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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"]) | |
| vectorstore = get_weaviate_store() | |
| def set_openai_api_key(api_key, agent): | |
| if api_key: | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| 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) | |