| Name | aiaas-falcon-light JSON |
| Version |
0.2.5
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| Summary | This python package help to interact with Generative AI - Large Language Models. It interacts with AIaaS LLM , AIaaS embedding , AIaaS Audio set of APIs to cater the request. |
| upload_time | 2024-01-17 07:43:19 |
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| docs_url | None |
| author | Your Name |
| requires_python | >=3.8.1,<4.0.0 |
| license | MIT |
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# AIaaS Falcon-Light
<h4 align="center">
<p>
<a href="#shield-installation">Installation</a> |
<a href="#fire-quickstart">Quickstart</a> |
<p>
</h4>
## Description
AIaaS_Falcon_Light is Generative AI - Logical & logging framework support AIaaS Falcon library
## :shield: Installation
Ensure you have the `requests` and `google-api-core` libraries installed:
```bash
pip install aiaas-falcon-light
```
if you want to install from source
```bash
git clone https://github.com/Praveengovianalytics/falcon_light && cd falcon_light
pip install -e .
```
### Methods
### `Light` Class
- `__init__ (config)`
Intialise the Falcon object with endpoint configs. \
Parameter:
- config: A object consisting parameter:
- api_key : API Key
- api_name: Name for endpoint
- api_endpoint: Type of endpoint ( can be azure, dev_quan, dev_full, prod)
- url: url of endpoint (eg: http://localhost:8443/)
- log_id: ID of log (Integer Number)
- use_pii: Activate Personal Identifier Information Limit Protection (Boolean)
- headers: header JSON for endpoint
- log_key: Auth Key to use the Application
- `current_pii()`
Check current Personal Identifier Information Protection activation status
- `switch_pii()`
Switch current Personal Identifier Information Protection activation status
- `list_models()`
List out models available
- `initalise_pii()`
Download and intialise PII Protection. \
Note: This does not activate PII but initialise dependencies
- `health()`
Check health of current endpoint
- `create_embedding(file_path)`
Create embeddings by sending files to the API. \
Parameter:
- file_path: Path to file
- `generate_text(query="",
context="",
use_file=0,
model="",
chat_history=[],
max_new_tokens: int = 200,
temperature: float = 0,
top_k: int = -1,
frequency_penalty: int = 0,
repetition_penalty: int = 1,
presence_penalty: float = 0,
fetch_k=100000,
select_k=4,
api_version='2023-05-15',
guardrail={'jailbreak': False, 'moderation': False},
custom_guardrail=None)` \
Generate text using LLM endpoint. Note: Some parameter of the endpoint is endpoint-specific. \
Parameter:
- query: a string of your prompt
- use_file: Whether to take file to context in generation. Only applies to dev_full and dev_quan. Need to `create_embedding` before use.
- model: a string on the model to use. You can use ` list_models` to check for model available.
- chat_history: an array of chat history between user and bot. Only applies to dev_full and dev_quan. (Beta)
- max_new_token: maximum new token to generate. Must be integer.
- temperature: Float that controls the randomness of the sampling. Lower
values make the model more deterministic, while higher values make
the model more random. Zero means greedy sampling.
- top_k: Integer that controls the number of top tokens to consider.
- frequency_penalty: Float that penalizes new tokens based on their
frequency in the generated text so far.
- repetition_penalty: Float that penalizes new tokens based on whether
they appear in the prompt and the generated text so far.
- presence_penalty: Float that penalizes new tokens based on whether they
appear in the generated text so far
- fetch_k: Use for document retrival. Include how many element in searching. Only applies when `use_file` is 1
- select k: Use to select number of document for document retrieval. Only applies when `use_file` is 1
- api_version: Only applies for azure endpoint
- guardrail: Whether to use the default jailbreak guardrail and moderation guardrail
- custom_guardrail: Path to custom guardrail .yaml file. The format can be found in sample.yaml
- ` evaluate_parameter(config)`
Carry out grid search for parameter \
Parameter:
- config: A dict. The dict must contain model and query. Parameter to grid search must be a list.
- model: a string of model
- query: a string of query
- **other parameter (eg: "temperature":list(np.arange(0,2,0.5))
- `decrypt_hash(encrypted_data)`
Decret the configuration from experiment id.
Parameter:
- encrypted_data: a string of id
## :fire: Quickstart
```
from aiaas_falcon import Falcon
model=Falcon(api_name="azure_1",protocol='https',host_name_port='example.com',api_key='API_KEY',api_endpoint='azure',log_key="KEY")
model.list_models()
model.generate_text_full(query="Hello, introduce yourself",model='gpt-35-turbo-0613-vanilla',api_version='2023-05-15')
```
## Conclusion
AIaaS_Falcon_Light library simplifies interactions with the AIaaS Falcon, providing a straightforward way to perform various operations such as fact-checking and logging.
## Authors
- [@Praveengovianalytics](https://github.com/Praveengovianalytics)
- [@zhuofan](https://github.com/zhuofan-16)
## Google Colab
- [Get start with aiaas_falcon](https://colab.research.google.com/drive/1haZ-1fD4htQuNF2zzyrUSTP90KRls1dC?usp=sharing)
## Badges
[](https://choosealicense.com/licenses/mit/)
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"description": "\n\n# AIaaS Falcon-Light\n\n\n<h4 align=\"center\">\n <p>\n <a href=\"#shield-installation\">Installation</a> |\n <a href=\"#fire-quickstart\">Quickstart</a> |\n <p>\n</h4>\n\n\n\n\n## Description\n\nAIaaS_Falcon_Light is Generative AI - Logical & logging framework support AIaaS Falcon library\n\n## :shield: Installation\n\nEnsure you have the `requests` and `google-api-core` libraries installed:\n\n```bash\npip install aiaas-falcon-light\n```\n\n\nif you want to install from source\n\n```bash\ngit clone https://github.com/Praveengovianalytics/falcon_light && cd falcon_light\npip install -e .\n```\n\n### Methods\n### `Light` Class\n- `__init__ (config)`\nIntialise the Falcon object with endpoint configs. \\\nParameter: \n - config: A object consisting parameter:\n - api_key : API Key\n - api_name: Name for endpoint\n - api_endpoint: Type of endpoint ( can be azure, dev_quan, dev_full, prod)\n - url: url of endpoint (eg: http://localhost:8443/)\n - log_id: ID of log (Integer Number)\n - use_pii: Activate Personal Identifier Information Limit Protection (Boolean)\n - headers: header JSON for endpoint\n - log_key: Auth Key to use the Application\n\n\n- `current_pii()`\nCheck current Personal Identifier Information Protection activation status\n\n- `switch_pii()`\nSwitch current Personal Identifier Information Protection activation status\n- `list_models()`\nList out models available\n- `initalise_pii()`\nDownload and intialise PII Protection. \\\nNote: This does not activate PII but initialise dependencies\n\n- `health()`\nCheck health of current endpoint\n\n- `create_embedding(file_path)`\nCreate embeddings by sending files to the API. \\\nParameter:\n - file_path: Path to file \n\n- `generate_text(query=\"\",\n context=\"\",\n use_file=0,\n model=\"\",\n chat_history=[],\n max_new_tokens: int = 200,\n temperature: float = 0,\n top_k: int = -1,\n frequency_penalty: int = 0,\n repetition_penalty: int = 1,\n presence_penalty: float = 0,\n fetch_k=100000,\n select_k=4,\n api_version='2023-05-15',\n guardrail={'jailbreak': False, 'moderation': False},\n custom_guardrail=None)` \\\n Generate text using LLM endpoint. Note: Some parameter of the endpoint is endpoint-specific. \\\n Parameter: \n - query: a string of your prompt\n - use_file: Whether to take file to context in generation. Only applies to dev_full and dev_quan. Need to `create_embedding` before use.\n - model: a string on the model to use. You can use ` list_models` to check for model available.\n - chat_history: an array of chat history between user and bot. Only applies to dev_full and dev_quan. (Beta)\n - max_new_token: maximum new token to generate. Must be integer.\n - temperature: Float that controls the randomness of the sampling. Lower\n values make the model more deterministic, while higher values make\n the model more random. Zero means greedy sampling.\n - top_k: Integer that controls the number of top tokens to consider.\n - frequency_penalty: Float that penalizes new tokens based on their\n frequency in the generated text so far.\n - repetition_penalty: Float that penalizes new tokens based on whether\n they appear in the prompt and the generated text so far.\n - presence_penalty: Float that penalizes new tokens based on whether they\n appear in the generated text so far\n - fetch_k: Use for document retrival. Include how many element in searching. Only applies when `use_file` is 1\n - select k: Use to select number of document for document retrieval. Only applies when `use_file` is 1\n - api_version: Only applies for azure endpoint\n - guardrail: Whether to use the default jailbreak guardrail and moderation guardrail\n - custom_guardrail: Path to custom guardrail .yaml file. The format can be found in sample.yaml\n \n- ` evaluate_parameter(config)`\nCarry out grid search for parameter \\\nParameter:\n - config: A dict. The dict must contain model and query. Parameter to grid search must be a list. \n - model: a string of model\n - query: a string of query\n - **other parameter (eg: \"temperature\":list(np.arange(0,2,0.5))\n- `decrypt_hash(encrypted_data)`\nDecret the configuration from experiment id.\nParameter:\n - encrypted_data: a string of id\n\n## :fire: Quickstart\n\n```\nfrom aiaas_falcon import Falcon\nmodel=Falcon(api_name=\"azure_1\",protocol='https',host_name_port='example.com',api_key='API_KEY',api_endpoint='azure',log_key=\"KEY\")\nmodel.list_models()\nmodel.generate_text_full(query=\"Hello, introduce yourself\",model='gpt-35-turbo-0613-vanilla',api_version='2023-05-15')\n```\n\n## Conclusion\n\nAIaaS_Falcon_Light library simplifies interactions with the AIaaS Falcon, providing a straightforward way to perform various operations such as fact-checking and logging.\n\n## Authors\n\n- [@Praveengovianalytics](https://github.com/Praveengovianalytics)\n- [@zhuofan](https://github.com/zhuofan-16)\n\n## Google Colab\n\n- [Get start with aiaas_falcon](https://colab.research.google.com/drive/1haZ-1fD4htQuNF2zzyrUSTP90KRls1dC?usp=sharing)\n\n## Badges\n\n[](https://choosealicense.com/licenses/mit/)\n",
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