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pgvector-r
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pgvector examples for R
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pgvector-r
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# pgvector-r [pgvector](https://github.com/pgvector/pgvector) examples for R Supports [DBI](https://github.com/r-dbi/DBI) and [dbx](https://github.com/ankane/dbx) [](https://github.com/pgvector/pgvector-r/actions) ## Getting Started Follow the instructions for your database library: - [DBI](#dbi) - [dbx](#dbx) Or check out an example: - [Embeddings](examples/openai/example.R) with OpenAI - [Binary embeddings](examples/cohere/example.R) with Cohere - [Sparse search](examples/sparse/example.R) with Text Embeddings Inference - [Molecular fingerprints](examples/fingerprint/example.R) with ChemmineR ## DBI Enable the extension ```r dbExecute(db, "CREATE EXTENSION IF NOT EXISTS vector") ``` Create a table ```r dbExecute(db, "CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))") ``` Insert vectors ```r encodeVector <- function(vec) { stopifnot(is.numeric(vec)) paste0("[", paste(vec, collapse=","), "]") } embeddings <- list( c(1, 1, 1), c(2, 2, 2), c(1, 1, 2) ) items <- data.frame(embedding=sapply(embeddings, encodeVector)) dbAppendTable(db, "items", items) ``` Get the nearest neighbors ```r params <- list(encodeVector(c(1, 2, 3))) dbGetQuery(db, "SELECT * FROM items ORDER BY embedding <-> $1 LIMIT 5", params=params) ``` Add an approximate index ```r dbExecute(db, "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)") # or dbExecute(db, "CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)") ``` Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance See a [full example](DBI/example.R) ## dbx Enable the extension ```r dbxExecute(db, "CREATE EXTENSION IF NOT EXISTS vector") ``` Create a table ```r dbxExecute(db, "CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3))") ``` Insert vectors ```r encodeVector <- function(vec) { stopifnot(is.numeric(vec)) paste0("[", paste(vec, collapse=","), "]") } embeddings <- list( c(1, 1, 1), c(2, 2, 2), c(1, 1, 2) ) items <- data.frame(embedding=sapply(embeddings, encodeVector)) dbxInsert(db, "items", items) ``` Get the nearest neighbors ```r params <- list(encodeVector(c(1, 2, 3))) dbxSelect(db, "SELECT * FROM items ORDER BY embedding <-> ? LIMIT 5", params=params) ``` Add an approximate index ```r dbxExecute(db, "CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)") # or dbxExecute(db, "CREATE INDEX ON items USING ivfflat (embedding vector_l2_ops) WITH (lists = 100)") ``` Use `vector_ip_ops` for inner product and `vector_cosine_ops` for cosine distance See a [full example](dbx/example.R) ## Contributing Everyone is encouraged to help improve this project. Here are a few ways you can help: - [Report bugs](https://github.com/pgvector/pgvector-r/issues) - Fix bugs and [submit pull requests](https://github.com/pgvector/pgvector-r/pulls) - Write, clarify, or fix documentation - Suggest or add new features To get started with development: ```sh git clone https://github.com/pgvector/pgvector-r.git cd pgvector-r createdb pgvector_r_test Rscript -e "install.packages('remotes', repos='https://cloud.r-project.org')" Rscript -e "remotes::install_deps(dependencies=TRUE)" Rscript DBI/example.R Rscript dbx/example.R ``` To run an example: ```sh cd examples/openai createdb pgvector_example Rscript -e "remotes::install_deps(dependencies=TRUE)" Rscript example.R ```
