airbyte/docs/integrations/destinations/qdrant.md

Qdrant

This page guides you through the process of setting up the Qdrant destination connector.

Features

Feature	Supported?(Yes/No)	Notes
Full Refresh Sync	Yes
Incremental - Append Sync	Yes
Incremental - Append + Deduped	Yes

Output Schema

Only one stream will exist to collect payload and vectors (optional) from all source streams. This will be in a collection in Qdrant whose name will be defined by the user. If the collection does not already exist in the Qdrant instance, a new collection with the same name will be created.

For each point in the collection, a UUID string is generated and used as the point id. The embeddings generated as defined or extracted from the source stream will be stored as the point vectors. The point payload will contain primarily the record metadata. The text field will then be stored in a field (as defined in the config) in the point payload.

Getting Started

You can connect to a Qdrant instance either in local mode or cloud mode.

• For the local mode, you will need to set it up using Docker. Check the Qdrant docs here for an official guide. After setting up, you would need your host, port and if applicable, your gRPC port.
• To setup to an instance in Qdrant cloud, check out this official guide to get started. After setting up the instance, you would need the instance url and an API key to connect.

Note that this connector does not support a local persistent mode. To test, use the docker option.

Requirements

To use the Qdrant destination, you'll need:

• An account with API access for OpenAI, Cohere (depending on which embedding method you want to use) or neither (if you want to extract the vectors from the source stream)
• A Qdrant db instance (local mode or cloud mode)
• Qdrant API Credentials (for cloud mode)
• Host and Port (for local mode)
• gRPC port (if applicable in local mode)

Configure Network Access

Make sure your Qdrant database can be accessed by Airbyte. If your database is within a VPC, you may need to allow access from the IP you're using to expose Airbyte.

Setup the Qdrant Destination in Airbyte

You should now have all the requirements needed to configure Qdrant as a destination in the UI. You'll need the following information to configure the Qdrant destination:

• (Required) Text fields to embed
• (Optional) Text splitter Options around configuring the chunking process provided by the Langchain Python library.
• (Required) Fields to store as metadata
• (Required) Collection The name of the collection in Qdrant db to store your data
• (Required) The field in the payload that contains the embedded text
• (Required) Prefer gRPC Whether to prefer gRPC over HTTP.
• (Required) Distance Metric The Distance metrics used to measure similarities among vectors. Select from:
  • Dot product
  • Cosine similarity
  • Euclidean distance
• (Required) Authentication method
  • For local mode
    • Host for example localhost
    • Port for example 8000
    • gRPC Port (Optional)
  • For cloud mode
    • Url The url of the cloud Qdrant instance.
    • API Key The API Key for the cloud Qdrant instance
• (Optional) Embedding
  • OpenAI API key if using OpenAI for embedding
  • Cohere API key if using Cohere for embedding
  • Embedding Field name and Embedding dimensions if getting the embeddings from stream records

Changelog

Expand to review

Version	Date	Pull Request	Subject
0.1.41	2025-05-10	59814	Update dependencies
0.1.40	2025-05-03	58718	Update dependencies
0.1.39	2025-04-19	58282	Update dependencies
0.1.38	2025-04-12	57610	Update dependencies
0.1.37	2025-04-05	57162	Update dependencies
0.1.36	2025-03-29	56564	Update dependencies
0.1.35	2025-03-22	56159	Update dependencies
0.1.34	2025-03-08	55363	Update dependencies
0.1.33	2025-03-01	54889	Update dependencies
0.1.32	2025-02-22	54246	Update dependencies
0.1.31	2025-02-15	53939	Update dependencies
0.1.30	2025-02-08	53389	Update dependencies
0.1.29	2025-02-01	52917	Update dependencies
0.1.28	2025-01-25	52171	Update dependencies
0.1.27	2025-01-18	51716	Update dependencies
0.1.26	2025-01-11	51232	Update dependencies
0.1.25	2025-01-04	50917	Update dependencies
0.1.24	2024-12-28	50459	Update dependencies
0.1.23	2024-12-21	50222	Update dependencies
0.1.22	2024-12-14	49290	Update dependencies
0.1.21	2024-11-25	48641	Update dependencies
0.1.20	2024-11-04	48191	Update dependencies
0.1.19	2024-10-29	47757	Update dependencies
0.1.18	2024-10-28	47621	Update dependencies
0.1.17	2024-10-28	47054	Update dependencies
0.1.16	2024-10-12	46774	Update dependencies
0.1.15	2024-10-05	46417	Update dependencies
0.1.14	2024-09-28	46137	Update dependencies
0.1.13	2024-09-21	45830	Update dependencies
0.1.12	2024-09-14	45526	Update dependencies
0.1.11	2024-09-07	45217	Update dependencies
0.1.10	2024-08-31	44678	Update dependencies
0.1.9	2024-08-17	44293	Update dependencies
0.1.8	2024-08-12	43744	Update dependencies
0.1.7	2024-08-10	43529	Update dependencies
0.1.6	2024-08-03	43219	Update dependencies
0.1.5	2024-07-27	42620	Update dependencies
0.1.4	2024-07-20	42384	Update dependencies
0.1.3	2024-07-13	41919	Update dependencies
0.1.2	2024-07-10	41530	Update dependencies
0.1.1	2024-07-09	41096	Update dependencies
0.1.0	2024-06-27	41020	Update to Airbyte CDK 2.3 and qdrant-client 1.10
0.0.13	2024-06-27	40215	Replaced deprecated AirbyteLogger with logging.Logger
0.0.12	2024-06-06	39172	[autopull] Upgrade base image to v1.2.2
0.0.11	2024-04-15	#37333	Updated CDK and pytest versions to fix security vulnerabilities
0.0.10	2023-12-11	#33303	Fix bug with embedding special tokens
0.0.9	2023-12-01	#32697	Allow omitting raw text
0.0.8	2023-11-29	#32608	Support deleting records for CDC sources and fix spec schema
0.0.7	2023-11-13	#32357	Improve spec schema
0.0.6	2023-10-23	#31563	Add field mapping option
0.0.5	2023-10-15	#31329	Add OpenAI-compatible embedder option
0.0.4	2023-10-04	#31075	Fix OpenAI embedder batch size
0.0.3	2023-09-29	#30820	Update CDK
0.0.2	2023-09-25	#30689	Update CDK to support Azure OpenAI embeddings and text splitting options
0.0.1	2023-09-22	#30332	🎉 New Destination: Qdrant (Vector Database)