Qdrant Manager

Agent: AIEngineer

Manage Qdrant Cloud collections for the Physical AI textbook RAG system. This skill handles vectorization of textbook chapters, metadata tagging, and collection management for efficient retrieval-augmented generation.

Quick Setup

bash

# Set environment variables
export QDRANT_URL="https://xxx.qdrant.io"
export QDRANT_API_KEY="your-api-key"
export OPENAI_API_KEY="sk-..."

# Initialize collection
.claude/skills/qdrant-manager/scripts/setup.sh --init

# Vectorize all chapters
.claude/skills/qdrant-manager/scripts/setup.sh --vectorize docs/

# Query the collection
.claude/skills/qdrant-manager/scripts/setup.sh --query "What is ROS 2?"

Command Options

Option	Description	Default
`--init`	Initialize/create the collection	-
`--vectorize PATH`	Vectorize markdown files in PATH	-
`--query TEXT`	Query the collection	-
`--delete`	Delete the collection	-
`--info`	Show collection info	-
`--list`	List all collections	-
`--collection NAME`	Collection name	`textbook_chapters`
`--model MODEL`	Embedding model	`text-embedding-3-small`
`--batch-size N`	Batch size for vectorization	`10`
`--top-k N`	Number of results for query	`5`
`-h, --help`	Show help message	-

What It Does

1. Collection Management

Creates and manages Qdrant collections with optimized settings for textbook content:

python

# Collection schema
{
    "vectors": {
        "size": 1536,  # text-embedding-3-small
        "distance": "Cosine"
    },
    "on_disk_payload": True  # Optimize for large documents
}

2. Chapter Vectorization

Processes markdown files with intelligent chunking:

Chunk Strategy: Split by headers (##, ###) for semantic coherence
Overlap: 100 tokens overlap between chunks
Max Chunk Size: 500 tokens per chunk
Metadata Extraction: Title, chapter, section, language, keywords

3. Metadata Schema

Each vector point includes rich metadata for filtered retrieval:

json

{
    "id": "uuid-v4",
    "vector": [0.1, 0.2, ...],
    "payload": {
        "content": "The actual text content...",
        "source": "docs/chapter-1/index.md",
        "chapter": 1,
        "section": "Introduction",
        "title": "Introduction to Physical AI & ROS 2",
        "language": "en",
        "locale": "en-US",
        "keywords": ["ros2", "physical-ai", "robotics"],
        "word_count": 245,
        "created_at": "2026-01-02T00:00:00Z",
        "chunk_index": 0,
        "total_chunks": 5
    }
}

4. RAG Query Support

Semantic search with metadata filtering:

python

# Example: Query with language filter
results = client.search(
    collection_name="textbook_chapters",
    query_vector=embed("What is ROS 2?"),
    query_filter={
        "must": [
            {"key": "language", "match": {"value": "en"}}
        ]
    },
    limit=5
)

Bundled Resources

1. Python Dependencies

File: requirements.txt

qdrant-client>=1.7.0
openai>=1.0.0
tiktoken>=0.5.0
python-dotenv>=1.0.0
rich>=13.0.0

2. Collection Configuration

File: assets/collection_config.json

json

{
    "vectors": {
        "size": 1536,
        "distance": "Cosine"
    },
    "optimizers_config": {
        "indexing_threshold": 20000
    },
    "on_disk_payload": true,
    "replication_factor": 1
}

3. Chunking Configuration

File: assets/chunking_config.json

json

{
    "chunk_size": 500,
    "chunk_overlap": 100,
    "separators": ["## ", "### ", "\n\n", "\n"],
    "length_function": "tiktoken",
    "model": "text-embedding-3-small"
}

4. Vectorization Script

File: scripts/vectorize.py

python

from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, VectorParams, Distance
from openai import OpenAI
import tiktoken
import uuid
import json
from pathlib import Path

class TextbookVectorizer:
    def __init__(self, qdrant_url, qdrant_api_key, openai_api_key):
        self.qdrant = QdrantClient(url=qdrant_url, api_key=qdrant_api_key)
        self.openai = OpenAI(api_key=openai_api_key)
        self.encoder = tiktoken.encoding_for_model("text-embedding-3-small")

    def create_collection(self, name: str, vector_size: int = 1536):
        self.qdrant.create_collection(
            collection_name=name,
            vectors_config=VectorParams(size=vector_size, distance=Distance.COSINE)
        )

    def chunk_markdown(self, content: str, source: str) -> list:
        # Split by headers for semantic coherence
        chunks = []
        current_chunk = ""
        current_section = "Introduction"

        for line in content.split("\n"):
            if line.startswith("## "):
                if current_chunk:
                    chunks.append({"content": current_chunk, "section": current_section})
                current_section = line[3:].strip()
                current_chunk = line + "\n"
            else:
                current_chunk += line + "\n"
                if len(self.encoder.encode(current_chunk)) > 500:
                    chunks.append({"content": current_chunk, "section": current_section})
                    current_chunk = ""

        if current_chunk:
            chunks.append({"content": current_chunk, "section": current_section})

        return chunks

    def embed(self, text: str) -> list:
        response = self.openai.embeddings.create(
            input=text,
            model="text-embedding-3-small"
        )
        return response.data[0].embedding

    def vectorize_file(self, filepath: Path, collection: str):
        content = filepath.read_text()
        chunks = self.chunk_markdown(content, str(filepath))

        points = []
        for i, chunk in enumerate(chunks):
            vector = self.embed(chunk["content"])
            points.append(PointStruct(
                id=str(uuid.uuid4()),
                vector=vector,
                payload={
                    "content": chunk["content"],
                    "source": str(filepath),
                    "section": chunk["section"],
                    "chunk_index": i,
                    "total_chunks": len(chunks)
                }
            ))

        self.qdrant.upsert(collection_name=collection, points=points)
        return len(points)

5. Input Variables

Variable	Required	Default	Description
`qdrant_url`	Yes	-	Qdrant Cloud cluster URL
`qdrant_api_key`	Yes	-	Qdrant API key
`collection_name`	No	`textbook_chapters`	Collection name
`embedding_model`	No	`text-embedding-3-small`	OpenAI embedding model
`vector_size`	No	`1536`	Vector dimension

Usage Instructions

Step 1: Set Environment Variables

bash

# Add to .env file
QDRANT_URL=https://your-cluster.qdrant.io
QDRANT_API_KEY=your-api-key
OPENAI_API_KEY=sk-...

Step 2: Initialize Collection

bash

.claude/skills/qdrant-manager/scripts/setup.sh --init --collection textbook_chapters

Step 3: Vectorize Chapters

bash

# Vectorize English chapters
.claude/skills/qdrant-manager/scripts/setup.sh --vectorize docs/

# Vectorize Urdu translations
.claude/skills/qdrant-manager/scripts/setup.sh --vectorize i18n/ur/docusaurus-plugin-content-docs/current/

Step 4: Query for RAG

bash

# Simple query
.claude/skills/qdrant-manager/scripts/setup.sh --query "How do I install ROS 2?"

# Query with top-k
.claude/skills/qdrant-manager/scripts/setup.sh --query "Gazebo simulation" --top-k 10

Step 5: Verify Collection

bash

# Show collection info
.claude/skills/qdrant-manager/scripts/setup.sh --info

# List all collections
.claude/skills/qdrant-manager/scripts/setup.sh --list

Verification Checklist

QDRANT_URL environment variable set
QDRANT_API_KEY environment variable set
OPENAI_API_KEY environment variable set
Collection created successfully
Chapters vectorized without errors
Query returns relevant results
Metadata filtering works correctly

Integration with RAG Chatbot

This skill provides the vector storage for the RAG chatbot (Feature 002). The chatbot queries this collection to retrieve relevant context for answering user questions.

python

# In RAG chatbot
from qdrant_client import QdrantClient

client = QdrantClient(url=QDRANT_URL, api_key=QDRANT_API_KEY)

def get_context(query: str, language: str = "en") -> list:
    query_vector = embed(query)
    results = client.search(
        collection_name="textbook_chapters",
        query_vector=query_vector,
        query_filter={"must": [{"key": "language", "match": {"value": language}}]},
        limit=5
    )
    return [hit.payload["content"] for hit in results]

Troubleshooting

Issue	Solution
Connection refused	Check QDRANT_URL is correct and cluster is running
Authentication failed	Verify QDRANT_API_KEY is valid
Embedding rate limit	Reduce --batch-size or add delays
Collection not found	Run --init first to create collection
Out of memory	Use smaller --batch-size
Slow vectorization	Use --batch-size 20 for faster processing

Requirements

Python 3.9+
Qdrant Cloud account (or self-hosted Qdrant)
OpenAI API key (for embeddings)
pip packages: qdrant-client, openai, tiktoken

Cost Considerations

Resource	Cost
Qdrant Cloud Free Tier	1GB storage, 1M vectors
OpenAI text-embedding-3-small	$0.02 / 1M tokens
Estimated for 3 chapters	~$0.01 (one-time)

Qdrant Documentation
OpenAI Embeddings
Skill: vercel-fastapi-link - Backend API with RAG endpoints
Feature: 002-rag-chatbot - RAG chatbot specification

Changelog

v1.0.0 (2026-01-02)

Initial Release

Collection creation and management
Markdown chunking with header-based splitting
OpenAI embedding integration (text-embedding-3-small)
Metadata extraction (chapter, section, language, keywords)
Query support with filtering
Batch vectorization with progress tracking

Search AI Tools

qdrant-manager

Install this agent skill to your Project

SKILL.md

Qdrant Manager

Quick Setup

Command Options

What It Does

1. Collection Management

2. Chapter Vectorization

3. Metadata Schema

4. RAG Query Support

Bundled Resources

1. Python Dependencies

2. Collection Configuration

3. Chunking Configuration

4. Vectorization Script

5. Input Variables

Usage Instructions

Step 1: Set Environment Variables

Step 2: Initialize Collection

Step 3: Vectorize Chapters

Step 4: Query for RAG

Step 5: Verify Collection

Verification Checklist

Integration with RAG Chatbot

Troubleshooting

Requirements

Cost Considerations

Related

Changelog

v1.0.0 (2026-01-02)