--- title: Asynchronous API weight: 18 --- # Using Qdrant asynchronously Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI [have embraced that new paradigm](https://fastapi.tiangolo.com/async/), but it's also becoming a standard for ML models served as SaaS. For example, the Cohere SDK [provides an async client](https://cohere-sdk.readthedocs.io/en/latest/cohere.html#asyncclient) next to its synchronous counterpart. Databases are often launched as separate services and are accessed via a network. All the interactions with them are IO-bound and can be performed asynchronously so as not to waste time actively waiting for a server response. If you use Python, that is achieved by using [`async/await`](https://docs.python.org/3/library/asyncio-task.html) syntax. That lets the interpreter switch to another task while waiting for a response from the server. Qdrant exposes two interfaces: HTTP and gRPC. The official SDKs for different languages are based on the autogenerated clients, and in Python [qdrant-client](https://github.com/qdrant/qdrant-client), you can call all the methods asynchronously. This tutorial presents how to do it. ## When the usage of async API is justified? If the application you are writing will never support multiple users at once, for example, it is a script run once daily, then there is no need to use async API. But if you are writing a web service that multiple users will use simultaneously, you shouldn't be blocking the threads of the web server as it limits the number of concurrent requests it can handle. In this case, you should use the async API. Modern web frameworks like [FastAPI](https://fastapi.tiangolo.com/) and [Quart](https://quart.palletsprojects.com/en/latest/) support async API out of the box. Mixing asynchronous code with an existing synchronous codebase might be a challenge. The `async/await` syntax cannot be used in synchronous functions. On the other hand, calling an IO-bound operation synchronously in async code is considered an antipattern. Therefore, if you build an async web service, exposed through an [ASGI](https://asgi.readthedocs.io/en/latest/) server, you should use the async API for all the interactions with Qdrant. ## How to use async API? Calling any method of Qdrant requires establishing a connection to the server. We need to create an instance of `QdrantClient` that will act as a gateway. If you want to do it locally, please make sure Qdrant server is running. If it's not, then you can launch it in a Docker container: ```bash docker run -p "6333:6333" -p "6334:6334" qdrant/qdrant:latest ``` Alternatively, you can also use a [Qdrant Cloud](https://cloud.qdrant.io/) cluster. With a Cloud cluster you will pass the API key to the client constructor. With that, we are ready to create a [`QdrantClient`](https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L13) instance, given you have the [`qdrant-client`](https://pypi.org/project/qdrant-client/) package installed: ```python from qdrant_client import QdrantClient client = QdrantClient("localhost") # If you use Qdrant Cloud, then pass the API key to the constructor: # client = QdrantClient("https://your-cluster-url.cloud.com", api_key="your-api-key") ``` The default client only exposes the synchronous methods for interacting with the server. To access the async client and it's underlying methods, we need to use autogenerated async clients. It is possible to use asynchronous HTTP API by using `qdrant_client.http.api_client.AsyncApis`. The asynchronous gRPC API if more efficient than the HTTP API, which is why we recommend you use it and we will be using it here. ### gRPC API Every instance of `QdrantClient` has two properties we are going to use: `async_grpc_collections` and `async_grpc_points`. They expose the autogenerated clients for [collections](/documentation/concepts/collections/) and [points](/documentation/concepts/points/) respectively. The gRPC client uses type definitions for all the interactions with the server. They are autogenerated from the source code of Qdrant server, and we have to import them if we want to call any of the available methods. ```python from qdrant_client import grpc ``` If your IDE does not support autocompletion for the autogenerated clients, you can refer to the documentation and [see the available fields for each of them](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md). #### Creating a collection Let's create a collection and add some points to it. ```python response = await client.async_grpc_collections.Create( grpc.CreateCollection( collection_name="my_collection", vectors_config=grpc.VectorsConfig( params=grpc.VectorParams( size=100, distance=grpc.Distance.Cosine, ) ), quantization_config=grpc.QuantizationConfig( scalar=grpc.ScalarQuantization( type=grpc.QuantizationType.Int8, always_ram=False, ) ), timeout=10, ) ) ``` Our collection was set up for 100-dimensional vectors, cosine distance and 8-bit quantization. The timeout is set to 10 seconds, so if the server does not respond within 10 seconds, the request will be aborted. #### Checking collection info We can check the collection info to see if it was created successfully, and configured as we expected: ```python response = await client.async_grpc_collections.Get( grpc.GetCollectionInfoRequest( collection_name="my_collection" ) ) ``` The `response` object is going to be an instance of `grpc.GetCollectionInfoResponse` and we can access the fields we need or simply display its content. ```python print(response) ``` ```text result { status: Green optimizer_status { ok: true } segments_count: 8 config { params { shard_number: 1 on_disk_payload: true vectors_config { params { size: 100 distance: Cosine } } replication_factor: 1 write_consistency_factor: 1 } hnsw_config { m: 16 ef_construct: 100 full_scan_threshold: 10000 max_indexing_threads: 0 on_disk: false } optimizer_config { deleted_threshold: 0.2 vacuum_min_vector_number: 1000 default_segment_number: 0 indexing_threshold: 20000 flush_interval_sec: 5 max_optimization_threads: 1 } wal_config { wal_capacity_mb: 32 wal_segments_ahead: 0 } quantization_config { scalar { type: Int8 always_ram: false } } } indexed_vectors_count: 0 } time: 1.3022e-05 ``` As you can see, the collection was created properly and is ready to be used. To understand more of the output, please refer to the [API specification](https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/get_collection) that describes the meaning of each parameter. ##### Handling exceptions If the collection does not exist, the server will return an error. We can handle it by catching the exception. We may expect some specific exceptions, or simply catch all the errors that subclass `RpcError` from `grpc` package: ```python from grpc import RpcError try: response = await client.async_grpc_collections.Get( grpc.GetCollectionInfoRequest( collection_name="my_collection_2" ) ) except RpcError as e: print(e) ``` Our exception contain the details about the error, including the status code and the message: ```text ``` We can use those details to properly handle some specific cases. For example, if we want to create a collection only if it does not exist, we can check the status code and create it if it is `StatusCode.NOT_FOUND`: #### Updating collection configuration It's quite likely that we will want to update the collection configuration. For example, we may want to change the HNSW parameters to improve the search precision. Let's change the `ef_construct` parameter to 200: ```python await client.async_grpc_collections.Update( grpc.UpdateCollection( collection_name="my_collection", hnsw_config=grpc.HnswConfigDiff( ef_construct=200, ), ) ) ``` #### Deleting a collection There is plenty of other methods available for collections, like creating aliases or checking the status of the cluster. We are not going to call all of them in this tutorial, but you can always [check the list of available methods and their parameters in the documentation](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md#collections_serviceproto). Right now, we are just going to delete the collection we created: ```python await client.async_grpc_collections.Delete( grpc.DeleteCollection( collection_name="my_collection" ) ) ``` That's it when it comes to collection management. Let's move on to points (individual vectors) allowing us to load data into our collection #### Adding points to the collection Assuming your collection created successfuly, we can now add some points to it. Let's add our first two points into the collection: ```python import numpy as np points = [ grpc.PointStruct( id=grpc.PointId(num=1), payload={ "int_param": grpc.Value(integer_value=32), "bool_param": grpc.Value(bool_value=True), }, vectors=grpc.Vectors( vector=grpc.Vector(data=np.random.random(100).tolist()), ), ), grpc.PointStruct( id=grpc.PointId(num=2), payload={ "int_param": grpc.Value(integer_value=64), "bool_param": grpc.Value(bool_value=False), }, vectors=grpc.Vectors( vector=grpc.Vector(data=np.random.random(100).tolist()), ), ), ] response = await client.async_grpc_points.Upsert( grpc.UpsertPoints( collection_name="my_collection", points=points, ) ) ``` Those points are automatically indexed and available for search. In the future, we can add more points, or update the existing ones. All the point related operations are [documented in the Qdrant server repository](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md#points_serviceproto). The main operation we need from the vector database is semantic search. Let's see how to perform it. #### Asynchronous semantic search Search operation requires a query vector and a collection name. We can also specify the number of results we want to retrieve and some other parameters, but let's take one step at a time. The minimal example may look like this: ```python response = await client.async_grpc_points.Search( grpc.SearchPoints( collection_name="my_collection", vector=np.random.rand(100).tolist(), limit=1, ) ) print(response) ``` The response is going to contain just the point id and similarity score for the vector most similar to the random vector generated in the code. For example, that might be the output of the code above: ```text result { id { num: 1 } score: 0.786471784 version: 1 } time: 0.000456134 ``` ##### Returning vectors and payloads If you need the vectors and payloads of the points, you can specify it in the request: ```python response = await client.async_grpc_points.Search( grpc.SearchPoints( collection_name="my_collection", vector=np.random.rand(100).tolist(), limit=1, with_payload=grpc.WithPayloadSelector(enable=True), with_vectors=grpc.WithVectorsSelector(enable=True), ) ) ``` That's all the code you need to bring back the vector and payload. ##### Applying filters If you ever used SQL, you are probably familiar with the `WHERE` clause. It allows you to filter the results of the query. Qdrant has similar functionality. Let's say we want to find all the points similar to given vector, but with `int_param` equal to 32. We can do it like this: ```python response = await client.async_grpc_points.Search( grpc.SearchPoints( collection_name="my_collection", vector=np.random.rand(100).tolist(), limit=1, with_payload=grpc.WithPayloadSelector(enable=True), filter=grpc.Filter( must=[ grpc.Condition( field=grpc.FieldCondition( key="int_param", match=grpc.Match(integer=32) ) ) ] ) ) ) ``` We can build even more sophisticated filters with the `must`, `should` and `must_not` clauses. You can find more details in the [filtering documentation](https://qdrant.tech/documentation/concepts/filtering/#filtering). Analogous to search, we can also [scroll our collection](https://qdrant.tech/documentation/concepts/points/#scroll-points) to retrieve all the points or use the [recommendation API](https://qdrant.tech/documentation/concepts/search/#recommendation-api) to find points similar to positive and dissimilar to negative examples. ## Support of Qdrant async API in Python libraries There is plenty of Python libraries that Qdrant integrates with. Until recently, only [Langchain]() provided async Python API support. Qdrant is the only vector database with full coverage of async API in Langchain. The documentation [describes how to use it](https://python.langchain.com/docs/modules/data_connection/vectorstores/#asynchronous-operations).