Create an Embedding Strategy — embedding

Construct an Embedding_Strategy object for semantic matching using embeddings. This is a distinct strategy type from token-based strategies created with search_strategy().

Embedding strategies:

Represent entire records as embedding vectors
Use cosine similarity for scoring
Support blocking variables to restrict comparisons
Require the tidyllm package for embedding computation

Usage

embedding_strategy(
  columns = NULL,
  embedding_model,
  threshold,
  collapse_sep = " ",
  normalize = TRUE,
  batch_size = 1000,
  block_by = NULL
)

Arguments

columns: Character vector of column names to embed, or NULL (default) to use all non-id character-like columns.
embedding_model: A tidyllm provider object (e.g., ollama(.model = "mxbai-embed-large")). This is passed directly to tidyllm's embed() function.
threshold: Numeric scalar in (0, 1). Cosine similarity threshold for filtering matches.
collapse_sep: Character scalar. Separator used when joining multiple columns into a single text string. Default is " ".
normalize: Logical scalar. If TRUE (default), apply L2 normalization to embeddings before computing cosine similarity.
batch_size: Numeric scalar. Number of records to process per batch when computing embeddings. Default is 1000.
block_by: Character vector of blocking variable names, or NULL (default). When specified, comparisons are only made within matching blocks.

Value

An Embedding_Strategy S7 object.

Examples

if (FALSE) { # \dontrun{
library(tidyllm)

# Create an embedding strategy using Ollama
emb_strat <- embedding_strategy(
  columns = c("name", "address"),
  embedding_model = ollama(.model = "mxbai-embed-large"),
  threshold = 0.85
)

# Use in multi-stage workflow
results <- multi_stage_search(
  base_table = customers_a,
  target_table = customers_b,
  base_id = "id_a",
  target_id = "id_b",
  strategies = list(
    token_stage = search_strategy(name ~ normalize_text() + word_tokens()),
    semantic_stage = emb_strat
  )
)
} # }