Qdrant Vector Search
Efficiently searches Qdrant vector databases using davila7's tool for semantic similarity and fast retrieval.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add qdrant-vector-search npx -- -y @trustedskills/qdrant-vector-search
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"qdrant-vector-search": {
"command": "npx",
"args": [
"-y",
"@trustedskills/qdrant-vector-search"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The qdrant-vector-search skill enables AI agents to perform efficient similarity searches using vector embeddings. It leverages the Qdrant vector database, allowing for fast and accurate retrieval of data based on semantic or numerical similarity.
When to use it
- Searching through large collections of text or image embeddings for similar content.
- Building recommendation systems that rely on vector-based similarity.
- Enhancing search functionality in AI applications with high-dimensional data.
- Integrating vector search capabilities into custom workflows requiring fast query responses.
Key capabilities
- Supports efficient similarity searches using vector embeddings.
- Integrates with Qdrant for scalable and performant vector storage and retrieval.
- Enables semantic search by comparing vectors derived from text or other data types.
Example prompts
- "Find the most similar documents to this query using vector search."
- "Retrieve images that are visually similar to the provided image embedding."
- "Search for products in the database that match this user's preferences based on embeddings."
Tips & gotchas
- Ensure your data is properly embedded and indexed in Qdrant before performing searches.
- Be mindful of vector dimensionality and similarity metrics when configuring search parameters.
Tags
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| Gen Agent Trust Hub | Pass |
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| Snyk | Pass |
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