## FAST API
akasha 提供get_response, ask_self, ask_whole_file, get_summary的api server

### 啟動

akasha api (–port {port} –host {host} –workers {num_of_workers})

### Example

import requests
import os

HOST = os.getenv("API_HOST", "http://127.0.0.1")
PORT = os.getenv("API_PORT", "8000")
urls = {
    "summary": f"{HOST}:{PORT}/get_summary",
    "qa": f"{HOST}:{PORT}/get_response",
    "self": f"{HOST}:{PORT}/ask_self",
    "file": f"{HOST}:{PORT}/ask_whole_file",
}

openai_config = {
    "azure_key": {your api key},
    "azure_base": {your api base},
}

self_data = {
    "prompt": "太陽電池技術?",
    "info": "太陽能電池技術5塊錢",
    "model": "openai:gpt-3.5-turbo",
    "system_prompt": "",
    "max_doc_len": 1500,
    "temperature": 0.0,
    "openai_config": openai_config
}

file_data = {
    "doc_path": "docs/mic/20230317_5軸工具機因應市場訴求改變的發展態勢.pdf",
    "prompt": "五軸是什麼?",
    "chunk_size": 1000,
    "model": "openai:gpt-3.5-turbo",
    "embedding_model": "openai:text-embedding-ada-002",
    "system_prompt": "",
    "max_doc_len": 1500,
    "temperature": 0.0,
    "openai_config": openai_config
}

chat_data = {
    "doc_path": "docs/pns/",
    "prompt": "太陽電池技術?",
    "chunk_size": 1000,
    "model": "openai:gpt-3.5-turbo",
    "embedding_model": "openai:text-embedding-ada-002",
    "threshold": 0.1,
    "search_type": 'auto',
    "system_prompt": "",
    "max_doc_len": 1500,
    "temperature": 0.0,
    "openai_config": openai_config
}
summary_data = {
    "file_path": "docs/pns/2484.txt",
    "model": "openai:gpt-3.5-turbo",
    "summary_type": "reduce_map",
    "summary_len": 500,
    "system_prompt": "",
    "openai_config": openai_config
}

# chat_response = requests.post(urls["qa"], json=chat_data).json()
# print(chat_response)

# sum_response = requests.post(
#     urls["summary"],
#     json=summary_data,
# ).json()

# print(sum_response)

self_response = requests.post(urls["self"], json=self_data).json()
print(self_response)
file_response = requests.post(urls["file"], json=file_data).json()
print(file_response)