File size: 4,453 Bytes
a557e27 907d604 a557e27 907d604 a557e27 faa90d7 a557e27 907d604 a557e27 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 |
from langchain.prompts.prompt import PromptTemplate
from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
import os
from typing import Optional, Tuple
import gradio as gr
import pickle
from threading import Lock
with open("vanguard_vectorstore.pkl", "rb") as f:
vectorstore = pickle.load(f)
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
You can assume the question about investing and the investment management industry.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
template = """You are an AI assistant for answering questions about investing and the investment management industry.
You are given the following extracted parts of a long document and a question. Provide a conversational answer.
If you don't know the answer, just say "Hmm, I'm not sure." Don't try to make up an answer.
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.
Question: {question}
=========
{context}
=========
Answer in Markdown:"""
QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])
def get_chain(vectorstore):
llm = OpenAI(temperature=0)
qa_chain = ChatVectorDBChain.from_llm(
llm,
vectorstore,
qa_prompt=QA_PROMPT,
condense_question_prompt=CONDENSE_QUESTION_PROMPT,
)
return qa_chain
def set_openai_api_key(api_key: str):
"""Set the api key and return chain.
If no api_key, then None is returned.
"""
if api_key:
os.environ["OPENAI_API_KEY"] = api_key
chain = get_chain(vectorstore)
os.environ["OPENAI_API_KEY"] = ""
return chain
class ChatWrapper:
def __init__(self):
self.lock = Lock()
def __call__(
self, api_key: str, inp: str, history: Optional[Tuple[str, str]], chain
):
"""Execute the chat functionality."""
self.lock.acquire()
try:
history = history or []
# If chain is None, that is because no API key was provided.
if chain is None:
history.append((inp, "Please paste your OpenAI key to use"))
return history, history
# Set OpenAI key
import openai
openai.api_key = api_key
# Run chain and append input.
output = chain({"question": inp, "chat_history": history})["answer"]
history.append((inp, output))
except Exception as e:
raise e
finally:
self.lock.release()
return history, history
chat = ChatWrapper()
block = gr.Blocks(css=".gradio-container {background-color: lightgray}")
with block:
with gr.Row():
gr.Markdown("<h3><center>Chat-Your-Data (Investor Education)</center></h3>")
openai_api_key_textbox = gr.Textbox(
placeholder="Paste your OpenAI API key (sk-...)",
show_label=False,
lines=1,
type="password",
)
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(
label="What's your question?",
placeholder="Ask questions about Investing",
lines=1,
)
submit = gr.Button(value="Send", variant="secondary").style(full_width=False)
gr.Examples(
examples=[
"What are the benefits of investing in ETFs?",
"What is the average cost of investing in a managed fund?",
"At what age can I start investing?",
"Do you offer investment accounts for kids?"
],
inputs=message,
)
gr.HTML("Demo application of a LangChain chain.")
gr.HTML(
"<center>Powered by <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></center>"
)
state = gr.State()
agent_state = gr.State()
submit.click(chat, inputs=[openai_api_key_textbox, message, state, agent_state], outputs=[chatbot, state])
message.submit(chat, inputs=[openai_api_key_textbox, message, state, agent_state], outputs=[chatbot, state])
openai_api_key_textbox.change(
set_openai_api_key,
inputs=[openai_api_key_textbox],
outputs=[agent_state],
)
block.launch(debug=True)
|