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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 | |
| # Pass the callable embedding function (embed_query) to FAISS | |
| vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings.embed_query) | |
| 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 | |