#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
- Install poetry https://python-poetry.org/docs/
- Run
poetry install - 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:
Messagemodel
Services
- Contain all business logic
- Work with models
- Stateless
- Handle:
- Object creation (factories)
- Model specific functionality
- Example:
MessageFactory- Create a
Messagefrom webhook data - Create a
Messagefrom an agent request to send it to a chat - Instantiate a
Messagefrom data base information - Create a
Messagefrom a Chatty Response
- Create a
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
Messagefrom webhook data - Create a
Messagefrom an agent request to send it to a chat - Instantiate a
Messagefrom data base information - Create a
Messagefrom a Chatty Response
- Create a
🚧 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.
2 maintainers
Metadata
2 maintainers
Release files for letschatty 0.4.557
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| letschatty-0.4.557.tar.gz | 535.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| letschatty-0.4.557-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / letschatty-0.4.557.tar.gz
| Download URL | letschatty-0.4.557.tar.gz |
|---|---|
| Size | 535.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
8b627dd5f7088de92530f295c33160e5093e3e1f21ec56a84e70ad203067982e
|
|
BLAKE2b-256 checksum How to use checksums |
198d20eaa98e9289baec6e31e5c8797b20d5ad905343beef1bea8154459fa0dd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.1 CPython/3.13.2 Darwin/23.5.0
|
Release files / letschatty-0.4.557-py3-none-any.whl
| Download URL | letschatty-0.4.557-py3-none-any.whl |
|---|---|
| Size | 714.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
0d37d7a2fb5486c5b3bf2d0f40e0f8d06f12c78f9b6724baaaf4a9d16dac5394
|
|
BLAKE2b-256 checksum How to use checksums |
dfb67f351d7c92ef3058258be02473125f56a2cc192864d895b466974b2afe98
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/2.1.1 CPython/3.13.2 Darwin/23.5.0
|
