app.py

from langchain.chains import RetrievalQA
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.document_loaders import TextLoader
from langchain.docstore.document import Document
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import OpenAI
import cohere
from langchain.embeddings.cohere import CohereEmbeddings
from langchain.llms import Cohere
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Chroma
import os
from tqdm import tqdm
import gradio as gr
# from langchain.memory import ConversationSummaryMemory
# from langchain.chains import ConversationalRetrievalChain
# from langchain.text_splitter import RecursiveCharacterTextSplitter
# from langchain.chains import LLMChain
# from langchain.prompts import (
#     ChatPromptTemplate,
#     HumanMessagePromptTemplate,
#     MessagesPlaceholder,
#     SystemMessagePromptTemplate,
# )
from langchain.schema import AIMessage,HumanMessage
documents=[]
path='./bios/'

for file in os.listdir(path):
    loader = TextLoader(f'{path}{file}',encoding='unicode_escape')
    # loader.load()[0].metadata['category']='biography'
    # print(loader.load()[0].metadata)
    documents += loader.load()
# print(documents)
print(len(documents))
'''This is the code used for without memory chat'''
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

embeddings = CohereEmbeddings(model='embed-english-v3.0')
docsearch = Chroma.from_documents(texts, embeddings)

qa = RetrievalQA.from_chain_type(llm=Cohere(model='command'), chain_type="stuff", \
    retriever=docsearch.as_retriever(search_kwargs={'k':1}),return_source_documents=True)

def predict(message, history):
    '''experimenation with memory and conversation retrieval chain has resulted in less
    performance, usefulness, and more halucination. Hence, this chat bot provides one 
    shot answers with zero memory. You can use the code in github notebooks to do this
    experimentation. github.com/mehrdad-es/Amazon-But-Better'''
    # history_langchain_format = []
    # for human, ai in history:
    #     history_langchain_format.append(HumanMessage(content=human))
    #     history_langchain_format.append(AIMessage(content=ai))
    # history_langchain_format.append(HumanMessage(content=message))
    # gpt_response = qa({'query':history_langchain_format})
    # return gpt_response['result']
    # gpt_response = qa({'query':''.join(history)+f'.\n given the previous conversation respond using the following prompt:{message}'})
    # # print(gpt_response)
    # history.append((f'HumanMessage:{message}',f'AIMessage: {gpt_response},'))
    # # history=history_langchain_format
    # return gpt_response['result']
    
    message="you are a language model that gives book recommendation based on your context"+message+\
    'just give the book title and author'
    result = qa({"query": message})
    # r1=docsearch.similarity_search_with_score(query=q,k=3)
    # print([(item[-2].metadata,item[-1]) for item in r1],\
    #       '\n\n',result['result'],f'|| {result["source_documents"][0].metadata}','\n*****\n')
    if result['result'] not in ["I don't know","I don't know."]:
        return result['result']+f'\n---\nIgnore the description below if the chatbot was unsure about its response \
        or if the response is not about the book shown below\nAmazon Kindle ebook description is:\n {result["source_documents"][0].page_content}'+\
        f'\nfrom this file: {result["source_documents"][0].metadata}'
    else:
        return result['result']

gr.ChatInterface(predict,
    chatbot=gr.Chatbot(height='auto'),
    textbox=gr.Textbox(placeholder="Recommend a book on someone who..."),
    title="Amazon But Better",
    description="Amazon started out with selling books. However, searching books on \
    Amazon is tedious and inaccurate if you don't know what you are exactly looking for. **Why not \
    make it faster and easier with LLMs:).**").launch()
# gr.ChatInterface(predict).launch()