refactor(scoring): improve context relevancy scoring

Remove MIN_RESULTS constant and use MULTI_FILE_THRESHOLD consistently for
minimum results filtering. Improve scoring by adding base score to symbol
matches and simplify spatial distance calculation. Add score bonus for
current file context to improve relevancy of local results.

These changes make the context selection more accurate by:
- Using consistent thresholds
- Better handling of symbol matching scores
- Prioritizing local context appropriately

lua/CopilotChat/context.lua

@@ -69,17 +69,15 @@ local OFF_SIDE_RULE_LANGUAGES = {
 local MIN_SYMBOL_SIMILARITY = 0.3
 local MIN_SEMANTIC_SIMILARITY = 0.4
 local MULTI_FILE_THRESHOLD = 5
-local MIN_RESULTS = 3
 local MAX_FILES = 2500
 
 --- Compute the cosine similarity between two vectors
 ---@param a table<number>
 ---@param b table<number>
----@param def number
 ---@return number
-local function spatial_distance_cosine(a, b, def)
+local function spatial_distance_cosine(a, b)
   if not a or not b then
-    return def or 0
+    return 0
   end
 
   local dot_product = 0
@@ -103,8 +101,9 @@ end
 local function data_ranked_by_relatedness(query, data, min_similarity)
   local results = {}
   for _, item in ipairs(data) do
-    local similarity = spatial_distance_cosine(item.embedding, query.embedding, item.score)
-    table.insert(results, vim.tbl_extend('force', item, { score = similarity }))
+    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 }))
   end
 
   table.sort(results, function(a, b)
@@ -114,7 +113,7 @@ local function data_ranked_by_relatedness(query, data, min_similarity)
   -- Take top MAX_RESULTS items that meet threshold, or at least MIN_RESULTS items
   local filtered = {}
   for i, result in ipairs(results) do
-    if (result.score >= min_similarity) or (i <= MIN_RESULTS) then
+    if (result.score >= min_similarity) or (i <= MULTI_FILE_THRESHOLD) then
       table.insert(filtered, result)
     end
   end
@@ -175,7 +174,6 @@ local function data_ranked_by_symbols(query, data, min_similarity)
   local max_score = 0
 
   for _, entry in ipairs(data) do
-    local score = entry.score or 0
     local basename = vim.fn.fnamemodify(entry.filename, ':t'):gsub('%..*$', '')
 
     -- Get trigrams for basename and compound version
@@ -187,7 +185,7 @@ local function data_ranked_by_symbols(query, data, min_similarity)
     local compound_sim = trigram_similarity(query_trigrams, compound_trigrams)
 
     -- Take best match
-    score = math.max(name_sim, compound_sim)
+    local score = (entry.score or 0) + math.max(name_sim, compound_sim)
 
     -- Add symbol matches
     if entry.symbols then
@@ -221,7 +219,7 @@ local function data_ranked_by_symbols(query, data, min_similarity)
   -- Filter results while preserving top scores
   local filtered_results = {}
   for i, result in ipairs(results) do
-    if (result.score >= min_similarity) or (i <= MIN_RESULTS) then
+    if (result.score >= min_similarity) or (i <= MULTI_FILE_THRESHOLD) then
       table.insert(filtered_results, result)
     end
   end
@@ -408,6 +406,7 @@ function M.files(winnr, with_content)
       content = table.concat(chunk, '\n'),
       filename = chunk_name,
       filetype = 'text',
+      score = 0.2, -- Score bonus
     })
   end