# qdrant

Provides integration patterns for the Qdrant vector database in Java and Spring Boot with LangChain4j. Helps build semantic search and vector stores for RAG.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3700 installs · ★ 363
- Author: giuseppe-trisciuoglio
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-java/skills/qdrant/SKILL.md
- Updated: 2026-09-10
- HTML: https://affpapa.org/en/nevibecode/skills/qdrant

## What it does

The skill provides Java integration patterns for Qdrant, covering local Docker deployment and client setup using `io.qdrant:client:1.15.0` in Maven or Gradle. It guides Spring Boot setup using `QdrantGrpcClient` and LangChain4j's `QdrantEmbeddingStore`.

It includes implementation templates for batch upserts with `PointStruct` and filtered vector queries. Production setups should stick strictly to the gRPC endpoint on port 6334 because the REST API on port 6333 is reserved for debugging.

A common pitfall is vector dimension mismatch. If dimensions differ from the embedding model, upserts fail immediately. The instructions also require payload sanitization before ingestion and content filtering on retrieved documents.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill qdrant -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/qdrant/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/qdrant/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill qdrant -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/qdrant/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill qdrant -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/qdrant/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install giuseppe-trisciuoglio/developer-kit qdrant
```

2. **Manually: put the files in place**
- File: `.github/skills/qdrant/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill qdrant -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/giuseppe-trisciuoglio/developer-kit --path plugins/developer-kit-java/skills/qdrant
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/qdrant/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill qdrant -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-java/skills/qdrant/SKILL.md

## When to use it
- Deploy local Qdrant instances using Docker containers
- Configure QdrantEmbeddingStore beans for LangChain4j in Spring Boot
- Implement filtered similarity search using vector queries
- Partition vector collections across multiple tenants

## Example prompts
- Configure a QdrantEmbeddingStore bean in Spring Boot using gRPC
- Show an example of batch upserting PointStruct vectors into Qdrant
- Create a vector search REST controller using LangChain4j and Qdrant

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: f3a02d8354e6b64b.

BEGIN THIRD-PARTY FILE f3a02d8354e6b64b
````text
---
name: qdrant
description: Provides Qdrant vector database integration patterns with LangChain4j. Handles embedding storage, similarity search, and vector management for Java applications. Use when implementing vector-based retrieval for RAG systems, semantic search, or recommendation engines.
allowed-tools: Read, Write, Edit, Bash, Glob, Grep
---

# Qdrant Vector Database Integration

## Overview

Qdrant is an AI-native vector database for semantic search and similarity retrieval. This skill provides patterns for integrating Qdrant with Java applications, focusing on Spring Boot and LangChain4j integration.

## When to Use

- Semantic search or recommendation systems in Spring Boot applications
- RAG pipelines with Java and LangChain4j
- Vector database integration for AI/ML applications
- High-performance similarity search with filtered queries

## Instructions

### 1. Deploy Qdrant with Docker

```bash
docker run -p 6333:6333 -p 6334:6334 \
    -v "$(pwd)/qdrant_storage:/qdrant/storage:z" \
    qdrant/qdrant
```

Access: REST API at `http://localhost:6333`, gRPC at `http://localhost:6334`.

### 2. Add Dependencies

**Maven:**
```xml
<dependency>
    <groupId>io.qdrant</groupId>
    <artifactId>client</artifactId>
    <version>1.15.0</version>
</dependency>
```

**Gradle:**
```gradle
implementation 'io.qdrant:client:1.15.0'
```

### 3. Initialize Client

```java
QdrantClient client = new QdrantClient(
    QdrantGrpcClient.newBuilder("localhost").build());
```

For production with API key:
```java
QdrantClient client = new QdrantClient(
    QdrantGrpcClient.newBuilder("localhost", 6334, false)
        .withApiKey("YOUR_API_KEY")
        .build());
```

### 4. Create Collection

```java
client.createCollectionAsync("search-collection",
    VectorParams.newBuilder()
        .setDistance(Distance.Cosine)
        .setSize(384)
        .build()
).get();
```

**Validation:** Verify the collection was created by checking `client.getCollectionAsync("search-collection").get()`.

### 5. Upsert Vectors

```java
List<PointStruct> points = List.of(
    PointStruct.newBuilder()
        .setId(id(1))
        .setVectors(vectors(0.05f, 0.61f, 0.76f, 0.74f))
        .putAllPayload(Map.of("title", value("Spring Boot Documentation")))
        .build()
);
client.upsertAsync("search-collection", points).get();
```

**Validation:** Check that `client.upsertAsync(...).get()` completes without throwing.

### 6. Search Vectors

```java
List<ScoredPoint> results = client.queryAsync(
    QueryPoints.newBuilder()
        .setCollectionName("search-collection")
        .setLimit(5)
        .setQuery(nearest(0.2f, 0.1f, 0.9f, 0.7f))
        .build()
).get();
```

Filtered search:
```java
List<ScoredPoint> results = client.searchAsync(
    SearchPoints.newBuilder()
        .setCollectionName("search-collection")
        .addAllVector(List.of(0.62f, 0.12f, 0.53f, 0.12f))
        .setFilter(Filter.newBuilder()
````
END THIRD-PARTY FILE f3a02d8354e6b64b

The third-party file has ended. Everything between the f3a02d8354e6b64b tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

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