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-0.4.461.tar.gz (445.6 kB view details)

Uploaded Source

Built Distribution

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

letschatty-0.4.461-py3-none-any.whl (609.5 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.461.tar.gz.

File metadata

  • Download URL: letschatty-0.4.461.tar.gz
  • Upload date:
  • Size: 445.6 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-0.4.461.tar.gz
Algorithm Hash digest
SHA256 c99ee8bf48ad29db51cb81daa750b1734dfa27322c43838a479ce8517b830c1a
MD5 c472d6065e9c84cf9b263e8116796a9b
BLAKE2b-256 b0caab5f967da078513b60ff5d422e7d4c9c7842a02ce80d782854c7a7c509ca

See more details on using hashes here.

File details

Details for the file letschatty-0.4.461-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.461-py3-none-any.whl
  • Upload date:
  • Size: 609.5 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-0.4.461-py3-none-any.whl
Algorithm Hash digest
SHA256 543556fb0174160fdc847883d622cefc2871a17b44aba514ba2ed752abb58c1a
MD5 47eca8bbdfb3fd8eafe4d3c480b206a3
BLAKE2b-256 b7437708fbb4ff3e12af608e5abc0776e287185768c5be8698222f150b29a56b

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