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from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
import json
from dotenv import load_dotenv
import os
load_dotenv()
HF_API_TOKEN = os.getenv('HUGGINGFACE_API_TOKEN')
model_id=os.getenv('LLM_MODEL')
LLM = HuggingFaceEndpoint(
repo_id=model_id,
temperature=0.1,
max_new_tokens=512,
repetition_penalty=1.2,
return_full_text=False,
huggingfacehub_api_token=HF_API_TOKEN)
def generate_keywords(document:dict,
llm_model:HuggingFaceEndpoint = LLM) -> str:
""" Generate a meaningful list of meta keywords for the provided document or chunk"""
template = (
"""
You are a SEO expert bot. Your task is to craft a meaningful list of 5 keywords to organize documents.
The keywords should help us in searching and retrieving the documents later.
You will only respond with the clear, concise and meaningful 5 of keywords separated by comma.
<<<
Document: {document}
>>>
Keywords:
"""
)
prompt = PromptTemplate.from_template(template=template)
chain = prompt | llm_model | StrOutputParser()
result = chain.invoke({'document': document})
return result.strip()
def generate_description(document:dict,
llm_model:HuggingFaceEndpoint = LLM) -> str:
""" Generate a meaningful document description based on document content """
template = (
"""
You are a SEO expert bot. Your task is to craft a meaningful summary to descripe and organize documents.
The description should be a meaningful summary of the document's content and help us in searching and retrieving the documents later.
You will only respond with the clear, concise and meaningful description.
<<<
Document: {document}
>>>
Description:
"""
)
prompt = PromptTemplate.from_template(template=template)
chain = prompt | llm_model | StrOutputParser()
result = chain.invoke({'document': document})
return result.strip() |