---
title: Qdrant
icon: /images/qdrant-logo.svg
description: Set up the Qdrant resource in Python, including self-hosted Qdrant and Qdrant Cloud, with local or server-side semantic search.
---

The Qdrant resource mounts a [Qdrant](https://qdrant.tech/) collection as a filesystem: group-by payload
fields become folders, points become files, and semantic search is the `search`
command. See [Qdrant Resource](/python/resource/qdrant) for the full layout and
command list.

## Dependencies

```bash
uv add 'mirage-ai[qdrant]'
```

The `qdrant` extra installs `qdrant-client[fastembed]`. `fastembed` powers
local, in-process query embedding for the `search` command; filesystem browsing
(`ls`/`cat`/`find`/`grep`) needs only the base client.

## Connection

The same `QdrantConfig` works for self-hosted and cloud.

### Self-hosted

```python
from mirage import MountMode, Workspace
from mirage.resource.qdrant import QdrantConfig, QdrantResource

config = QdrantConfig(
    host="localhost",
    port=6333,
    collection="fashion",
    group_by=["gender", "articleType", "baseColour"],
    id_field="id",
    text_field="productDisplayName",
    blob_field="image_b64",
    blob_ext="jpg",
)
ws = Workspace({"/fashion/": QdrantResource(config)}, mode=MountMode.READ)
```

### Qdrant Cloud

Use `url` plus an `api_key`:

```python
import os

config = QdrantConfig(
    url="https://xyz.cloud.qdrant.io",
    api_key=os.environ["QDRANT_API_KEY"],
    collection="fashion",
    group_by=["gender", "articleType", "baseColour"],
    id_field="id",
)
ws = Workspace({"/fashion/": QdrantResource(config)}, mode=MountMode.READ)
```

## Search setup

The `search` command embeds the query and runs Qdrant vector search. The
collection must already store vectors produced by the same model
(`embedding_model`, default `sentence-transformers/all-MiniLM-L6-v2`). Query
embedding happens one of two ways:

- **Local (default):** `fastembed` embeds the query in process.
- **Server-side:** set `cloud_inference=True` to have an inference-enabled
  Qdrant Cloud cluster embed the query. The query text is sent to the server
  instead of being embedded locally.

```python
config = QdrantConfig(
    url="https://xyz.cloud.qdrant.io",
    api_key=os.environ["QDRANT_API_KEY"],
    collection="fashion",
    group_by=["gender"],
    cloud_inference=True,
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
)
```

```bash
search "red running shoes" /fashion          # ranked <path>.txt:score + content
cat /fashion/Men/Shoes/White/3.txt            # follow a result to the real file
```

## Config reference

| Field              | Required | Default                                     | Description                                                  |
| ------------------ | -------- | ------------------------------------------- | ------------------------------------------------------------ |
| `url`              | No       |                                             | Qdrant URL (Cloud or self-hosted); overrides `host`/`port`   |
| `host`             | No       | `localhost`                                 | Host when `url` is unset                                     |
| `port`             | No       | `6333`                                      | Port when `url` is unset                                     |
| `https`            | No       | `false`                                     | Use TLS for `host`/`port` connections                        |
| `api_key`          | No       |                                             | Qdrant Cloud API key                                         |
| `collection`       | No       |                                             | Pin one collection; the mount root becomes that collection   |
| `group_by`         | No       | `[]`                                        | Payload fields that become nested folder levels              |
| `id_field`         | No       | `id`                                        | Field name shown for the point id; names point files         |
| `text_field`       | No       |                                             | Payload field served as the `<id>.txt` embedded source text  |
| `blob_field`       | No       |                                             | Payload field served as the raw blob/image file              |
| `blob_ext`         | No       | `bin`                                       | Extension for the blob file (`jpg`, `png`, ...)              |
| `vector_field`     | No       |                                             | Payload field holding a vector, omitted from `<id>.json`     |
| `search_limit`     | No       | `10`                                        | Default top-k returned by `search`                           |
| `max_rows`         | No       | `1000`                                      | Cap on points scanned per folder listing                     |
| `embedding_model`  | No       | `sentence-transformers/all-MiniLM-L6-v2`    | Model used to embed the query                                |
| `cloud_inference` | No       | `false`                                     | Embed the query server-side instead of with local fastembed  |

The mount is read-only. See [Qdrant Resource](/python/resource/qdrant) for the
filesystem layout and supported commands.
