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Qdrant

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giuseppe-trisciuoglio·vlatest·ai-ml·MIT

Qdrant enables seamless vector database integration for semantic search and retrieval, boosting AI applications with efficient similarity matching.

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  1. 1

    Run this in your project

    terminal
    $ npx skills add giuseppe-trisciuoglio/developer-kit --skill qdrant -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

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giuseppe-trisciuoglio

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Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

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    Makes no network calls.

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    Scanned giuseppe-trisciuoglio/developer-kit at fe73fb33a0.

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Commit
fe73fb33a0
Manifest
plugins/developer-kit-java/skills/qdrant/SKILL.md
Scanned
3 files, 891 lines
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About this skill

What it does

This skill enables integration with Qdrant, an AI-native vector database designed for semantic search and similarity retrieval. It provides patterns specifically tailored for Java applications using Spring Boot and LangChain4j. The skill facilitates efficient similarity matching within your AI applications by allowing you to store and query vectors in Qdrant.

When to use it

  • Building semantic search or recommendation systems within Spring Boot applications.
  • Implementing Retrieval-Augmented Generation (RAG) pipelines using Java and LangChain4j.
  • Integrating a vector database into existing AI/ML application workflows.
  • When you need high-performance similarity search capabilities with the ability to filter queries.

Key capabilities

  • Integration with Qdrant via REST API or gRPC.
  • Support for creating and managing collections within Qdrant.
  • Configuration options including specifying distance metrics (e.g., Cosine) and vector size.
  • Ability to upsert vectors into a collection, associating them with IDs and payloads.
  • Authentication via API key for production environments.

Example prompts

While this skill doesn't directly respond to natural language prompts, here are examples of Java code snippets demonstrating its use:

  • client.createCollectionAsync("search-collection", ...) - Creates a new collection named "search-collection".
  • client.upsertAsync("search-collection", points) - Adds data (vectors) to the "search-collection."
  • QdrantClient client = new QdrantClient(...) - Initializes a connection to the Qdrant server.

Tips & gotchas

  • Prerequisites: Requires Docker for deploying Qdrant and Java development environment with Maven or Gradle for dependency management.
  • API Key: For production use, configure authentication using an API key as shown in the initialization example.
  • Collection Validation: After creating a collection, verify its existence using client.getCollectionAsync("collection_name").get() to ensure successful setup.