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 = [ ["How to be happy"], ["Climate Change Challenges in Europe"], ["Philosophy in the world of Minimalism"], ["Hate Speech vs Freedom of Speech"], ["Articles by Noam Chomsky on US Politics"], ["The importance of values and reflection"] ] demo = gr.Interface(fn=greet, title="cicero-semantic-search", 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)