to prototype on your own infrastructure and validate your schema, embedding pipeline, and query patterns.
  • Scale or simplify ops: If you prefer a managed experience, migrate to Weaviate Cloud Service (WCS) to offload operational tasks.
  • Choose a vectorizer: Test multiple embedding providers or models (OpenAI, Cohere, Hugging Face, or custom modules) to find the best trade-off for accuracy, cost, and latency.
Alternatives and comparison Milvus is another popular open-source vector database. Compare both solutions on:
  • Performance and scale characteristics
  • Supported embedding integrations and modules
  • Operational complexity (self-hosted vs managed options)
  • Feature set for multimodal or hybrid search
Links and references That is it for this lesson on Weaviate.

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