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| # model under development | |
| import os | |
| import streamlit as st | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| #from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| #from langchain_text_splitters import CharacterTextSplitter | |
| from langchain_experimental.text_splitter import SemanticChunker | |
| #from langchain_core.prompts import ChatPromptTemplate | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.llms import CTransformers | |
| from langchain.chains import ConversationalRetrievalChain, LLMChain | |
| #from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler | |
| #from langchain.chains import LLMChain | |
| from langchain_core.prompts import PromptTemplate | |
| # adding separator | |
| def add_vertical_space(spaces=1): | |
| for _ in range(spaces): | |
| st.sidebar.markdown("---") | |
| # main method | |
| def main(): | |
| # page title | |
| st.set_page_config(page_title="Chatbot for NHPC", layout="wide") | |
| st.title("Chatbot for NHPC") | |
| st.write("##### 🚧 Under development 🚧") | |
| # faiss db directory | |
| DB_FAISS_PATH = "vectorstore/db_faiss" | |
| TEMP_DIR = "temp" | |
| # embedding model path | |
| EMBEDDING_MODEL_PATH = "embeddings/MiniLM-L6-v2" | |
| # creating faiss db direcoty if it doesnot exist already | |
| if not os.path.exists(TEMP_DIR): | |
| os.makedirs(TEMP_DIR) | |
| # uploading csv file | |
| uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'], help="Upload a CSV file") | |
| # adding vertical space | |
| add_vertical_space(1) | |
| # creating faiss vectorstore | |
| if uploaded_file is not None: | |
| file_path = os.path.join(TEMP_DIR, uploaded_file.name) | |
| with open(file_path, "wb") as f: | |
| f.write(uploaded_file.getvalue()) | |
| st.write(f"Uploaded file: {uploaded_file.name}") | |
| st.write("Processing CSV file...") | |
| st.sidebar.markdown('##### The model may sometime generate excessive or incorrect response.') | |
| # calling CSVLoader for loading CSV file | |
| loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','}) | |
| # loading the CSV file data | |
| data = loader.load() | |
| # creating embeddings using huggingface | |
| embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2') | |
| # creating chunks from CSV file | |
| #text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=50) | |
| text_splitter = SemanticChunker(embeddings, breakpoint_threshold_type="interquartile") | |
| #text_splitter = CharacterTextSplitter(chunk_size=800, chunk_overlap=100) | |
| text_chunks = text_splitter.split_documents(data) | |
| # chunks message to output | |
| st.write(f"Total text chunks: {len(text_chunks)}") | |
| st.write("---") | |
| # creating vectorstore from the text chunks | |
| docsearch = FAISS.from_documents(text_chunks, embeddings) | |
| # saving the vector store to local directory | |
| docsearch.save_local(DB_FAISS_PATH) | |
| # loading local llama model | |
| llm = CTransformers(#model="models/llama-2-7b-chat.ggmlv3.q8_0.bin", | |
| model="TheBloke/Llama-2-7B-Chat-GGML", | |
| model_type="llama", | |
| #callbacks=[StreamingStdOutCallbackHandler()], | |
| config={'max_new_tokens': 1024, | |
| 'temperature': 0.5, | |
| 'context_length' : 4096 | |
| #'repetition_penalty': 1.1 | |
| } | |
| ) | |
| # loading remote zephyr model | |
| #llm = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GGUF", | |
| # model_file="zephyr-7b-beta.Q5_K_M.gguf", | |
| # model_type="mistral", | |
| # gpu_layers=50, | |
| # max_new_tokens = 1000, | |
| # context_length = 6000) | |
| # question answering chain | |
| #memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True) | |
| # custom prompt | |
| custom_template=""" | |
| You are a smart personal assistant and your task is to provide the answer of the given question based only on the given context. \n | |
| If you can't find the answer in the context, just say that "I don't know, please look up the policy." and don't try to make up an answer. \n\n | |
| Please, give the answer in plain english and don't repeat your answer and don't mention that you found the answer form the context and don't mention that the answer can be found in the context. \n | |
| Question: "{question}" \n\n | |
| Context: "{context}" \n\n | |
| Helpful Answer: | |
| """ | |
| QA_PROMPT = PromptTemplate(template=custom_template,input_variables=["question", "context"]) | |
| # main llm chain | |
| qa = ConversationalRetrievalChain.from_llm(llm, | |
| #chain_type = "stuff", | |
| chain_type = "stuff", | |
| verbose=True, | |
| #retriever=docsearch.as_retriever() | |
| retriever=docsearch.as_retriever(search_kwargs = {"k" : 4, "search_type" : "similarity"}), | |
| combine_docs_chain_kwargs={"prompt": QA_PROMPT} | |
| #retriever=docsearch.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.1}) | |
| #memory=memory | |
| ) | |
| # taking question from user | |
| #st.write("### Enter your query:") | |
| query = st.chat_input("Ask a question to the chatbot.") | |
| if query: | |
| st.write("#### Query: "+query) | |
| with st.spinner("Processing your question..."): | |
| chat_history = [] | |
| result = qa({"question": query, "chat_history": chat_history}) | |
| #st.write("---") | |
| #st.write("### Response:") | |
| #st.write("#### Query: "+query) | |
| st.write(f"> {result['answer']}") | |
| os.remove(file_path) | |
| if __name__ == "__main__": | |
| main() | |