From: Vsevolod Stakhov <vsevolod@rspamd.com>
Date: Mon, 21 Jul 2025 09:07:27 +0000 (+0100)
Subject: [Minor] Fix various issues
X-Git-Tag: 3.13.0~38^2~22
X-Git-Url: http://git.ipfire.org/cgi-bin/gitweb.cgi?a=commitdiff_plain;h=a9c5cd3bbda25397b613bbee8dd87c464dd158c2;p=thirdparty%2Frspamd.git

[Minor] Fix various issues
---

diff --git a/lualib/lua_bayes_redis.lua b/lualib/lua_bayes_redis.lua
index 59952131ab..c086669fed 100644
--- a/lualib/lua_bayes_redis.lua
+++ b/lualib/lua_bayes_redis.lua
@@ -31,24 +31,9 @@ local function gen_classify_functor(redis_params, classify_script_id)
       if err then
         callback(task, false, err)
       else
-        -- Handle both binary and multi-class results
-        if type(data[1]) == "table" then
-          -- Multi-class format: [learned_counts_table, outputs_table]
-          -- Convert to binary format for backward compatibility if needed
-          local learned_counts = data[1]
-          local outputs = data[2]
-
-          -- For now, return ham/spam data if available for backward compatibility
-          local learned_ham = learned_counts["H"] or learned_counts["ham"] or 0
-          local learned_spam = learned_counts["S"] or learned_counts["spam"] or 0
-          local output_ham = outputs["H"] or outputs["ham"] or {}
-          local output_spam = outputs["S"] or outputs["spam"] or {}
-
-          callback(task, true, learned_ham, learned_spam, output_ham, output_spam)
-        else
-          -- Binary format: [learned_ham, learned_spam, output_ham, output_spam]
-          callback(task, true, data[1], data[2], data[3], data[4])
-        end
+        -- Pass the raw data table to the C++ callback for processing
+        -- The C++ callback will handle both binary and multi-class formats
+        callback(task, true, data)
       end
     end
 
diff --git a/src/libstat/backends/redis_backend.cxx b/src/libstat/backends/redis_backend.cxx
index 01ed818c47..0fe738de50 100644
--- a/src/libstat/backends/redis_backend.cxx
+++ b/src/libstat/backends/redis_backend.cxx
@@ -866,9 +866,20 @@ rspamd_redis_classified(lua_State *L)
 	bool result = lua_toboolean(L, 2);
 
 	if (result) {
-		/* Check if this is binary format [learned_ham, learned_spam, ham_tokens, spam_tokens]
-		 * or multi-class format [learned_counts_table, outputs_table]
+		/* Check we have enough arguments and the result data is a table */
+		if (lua_gettop(L) < 3 || !lua_istable(L, 3)) {
+			msg_err_task("internal error: expected table result from Redis script, got %s",
+						 lua_typename(L, lua_type(L, 3)));
+			rt->err = rspamd::util::error("invalid Redis script result format", 500);
+			return 0;
+		}
+
+		/* Check the array length to determine format:
+		 * - Length 4: binary format [learned_ham, learned_spam, ham_tokens, spam_tokens]
+		 * - Length 2: multi-class format [learned_counts_table, outputs_table]
 		 */
+		size_t result_len = rspamd_lua_table_size(L, 3);
+		msg_debug_bayes("Redis result array length: %uz", result_len);
 
 		auto filler_func = [](redis_stat_runtime<float> *rt, lua_State *L, unsigned learned, int tokens_pos) {
 			rt->learned = learned;
@@ -891,10 +902,7 @@ rspamd_redis_classified(lua_State *L)
 			rt->set_results(res);
 		};
 
-		/* Check if result[3] is a number (binary) or table (multi-class) */
-		lua_rawgeti(L, 3, 1); /* Get first element of result array */
-		bool is_binary_format = lua_isnumber(L, -1);
-		lua_pop(L, 1);
+		bool is_binary_format = (result_len == 4);
 
 		if (is_binary_format) {
 			/* Binary format: [learned_ham, learned_spam, ham_tokens, spam_tokens] */
@@ -935,7 +943,7 @@ rspamd_redis_classified(lua_State *L)
 		else {
 			/* Multi-class format: [learned_counts_table, outputs_table] */
 
-			/* Get learned counts table (index 3) and outputs table (index 4) */
+			/* Get learned counts table (index 1) and outputs table (index 2) */
 			lua_rawgeti(L, 3, 1); /* learned_counts */
 			lua_rawgeti(L, 3, 2); /* outputs */
 
@@ -984,10 +992,18 @@ rspamd_redis_classified(lua_State *L)
 	}
 	else {
 		/* Error message is on index 3 */
-		const auto *err_msg = lua_tostring(L, 3);
-		rt->err = rspamd::util::error(err_msg, 500);
-		msg_err_task("cannot classify task: %s",
-					 err_msg);
+		const char *err_msg = nullptr;
+		if (lua_gettop(L) >= 3 && lua_isstring(L, 3)) {
+			err_msg = lua_tostring(L, 3);
+		}
+		if (err_msg) {
+			rt->err = rspamd::util::error(err_msg, 500);
+			msg_err_task("cannot classify task: %s", err_msg);
+		}
+		else {
+			rt->err = rspamd::util::error("unknown Redis script error", 500);
+			msg_err_task("cannot classify task: unknown Redis script error");
+		}
 	}
 
 	return 0;
@@ -1102,9 +1118,18 @@ rspamd_redis_learned(lua_State *L)
 	}
 	else {
 		/* Error message is on index 3 */
-		const auto *err_msg = lua_tostring(L, 3);
-		rt->err = rspamd::util::error(err_msg, 500);
-		msg_err_task("cannot learn task: %s", err_msg);
+		const char *err_msg = nullptr;
+		if (lua_gettop(L) >= 3 && lua_isstring(L, 3)) {
+			err_msg = lua_tostring(L, 3);
+		}
+		if (err_msg) {
+			rt->err = rspamd::util::error(err_msg, 500);
+			msg_err_task("cannot learn task: %s", err_msg);
+		}
+		else {
+			rt->err = rspamd::util::error("unknown Redis script error", 500);
+			msg_err_task("cannot learn task: unknown Redis script error");
+		}
 	}
 
 	return 0;
diff --git a/src/libstat/classifiers/bayes.c b/src/libstat/classifiers/bayes.c
index 4a1b0cf32a..836c27c88d 100644
--- a/src/libstat/classifiers/bayes.c
+++ b/src/libstat/classifiers/bayes.c
@@ -1,11 +1,11 @@
-/*-
- * Copyright 2016 Vsevolod Stakhov
+/*
+ * Copyright 2025 Vsevolod Stakhov
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
  * You may obtain a copy of the License at
  *
- *   http://www.apache.org/licenses/LICENSE-2.0
+ *    http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing, software
  * distributed under the License is distributed on an "AS IS" BASIS,
@@ -422,7 +422,7 @@ bayes_classify_multiclass(struct rspamd_classifier *ctx,
 		for (i = 0; i < cl.num_classes; i++) {
 			if (cl.class_learns[i] < ctx->cfg->min_learns) {
 				msg_info_task("not classified as %s. The class needs more "
-							  "training samples. Currently: %ul; minimum %ud required",
+							  "training samples. Currently: %uL; minimum %ud required",
 							  cl.class_names[i], cl.class_learns[i], ctx->cfg->min_learns);
 				return TRUE;
 			}
@@ -602,7 +602,7 @@ bayes_classify(struct rspamd_classifier *ctx,
 		}
 
 		if (has_class_names) {
-			msg_debug_bayes("using multiclass classification with %u classes",
+			msg_debug_bayes("using multiclass classification with %ud classes",
 							(unsigned int) ctx->cfg->class_names->len);
 			return bayes_classify_multiclass(ctx, tokens, task);
 		}
@@ -617,14 +617,14 @@ bayes_classify(struct rspamd_classifier *ctx,
 	if (ctx->cfg->min_learns > 0) {
 		if (ctx->ham_learns < ctx->cfg->min_learns) {
 			msg_info_task("not classified as ham. The ham class needs more "
-						  "training samples. Currently: %ul; minimum %ud required",
+						  "training samples. Currently: %uL; minimum %ud required",
 						  ctx->ham_learns, ctx->cfg->min_learns);
 
 			return TRUE;
 		}
 		if (ctx->spam_learns < ctx->cfg->min_learns) {
 			msg_info_task("not classified as spam. The spam class needs more "
-						  "training samples. Currently: %ul; minimum %ud required",
+						  "training samples. Currently: %uL; minimum %ud required",
 						  ctx->spam_learns, ctx->cfg->min_learns);
 
 			return TRUE;
@@ -705,7 +705,7 @@ bayes_classify(struct rspamd_classifier *ctx,
 		final_prob = (s + 1.0 - h) / 2.;
 		msg_debug_bayes(
 			"got ham probability %.2f -> %.2f and spam probability %.2f -> %.2f,"
-			" %L tokens processed of %ud total tokens;"
+			" %uL tokens processed of %ud total tokens;"
 			" %uL text tokens found of %ud text tokens)",
 			cl.ham_prob,
 			h,