feat: add -v option and improve logging

.env.example

@@ -1,10 +1,17 @@
-OPENAI_API_KEY=key
-GOOGLE_API_KEY=key
+# Agent keys
+OPENAI_API_KEY=
+GOOGLE_API_KEY=
 
 # SERVICE_DESK Integration
 SERVICE_DESK_URL=
 SERVICE_DESK_USER=
 SERVICE_DESK_TOKEN=
 
+# Sentry configuration
+SENTRY_DSN=
+
 # Your time zone. Defaults to UTC.
-TIME_ZONE=
+TIME_ZONE=
+
+# Logging level
+LOG_LEVEL=

README.md

@@ -81,6 +81,7 @@ Lightman AI is an intelligent cybersecurity news aggregation and risk assessment
    agent = "openai"
    score_threshold = 8
    prompt = "development"
+   log_level = "INFO"
 
    [prompts]
    development = "Analyze cybersecurity news for relevance to our organization."' > lightman.toml
@@ -129,6 +130,8 @@ Lightman AI is an intelligent cybersecurity news aggregation and risk assessment
 | `--start-date` | Start date to retrieve articles | False |
 | `--today` | Retrieve articles from today | False |
 | `--yesterday` | Retrieve articles from yesterday | False |
+| `-v` | Be more verbose on output | False |
+
 
 ### Environment Variables:
 lightman-ai uses the following environment variables:

eval/cli.py

@@ -1,13 +1,13 @@
 import click
 from dotenv import load_dotenv
 from lightman_ai.ai.utils import AGENT_CHOICES
-from lightman_ai.constants import DEFAULT_CONFIG_FILE, DEFAULT_ENV_FILE
+from lightman_ai.constants import DEFAULT_AGENT, DEFAULT_CONFIG_FILE, DEFAULT_ENV_FILE, DEFAULT_SCORE
 from lightman_ai.core.config import PromptConfig
 
 from eval.classified_articles import NON_RELEVANT_ARTICLES, RELEVANT_ARTICLES
 from eval.constants import DEFAULT_EVAL_CONFIG_SECTION
 from eval.evaluator import eval
-from eval.utils import EvalConfig, EvalFileConfig, init_eval_settings
+from eval.utils import PARALLEL_WORKERS, EvalConfig, EvalFileConfig
 
 
 @click.command()
@@ -59,14 +59,14 @@ def run(
     model: str | None = None,
 ) -> None:
     load_dotenv(env_file)
-    settings = init_eval_settings(env_file)
+
     config_from_file = EvalFileConfig.get_config_from_file(config_section=config, path=config_file)
     configured_prompts = PromptConfig.get_config_from_file(path=prompt_file)
     eval_config = EvalConfig.init_from_dict(
         {
-            "agent": agent or config_from_file.agent or settings.AGENT,
+            "agent": agent or config_from_file.agent or DEFAULT_AGENT,
             "prompt": prompt or config_from_file.prompt,
-            "score_threshold": score or config_from_file.score_threshold or settings.SCORE,
+            "score_threshold": score or config_from_file.score_threshold or DEFAULT_SCORE,
             "samples": samples or config_from_file.samples,
             "model": model or config_from_file.model,
         }
@@ -81,7 +81,7 @@ def run(
         agent=eval_config.agent,
         prompt=configured_prompts.get_prompt(eval_config.prompt),
         model=eval_config.model,
-        parallel_workers=settings.PARALLEL_WORKERS,
+        parallel_workers=PARALLEL_WORKERS,
     )
 
 

eval/evaluator.py

@@ -41,7 +41,7 @@ def eval(
         prompt=prompt,
         score=score_threshold,
         logger=logger,
-        model=model or agent_class._default_model_name,
+        model=model or agent_class._DEFAULT_MODEL_NAME,
     )
 
     results_template.save()

eval/utils.py

@@ -2,23 +2,12 @@
 from collections.abc import Iterable
 from dataclasses import dataclass
 from decimal import Decimal
-from typing import Any
 
 from lightman_ai.article.models import SelectedArticle
 from lightman_ai.core.config import FileConfig, FinalConfig
-from lightman_ai.core.settings import Settings
 from pydantic import PositiveInt
 
-
-class EvalSettings(Settings):
-    def __init__(self, *args: Any, **kwargs: Any) -> None:
-        super().__init__(*args, **kwargs)
-
-    PARALLEL_WORKERS: int = 5
-
-
-def init_eval_settings(env_file: str | None = None) -> EvalSettings:
-    return EvalSettings(_env_file=env_file)
+PARALLEL_WORKERS: int = 5
 
 
 class EvalConfig(FinalConfig):

src/lightman_ai/ai/base/agent.py

@@ -1,6 +1,6 @@
 import logging
 from abc import ABC, abstractmethod
-from typing import Never
+from typing import ClassVar, Never, override
 
 from lightman_ai.article.models import SelectedArticlesList
 from pydantic_ai import Agent
@@ -9,18 +9,22 @@
 
 
 class BaseAgent(ABC):
-    _class: type[OpenAIChatModel] | type[GoogleModel]
-    _default_model_name: str
+    _AGENT_CLASS: type[OpenAIChatModel] | type[GoogleModel]
+    _DEFAULT_MODEL_NAME: str
+    _AGENT_NAME: ClassVar[str]
 
     def __init__(self, system_prompt: str, model: str | None = None, logger: logging.Logger | None = None) -> None:
-        agent_model = self._class(model or self._default_model_name)
+        selected_model = model or self._DEFAULT_MODEL_NAME
+        agent_model = self._AGENT_CLASS(selected_model)
         self.agent: Agent[Never, SelectedArticlesList] = Agent(
             model=agent_model, output_type=SelectedArticlesList, system_prompt=system_prompt
         )
         self.logger = logger or logging.getLogger("lightman")
+        self.logger.info("Selected %s's %s model", self, selected_model)
 
-    def get_prompt_result(self, prompt: str) -> SelectedArticlesList:
-        return self._run_prompt(prompt)
+    @override
+    def __str__(self) -> str:
+        return self._AGENT_NAME
 
     @abstractmethod
-    def _run_prompt(self, prompt: str) -> SelectedArticlesList: ...
+    def run_prompt(self, prompt: str) -> SelectedArticlesList: ...

src/lightman_ai/ai/gemini/agent.py

@@ -9,11 +9,12 @@
 class GeminiAgent(BaseAgent):
     """Class that provides an interface to operate with the Gemini model."""
 
-    _class = GoogleModel
-    _default_model_name = "gemini-2.5-pro"
+    _AGENT_CLASS = GoogleModel
+    _DEFAULT_MODEL_NAME = "gemini-2.5-pro"
+    _AGENT_NAME = "Gemini"
 
     @override
-    def _run_prompt(self, prompt: str) -> SelectedArticlesList:
+    def run_prompt(self, prompt: str) -> SelectedArticlesList:
         with map_gemini_exceptions():
             result = self.agent.run_sync(prompt)
         return result.output

src/lightman_ai/ai/openai/agent.py

@@ -11,15 +11,16 @@
 class OpenAIAgent(BaseAgent):
     """Class that provides an interface to operate with the OpenAI model."""
 
-    _class = OpenAIChatModel
-    _default_model_name = "gpt-4.1"
+    _AGENT_CLASS = OpenAIChatModel
+    _DEFAULT_MODEL_NAME = "gpt-4.1"
+    _AGENT_NAME = "OpenAI"
 
     def _execute_agent(self, prompt: str) -> AgentRunResult[SelectedArticlesList]:
         with map_openai_exceptions():
             return self.agent.run_sync(prompt)
 
     @override
-    def _run_prompt(self, prompt: str) -> SelectedArticlesList:
+    def run_prompt(self, prompt: str) -> SelectedArticlesList:
         try:
             result = self._execute_agent(prompt)
         except LimitTokensExceededError as err:

src/lightman_ai/cli.py

@@ -1,20 +1,33 @@
 import logging
+import os
 from datetime import date
 from importlib import metadata
 
 import click
 from dotenv import load_dotenv
 from lightman_ai.ai.utils import AGENT_CHOICES
-from lightman_ai.constants import DEFAULT_CONFIG_FILE, DEFAULT_CONFIG_SECTION, DEFAULT_ENV_FILE
+from lightman_ai.constants import (
+    DEFAULT_AGENT,
+    DEFAULT_CONFIG_FILE,
+    DEFAULT_CONFIG_SECTION,
+    DEFAULT_ENV_FILE,
+    DEFAULT_LOG_LEVEL,
+    DEFAULT_SCORE,
+    DEFAULT_TIME_ZONE,
+    VERBOSE_LOG_LEVEL,
+)
 from lightman_ai.core.config import FileConfig, FinalConfig, PromptConfig
 from lightman_ai.core.exceptions import ConfigNotFoundError, InvalidConfigError, PromptNotFoundError
 from lightman_ai.core.sentry import configure_sentry
-from lightman_ai.core.settings import Settings
 from lightman_ai.exceptions import MultipleDateSourcesError
 from lightman_ai.main import lightman
 from lightman_ai.utils import get_start_date
 
 logger = logging.getLogger("lightman")
+logging.basicConfig(
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
 
 
 def get_version() -> str:
@@ -72,6 +85,7 @@ def entry_point() -> None:
 @click.option("--start-date", type=click.DateTime(formats=["%Y-%m-%d"]), help="Start date to retrieve articles")
 @click.option("--today", is_flag=True, help="Retrieve articles from today.")
 @click.option("--yesterday", is_flag=True, help="Retrieve articles from yesterday.")
+@click.option("-v", is_flag=True, help="Be more verbose on output.")
 def run(
     agent: str,
     prompt: str,
@@ -85,18 +99,30 @@ def run(
     start_date: date | None,
     today: bool,
     yesterday: bool,
+    v: bool,
 ) -> int:
     """
     Entrypoint of the application.
 
     Holds no logic. It loads the configuration, calls the main method and returns 0 when succesful .
     """
     load_dotenv(env_file or DEFAULT_ENV_FILE)
-    configure_sentry()
 
-    settings = Settings.try_load_from_file(env_file)
+    if v:
+        logger.setLevel(VERBOSE_LOG_LEVEL)
+    else:
+        try:
+            env_log_level = os.getenv("LOG_LEVEL")
+            log_level = env_log_level.upper() if env_log_level else DEFAULT_LOG_LEVEL
+            logger.setLevel(log_level)
+        except ValueError:
+            logger.setLevel(DEFAULT_LOG_LEVEL)
+            logger.warning("Invalid logging level. Using default value.")
+
+    configure_sentry(logger.level)
+
     try:
-        start_datetime = get_start_date(settings, yesterday, today, start_date)
+        start_datetime = get_start_date(os.getenv("TIME_ZONE", DEFAULT_TIME_ZONE), yesterday, today, start_date)
     except MultipleDateSourcesError as e:
         raise click.UsageError(e.args[0]) from e
 
@@ -105,9 +131,9 @@ def run(
         config_from_file = FileConfig.get_config_from_file(config_section=config, path=config_file)
         final_config = FinalConfig.init_from_dict(
             data={
-                "agent": agent or config_from_file.agent or settings.AGENT,
+                "agent": agent or config_from_file.agent or DEFAULT_AGENT,
                 "prompt": prompt or config_from_file.prompt,
-                "score_threshold": score or config_from_file.score_threshold or settings.SCORE,
+                "score_threshold": score or config_from_file.score_threshold or DEFAULT_SCORE,
                 "model": model or config_from_file.model,
             }
         )
@@ -126,5 +152,11 @@ def run(
         start_date=start_datetime,
     )
     relevant_articles_metadata = [f"{article.title} ({article.link})" for article in relevant_articles]
-    logger.warning("Found these articles: \n- %s", "\n- ".join(relevant_articles_metadata))
+
+    if relevant_articles_metadata:
+        articles = f"Found these articles:\n* {'\n* '.join(relevant_articles_metadata)} "
+        click.echo(click.style(articles))
+    else:
+        click.echo(click.style("No relevant articles found."))
+
     return 0

src/lightman_ai/constants.py

@@ -3,3 +3,10 @@
 DEFAULT_CONFIG_FILE = "lightman.toml"
 
 DEFAULT_ENV_FILE = ".env"
+
+DEFAULT_LOG_LEVEL = "WARNING"
+VERBOSE_LOG_LEVEL = "INFO"
+
+DEFAULT_AGENT = "openai"
+DEFAULT_SCORE = 8
+DEFAULT_TIME_ZONE = "UTC"

src/lightman_ai/core/sentry.py

@@ -5,31 +5,24 @@
 logger = logging.getLogger("lightman")
 
 
-def configure_sentry() -> None:
+def configure_sentry(log_level: int) -> None:
     """Configure Sentry for error tracking."""
     try:
         import sentry_sdk  # noqa: PLC0415
         from sentry_sdk.integrations.logging import LoggingIntegration  # noqa: PLC0415
     except ImportError:
-        logger.warning(
-            "Could not initialize sentry, it is not installed! Add it by installing the project with `lightman-ai[sentry]`."
-        )
+        if os.getenv("SENTRY_DSN"):
+            logger.warning(
+                "Could not initialize sentry, it is not installed! Add it by installing the project with `lightman-ai[sentry]`."
+            )
         return
 
-    try:
-        if not os.getenv("SENTRY_DSN"):
-            logger.info("SENTRY_DSN not configured, skipping Sentry initialization")
-            return
-
-        logging_level_str = os.getenv("LOGGING_LEVEL", "ERROR").upper()
-        try:
-            logging_level = getattr(logging, logging_level_str, logging.ERROR)
-        except AttributeError:
-            logger.warning("The specified logging level `%s` does not exist. Defaulting to ERROR.", logging_level_str)
-            logging_level = logging.ERROR
+    if not os.getenv("SENTRY_DSN"):
+        logger.warning("SENTRY_DSN not configured, skipping Sentry initialization")
+        return
 
-        # Set up logging integration
-        sentry_logging = LoggingIntegration(level=logging.INFO, event_level=logging_level)
+    try:
+        sentry_logging = LoggingIntegration(level=logging.INFO, event_level=log_level)
 
         sentry_sdk.init(
             release=metadata.version("lightman-ai"),