Comments (1)
Langchain vector stores don't have a method to retrieve all the documents as such.
However, you can use qdrant_client
's QdrantClient#scroll()
method to retrieve the payload from all your points.
The method is documented at https://qdrant.tech/documentation/concepts/points/#scroll-points
from qdrant.
Related Issues (20)
- Feature request to control the flow of data in ALIAS feature HOT 1
- Failing example from qdrant documentation HOT 2
- Flaky test `multivector_filtrable_hnsw_test::test_multi_filterable_hnsw::case_4_discovery_multi` HOT 1
- [1.7.4] High-cardinality vector search inaccurate with Euclidean distance HOT 2
- Add request List shard_keys HOT 2
- Add deletion protection for collections HOT 3
- Metrics endpoint doesn't expose all metrics HOT 2
- is_empty slow even with index HOT 1
- Vector API call timeout HOT 3
- Flaky test `multivector_filtrable_hnsw_test::test_multi_filterable_hnsw::case_2_nearest_multi` HOT 1
- order_by is not working in distributed deployment (multi-node cluster) HOT 6
- Implement a SQL, Qdrant SQL? HOT 2
- Shard Not Active Error HOT 2
- Filtering by Distance HOT 1
- Batch recommend request fails if one or multiple points are not in the DB HOT 2
- i can not create docker container HOT 4
- Performance degradation in response time using range filters from Qdrant version 1.6 onwards HOT 4
- Collection status keeps red HOT 1
- Can Qdrant's search API directly handle the nested "if-else" condition in the filter? HOT 7
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