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import torch |
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import subprocess |
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import streamlit as st |
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from run_localGPT import load_model |
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from langchain.vectorstores import Chroma |
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from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY, MODEL_ID, MODEL_BASENAME |
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from langchain.embeddings import HuggingFaceInstructEmbeddings |
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from langchain.chains import RetrievalQA |
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from streamlit_extras.add_vertical_space import add_vertical_space |
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from langchain.prompts import PromptTemplate |
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from langchain.memory import ConversationBufferMemory |
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def model_memory(): |
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template = """Use the following pieces of context to answer the question at the end. If you don't know the answer,\ |
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just say that you don't know, don't try to make up an answer. |
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{context} |
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{history} |
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Question: {question} |
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Helpful Answer:""" |
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prompt = PromptTemplate(input_variables=["history", "context", "question"], template=template) |
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memory = ConversationBufferMemory(input_key="question", memory_key="history") |
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return prompt, memory |
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with st.sidebar: |
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st.title('🤗💬 Converse with your Data') |
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st.markdown(''' |
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## About |
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This app is an LLM-powered chatbot built using: |
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- [Streamlit](https://streamlit.io/) |
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- [LangChain](https://python.langchain.com/) |
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- [LocalGPT](https://github.com/PromtEngineer/localGPT) |
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''') |
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add_vertical_space(5) |
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st.write('Made with ❤️ by [Prompt Engineer](https://youtube.com/@engineerprompt)') |
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DEVICE_TYPE = "cuda" if torch.cuda.is_available() else "cpu" |
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if "result" not in st.session_state: |
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run_langest_commands = ["python", "ingest.py"] |
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run_langest_commands.append("--device_type") |
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run_langest_commands.append(DEVICE_TYPE) |
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result = subprocess.run(run_langest_commands, capture_output=True) |
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st.session_state.result = result |
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if "EMBEDDINGS" not in st.session_state: |
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EMBEDDINGS = HuggingFaceInstructEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": DEVICE_TYPE}) |
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st.session_state.EMBEDDINGS = EMBEDDINGS |
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if "DB" not in st.session_state: |
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DB = Chroma( |
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persist_directory=PERSIST_DIRECTORY, |
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embedding_function=st.session_state.EMBEDDINGS, |
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client_settings=CHROMA_SETTINGS, |
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) |
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st.session_state.DB = DB |
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if "RETRIEVER" not in st.session_state: |
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RETRIEVER = DB.as_retriever() |
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st.session_state.RETRIEVER = RETRIEVER |
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if "LLM" not in st.session_state: |
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LLM = load_model(device_type=DEVICE_TYPE, model_id=MODEL_ID, model_basename=MODEL_BASENAME) |
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st.session_state["LLM"] = LLM |
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if "QA" not in st.session_state: |
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prompt, memory = model_memory() |
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QA = RetrievalQA.from_chain_type( |
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llm=LLM, |
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chain_type="stuff", |
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retriever=RETRIEVER, |
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return_source_documents=True, |
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chain_type_kwargs={"prompt": prompt, "memory": memory}, |
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) |
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st.session_state["QA"] = QA |
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st.title('LocalGPT App 💬') |
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prompt = st.text_input('Input your prompt here') |
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if prompt: |
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response = st.session_state["QA"](prompt) |
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answer, docs = response["result"], response["source_documents"] |
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st.write(answer) |
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with st.expander('Document Similarity Search'): |
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search = st.session_state.DB.similarity_search_with_score(prompt) |
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for i, doc in enumerate(search): |
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st.write(f"Source Document # {i+1} : {doc[0].metadata['source'].split('/')[-1]}") |
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st.write(doc[0].page_content) |
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st.write("--------------------------------") |
