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-0.4.407.tar.gz (415.3 kB view details)

Uploaded Source

Built Distribution

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

letschatty-0.4.407-py3-none-any.whl (568.8 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.407.tar.gz.

File metadata

  • Download URL: letschatty-0.4.407.tar.gz
  • Upload date:
  • Size: 415.3 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-0.4.407.tar.gz
Algorithm Hash digest
SHA256 23637363bea725c6de9b8081d07a601fccd5723e5d91a7263c32bd69f450b0bb
MD5 6bc13c5c71dc7896c9a949d6084730ca
BLAKE2b-256 21503ea2a85431cfb5973e37e1c695b8360205233fcd2154664826364944c54e

See more details on using hashes here.

File details

Details for the file letschatty-0.4.407-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.407-py3-none-any.whl
  • Upload date:
  • Size: 568.8 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-0.4.407-py3-none-any.whl
Algorithm Hash digest
SHA256 98996a397cf7b4dd7bd74d95e5e7846112de976addfdb0286a803492cdaf53ed
MD5 e423e441a348af51aecd2e506dd7f025
BLAKE2b-256 50954e4b2b646198b7b6c3c26930bbcb824b053207bcdc965d507b7917cad4e8

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