Skip to main content

Models and custom classes to work across the Chattyverse

Project description

#PUSH

poetry version patch; poetry build; poetry publish

Chatty Analytics

Models and custom classes to work across the Chattyverse.

Lastest update: 2024-11-07

Development instrucions

  1. Install poetry https://python-poetry.org/docs/
  2. Run poetry install
  3. Install with pymongo: poetry install -E db to include pymongo dependencies

Architecture

Models

  • Data containers with Pydantic validation
  • No business logic
  • Little to no functionality (for that, see Services)
  • Used for:
    • Request/response validation
    • Database document mapping
    • Cross-service data transfer
  • Example:
    • Message model

Services

  • Contain all business logic
  • Work with models
  • Stateless
  • Handle:
    • Object creation (factories)
    • Model specific functionality
  • Example:
    • MessageFactory
      • Create a Message from webhook data
      • Create a Message from an agent request to send it to a chat
      • Instantiate a Message from data base information
      • Create a Message from a Chatty Response

Implementation Status

✅ Implemented

Models

  • Base message models
    • DBMessage: Database message model
    • MessageRequest: It models the intent of a message to be sent to a chat, still not instantiated as ChattyMessage.
    • BaseMessage (abstract)
      • Subtypes: AudioMessage, DocumentMessage, ImageMessage, TextMessage, VideoMessage, etc.
  • MetaNotificationJson: Models any notification from WhatsApp to the webhook
    • MetaMessageJson: Models the speicifc Notification with a messages object
    • MetaStatusJson: Models the specific Notification with a statuses object
    • MetaErrorJson: Models the specific Notification with an errors object
  • ChattyResponse: Models a list of pre-set responses in Chatty, that will be instantiated as a ChattyMessage when sent to a chat.
  • Auth0 company registrarion form model
  • Event models
  • Metrics models

Services

  • MessageFactory
    • Create a Message from webhook data
    • Create a Message from an agent request to send it to a chat
    • Instantiate a Message from data base information
    • Create a Message from a Chatty Response

🚧 In Progress

  • Chat and its modules and services
  • Service layer completion
  • Company Assets

Chatty Analytics is a proprietary tool developed by Axel Gualda and the Chatty Team. This software is for internal use only and is not licensed for distribution or use outside of authorized contexts.

Copyright (c) 2024 Axel Gualda. All Rights Reserved.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

letschatty-0.4.333.tar.gz (384.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

letschatty-0.4.333-py3-none-any.whl (516.4 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.333.tar.gz.

File metadata

  • Download URL: letschatty-0.4.333.tar.gz
  • Upload date:
  • Size: 384.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.1 CPython/3.13.2 Darwin/23.5.0

File hashes

Hashes for letschatty-0.4.333.tar.gz
Algorithm Hash digest
SHA256 414a7a09c2c679563cdc165f4974c6029d83b33d38495a8cd7598ff566c770a2
MD5 06a8ef2e9d2952aa0c33c7d07db15cbe
BLAKE2b-256 d50ac941594b19104e666807b5505e47b59ecd720ad89d66cc1254bec6c5ae83

See more details on using hashes here.

File details

Details for the file letschatty-0.4.333-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.333-py3-none-any.whl
  • Upload date:
  • Size: 516.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.1 CPython/3.13.2 Darwin/23.5.0

File hashes

Hashes for letschatty-0.4.333-py3-none-any.whl
Algorithm Hash digest
SHA256 61d43b85e6894736b94add1b141a318bc02b5d8e8b680b3d72df8a4c2bda2f49
MD5 8ea67c60b3fdd158135c81a1927a5a29
BLAKE2b-256 dbc7b95336b4cbceca99c667ab3b81c5f665471f51802b2988d075fa298faee3

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page