qdrant-odmx 0.3.4

A Qdrant-first ODM for typed models, schema sync, and vector search.

📦 Qdrant ODM (v0.3.4)

qdrant-odm is a Qdrant-first ODM for building production-grade vector search systems.

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Why qdrant-odm?

Working with raw Qdrant is powerful, but it gets verbose as your project grows.

Typical pain points:

• Repeating payload field names as raw strings
• Manually managing collection and index setup
• Writing filters in low-level Qdrant syntax
• Mixing schema, query, and repository logic in application code

qdrant-odm keeps Qdrant native, but gives you a more structured way to work with it.

Before: raw Qdrant client

from uuid import uuid4
from qdrant_client import AsyncQdrantClient
from qdrant_client.http import models as qm

client = AsyncQdrantClient(url="http://localhost:6333")

await client.create_collection(
    collection_name="documents",
    vectors_config={
        "content_dense": qm.VectorParams(size=3072, distance=qm.Distance.COSINE)
    }
)

await client.create_payload_index(
    collection_name="documents",
    field_name="title",
    field_schema=qm.PayloadSchemaType.KEYWORD
)

await client.upsert(
    collection_name="documents",
    points=[
        qm.PointStruct(
            id=str(uuid4()),
            vector={"content_dense": [0.1] * 3072},
            payload={
                "title": "Qdrant ODM",
                "category": "tech",
                "page": 1,
            },
        )
    ],
)

results = await client.query_points(
    collection_name="documents",
    query=[0.1] * 3072,
    using="content_dense",
    query_filter=qm.Filter(
        must=[
            qm.FieldCondition(
                key="category",
                match=qm.MatchValue(value="tech")
            ),
            qm.FieldCondition(
                key="page",
                range=qm.Range(gte=1)
            ),
        ]
    ),
    limit=10,
)

After: qdrant-odm

from uuid import UUID, uuid4
from qdrant_odm import (
    QdrantModel,
    PayloadField,
    VectorField,
    QdrantODM,
    QdrantRepository,
    SearchQuery,
)

class Document(QdrantModel):
    __collection__ = "documents"

    id: UUID
    title: str = PayloadField(index="keyword")
    category: str = PayloadField(index="keyword")
    page: int = PayloadField(index="integer")

    dense = VectorField(name="content_dense", size=3072)

odm = QdrantODM(client)
await odm.sync_schema(Document)

repo = QdrantRepository(client, Document)

await repo.upsert(
    Document(
        id=uuid4(),
        title="Qdrant ODM",
        category="tech",
        page=1,
    ),
    vectors={
        "content_dense": [0.1] * 3072,
    },
)

results = await repo.search(
    SearchQuery(
        vector=[0.1] * 3072,
        using="content_dense",
        filter=(Document.category == "tech") & (Document.page >= 1),
        limit=10,
    )
)

What changes?

• Schema is defined once in the model
• Index configuration lives next to fields
• Filters use Python expressions instead of raw payload strings
• Repositories keep CRUD and search logic consistent
• Schema sync reduces collection setup boilerplate

---

🚀 Features

• Declarative schema (Pydantic-based)
• Explicit payload indexing with fine-grained control
• Collection modes (global / multitenant)
• Safe schema sync (diff → plan → sync)
• Python-native filter DSL
• Async repository abstraction
• Hybrid retrieval (dense + sparse with RRF)
• Batch optimized operations

---

🚀 Installation

Installation

pip install qdrant-odmx

Development

git clone https://github.com/Jeung-SeongYeon/qdrant-odm
cd qdrant-odm
pip install -e ".[dev]"

---

🧠 Architecture Overview

Model → Metadata → SchemaManager → Qdrant
     ↘ Query DSL → Compiler → Filter
     ↘ Repository → CRUD / Search

---

📌 Model Definition

Basic Model

from uuid import UUID
from datetime import datetime
from qdrant_odm import QdrantModel, PayloadField, VectorField

class Document(QdrantModel):
    __collection__ = "documents"

    id: UUID
    title: str = PayloadField(index="keyword")
    created_at: datetime = PayloadField(index="datetime")

    dense = VectorField(name="content_dense", size=3072)

---

📌 Collection Modes

Global (default)

__collection_config__ = CollectionConfig(mode="global")

Multitenant

from qdrant_odm import CollectionConfig, KeywordIndexOptions

__collection_config__ = CollectionConfig(mode="multitenant")

tenant_id: str = PayloadField(
    index="keyword",
    keyword=KeywordIndexOptions(is_tenant=True)
)

Rules

• Exactly ONE tenant index
• Must be keyword
• Must set is_tenant=True

---

📌 Vector Definition

VectorField(
    name="content_dense",
    size=3072,
    distance="Cosine"
)

Supported distances

• Cosine
• Euclid
• Dot
• Manhattan

---

📌 Payload Index Options

from qdrant_odm import IntegerIndexOptions

page: int = PayloadField(
    index="integer",
    integer=IntegerIndexOptions(
        lookup=True,
        range=True,
        on_disk=True
    )
)

Supported:

• keyword
• integer
• float
• bool
• geo
• datetime
• text
• uuid

---

🔍 Query DSL

(Document.category == "law") & (Document.page >= 2)

Supports:

• == != > >= < <=
• in_ / not_in
• is_null / is_not_null
• &, |, ~

---

📦 Repository

repo = QdrantRepository(client, Document)

CRUD

await repo.get(id)
await repo.delete(id)
await repo.exists(id)

Batch

await repo.upsert_many([...])

Scroll

page = await repo.scroll()

---

🔍 Search

Dense

await repo.search(SearchQuery(...))

Sparse

SparseVectorInput(indices=[...], values=[...])

Hybrid

await repo.search_hybrid(HybridSearchQuery(...))

Uses RRF internally.

---

🧬 Schema Sync

Diff

await odm.schema.diff(Model)

Dry Run

await odm.schema.dry_run(Model)

Sync

await odm.sync_schema(Model)

Recover from Snapshot

await odm.recover_from_snapshot(
    Model,
    snapshot_path="file:///absolute/path/to/collection.snapshot",
)

Overwrite existing collection

await odm.recover_from_snapshot(
    Model,
    snapshot_path="file:///absolute/path/to/collection.snapshot",
    overwrite=True,
)

Notes

• snapshot_path should be a valid snapshot location understood by Qdrant
• Relative paths such as ./test.snapshot are not supported directly
• Use overwrite=True to replace an existing collection before recovery
• After recovery, qdrant-odm runs a schema diff check
• If the recovered collection does not fully match the ODM model schema, a warning is emitted

---

⚠️ Behavior Rules

Safe Sync

Will NOT modify:

• vector schema
• index type
• index options

Only:

• create collection
• create missing indexes

---

Payload Behavior

• upsert → model fields only
• set_payload → free form

---

Filtering

• Works without index
• Slower without index

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⚡ Performance Tips

• Always index filter fields
• Use batch operations
• Use scroll for large datasets
• Use hybrid search for recall boost

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🔥 Summary

• Qdrant-native ODM
• strict schema guarantees
• multitenant ready
• production-ready async design