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| import streamlit as st | |
| import os | |
| from langchain.chains.conversation.memory import ConversationBufferWindowMemory | |
| from data_extraction import Extraction | |
| import nest_asyncio | |
| from chunking import Chunker | |
| from embeddings import Embeddings | |
| from vectorstore import VectorDB | |
| from retriever import Retriever, CreateBM25Retriever | |
| from llm import LLM | |
| from langchain_core.prompts import PromptTemplate | |
| from chain import Chain | |
| from streamlit_chat import message | |
| if 'responses' not in st.session_state: | |
| st.session_state['responses'] = ["How can I assist you?"] | |
| if 'requests' not in st.session_state: | |
| st.session_state['requests'] = [] | |
| if 'buffer_memory' not in st.session_state: | |
| st.session_state.buffer_memory=ConversationBufferWindowMemory(k=3,return_messages=True) | |
| nest_asyncio.apply() | |
| ext = Extraction('fast') | |
| chnk = Chunker(chunk_size=1000, chunk_overlap=200) | |
| emb = Embeddings("hf", "all-MiniLM-L6-v2") | |
| _llm = LLM('vertex').get_llm() | |
| ch = Chain(_llm, st.session_state.buffer_memory) | |
| conversation = ch.get_chain_with_history() | |
| def query_refiner(conversation, query): | |
| prompt=f"Given the following user query and historical user queries, rephrase the users current query to form a meaningful and clear question.Previously user has asked the following: \n{conversation}\n\n User's Current Query: {query}. What will be the refined query? Only provide the query without any extra details or explanations.", | |
| ans = _llm.invoke(prompt).content | |
| return ans | |
| def get_conversation_string(): | |
| conversation_string = "" | |
| for i in range(len(st.session_state['responses'])-1): | |
| conversation_string += "Human: "+st.session_state['requests'][i] + "\n" | |
| # conversation_string += "Bot: "+ st.session_state['responses'][i+1] + "\n" | |
| return conversation_string | |
| def main(): | |
| inp_dir = "./inputs" | |
| db = 'pinecone' | |
| db_dir = 'pineconedb' | |
| st.set_page_config("Chat PDF") | |
| st.header("Chat with PDF") | |
| response_container = st.container() | |
| textcontainer = st.container() | |
| ret = None | |
| with textcontainer: | |
| query = st.text_input("Query: ", key="input") | |
| if query: | |
| if ret is None: | |
| ret = Retriever(db, db_dir, emb.embedding, 'ensemble', 5) | |
| with st.spinner("typing..."): | |
| conversation_string = get_conversation_string() | |
| if len(st.session_state['responses']) != 0: | |
| refined_query = query_refiner(conversation_string, query) | |
| else: | |
| refined_query = query | |
| st.subheader("Refined Query:") | |
| st.write(refined_query) | |
| context, context_list = ret.get_context(refined_query) | |
| response = conversation.predict(input=f"Context:\n {context} \n\n Query:\n{query}") | |
| # response += '\n' + "Source: " + src | |
| st.session_state.requests.append(query) | |
| st.session_state.responses.append(response) | |
| with response_container: | |
| if st.session_state['responses']: | |
| for i in range(len(st.session_state['responses'])): | |
| message(st.session_state['responses'][i],key=str(i)) | |
| if i < len(st.session_state['requests']): | |
| message(st.session_state["requests"][i], is_user=True,key=str(i)+ '_user') | |
| with st.sidebar: | |
| st.title("Menu:") | |
| pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True) | |
| pdfs = [] | |
| if pdf_docs: | |
| for pdf_file in pdf_docs: | |
| filename = pdf_file.name | |
| path = os.path.join(inp_dir,filename) | |
| with open(path, "wb") as f: | |
| f.write(pdf_file.getvalue()) | |
| pdfs.append(path) | |
| with st.spinner("Processing..."): | |
| texts, metas = ext.get_text(pdfs) | |
| docs = chnk.get_chunks(texts, metas) | |
| vs = VectorDB(db, emb.embedding, db_dir, docs=docs) | |
| bm = CreateBM25Retriever(docs) | |
| st.success("Done") | |
| if __name__ == "__main__": | |
| main() |