Separate llama generator class src file into method files

pipeline/CMakeLists.txt

@@ -83,7 +83,13 @@ set(PIPELINE_SOURCES
     src/data_generation/data_downloader.cpp
     src/database/database.cpp
     src/json_handling/json_loader.cpp
-    src/data_generation/llama_generator.cpp
+    src/data_generation/llama/destructor.cpp
+    src/data_generation/llama/set_sampling_options.cpp
+    src/data_generation/llama/load.cpp
+    src/data_generation/llama/infer.cpp
+    src/data_generation/llama/generate_brewery.cpp
+    src/data_generation/llama/generate_user.cpp
+    src/data_generation/llama/helpers.cpp
     src/data_generation/mock_generator.cpp
     src/json_handling/stream_parser.cpp
     src/wikipedia/wikipedia_service.cpp

pipeline/includes/biergarten_data_generator.h

@@ -106,8 +106,8 @@ private:
    * @brief Helper struct to store generated brewery data.
    */
   struct GeneratedBrewery {
-    int cityId;
+    int city_id;
-    std::string cityName;
+    std::string city_name;
     BreweryResult brewery;
   };
 

pipeline/includes/data_generation/llama_generator_helpers.h

@@ -0,0 +1,33 @@
+#ifndef BIERGARTEN_PIPELINE_DATA_GENERATION_LLAMA_GENERATOR_HELPERS_H_
+#define BIERGARTEN_PIPELINE_DATA_GENERATION_LLAMA_GENERATOR_HELPERS_H_
+
+#include <string>
+#include <utility>
+
+struct llama_model;
+struct llama_vocab;
+typedef int llama_token;
+
+// Helper functions for LlamaGenerator methods
+std::string PrepareRegionContextPublic(std::string_view region_context,
+                                       std::size_t max_chars = 700);
+
+std::pair<std::string, std::string>
+ParseTwoLineResponsePublic(const std::string& raw,
+                           const std::string& error_message);
+
+std::string ToChatPromptPublic(const llama_model *model,
+                               const std::string& user_prompt);
+
+std::string ToChatPromptPublic(const llama_model *model,
+                               const std::string& system_prompt,
+                               const std::string& user_prompt);
+
+void AppendTokenPiecePublic(const llama_vocab *vocab, llama_token token,
+                            std::string& output);
+
+std::string ValidateBreweryJsonPublic(const std::string& raw,
+                                      std::string& name_out,
+                                      std::string& description_out);
+
+#endif  // BIERGARTEN_PIPELINE_DATA_GENERATION_LLAMA_GENERATOR_HELPERS_H_

pipeline/src/biergarten_data_generator.cpp

@@ -111,8 +111,8 @@ void BiergartenDataGenerator::GenerateSampleBreweries() {
   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,
+    spdlog::info("{}. city_id={} city=\"{}\"", i + 1, entry.city_id,
-                 entry.cityName);
+                 entry.city_name);
     spdlog::info("   brewery_name=\"{}\"", entry.brewery.name);
     spdlog::info("   brewery_description=\"{}\"", entry.brewery.description);
   }

pipeline/src/data_generation/llama/destructor.cpp

@@ -0,0 +1,17 @@
+#include "llama.h"
+
+#include "data_generation/llama_generator.h"
+
+LlamaGenerator::~LlamaGenerator() {
+  if (context_ != nullptr) {
+    llama_free(context_);
+    context_ = nullptr;
+  }
+
+  if (model_ != nullptr) {
+    llama_model_free(model_);
+    model_ = nullptr;
+  }
+
+  llama_backend_free();
+}

pipeline/src/data_generation/llama/generate_brewery.cpp

@@ -0,0 +1,72 @@
+#include <stdexcept>
+#include <string>
+
+#include <spdlog/spdlog.h>
+
+#include "data_generation/llama_generator.h"
+#include "data_generation/llama_generator_helpers.h"
+
+BreweryResult
+LlamaGenerator::GenerateBrewery(const std::string& city_name,
+                                const std::string& country_name,
+                                const std::string& region_context) {
+  const std::string safe_region_context =
+      PrepareRegionContextPublic(region_context);
+
+  const std::string system_prompt =
+      "You are a copywriter for a craft beer travel guide. "
+      "Your writing is vivid, specific to place, and avoids generic beer "
+      "cliches. "
+      "You must output ONLY valid JSON. "
+      "The JSON schema must be exactly: {\"name\": \"string\", "
+      "\"description\": \"string\"}. "
+      "Do not include markdown formatting or backticks.";
+
+  std::string prompt =
+      "Write a brewery name and place-specific description for a craft "
+      "brewery in " +
+      city_name +
+      (country_name.empty() ? std::string("")
+                           : std::string(", ") + country_name) +
+      (safe_region_context.empty()
+           ? std::string(".")
+           : std::string(". Regional context: ") + safe_region_context);
+
+  const int max_attempts = 3;
+  std::string raw;
+  std::string last_error;
+  for (int attempt = 0; attempt < max_attempts; ++attempt) {
+    raw = Infer(system_prompt, prompt, 384);
+    spdlog::debug("LlamaGenerator: raw output (attempt {}): {}", attempt + 1,
+                  raw);
+
+    std::string name;
+    std::string description;
+    const std::string validation_error =
+        ValidateBreweryJsonPublic(raw, name, description);
+    if (validation_error.empty()) {
+      return {std::move(name), std::move(description)};
+    }
+
+    last_error = validation_error;
+    spdlog::warn("LlamaGenerator: malformed brewery JSON (attempt {}): {}",
+                 attempt + 1, validation_error);
+
+    prompt = "Your previous response was invalid. Error: " + validation_error +
+             "\nReturn ONLY valid JSON with this exact schema: "
+             "{\"name\": \"string\", \"description\": \"string\"}."
+             "\nDo not include markdown, comments, or extra keys."
+             "\n\nLocation: " +
+             city_name +
+             (country_name.empty() ? std::string("")
+                                  : std::string(", ") + country_name) +
+             (safe_region_context.empty()
+                  ? std::string("")
+                  : std::string("\nRegional context: ") + safe_region_context);
+  }
+
+  spdlog::error("LlamaGenerator: malformed brewery response after {} attempts: "
+                "{}",
+                max_attempts, last_error.empty() ? raw : last_error);
+  throw std::runtime_error("LlamaGenerator: malformed brewery response");
+}

pipeline/src/data_generation/llama/generate_user.cpp

@@ -0,0 +1,56 @@
+#include <algorithm>
+#include <stdexcept>
+#include <string>
+
+#include <spdlog/spdlog.h>
+
+#include "data_generation/llama_generator.h"
+#include "data_generation/llama_generator_helpers.h"
+
+UserResult LlamaGenerator::GenerateUser(const std::string& locale) {
+  const std::string system_prompt =
+      "You generate plausible social media profiles for craft beer "
+      "enthusiasts. "
+      "Respond with exactly two lines: "
+      "the first line is a username (lowercase, no spaces, 8-20 characters), "
+      "the second line is a one-sentence bio (20-40 words). "
+      "The profile should feel consistent with the locale. "
+      "No preamble, no labels.";
+
+  std::string prompt =
+      "Generate a craft beer enthusiast profile. Locale: " + locale;
+
+  const int max_attempts = 3;
+  std::string raw;
+  for (int attempt = 0; attempt < max_attempts; ++attempt) {
+    raw = Infer(system_prompt, prompt, 128);
+    spdlog::debug("LlamaGenerator (user): raw output (attempt {}): {}",
+                  attempt + 1, raw);
+
+    try {
+      auto [username, bio] = ParseTwoLineResponsePublic(
+          raw, "LlamaGenerator: malformed user response");
+
+      username.erase(
+          std::remove_if(username.begin(), username.end(),
+                         [](unsigned char ch) { return std::isspace(ch); }),
+          username.end());
+
+      if (username.empty() || bio.empty()) {
+        throw std::runtime_error("LlamaGenerator: malformed user response");
+      }
+
+      if (bio.size() > 200)
+        bio = bio.substr(0, 200);
+
+      return {username, bio};
+    } catch (const std::exception &e) {
+      spdlog::warn("LlamaGenerator: malformed user response (attempt {}): {}",
+                   attempt + 1, e.what());
+    }
+  }
+
+  spdlog::error("LlamaGenerator: malformed user response after {} attempts: {}",
+                max_attempts, raw);
+  throw std::runtime_error("LlamaGenerator: malformed user response");
+}

pipeline/src/data_generation/llama/helpers.cpp

@@ -0,0 +1,401 @@
+#include <algorithm>
+#include <array>
+#include <cctype>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+#include "llama.h"
+#include <boost/json.hpp>
+
+#include "data_generation/llama_generator.h"
+
+namespace {
+
+std::string Trim(std::string value) {
+  auto not_space = [](unsigned char ch) { return !std::isspace(ch); };
+
+  value.erase(value.begin(),
+              std::find_if(value.begin(), value.end(), not_space));
+  value.erase(std::find_if(value.rbegin(), value.rend(), not_space).base(),
+              value.end());
+
+  return value;
+}
+
+std::string CondenseWhitespace(std::string text) {
+  std::string out;
+  out.reserve(text.size());
+
+  bool in_whitespace = false;
+  for (unsigned char ch : text) {
+    if (std::isspace(ch)) {
+      if (!in_whitespace) {
+        out.push_back(' ');
+        in_whitespace = true;
+      }
+      continue;
+    }
+
+    in_whitespace = false;
+    out.push_back(static_cast<char>(ch));
+  }
+
+  return Trim(std::move(out));
+}
+
+std::string PrepareRegionContext(std::string_view region_context,
+                                 std::size_t max_chars) {
+  std::string normalized = CondenseWhitespace(std::string(region_context));
+  if (normalized.size() <= max_chars) {
+    return normalized;
+  }
+
+  normalized.resize(max_chars);
+  const std::size_t last_space = normalized.find_last_of(' ');
+  if (last_space != std::string::npos && last_space > max_chars / 2) {
+    normalized.resize(last_space);
+  }
+
+  normalized += "...";
+  return normalized;
+}
+
+std::string StripCommonPrefix(std::string line) {
+  line = Trim(std::move(line));
+
+  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 strip_label = [&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()));
+      }
+    }
+  };
+
+  strip_label("name:");
+  strip_label("brewery name:");
+  strip_label("description:");
+  strip_label("username:");
+  strip_label("bio:");
+
+  return Trim(std::move(line));
+}
+
+std::pair<std::string, std::string>
+ParseTwoLineResponse(const std::string& raw, const std::string& error_message) {
+  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));
+  }
+
+  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));
+    });
+    if (!l.empty() && l.front() == '<' && low.back() == '>')
+      continue;
+    if (low.rfind("okay,", 0) == 0 || low.rfind("hmm", 0) == 0)
+      continue;
+    filtered.push_back(std::move(l));
+  }
+
+  if (filtered.size() < 2)
+    throw std::runtime_error(error_message);
+
+  std::string first = Trim(filtered.front());
+  std::string second;
+  for (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(error_message);
+  return {first, second};
+}
+
+std::string ToChatPrompt(const llama_model *model,
+                         const std::string& user_prompt) {
+  const char *tmpl = llama_model_chat_template(model, nullptr);
+  if (tmpl == nullptr) {
+    return user_prompt;
+  }
+
+  const llama_chat_message message{"user", user_prompt.c_str()};
+
+  std::vector<char> buffer(std::max<std::size_t>(1024, user_prompt.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& system_prompt,
+                         const std::string& user_prompt) {
+  const char *tmpl = llama_model_chat_template(model, nullptr);
+  if (tmpl == nullptr) {
+    return system_prompt + "\n\n" + user_prompt;
+  }
+
+  const llama_chat_message messages[2] = {{"system", system_prompt.c_str()},
+                                          {"user", user_prompt.c_str()}};
+
+  std::vector<char> buffer(std::max<std::size_t>(
+      1024, (system_prompt.size() + user_prompt.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{};
+  int32_t bytes =
+      llama_token_to_piece(vocab, token, buffer.data(),
+                           static_cast<int32_t>(buffer.size()), 0, true);
+
+  if (bytes < 0) {
+    std::vector<char> dynamic_buffer(static_cast<std::size_t>(-bytes));
+    bytes = llama_token_to_piece(vocab, token, dynamic_buffer.data(),
+                                 static_cast<int32_t>(dynamic_buffer.size()), 0,
+                                 true);
+    if (bytes < 0) {
+      throw std::runtime_error(
+          "LlamaGenerator: failed to decode sampled token piece");
+    }
+
+    output.append(dynamic_buffer.data(), static_cast<std::size_t>(bytes));
+    return;
+  }
+
+  output.append(buffer.data(), static_cast<std::size_t>(bytes));
+}
+
+bool ExtractFirstJsonObject(const std::string& text, std::string& json_out) {
+  std::size_t start = std::string::npos;
+  int depth = 0;
+  bool in_string = false;
+  bool escaped = false;
+
+  for (std::size_t i = 0; i < text.size(); ++i) {
+    const char ch = text[i];
+
+    if (in_string) {
+      if (escaped) {
+        escaped = false;
+      } else if (ch == '\\') {
+        escaped = true;
+      } else if (ch == '"') {
+        in_string = false;
+      }
+      continue;
+    }
+
+    if (ch == '"') {
+      in_string = true;
+      continue;
+    }
+
+    if (ch == '{') {
+      if (depth == 0) {
+        start = i;
+      }
+      ++depth;
+      continue;
+    }
+
+    if (ch == '}') {
+      if (depth == 0) {
+        continue;
+      }
+      --depth;
+      if (depth == 0 && start != std::string::npos) {
+        json_out = text.substr(start, i - start + 1);
+        return true;
+      }
+    }
+  }
+
+  return false;
+}
+
+std::string ValidateBreweryJson(const std::string& raw, std::string& name_out,
+                                std::string& description_out) {
+  auto validate_object = [&](const boost::json::value& jv,
+                            std::string& error_out) -> bool {
+    if (!jv.is_object()) {
+      error_out = "JSON root must be an object";
+      return false;
+    }
+
+    const auto& obj = jv.get_object();
+    if (!obj.contains("name") || !obj.at("name").is_string()) {
+      error_out = "JSON field 'name' is missing or not a string";
+      return false;
+    }
+
+    if (!obj.contains("description") || !obj.at("description").is_string()) {
+      error_out = "JSON field 'description' is missing or not a string";
+      return false;
+    }
+
+    name_out = Trim(std::string(obj.at("name").as_string().c_str()));
+    description_out =
+        Trim(std::string(obj.at("description").as_string().c_str()));
+
+    if (name_out.empty()) {
+      error_out = "JSON field 'name' must not be empty";
+      return false;
+    }
+
+    if (description_out.empty()) {
+      error_out = "JSON field 'description' must not be empty";
+      return false;
+    }
+
+    std::string name_lower = name_out;
+    std::string description_lower = description_out;
+    std::transform(
+        name_lower.begin(), name_lower.end(), name_lower.begin(),
+        [](unsigned char c) { return static_cast<char>(std::tolower(c)); });
+    std::transform(description_lower.begin(), description_lower.end(),
+                   description_lower.begin(), [](unsigned char c) {
+                     return static_cast<char>(std::tolower(c));
+                   });
+
+    if (name_lower == "string" || description_lower == "string") {
+      error_out = "JSON appears to be a schema placeholder, not content";
+      return false;
+    }
+
+    error_out.clear();
+    return true;
+  };
+
+  boost::system::error_code ec;
+  boost::json::value jv = boost::json::parse(raw, ec);
+  std::string validation_error;
+  if (ec) {
+    std::string extracted;
+    if (!ExtractFirstJsonObject(raw, extracted)) {
+      return "JSON parse error: " + ec.message();
+    }
+
+    ec.clear();
+    jv = boost::json::parse(extracted, ec);
+    if (ec) {
+      return "JSON parse error: " + ec.message();
+    }
+
+    if (!validate_object(jv, validation_error)) {
+      return validation_error;
+    }
+
+    return {};
+  }
+
+  if (!validate_object(jv, validation_error)) {
+    return validation_error;
+  }
+
+  return {};
+}
+
+}  // namespace
+
+// Forward declarations for helper functions exposed to other translation units
+std::string PrepareRegionContextPublic(std::string_view region_context,
+                                       std::size_t max_chars) {
+  return PrepareRegionContext(region_context, max_chars);
+}
+
+std::pair<std::string, std::string>
+ParseTwoLineResponsePublic(const std::string& raw,
+                           const std::string& error_message) {
+  return ParseTwoLineResponse(raw, error_message);
+}
+
+std::string ToChatPromptPublic(const llama_model *model,
+                               const std::string& user_prompt) {
+  return ToChatPrompt(model, user_prompt);
+}
+
+std::string ToChatPromptPublic(const llama_model *model,
+                               const std::string& system_prompt,
+                               const std::string& user_prompt) {
+  return ToChatPrompt(model, system_prompt, user_prompt);
+}
+
+void AppendTokenPiecePublic(const llama_vocab *vocab, llama_token token,
+                            std::string& output) {
+  AppendTokenPiece(vocab, token, output);
+}
+
+std::string ValidateBreweryJsonPublic(const std::string& raw,
+                                      std::string& name_out,
+                                      std::string& description_out) {
+  return ValidateBreweryJson(raw, name_out, description_out);
+}

pipeline/src/data_generation/llama/infer.cpp

@@ -0,0 +1,195 @@
+#include <algorithm>
+#include <memory>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+#include "llama.h"
+#include <spdlog/spdlog.h>
+
+#include "data_generation/llama_generator.h"
+#include "data_generation/llama_generator_helpers.h"
+
+std::string LlamaGenerator::Infer(const std::string& prompt, int max_tokens) {
+  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 formatted_prompt = ToChatPromptPublic(model_, prompt);
+
+  std::vector<llama_token> prompt_tokens(formatted_prompt.size() + 8);
+  int32_t token_count = llama_tokenize(
+      vocab, formatted_prompt.c_str(),
+      static_cast<int32_t>(formatted_prompt.size()), prompt_tokens.data(),
+      static_cast<int32_t>(prompt_tokens.size()), true, true);
+
+  if (token_count < 0) {
+    prompt_tokens.resize(static_cast<std::size_t>(-token_count));
+    token_count = llama_tokenize(
+        vocab, formatted_prompt.c_str(),
+        static_cast<int32_t>(formatted_prompt.size()), prompt_tokens.data(),
+        static_cast<int32_t>(prompt_tokens.size()), true, true);
+  }
+
+  if (token_count < 0)
+    throw std::runtime_error("LlamaGenerator: prompt tokenization failed");
+
+  const int32_t n_ctx = static_cast<int32_t>(llama_n_ctx(context_));
+  const int32_t n_batch = static_cast<int32_t>(llama_n_batch(context_));
+  if (n_ctx <= 1 || n_batch <= 0) {
+    throw std::runtime_error("LlamaGenerator: invalid context or batch size");
+  }
+
+  const int32_t effective_max_tokens = std::max(1, std::min(max_tokens, n_ctx - 1));
+  int32_t prompt_budget = std::min(n_batch, n_ctx - effective_max_tokens);
+  prompt_budget = std::max<int32_t>(1, prompt_budget);
+
+  prompt_tokens.resize(static_cast<std::size_t>(token_count));
+  if (token_count > prompt_budget) {
+    spdlog::warn(
+        "LlamaGenerator: prompt too long ({} tokens), truncating to {} tokens "
+        "to fit n_batch/n_ctx limits",
+        token_count, prompt_budget);
+    prompt_tokens.resize(static_cast<std::size_t>(prompt_budget));
+    token_count = prompt_budget;
+  }
+
+  const llama_batch prompt_batch = llama_batch_get_one(
+      prompt_tokens.data(), static_cast<int32_t>(prompt_tokens.size()));
+  if (llama_decode(context_, prompt_batch) != 0)
+    throw std::runtime_error("LlamaGenerator: prompt decode failed");
+
+  llama_sampler_chain_params sampler_params =
+      llama_sampler_chain_default_params();
+  using SamplerPtr =
+      std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
+  SamplerPtr sampler(llama_sampler_chain_init(sampler_params),
+                     &llama_sampler_free);
+  if (!sampler)
+    throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
+
+  llama_sampler_chain_add(sampler.get(),
+                          llama_sampler_init_temp(sampling_temperature_));
+  llama_sampler_chain_add(sampler.get(),
+                          llama_sampler_init_top_p(sampling_top_p_, 1));
+  llama_sampler_chain_add(sampler.get(),
+                          llama_sampler_init_dist(sampling_seed_));
+
+  std::vector<llama_token> generated_tokens;
+  generated_tokens.reserve(static_cast<std::size_t>(max_tokens));
+
+  for (int i = 0; i < effective_max_tokens; ++i) {
+    const llama_token next = llama_sampler_sample(sampler.get(), context_, -1);
+    if (llama_vocab_is_eog(vocab, next))
+      break;
+    generated_tokens.push_back(next);
+    llama_token token = next;
+    const llama_batch one_token_batch = llama_batch_get_one(&token, 1);
+    if (llama_decode(context_, one_token_batch) != 0)
+      throw std::runtime_error(
+          "LlamaGenerator: decode failed during generation");
+  }
+
+  std::string output;
+  for (const llama_token token : generated_tokens)
+    AppendTokenPiecePublic(vocab, token, output);
+  return output;
+}
+
+std::string LlamaGenerator::Infer(const std::string& system_prompt,
+                                  const std::string& prompt, int max_tokens) {
+  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 formatted_prompt =
+      ToChatPromptPublic(model_, system_prompt, prompt);
+
+  std::vector<llama_token> prompt_tokens(formatted_prompt.size() + 8);
+  int32_t token_count = llama_tokenize(
+      vocab, formatted_prompt.c_str(),
+      static_cast<int32_t>(formatted_prompt.size()), prompt_tokens.data(),
+      static_cast<int32_t>(prompt_tokens.size()), true, true);
+
+  if (token_count < 0) {
+    prompt_tokens.resize(static_cast<std::size_t>(-token_count));
+    token_count = llama_tokenize(
+        vocab, formatted_prompt.c_str(),
+        static_cast<int32_t>(formatted_prompt.size()), prompt_tokens.data(),
+        static_cast<int32_t>(prompt_tokens.size()), true, true);
+  }
+
+  if (token_count < 0)
+    throw std::runtime_error("LlamaGenerator: prompt tokenization failed");
+
+  const int32_t n_ctx = static_cast<int32_t>(llama_n_ctx(context_));
+  const int32_t n_batch = static_cast<int32_t>(llama_n_batch(context_));
+  if (n_ctx <= 1 || n_batch <= 0) {
+    throw std::runtime_error("LlamaGenerator: invalid context or batch size");
+  }
+
+  const int32_t effective_max_tokens = std::max(1, std::min(max_tokens, n_ctx - 1));
+  int32_t prompt_budget = std::min(n_batch, n_ctx - effective_max_tokens);
+  prompt_budget = std::max<int32_t>(1, prompt_budget);
+
+  prompt_tokens.resize(static_cast<std::size_t>(token_count));
+  if (token_count > prompt_budget) {
+    spdlog::warn(
+        "LlamaGenerator: prompt too long ({} tokens), truncating to {} tokens "
+        "to fit n_batch/n_ctx limits",
+        token_count, prompt_budget);
+    prompt_tokens.resize(static_cast<std::size_t>(prompt_budget));
+    token_count = prompt_budget;
+  }
+
+  const llama_batch prompt_batch = llama_batch_get_one(
+      prompt_tokens.data(), static_cast<int32_t>(prompt_tokens.size()));
+  if (llama_decode(context_, prompt_batch) != 0)
+    throw std::runtime_error("LlamaGenerator: prompt decode failed");
+
+  llama_sampler_chain_params sampler_params =
+      llama_sampler_chain_default_params();
+  using SamplerPtr =
+      std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
+  SamplerPtr sampler(llama_sampler_chain_init(sampler_params),
+                     &llama_sampler_free);
+  if (!sampler)
+    throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
+
+  llama_sampler_chain_add(sampler.get(),
+                          llama_sampler_init_temp(sampling_temperature_));
+  llama_sampler_chain_add(sampler.get(),
+                          llama_sampler_init_top_p(sampling_top_p_, 1));
+  llama_sampler_chain_add(sampler.get(),
+                          llama_sampler_init_dist(sampling_seed_));
+
+  std::vector<llama_token> generated_tokens;
+  generated_tokens.reserve(static_cast<std::size_t>(max_tokens));
+
+  for (int i = 0; i < effective_max_tokens; ++i) {
+    const llama_token next = llama_sampler_sample(sampler.get(), context_, -1);
+    if (llama_vocab_is_eog(vocab, next))
+      break;
+    generated_tokens.push_back(next);
+    llama_token token = next;
+    const llama_batch one_token_batch = llama_batch_get_one(&token, 1);
+    if (llama_decode(context_, one_token_batch) != 0)
+      throw std::runtime_error(
+          "LlamaGenerator: decode failed during generation");
+  }
+
+  std::string output;
+  for (const llama_token token : generated_tokens)
+    AppendTokenPiecePublic(vocab, token, output);
+  return output;
+}

pipeline/src/data_generation/llama/load.cpp

@@ -0,0 +1,42 @@
+#include <stdexcept>
+#include <string>
+
+#include "llama.h"
+#include <spdlog/spdlog.h>
+
+#include "data_generation/llama_generator.h"
+
+void LlamaGenerator::Load(const std::string& model_path) {
+  if (model_path.empty())
+    throw std::runtime_error("LlamaGenerator: model path must not be empty");
+
+  if (context_ != nullptr) {
+    llama_free(context_);
+    context_ = nullptr;
+  }
+  if (model_ != nullptr) {
+    llama_model_free(model_);
+    model_ = nullptr;
+  }
+
+  llama_backend_init();
+
+  llama_model_params model_params = llama_model_default_params();
+  model_ = llama_model_load_from_file(model_path.c_str(), model_params);
+  if (model_ == nullptr) {
+    throw std::runtime_error(
+        "LlamaGenerator: failed to load model from path: " + model_path);
+  }
+
+  llama_context_params context_params = llama_context_default_params();
+  context_params.n_ctx = 2048;
+
+  context_ = llama_init_from_model(model_, context_params);
+  if (context_ == nullptr) {
+    llama_model_free(model_);
+    model_ = nullptr;
+    throw std::runtime_error("LlamaGenerator: failed to create context");
+  }
+
+  spdlog::info("[LlamaGenerator] Loaded model: {}", model_path);
+}

pipeline/src/data_generation/llama/set_sampling_options.cpp

@@ -0,0 +1,26 @@
+#include <stdexcept>
+
+#include "llama.h"
+
+#include "data_generation/llama_generator.h"
+
+void LlamaGenerator::SetSamplingOptions(float temperature, float top_p,
+                                        int seed) {
+  if (temperature < 0.0f) {
+    throw std::runtime_error(
+        "LlamaGenerator: sampling temperature must be >= 0");
+  }
+  if (!(top_p > 0.0f && top_p <= 1.0f)) {
+    throw std::runtime_error(
+        "LlamaGenerator: sampling top-p must be in (0, 1]");
+  }
+  if (seed < -1) {
+    throw std::runtime_error(
+        "LlamaGenerator: seed must be >= 0, or -1 for random");
+  }
+
+  sampling_temperature_ = temperature;
+  sampling_top_p_ = top_p;
+  sampling_seed_ = (seed < 0) ? static_cast<uint32_t>(LLAMA_DEFAULT_SEED)
+                              : static_cast<uint32_t>(seed);
+}

pipeline/src/data_generation/llama_generator.cpp

@@ -1,734 +0,0 @@
-#include <algorithm>
-#include <array>
-#include <cctype>
-#include <memory>
-#include <sstream>
-#include <stdexcept>
-#include <string>
-#include <vector>
-
-#include "llama.h"
-#include <boost/json.hpp>
-#include <spdlog/spdlog.h>
-
-#include "data_generation/llama_generator.h"
-
-namespace {
-
-std::string trim(std::string value) {
-  auto notSpace = [](unsigned char ch) { return !std::isspace(ch); };
-
-  value.erase(value.begin(),
-              std::find_if(value.begin(), value.end(), notSpace));
-  value.erase(std::find_if(value.rbegin(), value.rend(), notSpace).base(),
-              value.end());
-
-  return value;
-}
-
-std::string CondenseWhitespace(std::string text) {
-  std::string out;
-  out.reserve(text.size());
-
-  bool inWhitespace = false;
-  for (unsigned char ch : text) {
-    if (std::isspace(ch)) {
-      if (!inWhitespace) {
-        out.push_back(' ');
-        inWhitespace = true;
-      }
-      continue;
-    }
-
-    inWhitespace = false;
-    out.push_back(static_cast<char>(ch));
-  }
-
-  return trim(std::move(out));
-}
-
-std::string PrepareRegionContext(std::string_view regionContext,
-                                 std::size_t maxChars = 700) {
-  std::string normalized = CondenseWhitespace(std::string(regionContext));
-  if (normalized.size() <= maxChars) {
-    return normalized;
-  }
-
-  normalized.resize(maxChars);
-  const std::size_t lastSpace = normalized.find_last_of(' ');
-  if (lastSpace != std::string::npos && lastSpace > maxChars / 2) {
-    normalized.resize(lastSpace);
-  }
-
-  normalized += "...";
-  return normalized;
-}
-
-std::string stripCommonPrefix(std::string line) {
-  line = trim(std::move(line));
-
-  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::pair<std::string, std::string>
-parseTwoLineResponse(const std::string &raw, const std::string &errorMessage) {
-  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));
-  }
-
-  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));
-    });
-    if (!l.empty() && l.front() == '<' && low.back() == '>')
-      continue;
-    if (low.rfind("okay,", 0) == 0 || low.rfind("hmm", 0) == 0)
-      continue;
-    filtered.push_back(std::move(l));
-  }
-
-  if (filtered.size() < 2)
-    throw std::runtime_error(errorMessage);
-
-  std::string first = trim(filtered.front());
-  std::string second;
-  for (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);
-  return {first, second};
-}
-
-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 &system_prompt,
-                         const std::string &userPrompt) {
-  const char *tmpl = llama_model_chat_template(model, nullptr);
-  if (tmpl == nullptr) {
-    return system_prompt + "\n\n" + userPrompt;
-  }
-
-  const llama_chat_message messages[2] = {{"system", system_prompt.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{};
-  int32_t bytes =
-      llama_token_to_piece(vocab, token, buffer.data(),
-                           static_cast<int32_t>(buffer.size()), 0, true);
-
-  if (bytes < 0) {
-    std::vector<char> dynamicBuffer(static_cast<std::size_t>(-bytes));
-    bytes = llama_token_to_piece(vocab, token, dynamicBuffer.data(),
-                                 static_cast<int32_t>(dynamicBuffer.size()), 0,
-                                 true);
-    if (bytes < 0) {
-      throw std::runtime_error(
-          "LlamaGenerator: failed to decode sampled token piece");
-    }
-
-    output.append(dynamicBuffer.data(), static_cast<std::size_t>(bytes));
-    return;
-  }
-
-  output.append(buffer.data(), static_cast<std::size_t>(bytes));
-}
-
-bool extractFirstJsonObject(const std::string &text, std::string &jsonOut) {
-  std::size_t start = std::string::npos;
-  int depth = 0;
-  bool inString = false;
-  bool escaped = false;
-
-  for (std::size_t i = 0; i < text.size(); ++i) {
-    const char ch = text[i];
-
-    if (inString) {
-      if (escaped) {
-        escaped = false;
-      } else if (ch == '\\') {
-        escaped = true;
-      } else if (ch == '"') {
-        inString = false;
-      }
-      continue;
-    }
-
-    if (ch == '"') {
-      inString = true;
-      continue;
-    }
-
-    if (ch == '{') {
-      if (depth == 0) {
-        start = i;
-      }
-      ++depth;
-      continue;
-    }
-
-    if (ch == '}') {
-      if (depth == 0) {
-        continue;
-      }
-      --depth;
-      if (depth == 0 && start != std::string::npos) {
-        jsonOut = text.substr(start, i - start + 1);
-        return true;
-      }
-    }
-  }
-
-  return false;
-}
-
-std::string ValidateBreweryJson(const std::string &raw, std::string &nameOut,
-                                std::string &descriptionOut) {
-  auto validateObject = [&](const boost::json::value &jv,
-                            std::string &errorOut) -> bool {
-    if (!jv.is_object()) {
-      errorOut = "JSON root must be an object";
-      return false;
-    }
-
-    const auto &obj = jv.get_object();
-    if (!obj.contains("name") || !obj.at("name").is_string()) {
-      errorOut = "JSON field 'name' is missing or not a string";
-      return false;
-    }
-
-    if (!obj.contains("description") || !obj.at("description").is_string()) {
-      errorOut = "JSON field 'description' is missing or not a string";
-      return false;
-    }
-
-    nameOut = trim(std::string(obj.at("name").as_string().c_str()));
-    descriptionOut =
-        trim(std::string(obj.at("description").as_string().c_str()));
-
-    if (nameOut.empty()) {
-      errorOut = "JSON field 'name' must not be empty";
-      return false;
-    }
-
-    if (descriptionOut.empty()) {
-      errorOut = "JSON field 'description' must not be empty";
-      return false;
-    }
-
-    std::string nameLower = nameOut;
-    std::string descriptionLower = descriptionOut;
-    std::transform(
-        nameLower.begin(), nameLower.end(), nameLower.begin(),
-        [](unsigned char c) { return static_cast<char>(std::tolower(c)); });
-    std::transform(descriptionLower.begin(), descriptionLower.end(),
-                   descriptionLower.begin(), [](unsigned char c) {
-                     return static_cast<char>(std::tolower(c));
-                   });
-
-    if (nameLower == "string" || descriptionLower == "string") {
-      errorOut = "JSON appears to be a schema placeholder, not content";
-      return false;
-    }
-
-    errorOut.clear();
-    return true;
-  };
-
-  boost::system::error_code ec;
-  boost::json::value jv = boost::json::parse(raw, ec);
-  std::string validationError;
-  if (ec) {
-    std::string extracted;
-    if (!extractFirstJsonObject(raw, extracted)) {
-      return "JSON parse error: " + ec.message();
-    }
-
-    ec.clear();
-    jv = boost::json::parse(extracted, ec);
-    if (ec) {
-      return "JSON parse error: " + ec.message();
-    }
-
-    if (!validateObject(jv, validationError)) {
-      return validationError;
-    }
-
-    return {};
-  }
-
-  if (!validateObject(jv, validationError)) {
-    return validationError;
-  }
-
-  return {};
-}
-} // namespace
-
-LlamaGenerator::~LlamaGenerator() {
-  if (context_ != nullptr) {
-    llama_free(context_);
-    context_ = nullptr;
-  }
-
-  if (model_ != nullptr) {
-    llama_model_free(model_);
-    model_ = nullptr;
-  }
-
-  llama_backend_free();
-}
-
-void LlamaGenerator::SetSamplingOptions(float temperature, float top_p,
-                                        int seed) {
-  if (temperature < 0.0f) {
-    throw std::runtime_error(
-        "LlamaGenerator: sampling temperature must be >= 0");
-  }
-  if (!(top_p > 0.0f && top_p <= 1.0f)) {
-    throw std::runtime_error(
-        "LlamaGenerator: sampling top-p must be in (0, 1]");
-  }
-  if (seed < -1) {
-    throw std::runtime_error(
-        "LlamaGenerator: seed must be >= 0, or -1 for random");
-  }
-
-  sampling_temperature_ = temperature;
-  sampling_top_p_ = top_p;
-  sampling_seed_ = (seed < 0) ? static_cast<uint32_t>(LLAMA_DEFAULT_SEED)
-                              : static_cast<uint32_t>(seed);
-}
-
-void LlamaGenerator::Load(const std::string &model_path) {
-  if (model_path.empty())
-    throw std::runtime_error("LlamaGenerator: model path must not be empty");
-
-  if (context_ != nullptr) {
-    llama_free(context_);
-    context_ = nullptr;
-  }
-  if (model_ != nullptr) {
-    llama_model_free(model_);
-    model_ = nullptr;
-  }
-
-  llama_backend_init();
-
-  llama_model_params model_params = llama_model_default_params();
-  model_ = llama_model_load_from_file(model_path.c_str(), model_params);
-  if (model_ == nullptr) {
-    throw std::runtime_error(
-        "LlamaGenerator: failed to load model from path: " + model_path);
-  }
-
-  llama_context_params context_params = llama_context_default_params();
-  context_params.n_ctx = 2048;
-
-  context_ = llama_init_from_model(model_, context_params);
-  if (context_ == nullptr) {
-    llama_model_free(model_);
-    model_ = nullptr;
-    throw std::runtime_error("LlamaGenerator: failed to create context");
-  }
-
-  spdlog::info("[LlamaGenerator] Loaded model: {}", model_path);
-}
-
-std::string LlamaGenerator::Infer(const std::string &prompt, int max_tokens) {
-  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 formatted_prompt = toChatPrompt(model_, prompt);
-
-  std::vector<llama_token> promptTokens(formatted_prompt.size() + 8);
-  int32_t tokenCount = llama_tokenize(
-      vocab, formatted_prompt.c_str(),
-      static_cast<int32_t>(formatted_prompt.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, formatted_prompt.c_str(),
-        static_cast<int32_t>(formatted_prompt.size()), promptTokens.data(),
-        static_cast<int32_t>(promptTokens.size()), true, true);
-  }
-
-  if (tokenCount < 0)
-    throw std::runtime_error("LlamaGenerator: prompt tokenization failed");
-
-  const int32_t nCtx = static_cast<int32_t>(llama_n_ctx(context_));
-  const int32_t nBatch = static_cast<int32_t>(llama_n_batch(context_));
-  if (nCtx <= 1 || nBatch <= 0) {
-    throw std::runtime_error("LlamaGenerator: invalid context or batch size");
-  }
-
-  const int32_t effective_max_tokens = std::max(1, std::min(max_tokens, nCtx - 1));
-  const int32_t prompt_budget = std::min(nBatch, nCtx - effective_max_tokens);
-  prompt_budget = std::max<int32_t>(1, prompt_budget);
-
-  promptTokens.resize(static_cast<std::size_t>(tokenCount));
-  if (tokenCount > prompt_budget) {
-    spdlog::warn(
-        "LlamaGenerator: prompt too long ({} tokens), truncating to {} tokens "
-        "to fit n_batch/n_ctx limits",
-        tokenCount, prompt_budget);
-    promptTokens.resize(static_cast<std::size_t>(prompt_budget));
-    tokenCount = prompt_budget;
-  }
-
-  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 sampler_params =
-      llama_sampler_chain_default_params();
-  using SamplerPtr =
-      std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
-  SamplerPtr sampler(llama_sampler_chain_init(sampler_params),
-                     &llama_sampler_free);
-  if (!sampler)
-    throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
-
-  llama_sampler_chain_add(sampler.get(),
-                          llama_sampler_init_temp(sampling_temperature_));
-  llama_sampler_chain_add(sampler.get(),
-                          llama_sampler_init_top_p(sampling_top_p_, 1));
-  llama_sampler_chain_add(sampler.get(),
-                          llama_sampler_init_dist(sampling_seed_));
-
-  std::vector<llama_token> generated_tokens;
-  generated_tokens.reserve(static_cast<std::size_t>(max_tokens));
-
-  for (int i = 0; i < effective_max_tokens; ++i) {
-    const llama_token next = llama_sampler_sample(sampler.get(), context_, -1);
-    if (llama_vocab_is_eog(vocab, next))
-      break;
-    generated_tokens.push_back(next);
-    llama_token token = next;
-    const llama_batch one_token_batch = llama_batch_get_one(&token, 1);
-    if (llama_decode(context_, one_token_batch) != 0)
-      throw std::runtime_error(
-          "LlamaGenerator: decode failed during generation");
-  }
-
-  std::string output;
-  for (const llama_token token : generated_tokens)
-    appendTokenPiece(vocab, token, output);
-  return output;
-}
-
-std::string LlamaGenerator::Infer(const std::string &system_prompt,
-                                  const std::string &prompt, int max_tokens) {
-  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 formatted_prompt =
-      toChatPrompt(model_, system_prompt, prompt);
-
-  std::vector<llama_token> promptTokens(formatted_prompt.size() + 8);
-  int32_t tokenCount = llama_tokenize(
-      vocab, formatted_prompt.c_str(),
-      static_cast<int32_t>(formatted_prompt.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, formatted_prompt.c_str(),
-        static_cast<int32_t>(formatted_prompt.size()), promptTokens.data(),
-        static_cast<int32_t>(promptTokens.size()), true, true);
-  }
-
-  if (tokenCount < 0)
-    throw std::runtime_error("LlamaGenerator: prompt tokenization failed");
-
-  const int32_t nCtx = static_cast<int32_t>(llama_n_ctx(context_));
-  const int32_t nBatch = static_cast<int32_t>(llama_n_batch(context_));
-  if (nCtx <= 1 || nBatch <= 0) {
-    throw std::runtime_error("LlamaGenerator: invalid context or batch size");
-  }
-
-  const int32_t effective_max_tokens = std::max(1, std::min(max_tokens, nCtx - 1));
-  int32_t prompt_budget = std::min(nBatch, nCtx - effective_max_tokens);
-  prompt_budget = std::max<int32_t>(1, prompt_budget);
-
-  promptTokens.resize(static_cast<std::size_t>(tokenCount));
-  if (tokenCount > prompt_budget) {
-    spdlog::warn(
-        "LlamaGenerator: prompt too long ({} tokens), truncating to {} tokens "
-        "to fit n_batch/n_ctx limits",
-        tokenCount, prompt_budget);
-    promptTokens.resize(static_cast<std::size_t>(prompt_budget));
-    tokenCount = prompt_budget;
-  }
-
-  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 sampler_params =
-      llama_sampler_chain_default_params();
-  using SamplerPtr =
-      std::unique_ptr<llama_sampler, decltype(&llama_sampler_free)>;
-  SamplerPtr sampler(llama_sampler_chain_init(sampler_params),
-                     &llama_sampler_free);
-  if (!sampler)
-    throw std::runtime_error("LlamaGenerator: failed to initialize sampler");
-
-  llama_sampler_chain_add(sampler.get(),
-                          llama_sampler_init_temp(sampling_temperature_));
-  llama_sampler_chain_add(sampler.get(),
-                          llama_sampler_init_top_p(sampling_top_p_, 1));
-  llama_sampler_chain_add(sampler.get(),
-                          llama_sampler_init_dist(sampling_seed_));
-
-  std::vector<llama_token> generated_tokens;
-  generated_tokens.reserve(static_cast<std::size_t>(max_tokens));
-
-  for (int i = 0; i < effective_max_tokens; ++i) {
-    const llama_token next = llama_sampler_sample(sampler.get(), context_, -1);
-    if (llama_vocab_is_eog(vocab, next))
-      break;
-    generated_tokens.push_back(next);
-    llama_token token = next;
-    const llama_batch one_token_batch = llama_batch_get_one(&token, 1);
-    if (llama_decode(context_, one_token_batch) != 0)
-      throw std::runtime_error(
-          "LlamaGenerator: decode failed during generation");
-  }
-
-  std::string output;
-  for (const llama_token token : generated_tokens)
-    appendTokenPiece(vocab, token, output);
-  return output;
-}
-
-BreweryResult
-LlamaGenerator::GenerateBrewery(const std::string &city_name,
-                                const std::string &country_name,
-                                const std::string &region_context) {
-  const std::string safe_region_context = PrepareRegionContext(region_context);
-
-  const std::string system_prompt =
-      "You are a copywriter for a craft beer travel guide. "
-      "Your writing is vivid, specific to place, and avoids generic beer "
-      "cliches. "
-      "You must output ONLY valid JSON. "
-      "The JSON schema must be exactly: {\"name\": \"string\", "
-      "\"description\": \"string\"}. "
-      "Do not include markdown formatting or backticks.";
-
-  std::string prompt =
-      "Write a brewery name and place-specific description for a craft "
-      "brewery in " +
-      city_name +
-      (country_name.empty() ? std::string("")
-                           : std::string(", ") + country_name) +
-      (safe_region_context.empty()
-           ? std::string(".")
-           : std::string(". Regional context: ") + safe_region_context);
-
-  const int maxAttempts = 3;
-  std::string raw;
-  std::string lastError;
-  for (int attempt = 0; attempt < maxAttempts; ++attempt) {
-    raw = Infer(system_prompt, prompt, 384);
-    spdlog::debug("LlamaGenerator: raw output (attempt {}): {}", attempt + 1,
-                  raw);
-
-    std::string name;
-    std::string description;
-    const std::string validationError =
-        ValidateBreweryJson(raw, name, description);
-    if (validationError.empty()) {
-      return {std::move(name), std::move(description)};
-    }
-
-    lastError = validationError;
-    spdlog::warn("LlamaGenerator: malformed brewery JSON (attempt {}): {}",
-                 attempt + 1, validationError);
-
-    prompt = "Your previous response was invalid. Error: " + validationError +
-             "\nReturn ONLY valid JSON with this exact schema: "
-             "{\"name\": \"string\", \"description\": \"string\"}."
-             "\nDo not include markdown, comments, or extra keys."
-             "\n\nLocation: " +
-             city_name +
-             (country_name.empty() ? std::string("")
-                                  : std::string(", ") + country_name) +
-             (safe_region_context.empty()
-                  ? std::string("")
-                  : std::string("\nRegional context: ") + safe_region_context);
-  }
-
-  spdlog::error("LlamaGenerator: malformed brewery response after {} attempts: "
-                "{}",
-                maxAttempts, lastError.empty() ? raw : lastError);
-  throw std::runtime_error("LlamaGenerator: malformed brewery response");
-}
-
-UserResult LlamaGenerator::GenerateUser(const std::string &locale) {
-  const std::string system_prompt =
-      "You generate plausible social media profiles for craft beer "
-      "enthusiasts. "
-      "Respond with exactly two lines: "
-      "the first line is a username (lowercase, no spaces, 8-20 characters), "
-      "the second line is a one-sentence bio (20-40 words). "
-      "The profile should feel consistent with the locale. "
-      "No preamble, no labels.";
-
-  std::string prompt =
-      "Generate a craft beer enthusiast profile. Locale: " + locale;
-
-  const int maxAttempts = 3;
-  std::string raw;
-  for (int attempt = 0; attempt < maxAttempts; ++attempt) {
-    raw = Infer(system_prompt, prompt, 128);
-    spdlog::debug("LlamaGenerator (user): raw output (attempt {}): {}",
-                  attempt + 1, raw);
-
-    try {
-      auto [username, bio] =
-          parseTwoLineResponse(raw, "LlamaGenerator: malformed user response");
-
-      username.erase(
-          std::remove_if(username.begin(), username.end(),
-                         [](unsigned char ch) { return std::isspace(ch); }),
-          username.end());
-
-      if (username.empty() || bio.empty()) {
-        throw std::runtime_error("LlamaGenerator: malformed user response");
-      }
-
-      if (bio.size() > 200)
-        bio = bio.substr(0, 200);
-
-      return {username, bio};
-    } catch (const std::exception &e) {
-      spdlog::warn("LlamaGenerator: malformed user response (attempt {}): {}",
-                   attempt + 1, e.what());
-    }
-  }
-
-  spdlog::error("LlamaGenerator: malformed user response after {} attempts: {}",
-                maxAttempts, raw);
-  throw std::runtime_error("LlamaGenerator: malformed user response");
-}

pipeline/src/wikipedia/wikipedia_service.cpp

@@ -2,7 +2,7 @@
 #include <boost/json.hpp>
 #include <spdlog/spdlog.h>
 
-WikipediaService::WikipediaService(std::shared_ptr<IWebClient> client)
+WikipediaService::WikipediaService(std::shared_ptr<WebClient> client)
     : client_(std::move(client)) {}
 
 std::string WikipediaService::FetchExtract(std::string_view query) {