fix: include entries with zero score in results

The change removes the conditional check that filtered out entries with zero
scores. This ensures all entries are included in the results array, allowing
for proper normalization of scores across the entire dataset.

Signed-off-by: Tomas Slusny <[email protected]>

lua/CopilotChat/context.lua

@@ -99,20 +99,18 @@ end
 ---@param min_similarity number
 ---@return table<CopilotChat.context.embed>
 local function data_ranked_by_relatedness(query, data, min_similarity)
-  local results = {}
   for _, item in ipairs(data) do
     local score = spatial_distance_cosine(item.embedding, query.embedding)
-    score = score or item.score or 0
-    table.insert(results, vim.tbl_extend('force', item, { score = score }))
+    item.score = score or item.score or 0
   end
 
-  table.sort(results, function(a, b)
+  table.sort(data, function(a, b)
     return a.score > b.score
   end)
 
-  -- Take top MAX_RESULTS items that meet threshold, or at least MIN_RESULTS items
+  -- Return top items meeting threshold
   local filtered = {}
-  for i, result in ipairs(results) do
+  for i, result in ipairs(data) do
     if (result.score >= min_similarity) or (i <= MULTI_FILE_THRESHOLD) then
       table.insert(filtered, result)
     end
@@ -170,7 +168,6 @@ local function data_ranked_by_symbols(query, data, min_similarity)
     query_trigrams[trigram] = true
   end
 
-  local results = {}
   local max_score = 0
 
   for _, entry in ipairs(data) do
@@ -200,25 +197,23 @@ local function data_ranked_by_symbols(query, data, min_similarity)
       score = score + (symbol_score * 0.5) -- Weight symbol matches less
     end
 
-    if score > 0 then
-      max_score = math.max(max_score, score)
-      table.insert(results, vim.tbl_extend('force', entry, { score = score }))
-    end
+    max_score = math.max(max_score, score)
+    entry.score = score
   end
 
   -- Normalize scores
-  for _, result in ipairs(results) do
-    result.score = result.score / max_score
+  for _, entry in ipairs(data) do
+    entry.score = entry.score / max_score
   end
 
   -- Sort by score first
-  table.sort(results, function(a, b)
+  table.sort(data, function(a, b)
     return a.score > b.score
   end)
 
   -- Filter results while preserving top scores
   local filtered_results = {}
-  for i, result in ipairs(results) do
+  for i, result in ipairs(data) do
     if (result.score >= min_similarity) or (i <= MULTI_FILE_THRESHOLD) then
       table.insert(filtered_results, result)
     end