\n Installation |\n Quickstart |\n
\n\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", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "aiaas-falcon-light", "package_url": "https://pypi.org/project/aiaas-falcon-light/", "platform": null, "project_url": "https://pypi.org/project/aiaas-falcon-light/", "project_urls": null, "release_url": "https://pypi.org/project/aiaas-falcon-light/0.2.5/", "requires_dist": [ "google-api-core (>=2.12.0,<3.0.0)", "requests (>=2.31.0,<3.0.0)", "flake8 (>=6.1.0,<7.0.0)", "interrogate (>=1.5.0,<2.0.0)", "isort (>=5.12.0,<6.0.0)", "black (>=23.9.1,<24.0.0)", "pytest (>=7.4.2,<8.0.0)", "pandas (>=1.0.0,<2.1.4)", "python-logging-loki (>=0.3.1,<0.4.0)", "pycryptodome (>=3.19.0,<4.0.0)", "presidio-analyzer (>=2.2.351,<3.0.0)", "presidio-anonymizer (>=2.2.351,<3.0.0)" ], "requires_python": ">=3.8.1,<4.0.0", "summary": "This python package help to interact with Generative AI - Large Language Models. 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