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')
docsearch = Chroma.from_documents(texts, embeddings)
retriever=docsearch.as_retriever(search_kwargs={'k':1})
cohereLLM=Cohere(model='command')
# 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=docsearch.as_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):
    '''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'''
    
    message="you are a language model that gives book recommendation based on your context"+message+\
    'just give the book title and author'
    result=ai_msg = rag_chain({"question": message, "chat_history": chat_history})
    chat_history.extend([HumanMessage(content=message), AIMessage(content=ai_msg['answer'])])
    bookNamePath=result["source_documents"][0].metadata["source"]
    return result['answer'] +f'''---\nlink: {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()