fix llama grammar

pipeline/src/biergarten_data_generator/query_cities_with_countries.cc

@@ -13,7 +13,7 @@
 #include "biergarten_data_generator.h"
 #include "json_handling/json_loader.h"
 
-static constexpr size_t kBreweryAmount = 50;
+static constexpr size_t kBreweryAmount = 5;
 
 std::vector<Location> BiergartenDataGenerator::QueryCitiesWithCountries() {
   spdlog::info("\n=== GEOGRAPHIC DATA OVERVIEW ===");

pipeline/src/data_generation/llama/generate_brewery.cc

@@ -17,7 +17,7 @@
 #include "data_generation/llama_generator_helpers.h"
 
 static constexpr std::string_view kBreweryJsonGrammar = R"json_brewery(
-root ::= ws "{" ws "\"name\"" ws ":" ws string ws "," ws "\"description\"" ws ":" ws string ws "}" ws
+root ::= ws "{" ws "\"reasoning\"" ws ":" ws string ws "," ws "\"name\"" ws ":" ws string ws "," ws "\"description\"" ws ":" ws string ws "}" ws
 ws ::= [ \t\n\r]*
 string ::= "\"" char+ "\""
 char ::= [^"\\\x7F\x00-\x1F] | [\\] escape
@@ -36,11 +36,6 @@ BreweryResult LlamaGenerator::GenerateBrewery(
   const std::string country_suffix =
       location.country.empty() ? std::string{}
                                : std::format(", {}", location.country);
-  const std::string region_suffix =
-      safe_region_context.empty()
-          ? "."
-          : std::format(". Regional context: {}", safe_region_context);
-
   /**
    * Load brewery system prompt from file
    * Falls back to minimal inline prompt if file not found
@@ -53,9 +48,8 @@ BreweryResult LlamaGenerator::GenerateBrewery(
    * culturally relevant and locally-inspired brewery attributes
    */
   std::string prompt = std::format(
-      "Write a brewery name and place-specific long description for a craft "
-      "brewery in {}{}{}",
-      location.city, country_suffix, region_suffix);
+      "## CITY:\n{}\n\n## COUNTRY:\n{}\n\n## CONTEXT:\n{}",
+      location.city, location.country, safe_region_context);
 
   /**
    * Store location context for retry prompts (without repeating full context)
@@ -101,7 +95,7 @@ BreweryResult LlamaGenerator::GenerateBrewery(
     // Update prompt with error details to guide LLM toward correct output.
     prompt = std::format(
         R"(Your previous response was invalid. Error: {}
-Return ONLY valid JSON with exactly these keys: {{"name": "<brewery name>", "description": "<single-paragraph description>"}}.
+Return ONLY valid JSON with exactly these keys, in this exact order: {{"reasoning": "<brief planning summary>", "name": "<brewery name>", "description": "<single-paragraph description>"}}.
 Do not include markdown, comments, extra keys, or literal placeholder values.
 
 {})",

pipeline/src/data_generation/llama/helpers.cc

@@ -84,9 +84,8 @@ std::string PrepareRegionContext(std::string_view region_context,
 std::string ToChatPrompt(const llama_model* model,
                          const std::string& system_prompt,
                          const std::string& user_prompt) {
-  std::string combined_prompt = system_prompt;
-  combined_prompt.append("\n\n");
-  combined_prompt.append(user_prompt);
+  std::string combined_prompt =
+      std::format("{}\n\n{}", system_prompt, user_prompt);
 
   const char* tmpl = llama_model_chat_template(model, nullptr);
   if (tmpl == nullptr) {
@@ -103,9 +102,9 @@ std::string ToChatPrompt(const llama_model* model,
 
   constexpr std::size_t min_template_buffer_size = 1024;
 
-  std::vector<char> buffer(std::max<std::size_t>(
-      min_template_buffer_size,
-      (system_prompt.size() + user_prompt.size()) * 4));
+  std::vector<char> buffer(
+      std::max<std::size_t>(min_template_buffer_size,
+                            (system_prompt.size() + user_prompt.size()) * 4));
 
   auto apply_template_with_resize = [&](const llama_chat_message* chat_messages,
                                         int32_t message_count) -> int32_t {

pipeline/src/data_generation/llama/infer.cc

@@ -101,7 +101,7 @@ std::string LlamaGenerator::InferFormatted(const std::string& formatted_prompt,
       .temperature = sampling_temperature_,
       .top_k = sampling_top_k_,
       .top_p = sampling_top_p_,
-      .seed = rng_(),
+      .seed = static_cast<uint32_t>(rng_()),
   };
   auto sampler = MakeSamplerChain(vocab, sampler_config, grammar);