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-0.4.244.tar.gz (373.1 kB view details)

Uploaded Source

Built Distribution

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

letschatty-0.4.244-py3-none-any.whl (498.8 kB view details)

Uploaded Python 3

File details

Details for the file letschatty-0.4.244.tar.gz.

File metadata

  • Download URL: letschatty-0.4.244.tar.gz
  • Upload date:
  • Size: 373.1 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-0.4.244.tar.gz
Algorithm Hash digest
SHA256 1d9a159cfd1f55e5cc79637c386bc431c0f0c9d1fe58e03fc2cf9b1f51fc371d
MD5 2818328cd9e25c295d3ca46df2344bf1
BLAKE2b-256 04a370dfbb8ee79117730330799a2b173f6ca15420839e6e735c1cd156f762ed

See more details on using hashes here.

File details

Details for the file letschatty-0.4.244-py3-none-any.whl.

File metadata

  • Download URL: letschatty-0.4.244-py3-none-any.whl
  • Upload date:
  • Size: 498.8 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-0.4.244-py3-none-any.whl
Algorithm Hash digest
SHA256 f88cea191a1c81436a5aaf850c729615b35638538e214efd901ced32e489538a
MD5 cdb834e2120bf8bbe0bb3c68aafb65aa
BLAKE2b-256 29fc631a5e1e273c605ccd2688462bdfe0f95d1b143a55366a68bb685e720a75

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