A unified benchmarking and implementation suite for eight leading vector database backends using a common interface.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install vector-store-shootout
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install vector-store-shootout using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Vector Store Shootout?
The Vector Store Shootout provides a standardized abstraction layer for eight distinct vector database implementations, ranging from lightweight in-memory solutions to production-grade distributed systems. This skill allows developers to swap between backends like PGVector, Milvus, and Weaviate with a single line of code, making it an indispensable tool for anyone building RAG systems within the Openclaw Skills ecosystem.
By providing a common interface, it simplifies the evaluation process for embedding stores and hybrid search performance, ensuring that developers can focus on application logic rather than database-specific syntax. It is particularly valuable for teams needing to benchmark performance across different storage architectures before committing to a specific production stack.
Vector Store Shootout Use Cases
- Benchmarking different vector databases for RAG performance and accuracy.
- Rapidly prototyping search features using in-memory numpy before moving to production-ready databases.
- Implementing hybrid search (BM25 + Vector) specifically for technical documentation where keyword precision matters.
- Migrating between vector store providers without rewriting core application logic.
- Comparing graph-enhanced RAG via LightRAG against standard vector search methods.
How Vector Store Shootout Works
- Select a vector store backend from the eight available implementations (e.g., LanceDB, Qdrant, or PGVector).
- Initialize the backend using the standardized VectorStore abstract base class found in scripts/base.py.
- Add text data, embeddings, and metadata to your chosen store via the unified add method.
- Execute search queries across different databases using query_embedding to compare recall, speed, and relevance.
- Tweak specific configurations, such as Weaviate hybrid alpha settings, to optimize search results for your specific data types.
Vector Store Shootout Setup
To get started with this skill, ensure you have Python 3 installed and then install the dependencies required for your chosen backend:
# Install core dependencies for common backends
pip install numpy lancedb qdrant-client psycopg2 weaviate-client pymilvus lightrag
After installing the libraries, you can import the specific store implementation from the scripts directory. Ensure that for client-server backends like Milvus or Qdrant, the respective service is running locally or via a cloud provider as part of your Openclaw Skills workflow.
Vector Store Shootout Data Schema & Taxonomy
The skill utilizes a standardized interface to ensure data consistency across all eight backends. Data is organized according to the following schema:
| Attribute | Type | Description |
|---|---|---|
| texts | List[str] | The raw text chunks to be stored and retrieved. |
| embeddings | List[List[float]] | The vector representations of the text chunks. |
| metadatas | List[dict] | Key-value pairs used for filtering and providing additional context. |
| ids | List[str/int] | Unique identifiers for managing and deleting specific entries. |
Vector Store Shootout Advanced Features
- Drop-in replacement architecture allowing backend swaps by changing only the class instantiation.
- Optimized Weaviate Hybrid search configurations specifically tuned for high-precision technical terminology.
- Native support for Graph-enhanced RAG via LightRAG integration for complex relationship mapping.
- Scalable persistence options ranging from simple file-based Arrow-native storage (LanceDB) to GPU-accelerated clusters (Milvus).
- Uniform metadata handling and filtering capabilities across all Openclaw Skills implementations.
SKILL.md
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