| Name | chatbot-connectors JSON |
| Version |
0.4.0
JSON |
| download |
| home_page | None |
| Summary | A unified Python library for connecting to various chatbot APIs (RASA, MillionBot, Taskyto) |
| upload_time | 2025-10-09 12:47:12 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT |
| keywords |
api
chatbot
connectors
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Chatbot Connectors
[](https://github.com/Chatbot-TRACER/chatbot-connectors/actions/workflows/CI.yml)
[](https://pypi.org/project/chatbot-connectors/)
[](https://github.com/Chatbot-TRACER/chatbot-connectors/blob/main/LICENSE)
A Python library for connecting to various chatbot APIs with a unified interface.
## Installation
```bash
pip install chatbot-connectors
```
## Custom YAML Connector
If there is no connector for your chatbot and you are not willing to code one,
you can use the Custom Connector.
What this one does is read a YAML file with the info and try to work that way.
To see how to build these YAML files and use them see
[CUSTOM CONNECTOR GUIDE](docs/CUSTOM_CONNECTOR_GUIDE.md),
there are also examples in the `yaml-examples` directory.
If you want to directly try one, execute this in a Python shell:
```python
from chatbot_connectors.implementations.custom import CustomChatbot
bot = CustomChatbot("yaml-examples/ada-uam.yml")
success, response = bot.execute_with_input("Hola, necesito ayuda con Moodle")
print(response)
```
## Built-in Connectors
The library ships with several ready-to-use connectors. Each connector exposes the parameters listed via `--list-connector-params` in the CLI or `get_chatbot_parameters()` in code.
### Botlovers
- Base URL examples: `https://arthur.botslovers.com/`, `https://alcampo.botslovers.com/`
- Minimal Python usage:
```python
from chatbot_connectors.implementations.botlovers import BotloversChatbot
bot = BotloversChatbot(base_url="https://arthur.botslovers.com/", lang="en")
success, reply = bot.execute_with_input("Hi Arthur!")
print(reply)
```
### MillionBot
- Requires a `bot_id`. Known deployments:
- ADA UAM: `60a3be81f9a6b98f7659a6f9`
- SAIC Malaga: `64e5d1af081211d24e2cfec8`
- Madrid te cuida: `612cc0d871562c07747d3f0a`
- Genion: `65157185ba7cc62753c7d3e2`
- Gallo de Morón de la Frontera: `65ca19e7dbbb4e26cbeadf24`
- Ayto. de Arucas: `660d8b37876b1f546abde807`
- Gestri Diputación Valencia: `6141bc1e161c3d4e06ced69c`
- Quick example:
```python
from chatbot_connectors.implementations.millionbot import MillionBot
bot = MillionBot(bot_id="60a3be81f9a6b98f7659a6f9")
success, reply = bot.execute_with_input("Hola, ¿puedes ayudarme?")
print(reply)
```
### RASA
- Use the public REST webhook, e.g. `base_url="http://localhost:5005"`.
- Optional `sender_id` controls conversation tracking.
### Taskyto
- Requires the Taskyto server base URL and optional port (defaults to `5000`).
- Example: `ChatbotTaskyto(base_url="http://localhost", port=8080)`
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"description": "# Chatbot Connectors\n\n[](https://github.com/Chatbot-TRACER/chatbot-connectors/actions/workflows/CI.yml)\n[](https://pypi.org/project/chatbot-connectors/)\n[](https://github.com/Chatbot-TRACER/chatbot-connectors/blob/main/LICENSE)\n\nA Python library for connecting to various chatbot APIs with a unified interface.\n\n## Installation\n\n```bash\npip install chatbot-connectors\n```\n\n## Custom YAML Connector\n\nIf there is no connector for your chatbot and you are not willing to code one,\nyou can use the Custom Connector.\nWhat this one does is read a YAML file with the info and try to work that way.\n\nTo see how to build these YAML files and use them see\n[CUSTOM CONNECTOR GUIDE](docs/CUSTOM_CONNECTOR_GUIDE.md),\nthere are also examples in the `yaml-examples` directory.\n\nIf you want to directly try one, execute this in a Python shell:\n\n```python\nfrom chatbot_connectors.implementations.custom import CustomChatbot\n\nbot = CustomChatbot(\"yaml-examples/ada-uam.yml\")\nsuccess, response = bot.execute_with_input(\"Hola, necesito ayuda con Moodle\")\nprint(response)\n```\n\n## Built-in Connectors\n\nThe library ships with several ready-to-use connectors. Each connector exposes the parameters listed via `--list-connector-params` in the CLI or `get_chatbot_parameters()` in code.\n\n### Botlovers\n\n- Base URL examples: `https://arthur.botslovers.com/`, `https://alcampo.botslovers.com/`\n- Minimal Python usage:\n ```python\n from chatbot_connectors.implementations.botlovers import BotloversChatbot\n\n bot = BotloversChatbot(base_url=\"https://arthur.botslovers.com/\", lang=\"en\")\n success, reply = bot.execute_with_input(\"Hi Arthur!\")\n print(reply)\n ```\n\n### MillionBot\n\n- Requires a `bot_id`. Known deployments:\n - ADA UAM: `60a3be81f9a6b98f7659a6f9`\n - SAIC Malaga: `64e5d1af081211d24e2cfec8`\n - Madrid te cuida: `612cc0d871562c07747d3f0a`\n - Genion: `65157185ba7cc62753c7d3e2`\n - Gallo de Mor\u00f3n de la Frontera: `65ca19e7dbbb4e26cbeadf24`\n - Ayto. de Arucas: `660d8b37876b1f546abde807`\n - Gestri Diputaci\u00f3n Valencia: `6141bc1e161c3d4e06ced69c`\n- Quick example:\n ```python\n from chatbot_connectors.implementations.millionbot import MillionBot\n\n bot = MillionBot(bot_id=\"60a3be81f9a6b98f7659a6f9\")\n success, reply = bot.execute_with_input(\"Hola, \u00bfpuedes ayudarme?\")\n print(reply)\n ```\n\n### RASA\n\n- Use the public REST webhook, e.g. `base_url=\"http://localhost:5005\"`.\n- Optional `sender_id` controls conversation tracking.\n\n### Taskyto\n\n- Requires the Taskyto server base URL and optional port (defaults to `5000`).\n- Example: `ChatbotTaskyto(base_url=\"http://localhost\", port=8080)`\n",
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