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 pickle
import gradio as gr
from langchain import LLMChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
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
from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
# from langchain.memory import Memory
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank


documents=[]
path='./bios/'
Chroma().delete_collection()

for file in os.listdir(path):
    loader = TextLoader(f'{path}{file}',encoding='unicode_escape')
    documents += loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
# embeddings = CohereEmbeddings(model='embed-english-v3.0')
embeddings = OpenAIEmbeddings()
docsearch = Chroma.from_documents(texts, embeddings)
retriever=docsearch.as_retriever()
# cohereLLM=Cohere(model='command')
cohereLLM=OpenAI()
# Initialize the CohereRerank compressor and the ContextualCompressionRetriever
compressor = CohereRerank(user_agent='MyTool/1.0 (Linux; x86_64)')
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever
)

# delete this to return to production state
memory=ConversationSummaryMemory(
    llm=cohereLLM, memory_key="chat_history", return_messages=True
)
question_generator = LLMChain(llm=cohereLLM, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(cohereLLM, chain_type="refine")

rag_chain=chain = ConversationalRetrievalChain(
    retriever=retriever,
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
    return_source_documents=True
)
#

btuTuples=pickle.load(open('./bookTitleUrlTuples.pkl','rb'))
bookTitleUrlDict={x:y for x,y in btuTuples}
chat_history = []
def predict(message, history):
    
    message="you are a language model that gives book recommendation based on your context. "+message
    result=ai_msg = rag_chain({"question": message, "chat_history": chat_history})
    bookNamePath=result["source_documents"][0].metadata["source"]
    bookName=bookNamePath.split("/")[-1][:-4].replace('_','')
    # if bookName not in ai_msg['answer']:
        # ai_msg['answer']=cohereLLM(f'what is the answer to my question of {message}? either {bookName} + the reason is .... or you don"t know')
    chat_history.extend([HumanMessage(content=message), AIMessage(content=ai_msg['answer'])])
    # if bookNamePath.split("/")[-1][:-4] not in ai_msg['answer']:
    #     message=f"""{bookNamePath.split('/')[-1][:-4]} and the book referred to in your answer are different, please think again and recommend \
    #         a book that is in your context"""+message
    #     result=ai_msg = rag_chain({"question": message, "chat_history": chat_history})
    #     chat_history.extend([HumanMessage(content=message), AIMessage(content=ai_msg['answer'])])
                           
    return result['answer'] #+f'''\n---\nlinkToAmazon: {bookTitleUrlDict[bookNamePath.split("/")[-1][:-4]]}'''

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:).** This chatbot's context is based on almost all the non-sponsored \
    Kindle ebooks found in the biography section of amazon.ca (1195 items).",
    ).launch()