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Add pgvectorscale and update pgvector #555
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,189 @@ | ||
| { | ||
| "name": "pgvectorscale", | ||
| "links": { | ||
| "docs": "", | ||
| "github": "https://github.com/timescale/pgvectorscale", | ||
| "website": "https://github.com/timescale/pgvectorscale", | ||
| "vendor_discussion": "https://github.com/superlinked/VectorHub/discussions/554", | ||
| "poc_github": "https://github.com/cevian", | ||
| "slug": "pgvectorscale" | ||
| }, | ||
| "oss": { | ||
| "support": "full", | ||
| "source_url": "https://github.com/timescale/pgvectorscale", | ||
| "comment": "" | ||
| }, | ||
| "license": { | ||
| "value": "PostgreSQL Licence https://opensource.org/license/postgresql/", | ||
| "source_url": "https://opensource.org/license/postgresql/", | ||
| "comment": "" | ||
| }, | ||
| "dev_languages": { | ||
| "value": [ | ||
| "rust" | ||
| ], | ||
| "source_url": "https://github.com/timescale/pgvectorscale", | ||
| "comment": "" | ||
| }, | ||
| "vector_launch_year": 2023, | ||
| "metadata_filter": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "hybrid_search": { | ||
| "support": "full", | ||
| "source_url": "https://www.timescale.com/blog/postgresql-hybrid-search-using-pgvector-and-cohere", | ||
| "comment": "Hybrid search provided by combination of PostgreSQL full text search and pgvectorscale" | ||
| }, | ||
| "facets": { | ||
| "support": "", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "geo_search": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "Postgis" | ||
| }, | ||
| "multi_vec": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "sparse_vectors": { | ||
| "support": "full", | ||
| "source_url": "https://github.com/pgvector/pgvector#sparse-vectors", | ||
| "comment": "provided by pgvector" | ||
| }, | ||
| "bm25": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "provided by pgvector" | ||
| }, | ||
| "full_text": { | ||
| "support": "full", | ||
| "source_url": "https://www.postgresql.org/docs/current/textsearch.html", | ||
| "comment": "https://www.postgresql.org/docs/current/textsearch.html via GIST" | ||
| }, | ||
| "embeddings_text": { | ||
| "support": "full", | ||
| "source_url": "https://github.com/timescale/pgai", | ||
| "comment": "" | ||
| }, | ||
| "embeddings_image": { | ||
| "support": "full", | ||
| "source_url": "https://github.com/timescale/pgai", | ||
| "comment": "" | ||
| }, | ||
| "embeddings_structured": { | ||
| "support": "", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "rag": { | ||
| "support": "full", | ||
| "source_url": "https://github.com/timescale/pgai", | ||
| "comment": "" | ||
| }, | ||
| "recsys": { | ||
| "support": "", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "langchain": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "llamaindex": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "managed_cloud": { | ||
| "support": "full", | ||
| "source_url": "https://www.timescale.com/cloud", | ||
| "comment": "" | ||
| }, | ||
| "pricing": { | ||
| "value": "Timescale Cloud", | ||
| "source_url": "https://www.timescale.com/pricing", | ||
| "comment": "" | ||
| }, | ||
| "in_process": { | ||
| "support": "none", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "multi_tenancy": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "disk_index": { | ||
| "support": "full", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "ephemeral": { | ||
| "support": "none", | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "sharding": { | ||
| "support": "none", | ||
| "source_url": "", | ||
| "comment": "While pgvectorscale does not provide this natively, you can get this either from PostgreSQL functionality like \"postgres_fdw\" or from extensions. You can also choose to subdivide your index through partitioning." | ||
| }, | ||
| "doc_size": { | ||
| "bytes": 0, | ||
| "unlimited": false, | ||
| "source_url": "", | ||
| "comment": "" | ||
| }, | ||
| "vector_dims": { | ||
| "value": 16000, | ||
| "unlimited": false, | ||
| "source_url": "https://github.com/timescale/pgvectorscale/pull/181", | ||
| "comment": "" | ||
| }, | ||
| "index_types": { | ||
| "value": [ | ||
| "DiskANN", | ||
| "FreshDiskANN" | ||
| ], | ||
| "source_url": "https://www.timescale.com/blog/how-we-made-postgresql-as-fast-as-pinecone-for-vector-data", | ||
| "comment": "" | ||
| }, | ||
| "github_stars": { | ||
| "value": 2008, | ||
| "source_url": "https://github.com/timescale/pgvectorscale", | ||
| "comment": "", | ||
| "value_90_days": 0 | ||
| }, | ||
| "docker_pulls": { | ||
| "value": 0, | ||
| "source_url": "", | ||
| "comment": "", | ||
| "value_90_days": 0 | ||
| }, | ||
| "pypi_downloads": { | ||
| "value": 0, | ||
| "source_url": "", | ||
| "comment": "", | ||
| "value_90_days": 0 | ||
| }, | ||
| "npm_downloads": { | ||
| "value": 0, | ||
| "source_url": "", | ||
| "comment": "", | ||
| "value_90_days": 0 | ||
| }, | ||
| "crates_io_downloads": { | ||
| "value": 0, | ||
| "source_url": "", | ||
| "comment": "", | ||
| "value_90_days": 0 | ||
| } | ||
| } |
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hmmm, I think we would have to aggregate this entry across all the providers you listed in managed clouds, probably doesn't make sense to have 1 PSQL provider mentioned here alone
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just to clarify: pgai is an open-source python library that automates creating embeddings from Postgres data. It is not tied to any cloud.