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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.vectorstores import Pinecone
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from langchain_text_splitters import CharacterTextSplitter
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from langchain_openai import OpenAIEmbeddings
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from langchain_community.document_loaders import HuggingFaceDatasetLoader
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from langchain_pinecone import PineconeVectorStore
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from pinecone import Pinecone, ServerlessSpec
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from langchain_pinecone import PineconeVectorStore
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from langchain_openai import ChatOpenAI
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from langchain_core.output_parsers import StrOutputParser
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from langchain import hub
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from langchain_core.runnables import RunnablePassthrough
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import os
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import gradio as gr
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from dotenv import load_dotenv
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load_dotenv()
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dataset_name = "Pijush2023/Yale_Psychilogy"
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page_content_column = 'Biography'
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loader = HuggingFaceDatasetLoader(dataset_name, page_content_column)
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data = loader.load()
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text_splitter = CharacterTextSplitter(chunk_size=300, chunk_overlap=50)
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documents = text_splitter.split_documents(data)
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embeddings=OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY'])
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chat_model= ChatOpenAI(api_key=os.environ['OPENAI_API_KEY'], temperature=0.5, model='gpt-3.5-turbo-0125')
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pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
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index_name = "medical"
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if index_name not in pc.list_indexes().names():
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pc.create_index(
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name=index_name,
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dimension=1536,
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metric='cosine',
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spec=ServerlessSpec(
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cloud='aws',
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region='us-east-1'
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)
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)
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vectorstore = PineconeVectorStore(index_name=index_name, embedding=embeddings)
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vectorstore.add_documents(documents)
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query = "who is the best doctor for depression?"
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vectorstore.similarity_search(query,k=1)
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retriever = vectorstore.as_retriever(search_kwargs={'k':1})
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docs = retriever.invoke("who is the best doctors for depression ?")
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prompt=hub.pull("rlm/rag-prompt")
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rag_chain=(
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{"context":retriever , "question" : RunnablePassthrough()}
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| prompt
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| chat_model
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| StrOutputParser()
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)
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query="depression"
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rag_chain.invoke(query)
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def generate_answer(message, history):
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return rag_chain.invoke(message)
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answer_bot = gr.ChatInterface(
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generate_answer,
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chatbot=gr.Chatbot(height=300),
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textbox=gr.Textbox(placeholder="Ask me a question about Doctor on Psychiatry", container=False, scale=7),
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title="Psychiatry Doctor Chat-Bot",
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description="This is a chat bot related to top School in United States about Psychiatry",
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theme="soft",
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examples=["depression", "Mental-Stress", "Bipolar Disorder", "Eating Disorders" , "etc....."],
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cache_examples=False,
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retry_btn=None,
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undo_btn=None,
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clear_btn=None,
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submit_btn="Ask"
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)
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answer_bot.launch()
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