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Data / Knowledge library
Weaviate
vector keyword and hybrid retrieval with object metadata
Why learn Weaviate?
Useful when semantic and keyword retrieval need to work together. Model integration and database retrieval are separate parts of the system.
Where it came from
Weaviate is a database for vector-based and hybrid search over objects and their properties. Integrations can connect stored data with embedding and generative models.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Define a collection for a sample document corpus.
- Import objects with searchable properties.
- Compare keyword, vector, and hybrid results.
- Apply a metadata filter and verify exclusions.
- Evaluate retrieval quality on labeled queries.
Documentation and sources
- Official documentation (docs.weaviate.io)
Catalog research notes
- Research snapshot: September 9, 2026. This entry summarizes the linked primary documentation; it does not assert a latest release version.
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Real World Example
Optimizing Technical Documentation Retrieval for Global Support Teams
Global technical support teams managing thousands of complex product manuals and troubleshooting guides.
The business problem
Support agents struggle to find relevant solutions because traditional keyword-based search fails to capture the semantic intent of user queries, while pure vector search often misses specific product version numbers or error codes.
How this topic solves it
Weaviate enables hybrid retrieval by combining vector embeddings, which capture the conceptual meaning of a support ticket, with keyword-based filtering on object metadata like product version, OS, and error category. This dual-approach ensures that retrieved documentation is both semantically relevant and technically accurate for the specific environment.
- Define a collection schema in Weaviate that includes text fields for documentation content and metadata fields for versioning and category.
- Configure an embedding model to generate vector representations for the documentation text during the ingestion process.
- Implement hybrid search queries that pass both the natural language query for vector similarity and metadata filters for exact keyword matching.
- Integrate the retrieval results into a RAG pipeline to synthesize a concise answer for the support agent.
The flow shows how user queries are processed through vector and keyword channels before being merged.
Semantic similarity lookup
Metadata constraint check
Combines results
Relevant documentation
- User Query → Embeds query → Vector Search
- User Query → Extracts metadata → Keyword Filter
- Vector Search → Sends vector results → Hybrid Merger
- Keyword Filter → Sends filtered results → Hybrid Merger
- Hybrid Merger → Ranks and returns → Final Context
Expected business outcome
Support agents receive highly accurate, context-aware documentation snippets that reduce resolution time and minimize the need for manual document filtering.
Limitations and checks
A significant trade-off is the increased computational overhead of maintaining both vector indices and inverted keyword indices. To check for performance degradation, monitor the latency of hybrid queries as the collection size grows, as the intersection of vector and keyword results can become resource-intensive.