app.py

'''
Om Sri Sai Ram

Swami's Chatbot Alpha Version
'''

from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
from langchain import PromptTemplate
import textwrap
import gradio as gr
import time
import os

OPENAI_API_KEY=os.environ["OPENAI_API_KEY"]

vectordb = FAISS.load_local("faiss_index OPENAI", OpenAIEmbeddings())

# --------------------------------------------------------------------------------

prompt_template = """
Don't try to make up an answer, if you don't know just say that you don't know.
Answer in the same language the question was asked.
Use only the following pieces of context to answer the question at the end.

{context}

Question: {question}
Answer:"""


PROMPT = PromptTemplate(
    template= prompt_template,
    input_variables=["context", "question"]
)

chain = RetrievalQA.from_chain_type(llm= OpenAI(),
                                    chain_type="stuff",
                                    retriever= vectordb.as_retriever(),
                                    chain_type_kwargs= {'prompt': PROMPT},
                                    return_source_documents= True,
                                    verbose= False)

# --------------------------------------------------------------------------------

def wrap_text_preserve_newlines(text, width=200): # 110
    # Split the input text into lines based on newline characters
    lines = text.split('\n')

    # Wrap each line individually
    wrapped_lines = [textwrap.fill(line, width=width) for line in lines]

    # Join the wrapped lines back together using newline characters
    wrapped_text = '\n'.join(wrapped_lines)

    return wrapped_text

def process_llm_response(llm_response):
    ans = wrap_text_preserve_newlines(llm_response['result'])
    sources_used = ' \n'.join([str(source.metadata['source'].split('/')[-1][:-4]) for source in llm_response['source_documents']])
    ans = ans + '\n\nSources: \n' + sources_used
    return ans

def llm_ans(query):

    llm_response = chain(query)
    ans = process_llm_response(llm_response)

    return ans

def predict(message, history):
    # output = message # debug mode

    output = str(llm_ans(message))
    return output

demo = gr.ChatInterface(predict,
                        title = f'SAI Speaks')

if __name__ == "__main__":
    demo.launch()