Comments (4)
Hi, here is an example https://github.com/immich-app/immich/blob/main/docker/docker-compose.yml#L66:#L75
database:
container_name: immich_postgres
image: registry.hub.docker.com/tensorchord/pgvecto-rs:pg14-v0.2.0@sha256:90724186f0a3517cf6914295b5ab410db9ce23190a2d9d0b9dd6463e3fa298f0
environment:
POSTGRES_PASSWORD: ${DB_PASSWORD}
POSTGRES_USER: ${DB_USERNAME}
POSTGRES_DB: ${DB_DATABASE_NAME}
volumes:
- pgdata:/var/lib/postgresql/data
restart: always
Does it work for you?
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Yeah, you could check out our documentation for OpenAI 3072 dimension embedding model if you are interested.
https://docs.pgvecto.rs/use-case/adaptive-retrieval.html
from pgvecto.rs.
@gaocegege I'm going to find out because I'm in the middle of experimenting Spring AI and pgvector, and because pgvector has a dimension size limitation
from pgvecto.rs.
@kevintanhongann, have you encountered any difficulties in your investigation of Spring AI and pgvector/pgvecto.rs? We are more than willing to assist you.
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Related Issues (20)
- test: 0.3 release test HOT 1
- chore: sync return when index creation
- epic: Support SDKs in different programming languages HOT 4
- bug(cnpg): type "vector" does not exist HOT 1
- The default max_stack_depth parameter in the container image may be too small HOT 12
- How could I use AzureOpenAI to build LlamaIndex? HOT 2
- vector type not found in the database - pgvecto_rs.psycopg register_vector HOT 5
- copy with BINARY FORMAT fails with cannot find a dumper for type vector HOT 3
- sdk: Sparse vector indices type mismatch between sdk and function signature with numpy.ndarray
- How to find index size ? HOT 3
- Execute pg_resetwal in docker Unraid HOT 3
- feat: Support vector aggregation function HOT 1
- feat(fdw): How to be compatible with new pgvector types HOT 1
- feat: ANN benchmark HOT 3
- bench(fdw): Latency HOT 4
- fix(bench): Fix ZillizBench HOT 1
- feat: Add pgvecto.rs to vector hub HOT 2
- unknown x86 target feature HOT 2
- install patched pgrx failed HOT 4
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