weaviate

安装量: 83
排名: #9540

安装

npx skills add https://github.com/weaviate/agent-skills --skill weaviate
Weaviate Database Operations
This skill provides comprehensive access to Weaviate vector databases including search operations, natural language queries, schema inspection, data exploration, filtered fetching, collection creation, and data imports.
Weaviate Cloud Instance
If the user does not have an instance yet, direct them to the cloud console to register and create a free sandbox. Create a Weaviate instance via
Weaviate Cloud
.
Environment Variables
Required:
WEAVIATE_URL
- Your Weaviate Cloud cluster URL
WEAVIATE_API_KEY
- Your Weaviate API key
External Provider Keys (auto-detected):
Set only the keys your collections use, refer to
Environment Requirements
for more information.
Script Index
Search & Query
Query Agent - Ask Mode
Use when the user wants a
direct answer
to a question based on collection data. The Query Agent synthesizes information from one or more collections and returns a structured response with source citations (collection name and object ID).
Query Agent - Search Mode
Use when the user wants to
explore or browse raw objects
across one or more collections. Unlike ask mode, this returns the actual data objects rather than a synthesized answer.
Hybrid Search
:
Default choice for most searches.
Provides a good balance of semantic understanding and exact keyword matching. Use this when you are unsure which search type to pick.
Semantic Search
Use for finding
conceptually similar content
regardless of exact wording. Best when the intent matters more than specific keywords.
Keyword Search
Use for finding
exact terms, IDs, SKUs, or specific text patterns
. Best when precise keyword matching is needed rather than semantic similarity.
Collection Management
List Collections
Use to
discover what collections exist
in the Weaviate instance. This should typically be the first step before performing any search or data operation.
Get Collection Details
Use to
understand a collection's schema
— its properties, data types, vectorizer configuration, replication factor, and multi-tenancy status. Helpful before running searches or imports.
Explore Collection
Use to
analyze data distribution, top values, and inspect actual content
in a collection. Helpful for understanding what data looks like before querying.
Create Collection
Use to
create new collections with custom schemas
before importing data. Do not specify a vectorizer unless the user explicitly requests one (the default
text2vec_weaviate
is used).
Data Operations
Fetch and Filter
Use to
retrieve specific objects by ID
or
strictly filtered subsets
of data. Best for precise data retrieval rather than search.
Import Data
Use to
bulk import data
into an existing collection from CSV, JSON, or JSONL files.
Create Example Data
Use to create example data for immediate use of other skills, if no data is available or user requests some toy data. Recommendations Start by listing collections if you don't know what's available: uv run scripts/list_collections.py Ask the user if they want to create example data if nothing is available and the user requests it. Otherwise continue. uv run scripts/example_data.py Get collection details to understand the schema: uv run scripts/get_collection.py --name "COLLECTION_NAME" Explore collection data to see values and statistics: uv run scripts/explore_collection.py "COLLECTION_NAME" Import data to populate a new collection (if needed): uv run scripts/import.py "data.csv" --collection "CollectionName" Do not specify a vectorizer when creating collections unless requested: uv run scripts/create_collection.py Article \ --properties '[{"name": "title", "data_type": "text"}, {"name": "body", "data_type": "text"}]' Choose the right search type: Get AI-powered answers with source citations across multiple collections → ask.py Get raw objects from multiple collections → query_search.py General search → hybrid_search.py (default) Conceptual similarity → semantic_search.py Exact terms/IDs → keyword_search.py Output Formats All scripts support: Markdown tables (default and recommended) JSON ( --json flag) Error Handling Common errors: WEAVIATE_URL not set → Set the environment variable Collection not found → Use list_collections.py to see available collections Authentication error → Check API keys for both Weaviate and vectorizer providers
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