Weaviate Python Client
A Python native client for seamless interaction with Weaviate vector database instances. This skill helps you integrate Weaviate's powerful semantic search, vector embeddings, and ML-powered data management capabilities into your Python applications.
What This Skill Does
This skill provides comprehensive guidance for using the `weaviate-client` Python package (v4.X) to:
Connect to and configure Weaviate instancesCreate and manage schema definitionsImport and query vector dataPerform semantic and hybrid searchesWork with ML models and embeddingsHandle batch operations efficientlyInstallation
```bash
pip install weaviate-client
```
**Requirements:** Python 3.9 or higher
Instructions
1. Initial Setup and Connection
When a user needs to connect to Weaviate:
Import the client: `import weaviate`For v4.X (recommended), use the `weaviate.connect_to_*()` methodsSupport common connection patterns: - Local instance: `weaviate.connect_to_local()`
- Weaviate Cloud: `weaviate.connect_to_wcs()`
- Custom endpoint: `weaviate.connect_to_custom()`
Always use context managers (`with` statement) for proper resource cleanupHandle authentication (API keys, OIDC) when required2. Schema Management
When defining data structures:
Create collection definitions with proper vectorizer configurationsDefine properties with appropriate data types (text, number, boolean, date, etc.)Configure vector indexes (HNSW, flat) based on use caseSet up cross-references between collections when neededUse the v4 collections API: `client.collections.create()`3. Data Import and Batch Operations
When inserting data:
Use batch operations for bulk imports to optimize performanceConfigure batch size and threading based on data volumeHandle data validation and error loggingSupport auto-batching with `collection.data.insert_many()`Implement retry logic for failed operationsMonitor batch statistics for debugging4. Querying and Search
When retrieving data:
**Vector/Semantic Search:** Use `near_text()`, `near_vector()`, or `near_object()` for similarity searches**Keyword Search:** Use BM25 for traditional keyword-based queries**Hybrid Search:** Combine vector and keyword search with configurable alpha parameter**Filtering:** Apply where filters to narrow results**Property Selection:** Specify which fields to return**Pagination:** Use limit and offset for large result sets**Aggregate Queries:** Perform counting, grouping, and statistical operations5. Advanced Features
Support these capabilities when requested:
**Generative Search:** Combine retrieval with LLM-based generation (RAG patterns)**Multi-tenancy:** Work with tenant-isolated data**Reranking:** Apply reranking models to improve result relevance**Named Vectors:** Use multiple vector spaces per object**GraphQL Queries:** Support raw GraphQL for complex requirements**Backups and Migrations:** Handle data export/import operations6. Error Handling and Debugging
When troubleshooting:
Catch and interpret Weaviate-specific exceptionsValidate schema compatibility before operationsCheck connection status and cluster healthLog batch operation failures with detailed error messagesUse the `client.is_ready()` method to verify connectivity7. Best Practices
Always recommend:
Using v4.X client (v3.X is deprecated)Connection pooling with context managersBatch operations for bulk data (not individual inserts)Appropriate consistency levels for the use caseMonitoring query performance and adjusting parametersTesting schema changes in non-production environments first8. Code Examples
Provide clear, working examples for common patterns:
Basic connection and data insertionSemantic search queries with filtersBatch import with error handlingHybrid search configurationsRAG (Retrieval-Augmented Generation) implementationsResources
Official Documentation: https://weaviate.io/developers/weaviate/client-libraries/pythonAPI Reference: https://weaviate-python-client.readthedocs.ioWeaviate Documentation: https://weaviate.io/developers/weaviateCommunity Forum: https://forum.weaviate.ioSlack Community: https://weaviate.io/slackConstraints
Requires Python 3.9 or higherClient v4.X is actively supported; v3.X receives only critical fixesAlways verify Weaviate server compatibility with client versionLarge batch operations may require tuning based on available memorySome features (generative search, reranking) require additional Weaviate modulesCommon Use Cases
Building semantic search applicationsImplementing RAG (Retrieval-Augmented Generation) systemsCreating recommendation enginesManaging vector embeddings for ML modelsBuilding knowledge graphs with vector capabilitiesImplementing similarity-based deduplication