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import datetime | |
import os | |
import gradio as gr | |
import langchain | |
import weaviate | |
from langchain.vectorstores import Weaviate | |
from chain import get_new_chain1 | |
WEAVIATE_URL = os.environ["WEAVIATE_URL"] | |
def get_weaviate_store(): | |
client = weaviate.Client( | |
url=WEAVIATE_URL, | |
additional_headers={"X-OpenAI-Api-Key": os.environ["OPENAI_API_KEY"]}, | |
) | |
return Weaviate(client, "Paragraph", "content", attributes=["source"]) | |
def set_openai_api_key(api_key, agent): | |
if api_key: | |
os.environ["OPENAI_API_KEY"] = api_key | |
vectorstore = get_weaviate_store() | |
qa_chain = get_new_chain1(vectorstore) | |
os.environ["OPENAI_API_KEY"] = "" | |
return qa_chain | |
def chat(inp, history, agent): | |
history = history or [] | |
if agent is None: | |
history.append((inp, "Please paste your OpenAI key to use")) | |
return history, history | |
print("\n==== date/time: " + str(datetime.datetime.now()) + " ====") | |
print("inp: " + inp) | |
history = history or [] | |
output = agent({"question": inp, "chat_history": history}) | |
answer = output["answer"] | |
history.append((inp, answer)) | |
print(history) | |
return history, history | |
block = gr.Blocks(css=".gradio-container {background-color: lightgray}") | |
with block: | |
with gr.Row(): | |
gr.Markdown("<h3><center>LangChain AI</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="What's the answer to life, the universe, and everything?", | |
lines=1, | |
) | |
submit = gr.Button(value="Send", variant="secondary").style(full_width=False) | |
gr.Examples( | |
examples=[ | |
"What are agents?", | |
"How do I summarize a long document?", | |
"What types of memory exist?", | |
], | |
inputs=message, | |
) | |
gr.HTML( | |
""" | |
This simple application is an implementation of ChatGPT but over an external dataset (in this case, the LangChain documentation).""" | |
) | |
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=[message, state, agent_state], outputs=[chatbot, state]) | |
message.submit(chat, inputs=[message, state, agent_state], outputs=[chatbot, state]) | |
openai_api_key_textbox.change( | |
set_openai_api_key, | |
inputs=[openai_api_key_textbox, agent_state], | |
outputs=[agent_state], | |
) | |
block.launch(debug=True) | |