Update documentation and environment variables configuration

.env.template

@@ -1,8 +1,5 @@
 # Copy this file and modify. Do not save or commit the secrets!
 
-# Application Configuration
-DEBUG=TRUE
-
 # APScheduler Configuration
 SCHEDULER_TIMEZONE=UTC
 

README.md

@@ -10,47 +10,13 @@ short_description: Spring 2025 CTP Slack Bot RAG system
 ---
 
 
-
-
 # CTP Slack Bot
 
 ## _Modus Operandi_ in a Nutshell
 
-* Intelligently responds to Slack messages based on a repository of data.
+* Intelligently responds to Slack messages (when mentioned) based on a repository of data.
 * Periodically checks for new content to add to its repository.
 
-## Tech Stack
-
-* Hugging Face Spaces for hosting and serverless API
-* Google Drive for reference data (i.e., the material to be incorporated into the bot’s knowledge base)
-* MongoDB for data persistence
-* Docker for containerization
-* Python
-    * FastAPI for serving HTTP requests
-    * httpx for making HTTP requests
-    * APScheduler for running periodic tasks in the background
-    * See `pyproject.toml` for additional Python packages.
-
-## General Project Structure
-
-* `src/`
-    * `ctp_slack_bot/`
-        * `api/`: FastAPI application structure
-            * `routes.py`: API endpoint definitions
-        * `core/`: fundamental components like configuration (using pydantic), logging setup (loguru), and custom exceptions
-        * `db/`: database connection
-            * `repositories/`: repository pattern implementation
-        * `models/`: Pydantic models for data validation and serialization
-        * `services/`: business logic
-        * `tasks/`: background scheduled jobs
-        * `utils/`: reusable utilities
-* `tests/`: unit tests
-* `scripts/`: utility scripts for development, deployment, etc.
-    * `run-dev.sh`: script to run the application locally
-* `notebooks/`: Jupyter notebooks for exploration and model development
-* `.env`: local environment variables for development purposes (to be created for local use only from `.env.template`)
-* `Dockerfile`: Docker container build definition
-
 ## How to Run the Application
 
 ### Normally
@@ -66,7 +32,7 @@ docker build . -t ctp-slack-bot
 Run it with:
 
 ```sh
-docker run --env-file=.env -p 8000:8000 --name my-ctp-slack-bot-instance ctp-slack-bot
+docker run --volume ./logs:/app/logs/ --env-file=.env -p 8000:8000 --name my-ctp-slack-bot-instance ctp-slack-bot
 ```
 
 ### For Development
@@ -87,13 +53,45 @@ If `localhost` port `8000` is free, running the following will make the applicat
 scripts/run-dev.sh
 ```
 
-You can check that it’s reachable by visiting [http://localhost:8000/health](http://localhost:8000/health).
+## Tech Stack
 
-```text
-$ curl http://localhost:8000/health
-{"status":"healthy"}
-```
+* Hugging Face Spaces for hosting
+* OpenAI for embeddings and language models
+* Google Drive for reference data (i.e., the material to be incorporated into the bot’s knowledge base)
+* MongoDB for data persistence
+* Docker for containerization
+* Python
+    * Slack Bolt client for interfacing with Slack
+    * See `pyproject.toml` for additional Python packages.
 
-In debug mode (`DEBUG=true`), [http://localhost:8000/env](http://localhost:8000/env) will pretty-print the non-sensitive environment variables as JSON.
+## General Project Structure
 
-Uvicorn will restart the application automatically when any source files are changed.
+* `src/`
+    * `ctp_slack_bot/`
+        * `core/`: fundamental components like configuration (using pydantic), logging setup (loguru), and custom exceptions
+        * `db/`: database connection
+            * `repositories/`: repository pattern implementation
+        * `models/`: Pydantic models for data validation and serialization
+        * `services/`: business logic
+            * `answer_retrieval_service.py`: obtains an answer to a question from a language model using relevant context
+            * `content_ingestion_service.py`: converts content into chunks and stores them into the database
+            * `context_retrieval_service.py`: queries for relevant context from the database to answer a question
+            * `embeddings_model_service.py`: converts text to embeddings
+            * `event_brokerage_service.py`: brokers events between decoupled components
+            * `language_model_service.py`: answers questions using relevant context
+            * `question_dispatch_service.py`: listens for questions and retrieves relevant context to get answers
+            * `schedule_service.py`: runs background jobs
+            * `slack_service.py`: handles events from Slack and sends back responses
+            * `vector_database_service.py`: stores and queries chunks
+            * `vectorization_service.py`: converts chunks into chunks with embeddings
+        * `tasks/`: background scheduled jobs
+        * `utils/`: reusable utilities
+        * `app.py`: application entry point
+        * `containers.py`: the dependency injection container
+* `tests/`: unit tests
+* `scripts/`: utility scripts for development, deployment, etc.
+    * `run-dev.sh`: script to run the application locally
+* `notebooks/`: Jupyter notebooks for exploration and model development
+* `.env`: local environment variables for development purposes (to be created for local use only from `.env.template`)
+* `Dockerfile`: Docker container build definition
+* `pyproject.toml`: project definition and dependencies

src/ctp_slack_bot/core/config.py

@@ -1,17 +1,21 @@
+from loguru import logger
 from pydantic import Field, MongoDsn, NonNegativeFloat, NonNegativeInt, PositiveInt, SecretStr
 from pydantic_settings import BaseSettings, SettingsConfigDict
 from types import MappingProxyType
 from typing import Literal, Mapping, Optional, Self
 
-class Settings(BaseSettings): # TODO: Strong guarantees of validity, because garbage in = garbage out, and settings flow into all the nooks and crannies
+class Settings(BaseSettings):
     """
     Application settings loaded from environment variables.
     """
 
-    # Application Configuration
-    DEBUG: bool = False
+    def __init__(self: Self, **data) -> None:
+        super().__init__(**data)
+        logger.debug("Created {}", self.__class__.__name__)
+        if self.__pydantic_extra__:
+            logger.warning("Extra unrecognized environment variables were provided: {}", ", ".join(self.__pydantic_extra__))
 
-    # Logging Configuration
+    # Logging Configuration ― not actually used to configure Loguru, but defined to prevent warnings about “unknown” environment variables
     LOG_LEVEL: Literal["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"] = Field(default_factory=lambda data: "DEBUG" if data.get("DEBUG", False) else "INFO")
     LOG_FORMAT: Literal["text", "json"] = "json"