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| 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 | |
| import pickle | |
| 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) | |
| btuTuples=pickle.load(open('./bookTitleUrlTuples.pkl','rb')) | |
| bookTitleUrlDict={x:y for x,y in btuTuples} | |
| 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."]: | |
| bookNamePath=result["source_documents"][0].metadata["source"] | |
| return result['result']+f'\n---\n***Ignore 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'\n**from this file:** {bookNamePath}\n'+\ | |
| f'**link==>** {bookTitleUrlDict[bookNamePath.split("/")[-1][:-4]]}' | |
| 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:).** This chatbot's context is based on all the non-sponsored \ | |
| Kindle ebooks found in the biography section of amazon.ca (1195 items).").launch() | |
| # gr.ChatInterface(predict).launch() | |