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| import streamlit as st | |
| from langchain_community.document_loaders.pdf import PyPDFDirectoryLoader | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain_community.embeddings import HuggingFaceInstructEmbeddings | |
| from langchain_community.vectorstores import FAISS | |
| from langchain.chains import RetrievalQA | |
| from langchain_community.llms import HuggingFaceHub | |
| def make_vectorstore(embeddings): | |
| loader = PyPDFDirectoryLoader("data") | |
| documents = loader.load() | |
| text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=0) | |
| texts = text_splitter.split_documents(documents) | |
| docsearch = FAISS.from_documents(texts, embeddings) | |
| return docsearch | |
| def get_conversation(vectorstore, model): | |
| conversation_chain = RetrievalQA.from_llm( | |
| llm=model, | |
| # chain_type="stuff", | |
| retriever=vectorstore.as_retriever()) | |
| return conversation_chain | |
| def get_response(conversation_chain, query): | |
| # get the response | |
| response = conversation_chain.invoke(query) | |
| return response | |
| def response_formatter(resp_list): | |
| queries = [] | |
| responses = [] | |
| for resp in resp_list: | |
| # find the '\nQuestion: ' and '\nHelpful Answer: ' and take the text right in front of them a new list of query and responses | |
| content = resp["result"] | |
| # find '\nQuestion: ' in the text | |
| question = content.split('\nQuestion: ')[1].split('\nHelpful Answer: ')[0] | |
| queries.append(question) | |
| # find '\nHelpful Answer: ' in the text | |
| answer = content.split('\nHelpful Answer: ')[1] | |
| responses.append(answer) | |
| return queries, responses | |
| def main(): | |
| st.title("BetterZila RAG Enabled LLM") | |
| st.sidebar.title("About") | |
| st.sidebar.info("This app is for an assignment for BetterZila, RAG Enabled LLM that responds to a pre-defined book 48 Laws of Power by Robert Greene.") | |
| st.sidebar.write( | |
| """ | |
| This Space uses the the Zephyr 7B Beta LLM from HuggingFace to answer questions from the book 48 Laws of Power by Robert Greene using RAG with a vectorstore database. | |
| Embeddings used for the vectorstore is from the Instructor Base model from HuggingFace. | |
| The generated responses are not perfect and are just for demonstration purposes since the model is a qunatized model used in inference mode. | |
| This space is created by Pratik Dwivedi. | |
| GitHub - Dekode1859 | |
| """ | |
| ) | |
| response_list=[] | |
| print("Downloading Embeddings Model") | |
| with st.spinner('Downloading Embeddings Model...'): | |
| embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base", model_kwargs = {'device': 'cpu'}) | |
| print("Loading LLM from HuggingFace") | |
| with st.spinner('Loading LLM from HuggingFace...'): | |
| llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.7, "max_new_tokens":512, "top_p":0.95, "top_k":50}) | |
| print("Creating Vector Database of PDF file content") | |
| with st.spinner('Creating Vector Database of PDF file content...'): | |
| vectorstore = make_vectorstore(embeddings) | |
| print("Intializing LLM for inference with source material") | |
| with st.spinner('Intializing LLM for inference with source material...'): | |
| conversation_chain = get_conversation(vectorstore, llm) | |
| queries = ["Can you give me an example from history where the enemy was crushed totally from the book?", | |
| "What's the point of making myself less accessible?", | |
| "Can you tell me the story of Queen Elizabeth I from this 48 laws of power book?"] | |
| for query in queries: | |
| response = get_response(conversation_chain, query) | |
| response_list.append(response) | |
| queries, responses = response_formatter(response_list) | |
| for i in range(len(queries)): | |
| st.write("Query: ", queries[i]) | |
| st.write("Response: ", responses[i]) | |
| st.write("--------------------------------------------------") | |
| if __name__ == "__main__": | |
| main() |