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| import gradio as gr | |
| import tiktoken | |
| import os | |
| from langchain.text_splitter import CharacterTextSplitter | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain.vectorstores import Chroma | |
| from langchain.document_loaders import TextLoader | |
| from langchain import PromptTemplate | |
| from langchain.chains import LLMChain | |
| from langchain.chains.qa_with_sources import load_qa_with_sources_chain | |
| from langchain.llms import OpenAI | |
| from langchain.vectorstores import FAISS | |
| from langchain_openai import OpenAIEmbeddings | |
| from langchain_openai import ChatOpenAI | |
| # Load the FAISS index from the .pkl file | |
| openai_api_key = os.getenv("OPENAI_API_KEY") | |
| if not openai_api_key: | |
| raise ValueError("OPENAI_API_KEY environment variable is not set.") | |
| embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) | |
| #with open("index.pkl", "rb") as f: | |
| # db = faiss.read_index(f.read()) | |
| #with open("index.pkl", "rb") as f: | |
| #db = faiss.deserialize_index(f.read()) | |
| db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True) | |
| def get_response_from_query(db, query, k=3): | |
| docs = db.similarity_search(query, k=k) | |
| docs_page_content = " ".join([d.page_content for d in docs]) | |
| # llm = BardLLM() | |
| llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k",temperature=0) | |
| prompt = PromptTemplate( | |
| input_variables=["question", "docs"], | |
| template=""" | |
| A bot that is open to discussions about different cultural, philosophical and political exchanges. I will use do different analysis to the articles provided to me. Stay truthful and if you weren't provided any resources give your oppinion only. | |
| Answer the following question: {question} | |
| By searching the following articles: {docs} | |
| Only use the factual information from the documents. Make sure to mention key phrases from the articles. | |
| If you feel like you don't have enough information to answer the question, say "I don't know". | |
| """, | |
| ) | |
| chain = LLMChain(llm=llm, prompt=prompt) | |
| response = chain.run(question=query, docs=docs_page_content,return_source_documents=True) | |
| r_text = str(response) | |
| ##evaluation part | |
| prompt_eval = PromptTemplate( | |
| input_variables=["answer", "docs"], | |
| template=""" | |
| You job is to evaluate if the response to a given context is faithful. | |
| for the following: {answer} | |
| By searching the following article: {docs} | |
| Give a reason why they are similar or not, start with a Yes or a No. | |
| """, | |
| ) | |
| chain_part_2 = LLMChain(llm=llm, prompt=prompt_eval) | |
| evals = chain_part_2.run(answer=r_text, docs=docs_page_content) | |
| return response,docs,evals | |
| def greet(query): | |
| answer,sources,evals = get_response_from_query(db,query,2) | |
| return answer,sources,evals | |
| examples = [ | |
| ["What to do for Medicare and Continuing Care?"], | |
| ["What causes Prostate Cancer?"], | |
| ["What are the treatments for Kidney Disease?"], | |
| ["How to diagnose Alzheimer's Disease?"], | |
| ["What causes Low Vision?"], | |
| ["What is Diabetic Retinopathy?"] | |
| ] | |
| demo = gr.Interface(fn=greet, title="MedNavi", inputs="text", | |
| outputs=[gr.components.Textbox(lines=3, label="Response"), | |
| gr.components.Textbox(lines=3, label="Source"), | |
| gr.components.Textbox(lines=3, label="Evaluation")], | |
| examples=examples) | |
| demo.launch(share=True) |