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-0.4.317.tar.gz (365.9 kB view details)

Uploaded Source

Built Distribution

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

letschatty-0.4.317-py3-none-any.whl (478.4 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.317.tar.gz.

File metadata

  • Download URL: letschatty-0.4.317.tar.gz
  • Upload date:
  • Size: 365.9 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-0.4.317.tar.gz
Algorithm Hash digest
SHA256 05823fd8b1f05da7b70ab6e89cb3933c94b71abec77f235cdc5917c264df0350
MD5 f13c814558142cc5d8971df383a324a1
BLAKE2b-256 2d1e88370da05a2e79f4defb50749e9a66bebbb41cedbc4a88b910ef98a1ad61

See more details on using hashes here.

File details

Details for the file letschatty-0.4.317-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.317-py3-none-any.whl
  • Upload date:
  • Size: 478.4 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-0.4.317-py3-none-any.whl
Algorithm Hash digest
SHA256 7d25148905b174a707d22546627ac294515c7a5d27f4f817d907ac7c5fbd1ee0
MD5 3e75ad0aa004dba07245385938ce4550
BLAKE2b-256 7cf543cfd9c0d40a279fcff5e0bd8c5f33928037b90d78e7cb7d586e0752b59b

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