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from langchain.prompts.prompt import PromptTemplate |
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from langchain.llms import OpenAIChat |
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from langchain.chains import ConversationalRetrievalChain, LLMChain |
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from langchain.chains.question_answering import load_qa_chain |
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from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings |
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from langchain.callbacks import StdOutCallbackHandler |
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from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT |
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from langchain.vectorstores import FAISS |
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from langchain.memory import ConversationBufferMemory |
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import os |
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from typing import Optional, Tuple |
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import gradio as gr |
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import pickle |
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from threading import Lock |
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from langchain.prompts.chat import ( |
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ChatPromptTemplate, |
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SystemMessagePromptTemplate, |
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AIMessagePromptTemplate, |
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HumanMessagePromptTemplate, |
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) |
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from langchain.schema import ( |
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AIMessage, |
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HumanMessage, |
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SystemMessage |
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) |
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from langchain.prompts import PromptTemplate |
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prefix_messages = [{"role": "system", "content": "You are a helpful assistant that is very good at answering questions about investments using the information given."}] |
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model_options = {'all-mpnet-base-v2': "sentence-transformers/all-mpnet-base-v2", |
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'instructor-base': "hkunlp/instructor-base"} |
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model_options_list = list(model_options.keys()) |
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memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True, output_key='answer') |
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def load_prompt(): |
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system_template="""Use only the following pieces of context that has been scraped from a website to answer the users question accurately. |
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Do not use any information not provided in the website context. |
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If you don't know the answer, just say 'There is no relevant answer in the Investor Website', |
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don't try to make up an answer. |
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ALWAYS return a "SOURCES" part in your answer. |
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The "SOURCES" part should be a reference to the source of the document from which you got your answer. |
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Remember, do not reference any information not given in the context. |
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If the answer is not available in the given context just say 'There is no relevant answer in the website content' |
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Follow the below format when answering: |
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Question: {question} |
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SOURCES: [xyz] |
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Begin! |
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---------------- |
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{context}""" |
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messages = [ |
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SystemMessagePromptTemplate.from_template(system_template), |
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HumanMessagePromptTemplate.from_template("{question}") |
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] |
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prompt = ChatPromptTemplate.from_messages(messages) |
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return prompt |
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def load_vectorstore(model): |
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'''load embeddings and vectorstore''' |
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if 'mpnet' in model: |
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emb = HuggingFaceEmbeddings(model_name=model) |
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return FAISS.load_local('vanguard-embeddings', emb) |
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elif 'instructor'in model: |
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emb = HuggingFaceInstructEmbeddings(model_name=model, |
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query_instruction='Represent the Financial question for retrieving supporting paragraphs: ', |
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embed_instruction='Represent the Financial paragraph for retrieval: ') |
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return FAISS.load_local('vanguard_embeddings_inst', emb) |
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vectorstore = load_vectorstore(model_options['all-mpnet-base-v2']) |
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def on_value_change(change): |
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'''When radio changes, change the embeddings''' |
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global vectorstore |
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vectorstore = load_vectorstore(model_options[change]) |
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_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question. |
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You can assume the question about investing and the investment management industry. |
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Chat History: |
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{chat_history} |
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Follow Up Input: {question} |
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Standalone question:""" |
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CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) |
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template = """You are an AI assistant for answering questions about investing and the investment management industry. |
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You are given the following extracted parts of a long document and a question. Provide a conversational answer. |
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If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer. |
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If the question is not about investing, politely inform them that you are tuned to only answer questions about investing and the investment management industry. |
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Question: {question} |
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========= |
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{context} |
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========= |
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Answer in Markdown:""" |
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QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"]) |
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def get_chain(vectorstore): |
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llm = OpenAIChat(streaming=True, |
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callbacks=[StdOutCallbackHandler()], |
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verbose=True, |
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temperature=0, |
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model_name='gpt-4-0613') |
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question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) |
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doc_chain = load_qa_chain(llm=llm,chain_type="stuff",prompt=load_prompt()) |
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chain = ConversationalRetrievalChain(retriever=vectorstore.as_retriever(search_kwags={"k": 3}), |
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question_generator=question_generator, |
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combine_docs_chain=doc_chain, |
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memory=memory, |
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return_source_documents=True, |
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get_chat_history=lambda h :h) |
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return chain |
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def load_chain(): |
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chain = get_chain(vectorstore) |
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return chain |
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class ChatWrapper: |
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def __init__(self): |
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self.lock = Lock() |
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def __call__( |
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self, inp: str, history: Optional[Tuple[str, str]], chain |
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): |
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"""Execute the chat functionality.""" |
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self.lock.acquire() |
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try: |
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history = history or [] |
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output = chain({"question": inp})["answer"] |
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history.append((inp, output)) |
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except Exception as e: |
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raise e |
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finally: |
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self.lock.release() |
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return history, history |
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block = gr.Blocks(css=".gradio-container {background-color: lightgray}") |
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with block: |
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with gr.Row(): |
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gr.Markdown("<h3><center>Chat-Your-Data (Investor Education)</center></h3>") |
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embed_but = gr.Button(value='Load QA Chain') |
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with gr.Row(): |
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embeddings = gr.Radio(choices=model_options_list,value=model_options_list[0], label='Choose your Embedding Model', |
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interactive=True) |
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embeddings.change(on_value_change, embeddings) |
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vectorstore = load_vectorstore(embeddings.value) |
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chatbot = gr.Chatbot() |
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chat = ChatWrapper() |
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with gr.Row(): |
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message = gr.Textbox( |
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label="What's your question?", |
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placeholder="Ask questions about Investing", |
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lines=1, |
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) |
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submit = gr.Button(value="Send", variant="secondary").style(full_width=False) |
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gr.Examples( |
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examples=[ |
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"What are the benefits of investing in ETFs?", |
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"What is the average cost of investing in a managed fund?", |
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"At what age can I start investing?", |
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"Do you offer investment accounts for kids?" |
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], |
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inputs=message, |
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) |
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gr.HTML("Demo application of a LangChain chain.") |
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gr.HTML( |
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"<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>" |
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) |
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state = gr.State() |
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agent_state = gr.State() |
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submit.click(chat, inputs=[message, state, agent_state], outputs=[chatbot, state]) |
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message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state]) |
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embed_but.click( |
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load_chain, |
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outputs=[agent_state], |
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) |
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gr.Markdown("") |
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block.launch(debug=True) |
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