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-0.4.208.tar.gz (347.1 kB view details)

Uploaded Source

Built Distribution

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

letschatty-0.4.208-py3-none-any.whl (452.9 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.208.tar.gz.

File metadata

  • Download URL: letschatty-0.4.208.tar.gz
  • Upload date:
  • Size: 347.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.2 CPython/3.13.3 Darwin/23.6.0

File hashes

Hashes for letschatty-0.4.208.tar.gz
Algorithm Hash digest
SHA256 c66624b29916897aef5787528fd875679aaec65c79919a356f3f138b3beeda4c
MD5 1f83d1210155182bd8e3c4c26df6cf00
BLAKE2b-256 7aaeee62f687673ef69d1e53793a2e146dde337d64b108c60acc1d7c122667ac

See more details on using hashes here.

File details

Details for the file letschatty-0.4.208-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.208-py3-none-any.whl
  • Upload date:
  • Size: 452.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/2.1.2 CPython/3.13.3 Darwin/23.6.0

File hashes

Hashes for letschatty-0.4.208-py3-none-any.whl
Algorithm Hash digest
SHA256 b80a0c57e71d32900030d853dfa76d319a50a9cde156c7f79d4556eed245097e
MD5 eab47f81b0944a2ba8a211b73222b416
BLAKE2b-256 0e83466b8807a647d86e5cf9973bc510a564ead2b18a21aa65212d55fab0046d

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