app.py

import os 
import gradio as gr
import pandas as pd

from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
#from langchain.vectorstores import Chroma
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import TextLoader
from langchain_community.chat_models import ChatOpenAI
# from langchain import PromptTemplate
from langchain_core.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
#from langchain.vectorstores import FAISS
from langchain_community.vectorstores.faiss import FAISS

# embeddings = OpenAIEmbeddings()

openai_api_key = os.getenv("OPENAI_API_KEY")
try:
    embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
except ValidationError as e:
    print(f"Error: {e}")


#------------------------------------------------------------------------------
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 = 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)
    # chain = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, prompt=prompt,
    #                                                     chain_type="stuff", retriever=db.as_retriever(), return_source_documents=True)

    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

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")])

demo.launch(share=True, debug=True)