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import logging
import requests
from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint
from langchain_core.language_models import LLM
from global_config import GlobalConfig
HF_API_URL = f"https://api-inference.huggingface.co/models/{GlobalConfig.HF_LLM_MODEL_NAME}"
HF_API_HEADERS = {"Authorization": f"Bearer {GlobalConfig.HUGGINGFACEHUB_API_TOKEN}"}
logger = logging.getLogger(__name__)
def get_hf_endpoint() -> LLM:
"""
Get an LLM via the HuggingFaceEndpoint.
:return: The LLM.
"""
logger.debug('Getting LLM via HF endpoint')
return HuggingFaceEndpoint(
repo_id=GlobalConfig.HF_LLM_MODEL_NAME,
max_new_tokens=GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
top_k=40,
top_p=0.95,
temperature=GlobalConfig.LLM_MODEL_TEMPERATURE,
repetition_penalty=1.03,
streaming=True,
huggingfacehub_api_token=GlobalConfig.HUGGINGFACEHUB_API_TOKEN,
return_full_text=False,
stop_sequences=['</s>'],
)
def hf_api_query(payload: dict) -> dict:
"""
Invoke HF inference end-point API.
:param payload: The prompt for the LLM and related parameters.
:return: The output from the LLM.
"""
try:
response = requests.post(HF_API_URL, headers=HF_API_HEADERS, json=payload, timeout=15)
result = response.json()
except requests.exceptions.Timeout as te:
logger.error('*** Error: hf_api_query timeout! %s', str(te))
result = {}
return result
def generate_slides_content(topic: str) -> str:
"""
Generate the outline/contents of slides for a presentation on a given topic.
:param topic: Topic on which slides are to be generated.
:return: The content in JSON format.
"""
with open(GlobalConfig.SLIDES_TEMPLATE_FILE, 'r', encoding='utf-8') as in_file:
template_txt = in_file.read().strip()
template_txt = template_txt.replace('<REPLACE_PLACEHOLDER>', topic)
output = hf_api_query({
'inputs': template_txt,
'parameters': {
'temperature': GlobalConfig.LLM_MODEL_TEMPERATURE,
'min_length': GlobalConfig.LLM_MODEL_MIN_OUTPUT_LENGTH,
'max_length': GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
'max_new_tokens': GlobalConfig.LLM_MODEL_MAX_OUTPUT_LENGTH,
'num_return_sequences': 1,
'return_full_text': False,
# "repetition_penalty": 0.0001
},
'options': {
'wait_for_model': True,
'use_cache': True
}
})
output = output[0]['generated_text'].strip()
# output = output[len(template_txt):]
json_end_idx = output.rfind('```')
if json_end_idx != -1:
# logging.debug(f'{json_end_idx=}')
output = output[:json_end_idx]
logger.debug('generate_slides_content: output: %s', output)
return output
if __name__ == '__main__':
# results = get_related_websites('5G AI WiFi 6')
#
# for a_result in results.results:
# print(a_result.title, a_result.url, a_result.extract)
# get_ai_image('A talk on AI, covering pros and cons')
pass
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