🌟
HNSW + PostgreSQL Indexer
HNSWPostgreSQLIndexer Jina is a production-ready, scalable Indexer for the Jina neural search framework.
It combines the reliability of PostgreSQL with the speed and efficiency of the HNSWlib nearest neighbor library.
It thus provides all the CRUD operations expected of a database system, while also offering fast and reliable vector lookup.
Requires a running PostgreSQL database service. For quick testing, you can run a containerized version locally with:
docker run -e POSTGRES_PASSWORD=123456 -p 127.0.0.1:5432:5432/tcp postgres:13.2
Syncing between PSQL and HNSW
By default, all data is stored in a PSQL database (as defined in the arguments). In order to add data to / build a HNSW index with your data, you need to manually call the /sync endpoint. This iterates through the data you have stored, and adds it to the HNSW index. By default, this is done incrementally, on top of whatever data the HNSW index already has. If you want to completely rebuild the index, use the parameter rebuild, like so:
flow.post(on='/sync', parameters={'rebuild': True})
At start-up time, the data from PSQL is synced into HNSW automatically. You can disable this with:
Flow().add(
uses='jinahub://HNSWPostgresIndexer',
uses_with={'startup_sync': False}
)
Automatic background syncing
Optionally, you can enable the option for automatic background syncing of the data into HNSW. This creates a thread in the background of the main operations, that will regularly perform the synchronization. This can be done with the sync_interval constructor argument, like so:
Flow().add(
uses='jinahub://HNSWPostgresIndexer',
uses_with={'sync_interval': 5}
)
sync_interval argument accepts an integer that represents the amount of seconds to wait between synchronization attempts. This should be adjusted based on your specific data amounts. For the duration of the background sync, the HNSW index will be locked to avoid invalid state, so searching will be queued. When sync_interval is enabled, the index will also be locked during search mode, so that syncing will be queued.
CRUD operations
You can perform all the usual operations on the respective endpoints
/index. Add new data to PostgreSQL/search. Query the HNSW index with your Documents./update. Update documents in PostgreSQL/delete. Delete documents in PostgreSQL.
Note. This only performs soft-deletion by default. This is done in order to not break the look-up of the document id after doing a search. For a hard delete, add 'soft_delete': False' to parameters. You might also perform a cleanup after a full rebuild of the HNSW index, by calling /cleanup.
Status endpoint
You can also get the information about the status of your data via the /status endpoint. This returns a Document whose tags contain the relevant information. The information can be returned via the following keys:
'psql_docs': number of Documents stored in the PSQL database (includes entries that have been "soft-deleted")'hnsw_docs': the number of Documents indexed in the HNSW index'last_sync': the time of the last synchronization of PSQL into HNSW'pea_id': the shard number
In a sharded environment (parallel>1) you will get one Document from each shard. Each shard will have its own 'hnsw_docs', 'last_sync', 'pea_id', but they will all report the same 'psql_docs' (The PSQL database is available to all your shards). You need to sum the 'hnsw_docs' across these Documents, like so
result = f.post('/status', None, return_results=True)
result_docs = result[0].docs
total_hnsw_docs = sum(d.tags['hnsw_docs'] for d in result_docs)
2.4k Jan 05, 2023
115 Nov 21, 2022
10 Jul 14, 2022
39 Oct 22, 2022
3k Jan 03, 2023
0 Oct 05, 2021
3 Jun 02, 2022
27 Oct 06, 2022
2 Sep 27, 2022
19 Dec 10, 2022
1 Dec 13, 2021
175 Jan 07, 2023
16 Aug 07, 2022
101 Jan 04, 2023
66 Nov 03, 2022
294 Dec 12, 2022
308 Jan 05, 2023
12 Sep 26, 2022
3 May 30, 2022
15 Jun 11, 2022