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-0.4.437.tar.gz (432.9 kB view details)

Uploaded Source

Built Distribution

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

letschatty-0.4.437-py3-none-any.whl (595.4 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.437.tar.gz.

File metadata

  • Download URL: letschatty-0.4.437.tar.gz
  • Upload date:
  • Size: 432.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.2 CPython/3.13.12 Darwin/23.6.0

File hashes

Hashes for letschatty-0.4.437.tar.gz
Algorithm Hash digest
SHA256 a67a705754847a42400a537d3dd44df26970aba6388c048fa36a376bf117c5ae
MD5 967081f0021856c4fc5a5774bfe62c30
BLAKE2b-256 0b7d99763b30fd78da4be1576106f464e811b3bba52daa7876c779e71fc1dc9d

See more details on using hashes here.

File details

Details for the file letschatty-0.4.437-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.437-py3-none-any.whl
  • Upload date:
  • Size: 595.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.2 CPython/3.13.12 Darwin/23.6.0

File hashes

Hashes for letschatty-0.4.437-py3-none-any.whl
Algorithm Hash digest
SHA256 eaea8f5f88e892638169c9af511aff8adbc7200864e390f768ca97f8e5c6fbce
MD5 1bdd3039df7dba2f4dbe5b91f76889cc
BLAKE2b-256 ee7b86eaed8ec6d61688f6622b06a79460cf693f83192af1fff0af7ee2aaa344

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