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remove local mode

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David Myriel
2023-09-04 09:40:46 +02:00
parent a764e58be9
commit e43cfe0798
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qdrant-landing/content/documentation/quick-start.md
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@@ -6,46 +6,19 @@ aliases:
---
# Quickstart
## Recommended Workflow
In this short example, you will use the Python Client to create a Collection, load data into it and run a basic search query.
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.
<aside role="status">Before you start, please make sure Docker is installed and running on your system.</aside>
|[Local Mode](#option-1-local-mode)|[Docker Mode](#option-2-docker-mode)|[Qdrant Cloud](../cloud/quickstart-cloud/)|
|:-:|:-:|:-:|
## Download and run
![Local mode workflow](/docs/recommended.png)
## Option 1: Local Mode
**Prerequisite:** First make sure you have the latest version of Python installed.
### Install Qdrant
```bash
pip install qdrant-client
```
### Initialize Qdrant Client
```python
from qdrant_client import QdrantClient
client = QdrantClient(":memory:")
# or
client = QdrantClient(path="path/to/db") # Persists changes to disk
```
## Option 2: Docker Mode
**Prerequisite:** First make sure Docker is installed and running on your system.
### Download the image
First, download the latest Qdrant image from Dockerhub:
```bash
docker pull qdrant/qdrant
```
### Run the service
Then, run the service:
```bash
docker run -p 6333:6333 \
@@ -57,7 +30,7 @@ Under the default configuration all data will be stored in the `./qdrant_storage
Qdrant should now be accessible at [localhost:6333](http://localhost:6333)
### Initialize Qdrant Client
## Initialize the client
```python
from qdrant_client import QdrantClient
@@ -65,20 +38,13 @@ from qdrant_client import QdrantClient
client = QdrantClient("localhost", port=6333)
```
<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>
<aside role="status">By default, Qdrant starts with no 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>
# Running vector search queries
## Create a collection
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.
![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 product distance metric to compare vectors.
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 dot product distance metric to compare vectors.
```python
from qdrant_client.http.models import Distance, VectorParams
client.recreate_collection(
@@ -93,7 +59,7 @@ client.recreate_collection(
True
```
## Step 2: Add vectors
## Add vectors
Let's now add a few vectors with a payload. Payloads are other data you want to associate with the vector:
@@ -105,11 +71,11 @@ operation_info = client.upsert(
wait=True,
points=[
PointStruct(id=1, vector=[0.05, 0.61, 0.76, 0.74], payload={"city": "Berlin"}),
PointStruct(id=2, vector=[0.19, 0.81, 0.75, 0.11], payload={"city": ["Berlin", "London"]}),
PointStruct(id=3, vector=[0.36, 0.55, 0.47, 0.94], payload={"city": ["Berlin", "Moscow"]}),
PointStruct(id=4, vector=[0.18, 0.01, 0.85, 0.80], payload={"city": ["London", "Moscow"]}),
PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"count": [0]}),
PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44]),
PointStruct(id=2, vector=[0.19, 0.81, 0.75, 0.11], payload={"city": "London"}),
PointStruct(id=3, vector=[0.36, 0.55, 0.47, 0.94], payload={"city": "Moscow"}),
PointStruct(id=4, vector=[0.18, 0.01, 0.85, 0.80], payload={"city": "New York"}),
PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={"city": "Beijing"}),
PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44], payload={"city": "Mumbai"}),
]
)
print(operation_info)
@@ -121,7 +87,7 @@ print(operation_info)
operation_id=0 status=<UpdateStatus.COMPLETED: 'completed'>
```
## Step 3: Run a query
## 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
@@ -136,22 +102,21 @@ print(search_result)
**Response:**
```python
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)
ScoredPoint(id=4, version=0, score=1.362, payload={'city': 'New York'}, vector=None),
ScoredPoint(id=1, version=0, score=1.273, payload={'city': 'Berlin'}, vector=None),
ScoredPoint(id=3, version=0, score=1.208, payload={'city': 'Moscow'}, vector=None)
```
The results are returned in decreasing si,ilarity order. Note that payload and vector data is missing in these results by default.
The results are returned in decreasing similarity order. 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.
## Step 4: Add a filter
## Add a filter
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
search_result = client.search(
collection_name="test_collection",
query_vector=[0.2, 0.1, 0.9, 0.7],
@@ -171,14 +136,17 @@ print(search_result)
**Response:**
```python
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)
ScoredPoint(id=2, version=0, score=0.871, payload={'city': '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
## Next steps
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.
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/).
**Note:** There is another way of running Qdrant locally. If you are a Python developer, we recommend that you try Local Mode in [Qdrant Client](https://github.com/qdrant/qdrant-client), as it only takes a few moments to get setup.