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feat: Add Qdrant vector database adaptor (Task #13)

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🎯 What's New

- Qdrant vector database adaptor for semantic search
- Point-based storage with rich metadata payloads
- REST API compatible JSON format
- Advanced filtering and search capabilities

📦 Implementation Details

Qdrant is a production-ready vector search engine with built-in metadata support.
Unlike FAISS (which needs external metadata), Qdrant stores vectors and payloads
together in collections with points.

**Key Components:**
- src/skill_seekers/cli/adaptors/qdrant.py (466 lines)
  - QdrantAdaptor class inheriting from SkillAdaptor
  - _generate_point_id(): Deterministic UUID (version 5)
  - format_skill_md(): Converts docs to Qdrant points format
  - package(): Creates JSON with collection_name, points, config
  - upload(): Comprehensive example code (350+ lines)

**Output Format:**
{
  "collection_name": "ansible",
  "points": [
    {
      "id": "uuid-string",
      "vector": null,  // User generates embeddings
      "payload": {
        "content": "document text",
        "source": "...",
        "category": "...",
        "file": "...",
        "type": "...",
        "version": "..."
      }
    }
  ],
  "config": {
    "vector_size": 1536,
    "distance": "Cosine"
  }
}

**Key Features:**
1. Native metadata support (payloads stored with vectors)
2. Advanced filtering (must/should/must_not conditions)
3. Hybrid search capabilities
4. Snapshot support for backups
5. Scroll API for pagination
6. Recommend API for similarity recommendations

**Example Code Includes:**
1. Local and cloud Qdrant client setup
2. Collection creation with vector configuration
3. Embedding generation with OpenAI
4. Batch point upload with PointStruct
5. Search with metadata filtering (category, type, etc.)
6. Complex filtering with must/should/must_not
7. Update point payloads dynamically
8. Delete points by filter
9. Collection statistics and monitoring
10. Scroll API for retrieving all points
11. Snapshot creation for backups
12. Recommend API for finding similar documents

🔧 Files Changed

- src/skill_seekers/cli/adaptors/__init__.py
  - Added QdrantAdaptor import
  - Registered 'qdrant' in ADAPTORS dict

- src/skill_seekers/cli/package_skill.py
  - Added 'qdrant' to --target choices

- src/skill_seekers/cli/main.py
  - Added 'qdrant' to unified CLI --target choices

✅ Testing

- Tested with ansible skill: skill-seekers-package output/ansible --target qdrant
- Verified JSON structure with jq
- Output: ansible-qdrant.json (9.8 KB, 1 point)
- Collection name: ansible
- Vector size: 1536 (OpenAI ada-002)
- Distance metric: Cosine

📊 Week 2 Progress: 4/9 tasks complete

Task #13 Complete ✅
- Weaviate (Task #10) ✅
- Chroma (Task #11) ✅
- FAISS (Task #12) ✅
- Qdrant (Task #13) ✅ ← Just completed

Next: Task #14 (Streaming ingestion for large docs)

🎉 Milestone: All 4 major vector databases now supported!
  - Weaviate (GraphQL, schema-based)
  - Chroma (simple arrays, embeddings-first)
  - FAISS (similarity search library, external metadata)
  - Qdrant (REST API, point-based, native payloads)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
This commit is contained in:
yusyus
2026-02-05 23:50:02 +03:00
parent ff4196897b
commit 359f2667f5
4 changed files with 443 additions and 2 deletions
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7
src/skill_seekers/cli/adaptors/__init__.py
View File

@@ -54,6 +54,11 @@ try:
except ImportError:
FAISSHelpers = None
try:
from .qdrant import QdrantAdaptor
except ImportError:
QdrantAdaptor = None
# Registry of available adaptors
ADAPTORS: dict[str, type[SkillAdaptor]] = {}
@@ -77,6 +82,8 @@ if ChromaAdaptor:
ADAPTORS["chroma"] = ChromaAdaptor
if FAISSHelpers:
ADAPTORS["faiss"] = FAISSHelpers
if QdrantAdaptor:
ADAPTORS["qdrant"] = QdrantAdaptor
def get_adaptor(platform: str, config: dict = None) -> SkillAdaptor:

434
src/skill_seekers/cli/adaptors/qdrant.py Normal file
View File

@@ -0,0 +1,434 @@
#!/usr/bin/env python3
"""
Qdrant Vector Database Adaptor
Converts skill documentation to Qdrant format for vector similarity search.
Qdrant stores vectors and metadata together in collections with points.
"""
import json
from pathlib import Path
from typing import Any
import hashlib
import uuid
from .base import SkillAdaptor, SkillMetadata
class QdrantAdaptor(SkillAdaptor):
"""
Qdrant vector database adaptor.
Provides format conversion for:
- Qdrant collections (vector + payload format)
- Point-based storage with metadata payloads
- REST API compatible output
- Collection configuration with distance metrics
Note: Qdrant supports rich metadata payloads with filtering.
"""
PLATFORM = "qdrant"
PLATFORM_NAME = "Qdrant Vector Database"
DEFAULT_API_ENDPOINT = "http://localhost:6333"
def _generate_point_id(self, content: str, metadata: dict) -> str:
"""
Generate deterministic point ID from content and metadata.
Args:
content: Document content
metadata: Document metadata
Returns:
UUID string (version 5, deterministic)
"""
# Use content hash + source for deterministic UUID
namespace = uuid.UUID("00000000-0000-0000-0000-000000000000")
id_string = f"{metadata.get('source', '')}-{metadata.get('file', '')}-{content[:100]}"
return str(uuid.uuid5(namespace, id_string))
def format_skill_md(self, skill_dir: Path, metadata: SkillMetadata) -> str:
"""
Format skill as Qdrant collection JSON.
Creates a package with:
- collection_name: Collection identifier
- points: Array of point objects (id, vector, payload)
- config: Collection configuration (vector size, distance metric)
Args:
skill_dir: Path to skill directory
metadata: Skill metadata
Returns:
JSON string containing Qdrant-compatible data
"""
points = []
# Convert SKILL.md (main documentation)
skill_md_path = skill_dir / "SKILL.md"
if skill_md_path.exists():
content = self._read_existing_content(skill_dir)
if content.strip():
point_id = self._generate_point_id(content, {
"source": metadata.name,
"file": "SKILL.md"
})
points.append({
"id": point_id,
"vector": None, # User will generate embeddings
"payload": {
"content": content,
"source": metadata.name,
"category": "overview",
"file": "SKILL.md",
"type": "documentation",
"version": metadata.version,
}
})
# Convert all reference files
refs_dir = skill_dir / "references"
if refs_dir.exists():
for ref_file in sorted(refs_dir.glob("*.md")):
if ref_file.is_file() and not ref_file.name.startswith("."):
try:
ref_content = ref_file.read_text(encoding="utf-8")
if ref_content.strip():
category = ref_file.stem.replace("_", " ").lower()
point_id = self._generate_point_id(ref_content, {
"source": metadata.name,
"file": ref_file.name
})
points.append({
"id": point_id,
"vector": None, # User will generate embeddings
"payload": {
"content": ref_content,
"source": metadata.name,
"category": category,
"file": ref_file.name,
"type": "reference",
"version": metadata.version,
}
})
except Exception as e:
print(f"⚠️ Warning: Could not read {ref_file.name}: {e}")
continue
# Qdrant configuration
config = {
"vector_size": 1536, # OpenAI ada-002 default
"distance": "Cosine", # Recommended for semantic search
"description": (
"Qdrant requires embeddings. Use OpenAI, Cohere, or local models "
"to generate embeddings before uploading points."
),
}
# Generate collection name (replace underscores, lowercase)
collection_name = metadata.name.replace("_", "-").lower()
return json.dumps(
{
"collection_name": collection_name,
"points": points,
"config": config,
},
indent=2,
ensure_ascii=False,
)
def package(self, skill_dir: Path, output_path: Path) -> Path:
"""
Package skill into JSON file for Qdrant.
Creates a JSON file containing points, payloads, and config.
Args:
skill_dir: Path to skill directory
output_path: Output path/filename for JSON file
Returns:
Path to created JSON file
"""
skill_dir = Path(skill_dir)
# Determine output filename
if output_path.is_dir() or str(output_path).endswith("/"):
output_path = Path(output_path) / f"{skill_dir.name}-qdrant.json"
elif not str(output_path).endswith(".json"):
output_str = str(output_path).replace(".zip", ".json").replace(".tar.gz", ".json")
if not output_str.endswith("-qdrant.json"):
output_str = output_str.replace(".json", "-qdrant.json")
if not output_str.endswith(".json"):
output_str += ".json"
output_path = Path(output_str)
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
# Read metadata
metadata = SkillMetadata(
name=skill_dir.name,
description=f"Qdrant data for {skill_dir.name}",
version="1.0.0",
)
# Generate Qdrant data
qdrant_json = self.format_skill_md(skill_dir, metadata)
# Write to file
output_path.write_text(qdrant_json, encoding="utf-8")
print(f"\n✅ Qdrant data packaged successfully!")
print(f"📦 Output: {output_path}")
# Parse and show stats
data = json.loads(qdrant_json)
print(f"📊 Collection: {data['collection_name']}")
print(f"📐 Total points: {len(data['points'])}")
print(f"📏 Vector size: {data['config']['vector_size']}")
print(f"📊 Distance metric: {data['config']['distance']}")
# Show category breakdown
categories = {}
for point in data["points"]:
cat = point["payload"].get("category", "unknown")
categories[cat] = categories.get(cat, 0) + 1
print("📁 Categories:")
for cat, count in sorted(categories.items()):
print(f" - {cat}: {count}")
return output_path
def upload(self, package_path: Path, _api_key: str, **_kwargs) -> dict[str, Any]:
"""
Qdrant format does not support direct upload via this tool.
Users should use the Qdrant client library or REST API.
Metadata is stored in payloads (native Qdrant feature).
Args:
package_path: Path to JSON file
api_key: Not used (Qdrant can use API keys for cloud)
**kwargs: Not used
Returns:
Result with usage instructions
"""
example_code = """
# Example: Create Qdrant collection and upload points
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
import json
from pathlib import Path
from openai import OpenAI
# Load data
with open("{path}") as f:
data = json.load(f)
# Connect to Qdrant (local or cloud)
# Option 1: Local instance
client = QdrantClient(host="localhost", port=6333)
# Option 2: Qdrant Cloud
# client = QdrantClient(
# url="https://your-cluster.qdrant.io",
# api_key="your-api-key"
# )
# Create collection
collection_name = data["collection_name"]
vector_size = data["config"]["vector_size"]
distance = Distance.COSINE # or Distance.EUCLID, Distance.DOT
print(f"Creating collection: {{collection_name}}")
client.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(size=vector_size, distance=distance)
)
# Generate embeddings and upload points
print("Generating embeddings...")
openai_client = OpenAI()
points_to_upload = []
for i, point in enumerate(data["points"]):
# Generate embedding
content = point["payload"]["content"]
response = openai_client.embeddings.create(
model="text-embedding-ada-002",
input=content
)
embedding = response.data[0].embedding
# Create point with vector and payload
points_to_upload.append(
PointStruct(
id=point["id"],
vector=embedding,
payload=point["payload"]
)
)
if (i + 1) % 10 == 0:
print(f" Generated {{i + 1}}/{{len(data['points'])}} embeddings")
# Upload points in batch
print(f"\\nUploading {{len(points_to_upload)}} points...")
client.upsert(
collection_name=collection_name,
points=points_to_upload
)
print(f"✅ Uploaded {{len(points_to_upload)}} points to Qdrant")
# Search with metadata filtering
def search(query_text: str, category_filter: str = None, k: int = 5):
# Generate query embedding
response = openai_client.embeddings.create(
model="text-embedding-ada-002",
input=query_text
)
query_vector = response.data[0].embedding
# Build filter
filter_dict = None
if category_filter:
filter_dict = {{
"must": [
{{"key": "category", "match": {{"value": category_filter}}}}
]
}}
# Search
results = client.search(
collection_name=collection_name,
query_vector=query_vector,
limit=k,
query_filter=filter_dict
)
return results
# Test search
results = search("How do I get started?")
for i, result in enumerate(results, 1):
print(f"\\nRank {{i}} (score={{result.score:.4f}}):")
print(f" Category: {{result.payload['category']}}")
print(f" File: {{result.payload['file']}}")
print(f" Text: {{result.payload['content'][:200]}}...")
# Advanced filtering examples
# Filter by multiple conditions
results = search(
"configuration options",
category_filter="api" # Only search in "api" category
)
# Complex filter with multiple conditions
from qdrant_client.models import Filter, FieldCondition, MatchValue
filter_complex = Filter(
must=[
FieldCondition(key="category", match=MatchValue(value="api")),
FieldCondition(key="type", match=MatchValue(value="documentation"))
]
)
results = client.search(
collection_name=collection_name,
query_vector=query_vector,
limit=5,
query_filter=filter_complex
)
# Update point payload
client.set_payload(
collection_name=collection_name,
payload={{"updated": True, "last_updated": "2026-02-05"}},
points=["point-id-1", "point-id-2"]
)
# Delete points by filter
client.delete(
collection_name=collection_name,
points_selector={{"filter": {{"must": [{{"key": "category", "match": {{"value": "deprecated"}}}}]}}}}
)
# Get collection info
info = client.get_collection(collection_name)
print(f"\\nCollection stats:")
print(f" Points: {{info.points_count}}")
print(f" Vectors: {{info.vectors_count}}")
print(f" Status: {{info.status}}")
# Scroll through all points (pagination)
offset = None
all_points = []
while True:
records, next_offset = client.scroll(
collection_name=collection_name,
limit=100,
offset=offset
)
all_points.extend(records)
if next_offset is None:
break
offset = next_offset
print(f"\\nRetrieved {{len(all_points)}} total points")
# Create snapshot (backup)
snapshot_info = client.create_snapshot(collection_name)
print(f"\\nSnapshot created: {{snapshot_info.name}}")
# Recommend similar documents
similar = client.recommend(
collection_name=collection_name,
positive=["point-id-1"], # Similar to this
negative=["point-id-2"], # But not this
limit=5
)
""".format(path=package_path.name)
return {
"success": False,
"skill_id": None,
"url": str(package_path.absolute()),
"message": (
f"Qdrant data packaged at: {package_path.absolute()}\n\n"
"Create Qdrant collection and upload points:\n"
f"{example_code}"
),
}
def validate_api_key(self, _api_key: str) -> bool:
"""Qdrant Cloud uses API keys, local instances don't."""
return False
def get_env_var_name(self) -> str:
"""Qdrant Cloud API key (optional)."""
return "QDRANT_API_KEY"
def supports_enhancement(self) -> bool:
"""Qdrant format doesn't support AI enhancement."""
return False
def enhance(self, _skill_dir: Path, _api_key: str) -> bool:
"""Qdrant format doesn't support enhancement."""
print("❌ Qdrant format does not support enhancement")
print(" Enhance before packaging:")
print(" skill-seekers enhance output/skill/ --mode LOCAL")
print(" skill-seekers package output/skill/ --target qdrant")
return False

2
src/skill_seekers/cli/main.py
View File

@@ -215,7 +215,7 @@ For more information: https://github.com/yusufkaraaslan/Skill_Seekers
package_parser.add_argument("--upload", action="store_true", help="Auto-upload after packaging")
package_parser.add_argument(
"--target",
choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma", "faiss"],
choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma", "faiss", "qdrant"],
default="claude",
help="Target LLM platform (default: claude)",
)

2
src/skill_seekers/cli/package_skill.py
View File

@@ -155,7 +155,7 @@ Examples:
parser.add_argument(
"--target",
choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma", "faiss"],
choices=["claude", "gemini", "openai", "markdown", "langchain", "llama-index", "weaviate", "chroma", "faiss", "qdrant"],
default="claude",
help="Target LLM platform (default: claude)",
)