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| # Load reticulate for Python integration | |
| library(reticulate) | |
| # Import necessary Python libraries | |
| pd <- import("pandas") | |
| torch <- import("torch") | |
| sentence_transformers <- import("sentence_transformers") | |
| # Load the sentence-transformer model | |
| load_model <- function(model_name) { | |
| cat("Loading model:", model_name, "\n") | |
| embedder <- sentence_transformers$SentenceTransformer(model_name) | |
| # Check and use GPU if available | |
| if (torch$cuda$is_available()) { | |
| cat("GPU found! Using CUDA.\n") | |
| embedder$to("cuda") | |
| } else { | |
| cat("No GPU found. Using CPU.\n") | |
| } | |
| return(embedder) | |
| } | |
| # Function to generate embedding for a query | |
| generate_query_embedding <- function(query, model_name = "sentence-transformers/all-MiniLM-L6-v2") { | |
| # Load the model | |
| embedder <- load_model(model_name) | |
| # Generate embedding | |
| cat("Generating embedding for the query...\n") | |
| embedding <- embedder$encode(query) | |
| # Convert the embedding to a numeric vector | |
| return(as.numeric(embedding)) | |
| } | |
| # # Example usage | |
| # query <- "I need a package for data visualization" | |
| # model_name <- "sentence-transformers/all-MiniLM-L6-v2" | |
| # query_embedding <- generate_query_embedding(query, model_name) | |
| # | |
| # # Print the embedding | |
| # cat("Embedding for query:\n") | |
| # print(query_embedding) | |