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| import gradio as gr | |
| import pinecone | |
| import openai | |
| import os | |
| from langchain.embeddings.openai import OpenAIEmbeddings | |
| from langchain.chains import ConversationalRetrievalChain | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.memory import ConversationBufferMemory | |
| from langchain.vectorstores import Pinecone | |
| from langchain.prompts.prompt import PromptTemplate | |
| BOOK_TOKEN = os.getenv("book") | |
| pine = os.getenv("pine") | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| os.environ["OPENAI_API_KEY"] = BOOK_TOKEN | |
| OPENAI_API_KEY = "" | |
| PINECONE_API_KEY = "" | |
| PINECONE_API_ENV = "gcp-starter" | |
| #embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEYs) | |
| embed_model = "text-embedding-ada-003" | |
| pinecone.init( | |
| api_key=pine, | |
| environment=PINECONE_API_ENV | |
| ) | |
| openai.api_key=BOOK_TOKEN | |
| index_n = "hospital-code" | |
| index = pinecone.Index(index_n) | |
| index.describe_index_stats() | |
| limit = 3750 | |
| llm = ChatOpenAI(temperature=0, model_name="gpt-4" ) | |
| embeddings = OpenAIEmbeddings( | |
| model="text-embedding-3-large" | |
| ) | |
| #get the db index | |
| db = Pinecone.from_existing_index(index_name=index_n, embedding=embeddings) | |
| theme = gr.themes.Soft( | |
| primary_hue="emerald", | |
| ).set( | |
| block_background_fill='black') | |
| with gr.Blocks(theme=theme) as demo: | |
| chatbot = gr.Chatbot(label="Talk to the Bot", show_copy_button=True, show_label=True) | |
| msg = gr.Textbox() | |
| clear = gr.Button("Clear") | |
| chat_history = [] | |
| def vote(data: gr.LikeData): | |
| if data.liked: | |
| print("You upvoted this response: " + data.value) | |
| else: | |
| print("You downvoted this reposnse: " + data.value) | |
| def user(user_message, chat_history): | |
| memory = ConversationBufferMemory( | |
| memory_key='chat_history', | |
| return_messages=False | |
| ) | |
| #PUT IT IN A PROMPT TEMPLATE | |
| #template = """The following is chat between a human and an AI assistant. The AI provides the answer along with the section it referred to for the answer. | |
| #Current Conversation: | |
| #{history} | |
| #Friend: {input} | |
| #AI: | |
| #""" | |
| #PROMPT = PromptTemplate(input_variables=["history", "input"], template=template) | |
| #Initalize lanchain - Conversation Retrieval Chain | |
| qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), retriever=db.as_retriever(), memory=memory) | |
| #get response from QA Chain | |
| response = qa({'question': user_message, "chat_history": chat_history}) | |
| #append user message and respone to chat history | |
| chat_history.append((user_message, response["answer"])) | |
| return gr.update(value=""), chat_history | |
| msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False) | |
| chatbot.like(vote, None, None) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) |