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@@ -4,47 +4,28 @@ weight: 11
aliases:
- quick_start
---
# Quickstart
## Installation
## Recommended Workflow
The easiest way to use Qdrant is to run a pre-built image. To do this, make sure Docker is installed on your system.
There are two ways of using Qdrant locally: [1) Local Mode](#option-1-local-mode) and [2) Docker Mode](#option-2-docker-mode). We recommend that you first try Local Mode in [Qdrant Client](https://github.com/qdrant/qdrant-client), as it only takes a few moments to get setup. Then, you may further experiment with Qdrant Docker containers. When you are more comfortable with Qdrant, then you should deploy your app to a Free Tier [Qdrant Cloud](../cloud/quickstart-cloud/) cluster.
Download image from [DockerHub](https://hub.docker.com/r/qdrant/qdrant):
|[Local Mode](#option-1-local-mode)|[Docker Mode](#option-2-docker-mode)|[Qdrant Cloud](../cloud/quickstart-cloud/)|
|:-:|:-:|:-:|
```bash
docker pull qdrant/qdrant
```
![Local mode workflow](/docs/recommended.png)
And run the service inside the docker:
## Option 1: Local Mode
```bash
docker run -p 6333:6333 \
-v $(pwd)/qdrant_storage:/qdrant/storage \
qdrant/qdrant
```
**Prerequisite:** First make sure you have the latest version of Python installed.
In this case Qdrant will use default configuration and store all data under `./qdrant_storage` directory.
Now Qdrant should be accessible at [localhost:6333](http://localhost:6333)
<aside role="status">Qdrant has no encryption or authentication by default and new instances are open to everyone. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
### Local mode
![Local mode workflow](https://raw.githubusercontent.com/qdrant/qdrant-client/master/docs/images/try-develop-deploy.png)
With python client it is possible to try Qdrant without running docker container at all.
Simply install it
### Install Qdrant
```bash
pip install qdrant-client
```
and initialize client like this:
### Initialize Qdrant Client
```python
from qdrant_client import QdrantClient
@@ -54,151 +35,61 @@ client = QdrantClient(":memory:")
client = QdrantClient(path="path/to/db") # Persists changes to disk
```
Local mode is useful for development, prototyping and testing.
## Option 2: Docker Mode
* You can use it to run tests in your CI/CD pipeline.
* Run it in Colab or Jupyter Notebook, no extra dependencies required. See a [Colab Example](https://colab.research.google.com/drive/1Bz8RSVHwnNDaNtDwotfPj0w7AYzsdXZ-?usp=sharing)
* When you need to scale, simply switch to server mode.
**Prerequisite:** First make sure Docker is installed and running on your system.
## API
All interaction with Qdrant takes place via the REST API.
This example covers the most basic use-case - collection creation and basic vector search.
For additional information please refer to the [API documentation](https://qdrant.github.io/qdrant/redoc/index.html).
### gRPC
In addition to REST API, Qdrant also supports gRPC interface.
To enable gPRC interface, specify the following lines in the [configuration file](https://github.com/qdrant/qdrant/blob/master/config/config.yaml):
```yaml
service:
grpc_port: 6334
```
gRPC interface will be available on the specified port.
The gRPC methods follow the same principles as REST. For each REST endpoint, there is a corresponding gRPC method.
Documentation on all available gRPC methods and structures is available here - [gRPC Documentation](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md).
The choice between gRPC and the REST API is a trade-off between convenience and speed.
gRPC is a binary protocol and can be more challenging to debug.
We recommend switching to it if you are already familiar with Qdrant and are trying to optimize the performance of your application.
If you are applying Qdrant for the first time or working on a prototype, you might prefer to use REST.
### Clients
Qdrant provides a set of clients for different programming languages. You can find them here:
* [Python](https://github.com/qdrant/qdrant-client) - `pip install qdrant-client`
* [Rust](https://github.com/qdrant/rust-client) - `cargo add qdrant-client`
* [Go](https://github.com/qdrant/go-client) - `go get github.com/qdrant/go-client`
* [Javascript](https://github.com/qdrant/qdrant-js) - `npm install @qdrant/js-client-rest`
If you are using a language that is not listed here, you can use the REST API directly or generate a client for your language
using [OpenAPI](https://github.com/qdrant/qdrant/blob/master/docs/redoc/master/openapi.json)
or [protobuf](https://github.com/qdrant/qdrant/tree/master/lib/api/src/grpc/proto) definitions.
### Create collection
First - let's create a collection with dot-production metric.
### Download the image
```bash
curl -X PUT 'http://localhost:6333/collections/test_collection' \
-H 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 4,
"distance": "Dot"
}
}'
docker pull qdrant/qdrant
```
### Run the service
```bash
docker run -p 6333:6333 \
-v $(pwd)/qdrant_storage:/qdrant/storage \
qdrant/qdrant
```
Under the default configuration all data will be stored in the `./qdrant_storage` directory.
Qdrant should now be accessible at [localhost:6333](http://localhost:6333)
<aside role="status">Qdrant has no encryption or authentication by default and new instances are open to everyone. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
# Running vector search queries
In this simple example, you will create a Qdrant collection, load data into it and run a basic search query.
![Qdrant Quickstart](/docs/quickstart.png)
## Step 1. Create a collection
You will be storing all of your vector data in a Qdrant collection. Let's call it `test_collection`. This collection will be using a doc production metric.
```python
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams
client = QdrantClient("localhost", port=6333)
# specify port if using Docker
# client = QdrantClient("localhost", port=6333)
client.recreate_collection(
collection_name="test_collection",
vectors_config=VectorParams(size=4, distance=Distance.DOT),
)
```
Expected response:
```json
{
"result": true,
"status": "ok",
"time": 0.031095451
}
```
We can ensure that collection was created:
```bash
curl 'http://localhost:6333/collections/test_collection'
```
**Response:**
```python
collection_info = client.get_collection(collection_name="test_collection")
True
```
Expected response:
## Step 2: Add vectors
```json
{
"result": {
"status": "green",
"vectors_count": 0,
"segments_count": 5,
"disk_data_size": 0,
"ram_data_size": 0,
"config": {
"params": {
"vectors": {
"size": 4,
"distance": "Dot"
}
},
"hnsw_config": { ... },
"optimizer_config": { ... },
"wal_config": { ... }
}
},
"status": "ok",
"time": 2.1199e-05
}
```
```python
from qdrant_client.http.models import CollectionStatus
assert collection_info.status == CollectionStatus.GREEN
assert collection_info.vectors_count == 0
```
### Add points
Let's now add vectors with some payload:
```bash
curl -L -X PUT 'http://localhost:6333/collections/test_collection/points?wait=true' \
-H 'Content-Type: application/json' \
--data-raw '{
"points": [
{"id": 1, "vector": [0.05, 0.61, 0.76, 0.74], "payload": {"city": "Berlin" }},
{"id": 2, "vector": [0.19, 0.81, 0.75, 0.11], "payload": {"city": ["Berlin", "London"] }},
{"id": 3, "vector": [0.36, 0.55, 0.47, 0.94], "payload": {"city": ["Berlin", "Moscow"] }},
{"id": 4, "vector": [0.18, 0.01, 0.85, 0.80], "payload": {"city": ["London", "Moscow"] }},
{"id": 5, "vector": [0.24, 0.18, 0.22, 0.44], "payload": {"count": [0] }},
{"id": 6, "vector": [0.35, 0.08, 0.11, 0.44]}
]
}'
```
Let's now add a few vectors with a payload:
```python
from qdrant_client.http.models import PointStruct
@@ -215,40 +106,17 @@ operation_info = client.upsert(
PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44]),
]
)
print(operation_info)
```
Expected response:
```json
{
"result": {
"operation_id": 0,
"status": "completed"
},
"status": "ok",
"time": 0.000206061
}
```
**Response:**
```python
from qdrant_client.http.models import UpdateStatus
assert operation_info.status == UpdateStatus.COMPLETED
operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>
```
### Search with filtering
Let's start with a basic request:
```bash
curl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \
-H 'Content-Type: application/json' \
--data-raw '{
"vector": [0.2,0.1,0.9,0.7],
"limit": 3
}'
```
## Step 3: Run a query
Let's ask a basic question - Which of our stored vectors are most similar to the query vector `[0.2, 0.1, 0.9, 0.7]`?
```python
search_result = client.search(
@@ -256,58 +124,23 @@ search_result = client.search(
query_vector=[0.2, 0.1, 0.9, 0.7],
limit=3
)
print(search_result)
```
Expected response:
```json
{
"result": [
{ "id": 4, "score": 1.362 },
{ "id": 1, "score": 1.273 },
{ "id": 3, "score": 1.208 }
],
"status": "ok",
"time": 0.000055785
}
```
**Response:**
```python
assert len(search_result) == 3
print(search_result[0])
# ScoredPoint(id=4, score=1.362, ...)
print(search_result[1])
# ScoredPoint(id=1, score=1.273, ...)
print(search_result[2])
# ScoredPoint(id=3, score=1.208, ...)
ScoredPoint(id=4, version=0, score=1.362000031210482, payload={'city': ['London', 'Moscow']}, vector=None),
ScoredPoint(id=1, version=0, score=1.2729999996721744, payload={'city': 'Berlin'}, vector=None),
ScoredPoint(id=3, version=0, score=1.2080000013113021, payload={'city': ['Berlin', 'Moscow']}, vector=None)
```
Note that payload and vector data is missing in these results by default.
See [payload and vector in the result](../concepts/search#payload-and-vector-in-the-result) on how to enable it.
But the result is different if we add a filter:
## Step 4: Add a filter
```bash
curl -L -X POST 'http://localhost:6333/collections/test_collection/points/search' \
-H 'Content-Type: application/json' \
--data-raw '{
"filter": {
"should": [
{
"key": "city",
"match": {
"value": "London"
}
}
]
},
"vector": [0.2, 0.1, 0.9, 0.7],
"limit": 3
}'
```
We can narrow down the results further by filtering by payload. Let's find the closest results that include "London".
```python
from qdrant_client.http.models import Filter, FieldCondition, MatchValue
@@ -326,28 +159,20 @@ search_result = client.search(
),
limit=3
)
print(search_result)
```
Expected response:
```json
{
"result": [
{ "id": 4, "score": 1.362 },
{ "id": 2, "score": 0.871 }
],
"status": "ok",
"time": 0.000093972
}
```
**Response:**
```python
assert len(search_result) == 2
print(search_result[0])
# ScoredPoint(id=4, score=1.362, ...)
print(search_result[1])
# ScoredPoint(id=2, score=0.871, ...)
ScoredPoint(id=4, version=0, score=1.362000031210482, payload={'city': ['London', 'Moscow']}, vector=None),
ScoredPoint(id=2, version=0, score=0.8709999993443489, payload={'city': ['Berlin', 'London']}, vector=None)
```
You have just conducted vector search. You loaded vectors into a database and queried the database with a vector of your own. Qdrant found the closest results and presented you with a similarity score.
## Next Steps
Now you know how Qdrant works. Getting started with [Qdrant Cloud](../cloud/quickstart-cloud/)| is even simpler. [Create an account](https://qdrant.to/cloud) and use our SaaS completely free. We will take care of infrastructure maintenance and software updates.
To move onto some more complex examples of vector search, read our [Tutorials](tutorials/) and create your own app with the help of our [Examples](examples/).