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| import pysqlite3 | |
| import sys, os | |
| sys.modules["sqlite3"] = sys.modules.pop("pysqlite3") | |
| from langchain_community.document_loaders import PyPDFLoader | |
| from langchain.llms import HuggingFaceHub | |
| from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings | |
| from langchain.vectorstores import Chroma | |
| from langchain_core.prompts import ChatPromptTemplate | |
| from langchain_core.runnables import RunnablePassthrough | |
| from langchain_core.output_parsers import StrOutputParser | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| import streamlit as st | |
| HF_TOKEN = st.secrets["HF_TOKEN"] | |
| os.environ["HUGGINGFACEHUB_API_TOKEN"] = HF_TOKEN | |
| def retrieve_documents(): | |
| embeddings = HuggingFaceInferenceAPIEmbeddings( | |
| api_key=HF_TOKEN, model_name="BAAI/bge-base-en-v1.5") | |
| #api_key=HF_TOKEN, model_name="local:BAAI/bge-m3") | |
| db = Chroma(persist_directory="./db", | |
| embedding_function=embeddings) | |
| retriever = db.as_retriever(search_kwargs = {"k":3}) | |
| return retriever | |
| def create_chain(_retriever): | |
| template = """ | |
| User: You are an AI Assistant that follows instructions well. | |
| Please be truthful and give direct answers. Please tell 'I don't know' if user query is not in CONTEXT | |
| Keep in mind, you will lose the job, if you answer out of CONTEXT questions | |
| CONTEXT: {context} | |
| Query: {question} | |
| Remember only return AI answer | |
| Assistant: | |
| """ | |
| llm = HuggingFaceEndpoint( | |
| endpoint_url = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.2", | |
| max_new_tokens=2048, | |
| top_k=3, | |
| top_p=0.9, | |
| temperature=0.5, | |
| repetition_penalty=1.1, | |
| streaming=False, | |
| ) | |
| prompt = ChatPromptTemplate.from_template(template) | |
| output_parser = StrOutputParser() | |
| chain = ({ | |
| "context": _retriever.with_config(run_name="Docs"), | |
| "question":RunnablePassthrough() | |
| } | |
| | prompt | |
| | llm | |
| | output_parser | |
| ) | |
| return chain | |
| def main(): | |
| st.title("All About Sungwon") | |
| st.header("Ask anything about Sungwon. Professional or Personal") | |
| prompt = st.text_input("Enter your question") | |
| text_container = st.empty() | |
| text_debugger = st.empty() | |
| full_text = "" | |
| chain = create_chain(retrieve_documents()) | |
| chunk = chain.invoke(prompt) | |
| text_container.write(chunk) | |
| st.write("check out my personalized diffuion model site to see my picture[link](https://huggingface.co/spaces/sorg20/sorg20-autotrain-sd-pic)") | |
| if __name__ == "__main__": | |
| main() | |