Enhance brewery generation: add country name parameter and improve prompt handling

pipeline/includes/data_generator.h

@@ -19,6 +19,7 @@ public:
   virtual void load(const std::string &modelPath) = 0;
 
   virtual BreweryResult generateBrewery(const std::string &cityName,
+                                        const std::string &countryName,
                                         const std::string &regionContext) = 0;
 
   virtual UserResult generateUser(const std::string &locale) = 0;

pipeline/includes/llama_generator.h

@@ -13,11 +13,18 @@ public:
 
   void load(const std::string &modelPath) override;
   BreweryResult generateBrewery(const std::string &cityName,
+                                const std::string &countryName,
                                 const std::string &regionContext) override;
   UserResult generateUser(const std::string &locale) override;
 
 private:
-  std::string infer(const std::string &prompt, int maxTokens = 256);
+  std::string infer(const std::string &prompt, int maxTokens = 5000);
+  // Overload that allows passing a system message separately so chat-capable
+  // models receive a proper system role instead of having the system text
+  // concatenated into the user prompt (helps avoid revealing internal
+  // reasoning or instructions in model output).
+  std::string infer(const std::string &systemPrompt, const std::string &prompt,
+                    int maxTokens = 5000);
 
   llama_model *model_ = nullptr;
   llama_context *context_ = nullptr;

pipeline/includes/mock_generator.h

@@ -8,6 +8,7 @@ class MockGenerator final : public IDataGenerator {
 public:
   void load(const std::string &modelPath) override;
   BreweryResult generateBrewery(const std::string &cityName,
+                                const std::string &countryName,
                                 const std::string &regionContext) override;
   UserResult generateUser(const std::string &locale) override;
 

pipeline/src/llama_generator.cpp

@@ -6,6 +6,7 @@
 #include <array>
 #include <cctype>
 #include <memory>
+#include <sstream>
 #include <stdexcept>
 #include <string>
 #include <vector>
@@ -25,6 +26,117 @@ std::string trim(std::string value) {
   return value;
 }
 
+std::string stripCommonPrefix(std::string line) {
+  line = trim(std::move(line));
+
+  // Strip simple list markers like "- ", "* ", "1. ", "2) ".
+  if (!line.empty() && (line[0] == '-' || line[0] == '*')) {
+    line = trim(line.substr(1));
+  } else {
+    std::size_t i = 0;
+    while (i < line.size() &&
+           std::isdigit(static_cast<unsigned char>(line[i]))) {
+      ++i;
+    }
+    if (i > 0 && i < line.size() && (line[i] == '.' || line[i] == ')')) {
+      line = trim(line.substr(i + 1));
+    }
+  }
+
+  auto stripLabel = [&line](const std::string &label) {
+    if (line.size() >= label.size()) {
+      bool matches = true;
+      for (std::size_t i = 0; i < label.size(); ++i) {
+        if (std::tolower(static_cast<unsigned char>(line[i])) !=
+            std::tolower(static_cast<unsigned char>(label[i]))) {
+          matches = false;
+          break;
+        }
+      }
+      if (matches) {
+        line = trim(line.substr(label.size()));
+      }
+    }
+  };
+
+  stripLabel("name:");
+  stripLabel("brewery name:");
+  stripLabel("description:");
+  stripLabel("username:");
+  stripLabel("bio:");
+
+  return trim(std::move(line));
+}
+
+std::string toChatPrompt(const llama_model *model,
+                         const std::string &userPrompt) {
+  const char *tmpl = llama_model_chat_template(model, nullptr);
+  if (tmpl == nullptr) {
+    return userPrompt;
+  }
+
+  const llama_chat_message message{
+      "user",
+      userPrompt.c_str(),
+  };
+
+  std::vector<char> buffer(std::max<std::size_t>(1024, userPrompt.size() * 4));
+  int32_t required =
+      llama_chat_apply_template(tmpl, &message, 1, true, buffer.data(),
+                                static_cast<int32_t>(buffer.size()));
+
+  if (required < 0) {
+    throw std::runtime_error("LlamaGenerator: failed to apply chat template");
+  }
+
+  if (required >= static_cast<int32_t>(buffer.size())) {
+    buffer.resize(static_cast<std::size_t>(required) + 1);
+    required = llama_chat_apply_template(tmpl, &message, 1, true, buffer.data(),
+                                         static_cast<int32_t>(buffer.size()));
+    if (required < 0) {
+      throw std::runtime_error("LlamaGenerator: failed to apply chat template");
+    }
+  }
+
+  return std::string(buffer.data(), static_cast<std::size_t>(required));
+}
+
+std::string toChatPrompt(const llama_model *model,
+                         const std::string &systemPrompt,
+                         const std::string &userPrompt) {
+  const char *tmpl = llama_model_chat_template(model, nullptr);
+  if (tmpl == nullptr) {
+    // Fall back to concatenating but keep system and user parts distinct.
+    return systemPrompt + "\n\n" + userPrompt;
+  }
+
+  const llama_chat_message messages[2] = {
+      {"system", systemPrompt.c_str()},
+      {"user", userPrompt.c_str()},
+  };
+
+  std::vector<char> buffer(std::max<std::size_t>(
+      1024, (systemPrompt.size() + userPrompt.size()) * 4));
+  int32_t required =
+      llama_chat_apply_template(tmpl, messages, 2, true, buffer.data(),
+                                static_cast<int32_t>(buffer.size()));
+
+  if (required < 0) {
+    throw std::runtime_error("LlamaGenerator: failed to apply chat template");
+  }
+
+  if (required >= static_cast<int32_t>(buffer.size())) {
+    buffer.resize(static_cast<std::size_t>(required) + 1);
+    required = llama_chat_apply_template(tmpl, messages, 2, true, buffer.data(),
+                                         static_cast<int32_t>(buffer.size()));
+    if (required < 0) {
+      throw std::runtime_error("LlamaGenerator: failed to apply chat template");
+    }
+  }
+
+  return std::string(buffer.data(), static_cast<std::size_t>(required));
+}
+
 void appendTokenPiece(const llama_vocab *vocab, llama_token token,
                       std::string &output) {
   std::array<char, 256> buffer{};
@@ -51,13 +163,63 @@ void appendTokenPiece(const llama_vocab *vocab, llama_token token,
 
 std::pair<std::string, std::string>
 parseTwoLineResponse(const std::string &raw, const std::string &errorMessage) {
-  const auto newlinePos = raw.find('\n');
-  if (newlinePos == std::string::npos) {
+  std::string normalized = raw;
+  std::replace(normalized.begin(), normalized.end(), '\r', '\n');
+
+  std::vector<std::string> lines;
+  std::stringstream stream(normalized);
+  std::string line;
+  while (std::getline(stream, line)) {
+    line = stripCommonPrefix(std::move(line));
+    if (!line.empty()) {
+      lines.push_back(std::move(line));
+    }
+  }
+
+  // Filter out obvious internal-thought / meta lines that sometimes leak from
+  // models (e.g. "<think>", "Okay, so the user is asking me...").
+  std::vector<std::string> filtered;
+  for (auto &l : lines) {
+    std::string low = l;
+    std::transform(low.begin(), low.end(), low.begin(), [](unsigned char c) {
+      return static_cast<char>(std::tolower(c));
+    });
+
+    // Skip single-token angle-bracket markers like <think> or <...>
+    if (!l.empty() && l.front() == '<' && l.back() == '>') {
+      continue;
+    }
+
+    // Skip short internal commentary that starts with common discourse markers
+    if (low.rfind("okay,", 0) == 0 || low.rfind("wait,", 0) == 0 ||
+        low.rfind("hmm", 0) == 0) {
+      continue;
+    }
+
+    // Skip lines that look like self-descriptions of what the model is doing
+    if (low.find("user is asking") != std::string::npos ||
+        low.find("protocol") != std::string::npos ||
+        low.find("parse") != std::string::npos ||
+        low.find("return only") != std::string::npos) {
+      continue;
+    }
+
+    filtered.push_back(std::move(l));
+  }
+
+  if (filtered.size() < 2) {
     throw std::runtime_error(errorMessage);
   }
 
-  std::string first = trim(raw.substr(0, newlinePos));
-  std::string second = trim(raw.substr(newlinePos + 1));
+  std::string first = trim(filtered.front());
+  std::string second;
+  for (std::size_t i = 1; i < filtered.size(); ++i) {
+    if (!second.empty()) {
+      second += ' ';
+    }
+    second += filtered[i];
+  }
+  second = trim(std::move(second));
 
   if (first.empty() || second.empty()) {
     throw std::runtime_error(errorMessage);
@@ -128,18 +290,22 @@ std::string LlamaGenerator::infer(const std::string &prompt, int maxTokens) {
     throw std::runtime_error("LlamaGenerator: vocab unavailable");
   }
 
-  std::vector<llama_token> promptTokens(prompt.size() + 8);
-  int32_t tokenCount =
-      llama_tokenize(vocab, prompt.c_str(), static_cast<int32_t>(prompt.size()),
-                     promptTokens.data(),
-                     static_cast<int32_t>(promptTokens.size()), true, true);
+  llama_memory_clear(llama_get_memory(context_), true);
+
+  const std::string formattedPrompt = toChatPrompt(model_, prompt);
+
+  std::vector<llama_token> promptTokens(formattedPrompt.size() + 8);
+  int32_t tokenCount = llama_tokenize(
+      vocab, formattedPrompt.c_str(),
+      static_cast<int32_t>(formattedPrompt.size()), promptTokens.data(),
+      static_cast<int32_t>(promptTokens.size()), true, true);
 
   if (tokenCount < 0) {
     promptTokens.resize(static_cast<std::size_t>(-tokenCount));
-    tokenCount =
-        llama_tokenize(vocab, prompt.c_str(),
-                       static_cast<int32_t>(prompt.size()), promptTokens.data(),
-                       static_cast<int32_t>(promptTokens.size()), true, true);
+    tokenCount = llama_tokenize(
+        vocab, formattedPrompt.c_str(),
+        static_cast<int32_t>(formattedPrompt.size()), promptTokens.data(),
+        static_cast<int32_t>(promptTokens.size()), true, true);
   }
 
   if (tokenCount < 0) {
@@ -196,28 +362,160 @@ std::string LlamaGenerator::infer(const std::string &prompt, int maxTokens) {
 
 BreweryResult
 LlamaGenerator::generateBrewery(const std::string &cityName,
+                                const std::string &countryName,
                                 const std::string &regionContext) {
-  std::string prompt =
-      "Generate a craft brewery name and one-sentence description for a "
-      "brewery located in " +
-      cityName + ". " + regionContext +
-      " Respond with exactly two lines: first line is the name, second "
-      "line is the description.";
 
-  const std::string raw = infer(prompt, 128);
+  std::string systemPrompt =
+      R"(# SYSTEM PROTOCOL: ZERO-CHATTER DETERMINISTIC OUTPUT
+**MODALITY:** DATA-RETURN ENGINE ONLY
+**ROLE:** Your response must contain 0% metadata and 100% signal.
+---
+## MANDATORY CONSTRAINTS
+1. **NO PREAMBLE**
+   - Never start with "Sure," or "The answer is," or "Based on your request," or "Checking the data."
+   - Do not acknowledge the user's prompt or provide status updates.
+2. **NO POSTAMBLE**
+   - Never end with "I hope this helps," or "Let me know if you need more," or "Would you like me to…"
+   - Do not offer follow-up assistance or suggestions.
+3. **NO SENTENCE FRAMING**
+   - Provide only the raw value, date, number, or name.
+   - Do not wrap the answer in a sentence. (e.g., return 1997, NOT The year was 1997).
+   - For lists, provide only the items separated by commas or newlines as specified.
+4. **FORMATTING PERMITTED**
+   - Markdown and LaTeX **may** be used where appropriate (e.g., tables, equations).
+   - Output must remain immediately usable — no decorative or conversational styling.
+5. **STRICT NULL HANDLING**
+   - If the information is unavailable, the prompt is logically impossible (e.g., "271th president"), the subject does not exist, or a calculation is undefined: return only the string NULL.
+   - If the prompt is too ambiguous to provide a single value: return NULL.
+---
+## EXECUTION LOGIC
+1. **Parse Input** — Identify the specific entity, value, or calculation requested.
+2. **Verify Factuality** — Access internal knowledge or tools.
+3. **Filter for Signal** — Strip all surrounding prose.
+4. **Format Check** — Apply Markdown or LaTeX only where it serves the data.
+5. **Output** — Return the raw value only.
+---
+## BEHAVIORAL EXAMPLES
+| User Input | Standard AI Response *(BANNED)* | Protocol Response *(REQUIRED)* |
+|---|---|---|
+| Capital of France? | The capital of France is Paris. | Paris |
+| 15% of 200 | 15% of 200 is 30. | 30 |
+| Who wrote '1984'? | George Orwell wrote that novel. | George Orwell |
+| ISO code for Japan | The code is JP. | JP |
+| $\sqrt{x}$ where $x$ is a potato | A potato has no square root. | NULL |
+| 500th US President | There haven't been that many. | NULL |
+| Pythagorean theorem | The theorem states... | $a^2 + b^2 = c^2$ |
+---
+## FINAL INSTRUCTION
+Total silence is preferred over conversational error. Any deviation from the raw-value-only format is a protocol failure. Proceed with next input.)";
+
+  std::string prompt =
+      "Generate a craft brewery name and 1000 character description for a "
+      "brewery located in " +
+      cityName +
+      (countryName.empty() ? std::string("")
+                           : std::string(", ") + countryName) +
+      ". " + regionContext +
+      " Respond with exactly two lines: first line is the name, second line is "
+      "the description. Do not include bullets, numbering, or any extra text.";
+
+  const std::string raw = infer(systemPrompt, prompt, 512);
   auto [name, description] =
       parseTwoLineResponse(raw, "LlamaGenerator: malformed brewery response");
 
   return {name, description};
 }
 
+std::string LlamaGenerator::infer(const std::string &systemPrompt,
+                                  const std::string &prompt, int maxTokens) {
+  if (model_ == nullptr || context_ == nullptr) {
+    throw std::runtime_error("LlamaGenerator: model not loaded");
+  }
+
+  const llama_vocab *vocab = llama_model_get_vocab(model_);
+  if (vocab == nullptr) {
+    throw std::runtime_error("LlamaGenerator: vocab unavailable");
+  }
+
+  llama_memory_clear(llama_get_memory(context_), true);
+
+  const std::string formattedPrompt =
+      toChatPrompt(model_, systemPrompt, prompt);
+
+  std::vector<llama_token> promptTokens(formattedPrompt.size() + 8);
+  int32_t tokenCount = llama_tokenize(
+      vocab, formattedPrompt.c_str(),
+      static_cast<int32_t>(formattedPrompt.size()), promptTokens.data(),
+      static_cast<int32_t>(promptTokens.size()), true, true);
+
+  if (tokenCount < 0) {
+    promptTokens.resize(static_cast<std::size_t>(-tokenCount));
+    tokenCount = llama_tokenize(
+        vocab, formattedPrompt.c_str(),
+        static_cast<int32_t>(formattedPrompt.size()), promptTokens.data(),
+        static_cast<int32_t>(promptTokens.size()), true, true);
+  }
+
+  if (tokenCount < 0) {
+    throw std::runtime_error("LlamaGenerator: prompt tokenization failed");
+  }
+
+  promptTokens.resize(static_cast<std::size_t>(tokenCount));
+
+  const llama_batch promptBatch = llama_batch_get_one(
+      promptTokens.data(), static_cast<int32_t>(promptTokens.size()));
+  if (llama_decode(context_, promptBatch) != 0) {
+    throw std::runtime_error("LlamaGenerator: prompt decode failed");
+  }
+
+  llama_sampler_chain_params samplerParams =
+      llama_sampler_chain_default_params();
+  using SamplerPtr =
+      std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
+  SamplerPtr sampler(llama_sampler_chain_init(samplerParams),
+                     &llama_sampler_free);
+
+  if (!sampler) {
+    throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
+  }
+
+  llama_sampler_chain_add(sampler.get(), llama_sampler_init_greedy());
+
+  std::vector<llama_token> generatedTokens;
+  generatedTokens.reserve(static_cast<std::size_t>(maxTokens));
+
+  for (int i = 0; i < maxTokens; ++i) {
+    const llama_token next = llama_sampler_sample(sampler.get(), context_, -1);
+    if (llama_vocab_is_eog(vocab, next)) {
+      break;
+    }
+
+    generatedTokens.push_back(next);
+
+    llama_token token = next;
+    const llama_batch oneTokenBatch = llama_batch_get_one(&token, 1);
+    if (llama_decode(context_, oneTokenBatch) != 0) {
+      throw std::runtime_error(
+          "LlamaGenerator: decode failed during generation");
+    }
+  }
+
+  std::string output;
+  for (const llama_token token : generatedTokens) {
+    appendTokenPiece(vocab, token, output);
+  }
+
+  return output;
+}
+
 UserResult LlamaGenerator::generateUser(const std::string &locale) {
   std::string prompt =
       "Generate a plausible craft beer enthusiast username and a one-sentence "
       "bio. Locale: " +
       locale +
       ". Respond with exactly two lines: first line is the username (no "
-      "spaces), second line is the bio.";
+      "spaces), second line is the bio. Do not include bullets, numbering, "
+      "or any extra text.";
 
   const std::string raw = infer(prompt, 128);
   auto [username, bio] =

pipeline/src/main.cpp

@@ -8,6 +8,7 @@
 #include <filesystem>
 #include <memory>
 #include <spdlog/spdlog.h>
+#include <vector>
 
 static bool FileExists(const std::string &filePath) {
   return std::filesystem::exists(filePath);
@@ -22,6 +23,8 @@ int main(int argc, char *argv[]) {
     std::string commit =
         argc > 3 ? argv[3] : "c5eb7772"; // Default: stable 2026-03-28
 
+    std::string countryName = argc > 4 ? argv[4] : "";
+
     std::string jsonPath = cacheDir + "/countries+states+cities.json";
     std::string dbPath = cacheDir + "/biergarten-pipeline.db";
 
@@ -65,28 +68,29 @@ int main(int argc, char *argv[]) {
     spdlog::info("  States: {}", db.QueryStates(0).size());
     spdlog::info("  Cities: {}", cities.size());
 
-    spdlog::info("\n--- 50 COUNTRIES ---");
-    for (size_t i = 0; i < countries.size(); i++) {
-      spdlog::info("{}. {} ({}) {}", (i + 1), countries[i].iso2,
-                   countries[i].iso3, countries[i].name);
-    }
+    struct GeneratedBrewery {
+      int cityId;
+      std::string cityName;
+      BreweryResult brewery;
+    };
 
-    spdlog::info("\n--- 50 STATES ---");
-    for (size_t i = 0; i < states.size(); i++) {
-      spdlog::info("{}. {}: {}", (i + 1), states[i].iso2, states[i].name);
-    }
+    std::vector<GeneratedBrewery> generatedBreweries;
+    const size_t sampleCount = std::min(size_t(30), cities.size());
 
-    spdlog::info("\n--- 50 CITIES ---");
-    for (size_t i = 0; i < std::min(size_t(50), cities.size()); i++) {
-      spdlog::info("{}. {}", (i + 1), cities[i].second);
-    }
-
-    spdlog::info("\n=== SAMPLE BREWERY GENERATION ===\n");
-    for (size_t i = 0; i < std::min(size_t(5), cities.size()); i++) {
+    spdlog::info("\n=== SAMPLE BREWERY GENERATION ===");
+    for (size_t i = 0; i < sampleCount; i++) {
       const auto &[cityId, cityName] = cities[i];
-      auto brewery = generator->generateBrewery(cityName, "");
-      spdlog::info("  {}: {}", cityName, brewery.name);
-      spdlog::info("    -> {}", brewery.description);
+      auto brewery = generator->generateBrewery(cityName, countryName, "");
+      generatedBreweries.push_back({cityId, cityName, brewery});
+    }
+
+    spdlog::info("\n=== GENERATED DATA DUMP ===");
+    for (size_t i = 0; i < generatedBreweries.size(); i++) {
+      const auto &entry = generatedBreweries[i];
+      spdlog::info("{}. city_id={} city=\"{}\"", i + 1, entry.cityId,
+                   entry.cityName);
+      spdlog::info("   brewery_name=\"{}\"", entry.brewery.name);
+      spdlog::info("   brewery_description=\"{}\"", entry.brewery.description);
     }
 
     spdlog::info("\nOK: Pipeline completed successfully");

pipeline/src/mock_generator.cpp

@@ -78,10 +78,13 @@ std::size_t MockGenerator::deterministicHash(const std::string &a,
 }
 
 BreweryResult MockGenerator::generateBrewery(const std::string &cityName,
+                                             const std::string &countryName,
                                              const std::string &regionContext) {
+  const std::string locationKey =
+      countryName.empty() ? cityName : cityName + "," + countryName;
   const std::size_t hash = regionContext.empty()
-                               ? std::hash<std::string>{}(cityName)
-                               : deterministicHash(cityName, regionContext);
+                               ? std::hash<std::string>{}(locationKey)
+                               : deterministicHash(locationKey, regionContext);
 
   BreweryResult result;
   result.name = kBreweryAdjectives[hash % kBreweryAdjectives.size()] + " " +