rag-mongo-logger 0.1.2

Async buffered logger with MongoDB and PostgreSQL support for your RAG applications

rag-mongo-logger

🚀 A robust, async-buffered Python logging library supporting MongoDB, PostgreSQL, and local file fallback. Designed for chatbots, training pipelines, or any system needing conversation-oriented logging.

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📦 Installation

Install from PyPI:

pip install rag-mongo-logger

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⚙️ Configuration

config = {
    "uri": "mongodb://localhost:27017/",  # or PostgreSQL URI
    "db": "conversational_logs",          # MongoDB database name
    "env": "prod",                        # Optional: "dev", "staging", "prod"
    "logger_mode": "chat",                # "chat" or "training"
    "log_batch_size": 5,                  # Buffer size before flushing
    "log_fallback_file": "fallback.txt"   # Local file fallback on DB failure
}

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🧠 Modes of Operation

1. Chat Mode (logger_mode='chat')

Logs conversations with optional metadata like conversation_id, bot_id, user_id.

from rag_mongo_logger.singleton import LoggerSingleton
from rag_mongo_logger.context_logging import conversation_logger

logger = LoggerSingleton.get_logger(config)

with conversation_logger(logger, conversation_id="conv-123", bot_id="bot-xyz", user_id="admin_001") as log:
    log.info("User started the conversation.")
    log.debug("Fetching documents...")
    log.warning("Timeout fetching from external API.")

2. Training Mode (logger_mode='training')

Logs training operations with context like bot_id and training_id.

from rag_mongo_logger.context_logging import training_logger

with training_logger(logger, bot_id="bot-abc", training_id="train-001") as log:
    log.info("Training session started.")
    log.debug("Processing document 3 of 10.")
    log.warning("Skipped document due to format issue.")

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🔁 Manual DB Reconnect

If your DB (Mongo/PostgreSQL) crashes and comes back up mid-execution:

logger.reopen()

Or expose a FastAPI endpoint:

@app.post("/logger/reopen")
def trigger_reconnect():
    logger.reopen()
    return {"status": "Reconnection attempted"}

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🧪 Example Usage

from rag_mongo_logger.singleton import LoggerSingleton
from rag_mongo_logger.context_logging import conversation_logger, training_logger

config = {
    "uri": "mongodb://localhost:27017/",
    "db": "conversational_logs",
    "env": "prod",
    "logger_mode": "training",
    "log_batch_size": 5,
    "log_fallback_file": "app_fallback_logs.txt"
}

logger = LoggerSingleton.get_logger(config)

# Training mode
with training_logger(logger, bot_id="bot_v2", training_id="train_123") as log:
    log.info("Training started.")
    log.debug("Document 1 processed.")
    log.warning("Skipped doc due to malformed content.")

# Switch to chat mode
LoggerSingleton.close_logger()
config["logger_mode"] = "chat"
LoggerSingleton.reopen()
logger = LoggerSingleton.get_logger(config)

# Chat mode
with conversation_logger(logger, conversation_id="conv_42", bot_id="bot_v2", user_id="admin") as log:
    log.info("Conversation started.")
    log.debug("User asked about refund policy.")

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✅ Best Practices (Do's)

• ✅ Use LoggerSingleton.get_logger(config) once per mode/config.
• ✅ Always use context loggers (conversation_logger or training_logger) for automatic flushing and tagging.
• ✅ Call LoggerSingleton.close_logger() before switching modes.
• ✅ Use .reopen() when recovering from DB crashes mid-run.
• ✅ Provide consistent conversation_id, bot_id, user_id for grouping logs.

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❌ Pitfalls to Avoid (Don'ts)

• ❌ Don't call get_logger() repeatedly without closing — it’s a singleton.
• ❌ Don't log outside the context manager unless necessary — flushing won't be guaranteed.
• ❌ Don't forget to close the logger at the end of scripts (LoggerSingleton.close_logger()).
• ❌ Don't rely solely on the DB connection — always configure a fallback file.

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📁 Fallback Logs

If the DB is down, logs are written to log_fallback_file (e.g., fallback.txt), using a rotating file handler (5MB x 3 backups).

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📜 License

MIT License © Siddhesh Dosi

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🙋 Need Help?

Open an issue or contribute at GitHub Repository