diff --git a/tests/tests_rafa/fts_extension.R b/tests/tests_rafa/fts_extension.R
index b442f46..1c7f383 100644
--- a/tests/tests_rafa/fts_extension.R
+++ b/tests/tests_rafa/fts_extension.R
@@ -103,14 +103,11 @@ DBI::dbListTables(con)
 # Perform the LEFT JOIN with match_bm25 scoring
 query_left_join <- glue::glue(
   "SELECT
+      input_db.id,
       input_db.logradouro AS input_logradouro,
-      input_db.numero AS input_numero,
-      input_db.cep AS input_cep,
       cnefe.logradouro AS cnefe_logradouro,
-      cnefe.numero AS cnefe_numero,
-      cnefe.cep AS cnefe_cep,
       cnefe.lat AS cnefe_lat,
-     fts_main_cnefe.match_bm25(input_db.logradouro, cnefe.logradouro) AS score
+     fts_main_cnefe.match_bm25(input_logradouro, cnefe_logradouro) AS score
     FROM
       input_db
     LEFT JOIN
@@ -131,3 +128,80 @@ result
 print(result)
 
 
+
+
+
+
+# solucao sem fts -------------------------------------
+
+#' https://duckdb.org/docs/sql/functions/char.html
+#' damerau_levenshtein
+#' jaccard
+#' jaro_similarity
+#' jaro_winkler_similarity
+#'
+
+# threshold
+query_join <- glue::glue(
+  "WITH similarity_scores AS (
+     SELECT
+       i.id AS input_id,
+       c.cnefe_id,
+       i.municipio AS input_municipio,
+       c.municipio AS cnefe_municipio,
+       i.logradouro AS input_logradouro,
+       c.logradouro AS cnefe_logradouro,
+       jaro_winkler_similarity(i.logradouro, c.logradouro) AS similarity
+     FROM input_db i
+     JOIN cnefe c
+     ON i.municipio = c.municipio
+   )
+   SELECT
+     input_id,
+     cnefe_id,
+     input_municipio,
+     cnefe_municipio,
+     input_logradouro,
+     cnefe_logradouro,
+     similarity
+   FROM similarity_scores
+   WHERE similarity > 0.8 -- Adjust the threshold as needed
+   ORDER BY similarity DESC;"
+)
+
+# return max similariy
+query_max_similarity <- glue::glue(
+  "WITH similarity_scores AS (
+     SELECT
+       i.id AS input_id,
+       c.cnefe_id,
+       i.municipio AS input_municipio,
+       c.municipio AS cnefe_municipio,
+       i.logradouro AS input_logradouro,
+       c.logradouro AS cnefe_logradouro,
+       jaro_winkler_similarity(i.logradouro, c.logradouro) AS similarity,
+       RANK() OVER (PARTITION BY i.id ORDER BY jaro_winkler_similarity(i.logradouro, c.logradouro) DESC) AS rank
+     FROM input_db i
+     JOIN cnefe c
+     ON i.municipio = c.municipio
+   )
+   SELECT
+     input_id,
+     cnefe_id,
+     input_municipio,
+     cnefe_municipio,
+     input_logradouro,
+     cnefe_logradouro,
+     similarity,
+     rank
+   FROM similarity_scores
+   WHERE similarity > 0.8 -- Adjust the threshold as needed
+   AND rank = 1 -- Only keep the best match per input;"
+)
+
+
+
+# Execute the query
+DBI::dbGetQuery(con, query_join)
+DBI::dbGetQuery(con, query_max_similarity)
+
diff --git a/tests/tests_rafa/geocode_like.R b/tests/tests_rafa/geocode_like.R
deleted file mode 100644
index c8f6337..0000000
--- a/tests/tests_rafa/geocode_like.R
+++ /dev/null
@@ -1,328 +0,0 @@
-#' Geocode Brazilian addresses
-#'
-#' Geocodes Brazilian addresses based on CNEFE data. Addresses must be passed as
-#' a data frame in which each column describes one address field (street name,
-#' street number, neighborhood, etc). The input addresses are matched with CNEFE
-#' following 12 different case patterns. For more info, please see the Details
-#' section.
-#'
-#' @param addresses_table A data frame. The addresses to be geocoded. Each
-#'   column must represent an address field.
-#' @param address_fields A character vector. The correspondence between each
-#'   address field and the name of the column that describes it in
-#'   `addresses_table`. The [setup_address_fields()] function helps creating
-#'   this vector and performs some checks on the input. Address fields
-#'   passed as `NULL` are ignored and the function must receive at least one
-#'   non-null field. If manually creating the vector, please note that the
-#'   vector names should be the same names used in the [setup_address_fields()]
-#'   parameters.
-#' @template n_cores
-#' @template progress
-#' @template cache
-#'
-#' @return Returns the data frame passed in `addresses_table` with the latitude
-#'   (`lat`) and longitude (`lon`) of each matched address, as well as another
-#'   column (`match_type`) indicating the match level with which the address was
-#'   matched.
-#'
-#' @details
-#' The input addresses are deterministically matched with CNEFE following 12
-#' different case patterns. The type of match found for each address in the
-#' input data is indicated by the `match_type` column in the output. In every
-#' match type, the function always calculates the average latitude and longitude
-#' of all addresses in CNEFE that match the input address. In the strictest case,
-#' the function finds a perfect match for all of the fields of a given address.
-#' Think for example of a building with several apartments that match the same
-#' street address. In such case, the coordinates of the apartments will differ
-#' very slightly, and {geocodebr} take the average of those coordinates. On the
-#' other hand, in the loosest case, in which only the state and the city are
-#' matched, geocodebr takes the city-wide average coordinates, which tends to
-#' favor more densely populated areas. The columns considered in each of the 12
-#' different match types are described below:
-#'
-#' - Case 01: estado, município, logradouro, número, cep e bairro;
-#' - Case 02: estado, município, logradouro, número e cep;
-#' - Case 03: estado, município, logradouro, número e bairro;
-#' - Case 04: estado, município, logradouro e número;
-#' - Case 05: estado, município, logradouro, cep e bairro;
-#' - Case 06: estado, município, logradouro e cep;
-#' - Case 07: estado, município, logradouro e bairro;
-#' - Case 08: estado, município e logradouro;
-#' - Case 09: estado, município, cep e bairro;
-#' - Case 10: estado, município e cep;
-#' - Case 11: estado, município e bairro;
-#' - Case 12: estado, município.
-#'
-#' @examplesIf identical(tolower(Sys.getenv("NOT_CRAN")), "true")
-#'
-#' data_path <- system.file("extdata/small_sample.csv", package = "geocodebr")
-#' input_df <- read.csv(data_path)
-#'
-#' fields <- setup_address_fields(
-#'   logradouro = "nm_logradouro",
-#'   numero = "Numero",
-#'   cep = "Cep",
-#'   bairro = "Bairro",
-#'   municipio = "nm_municipio",
-#'   estado = "nm_uf"
-#' )
-#'
-#' df <- geocode(input_df, address_fields = fields, progress = FALSE)
-#' df
-#'
-geocode_like <- function(addresses_table,
-                    address_fields = setup_address_fields(),
-                    n_cores = 1,
-                    progress = TRUE,
-                    cache = TRUE) {
-
-  # check input
-  assert_address_fields(address_fields, addresses_table)
-  checkmate::assert_data_frame(addresses_table)
-  checkmate::assert_number(n_cores, lower = 1, finite = TRUE)
-  checkmate::assert_logical(progress, any.missing = FALSE, len = 1)
-  checkmate::assert_logical(cache, any.missing = FALSE, len = 1)
-
-  # standardizing the addresses table to increase the chances of finding a match
-  # in the CNEFE data
-
-  if (progress) message_standardizing_addresses()
-
-  standard_locations <- enderecobr::padronizar_enderecos(
-    addresses_table,
-    campos_do_endereco = enderecobr::correspondencia_campos(
-      logradouro = address_fields[["logradouro"]],
-      numero = address_fields[["numero"]],
-      cep = address_fields[["cep"]],
-      bairro = address_fields[["bairro"]],
-      municipio = address_fields[["municipio"]],
-      estado = address_fields[["estado"]]
-    ),
-    formato_estados = "sigla"
-  )
-
-  # downloading cnefe. we only need to download the states present in the
-  # addresses table, which may save us some time.
-
-  present_states <- unique(standard_locations$estado_padr)
-  cnefe_dir <- download_cnefe(
-    present_states,
-    progress = progress,
-    cache = cache
-  )
-
-  # creating a temporary db and register the input table data
-
-  tmpdb <- tempfile(fileext = ".duckdb")
-  con <- duckdb::dbConnect(duckdb::duckdb(), dbdir = tmpdb)
-
-  DBI::dbExecute(con, glue::glue("SET threads = {n_cores}"))
-
-  duckdb::dbWriteTable(
-    con,
-    name = "standard_locations",
-    value = standard_locations,
-    temporary = TRUE
-  )
-
-  # register cnefe data to db, but only include states and municipalities
-  # present in the input table, reducing the search scope and consequently
-  # reducing processing time and memory usage
-
-  unique_muns <- unique(standard_locations$municipio_padr)
-
-  filtered_cnefe <- arrow::open_dataset(cnefe_dir)
-  filtered_cnefe <- dplyr::filter(
-    filtered_cnefe,
-    estado %in% present_states & municipio %in% unique_muns
-  )
-
-  duckdb::duckdb_register_arrow(con, "filtered_cnefe", filtered_cnefe)
-
-  # to find the coordinates of the addresses, we merge the input table with the
-  # cnefe data. the column names used in the input table are different than the
-  # ones used in cnefe, so we create a helper object to "translate" the column
-  # names between datasets
-
-  equivalent_colnames <- tibble::tribble(
-    ~standard_locations, ~cnefe,
-    "logradouro_padr",   "logradouro_sem_numero",
-    "numero_padr",       "numero",
-    "cep_padr",          "cep",
-    "bairro_padr",       "localidade",
-    "municipio_padr",    "municipio",
-    "estado_padr",       "estado"
-  )
-
-  lookup_vector <- equivalent_colnames$cnefe
-  names(lookup_vector) <- equivalent_colnames$standard_locations
-
-  # when merging the data, we have several different cases with different confidence
-  # levels. from best to worst, they are:
-  #
-  # - case 01: match estado, municipio, logradouro, numero, cep, localidade
-  # - case 02: match estado, municipio, logradouro, numero, cep
-  # - case 03: match estado, municipio, logradouro, numero, localidade
-  # - case 04: match estado, municipio, logradouro, numero
-  # - case 05: match estado, municipio, logradouro, cep, localidade
-  # - case 06: match estado, municipio, logradouro, cep
-  # - case 07: match estado, municipio, logradouro, localidade
-  # - case 08: match estado, municipio, logradouro
-  # - case 09: match estado, municipio, cep, localidade
-  # - case 10: match estado, municipio, cep
-  # - case 11: match estado, municipio, localidade
-  # - case 12: match estado, municipio
-
-  DBI::dbExecute(con, "ALTER TABLE standard_locations ADD COLUMN match_type VARCHAR DEFAULT NULL")
-  DBI::dbExecute(con, "ALTER TABLE standard_locations ADD COLUMN lon DOUBLE DEFAULT NULL")
-  DBI::dbExecute(con, "ALTER TABLE standard_locations ADD COLUMN lat DOUBLE DEFAULT NULL")
-
-  if (progress) {
-    prog <- create_progress_bar(standard_locations)
-    n_rows_affected <- 0
-
-    message_looking_for_matches()
-  }
-
-  for (case in 1:12) {
-    relevant_cols <- get_relevant_cols(case)
-    formatted_case <- formatC(case, width = 2, flag = "0")
-
-    if (progress) update_progress_bar(n_rows_affected, formatted_case)
-
-    if (all(relevant_cols %in% names(standard_locations))) {
-      join_condition <- paste(
-        glue::glue("aggregated_cnefe.{lookup_vector[relevant_cols]} = standard_locations.{relevant_cols}"),
-        collapse = " AND "
-      )
-
-      # use LIKE for logradouro
-      join_condition <- gsub("= standard_locations.logradouro_padr", "LIKE '%' || standard_locations.logradouro_padr || '%'", join_condition)
-
-
-
-
-      cnefe_cols <- paste(lookup_vector[relevant_cols], collapse = ", ")
-
-      n_rows_affected <- DBI::dbExecute(
-        con,
-        glue::glue(
-          "UPDATE standard_locations ",
-          "SET lat = aggregated_cnefe.lat, lon = aggregated_cnefe.lon, match_type = 'case_{formatted_case}' ",
-          "FROM ",
-          "  (SELECT {cnefe_cols}, AVG(lon) AS lon, AVG(lat) AS lat FROM filtered_cnefe GROUP BY {cnefe_cols}) AS aggregated_cnefe ",
-          "WHERE match_type IS NULL AND {join_condition}"
-        )
-      )
-    }
-  }
-
-  if (progress) finish_progress_bar(n_rows_affected)
-
-  cols_to_keep <- names(standard_locations)
-  cols_to_keep <- cols_to_keep[!grepl("_padr$", cols_to_keep)]
-  cols_to_keep <- c(cols_to_keep, "match_type", "lon", "lat")
-  cols_to_keep <- paste(cols_to_keep, collapse = ", ")
-
-  output <- dplyr::tbl(
-    con,
-    dplyr::sql(glue::glue("SELECT {cols_to_keep} FROM standard_locations"))
-  )
-  output <- dplyr::collect(output)
-
-  duckdb::dbDisconnect(con, shutdown = TRUE)
-
-  return(output)
-}
-
-assert_address_fields <- function(address_fields, addresses_table) {
-  col <- checkmate::makeAssertCollection()
-  checkmate::assert_names(
-    names(address_fields),
-    type = "unique",
-    subset.of = c(
-      "logradouro",
-      "numero",
-      "cep",
-      "bairro",
-      "municipio",
-      "estado"
-    ),
-    add = col
-  )
-  checkmate::assert_names(
-    address_fields,
-    subset.of = names(addresses_table),
-    add = col
-  )
-  checkmate::reportAssertions(col)
-
-  return(invisible(TRUE))
-}
-
-message_standardizing_addresses <- function() {
-  geocodebr_message(c("i" = "Standardizing input addresses"))
-}
-
-message_looking_for_matches <- function() {
-  geocodebr_message(c("i" = "Looking for matches in CNEFE"))
-}
-
-get_relevant_cols <- function(case) {
-  relevant_cols <- if (case == 1) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "numero_padr", "cep_padr", "bairro_padr")
-  } else if (case == 2) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "numero_padr", "cep_padr")
-  } else if (case == 3) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "numero_padr", "bairro_padr")
-  } else if (case == 4) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "numero_padr")
-  } else if (case == 5) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "cep_padr", "bairro_padr")
-  } else if (case == 6) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "cep_padr")
-  } else if (case == 7) {
-    c("estado_padr", "municipio_padr", "logradouro_padr", "bairro_padr")
-  } else if (case == 8) {
-    c("estado_padr", "municipio_padr", "logradouro_padr")
-  } else if (case == 9) {
-    c("estado_padr", "municipio_padr", "cep_padr", "bairro_padr")
-  } else if (case == 10) {
-    c("estado_padr", "municipio_padr", "cep_padr")
-  } else if (case == 11) {
-    c("estado_padr", "municipio_padr", "bairro_padr")
-  } else if (case == 12) {
-    c("estado_padr", "municipio_padr")
-  }
-
-  return(relevant_cols)
-}
-
-create_progress_bar <- function(standard_locations, .envir = parent.frame()) {
-  cli::cli_progress_bar(
-    total = nrow(standard_locations),
-    format = "Matched addresses: {formatC(cli::pb_current, big.mark = ',', format = 'd')}/{formatC(cli::pb_total, big.mark = ',', format = 'd')} {cli::pb_bar} {cli::pb_percent} - {cli::pb_status}",
-    clear = FALSE,
-    .envir = .envir
-  )
-}
-
-update_progress_bar <- function(n_rows_affected,
-                                formatted_case,
-                                .envir = parent.frame()) {
-  cli::cli_progress_update(
-    inc = n_rows_affected,
-    status = glue::glue("Looking for case {formatted_case} matches"),
-    force = TRUE,
-    .envir = .envir
-  )
-}
-
-finish_progress_bar <- function(n_rows_affected, .envir = parent.frame()) {
-  cli::cli_progress_update(
-    inc = n_rows_affected,
-    status = "Done!",
-    force = TRUE,
-    .envir = .envir
-  )
-}