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from pypdf import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
from langchain_community.embeddings.ollama import OllamaEmbeddings
from langchain_community.embeddings.bedrock import BedrockEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain
from dotenv import load_dotenv
import streamlit as st
import os
load_dotenv()
# Function to get the API key
def get_api_key():
# Try to get the API key from st.secrets first
try:
groq_api_key = os.getenv("GROQ_API_KEY", "")
return groq_api_key
except Exception as e:
print(e)
def get_inference_api_key():
try:
inference_api_key = os.getenv("INFERENCE_API_KEY", "")
return inference_api_key
except Exception as e:
print(e)
# Function for API configuration at sidebar
def sidebar_api_key_configuration():
groq_api_key = get_api_key()
if groq_api_key == '':
st.sidebar.warning('Enter the API Key(s) 🗝️')
st.session_state.prompt_activation = False
elif (groq_api_key.startswith('gsk_') and (len(groq_api_key) == 56)):
st.sidebar.success('Lets Proceed!', icon='️👉')
st.session_state.prompt_activation = True
else:
st.sidebar.warning('Please enter the correct API Key 🗝️!', icon='⚠️')
st.session_state.prompt_activation = False
return groq_api_key
def sidebar_groq_model_selection():
st.sidebar.subheader("Model Selection")
model = st.sidebar.selectbox('Select the Model', ('Llama3-8b-8192', 'Llama3-70b-8192', 'Mixtral-8x7b-32768',
'Gemma-7b-it'), label_visibility="collapsed")
return model
# Read PDF data
def read_pdf_data(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
text += page.extract_text()
return text
# Split data into chunks
def split_data(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
text_chunks = text_splitter.split_text(text)
return text_chunks
def get_embedding_function():
# embeddings = BedrockEmbeddings(
# credentials_profile_name="default", region_name="us-east-1"
# )
#embeddings = OllamaEmbeddings(model="nomic-embed-text")
inference_api_key = get_inference_api_key()
embeddings = HuggingFaceInferenceAPIEmbeddings(
api_key=inference_api_key, model_name="sentence-transformers/all-MiniLM-l6-v2"
)
return embeddings
# Create vectorstore
def create_vectorstore(pdf_docs):
raw_text = read_pdf_data(pdf_docs) # Get PDF text
text_chunks = split_data(raw_text) # Get the text chunks
embeddings = get_embedding_function() # Get the embedding function
vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
return vectorstore
# Get response from llm of user asked question
def get_llm_response(llm, prompt, question):
document_chain = create_stuff_documents_chain(llm, prompt)
retrieval_chain = create_retrieval_chain(st.session_state.vector_store.as_retriever(), document_chain)
response = retrieval_chain.invoke({'input': question})
return response
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