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from PyPDF2 import PdfReader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
import streamlit as st
def get_response_from_OpenAI_LangChain(uploaded_file, prompt):
try:
reader = PdfReader(uploaded_file)
raw_text = ""
for page in reader.pages:
text = page.extract_text()
if text:
raw_text += text
text_splitter = CharacterTextSplitter(separator = "\n",
chunk_size = 1000,
chunk_overlap = 200,
length_function = len)
texts = text_splitter.split_text(raw_text)
with st.spinner('Processing Embeddings...'):
embeddings = OpenAIEmbeddings()
doc_search = FAISS.from_texts(texts, embeddings)
chain = load_qa_chain(OpenAI(), chain_type='map_reduce')
query = prompt
docs = doc_search.similarity_search(query)
with st.spinner('Generating Answer...'):
response = chain.run(input_documents=docs, question=query) # response
from components.sidebar.Auth import upload_data
data = {"prompt": prompt,
"response": response}
st.session_state['response'] = response
upload_data(st.session_state['uuid'], data, uploaded_file.name[:-4])
return response
except Exception as e:
if "You exceeded your current quota" in str(e):
st.error('Oops! You may have exceeded your API rate limit.\nPlease check you OpenAI API key usage at https://platform.openai.com/account/usage')
else:
st.error("Oops! Something went wrong. Please try again. Please check your OpenAI API key in the sidebar.")
st.stop()
return |