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185 lines
6.0 KiB
Markdown
185 lines
6.0 KiB
Markdown
---
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title: Quickstart
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weight: 11
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aliases:
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- quick_start
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---
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# Quickstart
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## Recommended Workflow
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There are two ways of using Qdrant locally: [1) Local Mode](#option-1-local-mode) and [2) Docker Mode](#option-2-docker-mode). If you are a Python developer, 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.
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|[Local Mode](#option-1-local-mode)|[Docker Mode](#option-2-docker-mode)|[Qdrant Cloud](../cloud/quickstart-cloud/)|
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## Option 1: Local Mode
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**Prerequisite:** First make sure you have the latest version of Python installed.
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### Install Qdrant
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```bash
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pip install qdrant-client
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```
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### Initialize Qdrant Client
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```python
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from qdrant_client import QdrantClient
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client = QdrantClient(":memory:")
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# or
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client = QdrantClient(path="path/to/db") # Persists changes to disk
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```
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## Option 2: Docker Mode
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**Prerequisite:** First make sure Docker is installed and running on your system.
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### Download the image
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```bash
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docker pull qdrant/qdrant
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```
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### Run the service
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```bash
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docker run -p 6333:6333 \
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-v $(pwd)/qdrant_storage:/qdrant/storage:z \
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qdrant/qdrant
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```
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Under the default configuration all data will be stored in the `./qdrant_storage` directory. This will also be the only directory that both the Container and the host machine can both see.
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Qdrant should now be accessible at [localhost:6333](http://localhost:6333)
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### Initialize Qdrant Client
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```python
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from qdrant_client import QdrantClient
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client = QdrantClient("localhost", port=6333)
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```
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<aside role="status">By default, Qdrant starts with encryption or authentication . This means anyone with network access to your machine can access your Qdrant container instance. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
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# Running vector search queries
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In this short example, you will use the Python client to create a Qdrant collection, load data into it and run a basic search query.
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## Step 1. Create a collection
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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 product distance metric to compare vectors.
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```python
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from qdrant_client.http.models import Distance, VectorParams
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client.recreate_collection(
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collection_name="test_collection",
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vectors_config=VectorParams(size=4, distance=Distance.DOT),
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)
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```
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**Response:**
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```python
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True
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```
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## Step 2: Add vectors
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Let's now add a few vectors with a payload. Payloads are other data you want to associate with the vector:
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```python
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from qdrant_client.http.models import PointStruct
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operation_info = client.upsert(
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collection_name="test_collection",
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wait=True,
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points=[
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PointStruct(id=1, vector=[0.05, 0.61, 0.76, 0.74], payload={"city": "Berlin"}),
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PointStruct(id=2, vector=[0.19, 0.81, 0.75, 0.11], payload={"city": ["Berlin", "London"]}),
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PointStruct(id=3, vector=[0.36, 0.55, 0.47, 0.94], payload={"city": ["Berlin", "Moscow"]}),
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PointStruct(id=4, vector=[0.18, 0.01, 0.85, 0.80], payload={"city": ["London", "Moscow"]}),
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PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"count": [0]}),
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PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44]),
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]
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)
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print(operation_info)
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```
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**Response:**
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```python
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operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>
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```
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## Step 3: Run a query
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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]`?
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```python
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search_result = client.search(
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collection_name="test_collection",
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query_vector=[0.2, 0.1, 0.9, 0.7],
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limit=3
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)
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print(search_result)
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```
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**Response:**
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```python
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ScoredPoint(id=4, version=0, score=1.362000031210482, payload={'city': ['London', 'Moscow']}, vector=None),
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ScoredPoint(id=1, version=0, score=1.2729999996721744, payload={'city': 'Berlin'}, vector=None),
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ScoredPoint(id=3, version=0, score=1.2080000013113021, payload={'city': ['Berlin', 'Moscow']}, vector=None)
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```
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The results are returned in decreasing si,ilarity order. Note that payload and vector data is missing in these results by default.
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See [payload and vector in the result](../concepts/search#payload-and-vector-in-the-result) on how to enable it.
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## Step 4: Add a filter
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We can narrow down the results further by filtering by payload. Let's find the closest results that include "London".
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```python
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from qdrant_client.http.models import Filter, FieldCondition, MatchValue
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search_result = client.search(
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collection_name="test_collection",
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query_vector=[0.2, 0.1, 0.9, 0.7],
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query_filter=Filter(
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must=[
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FieldCondition(
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key="city",
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match=MatchValue(value="London")
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)
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]
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),
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limit=3
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)
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print(search_result)
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```
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**Response:**
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```python
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ScoredPoint(id=4, version=0, score=1.362000031210482, payload={'city': ['London', 'Moscow']}, vector=None),
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ScoredPoint(id=2, version=0, score=0.8709999993443489, payload={'city': ['Berlin', 'London']}, vector=None)
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```
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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.
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## Next Steps
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Now you know how Qdrant works. Getting started with [Qdrant Cloud](../cloud/quickstart-cloud/) is just as easy. [Create an account](https://qdrant.to/cloud) and use our SaaS completely free. We will take care of infrastructure maintenance and software updates.
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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/).
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