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"""
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Script for English retrieval based conversation system backed by LLaMa2
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"""
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import argparse
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import os
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from colossalqa.chain.retrieval_qa.base import RetrievalQA
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from colossalqa.data_loader.document_loader import DocumentLoader
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from colossalqa.local.llm import ColossalAPI, ColossalLLM
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from colossalqa.memory import ConversationBufferWithSummary
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from colossalqa.prompt.prompt import (
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EN_RETRIEVAL_QA_REJECTION_ANSWER,
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EN_RETRIEVAL_QA_TRIGGER_KEYWORDS,
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PROMPT_DISAMBIGUATE_EN,
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PROMPT_RETRIEVAL_QA_EN,
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)
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from colossalqa.retriever import CustomRetriever
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from langchain import LLMChain
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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if __name__ == "__main__":
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# Parse arguments
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parser = argparse.ArgumentParser(description="English retrieval based conversation system backed by LLaMa2")
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parser.add_argument("--model_path", type=str, default=None, help="path to the model")
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parser.add_argument("--model_name", type=str, default=None, help="name of the model")
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parser.add_argument(
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"--sql_file_path", type=str, default=None, help="path to the a empty folder for storing sql files for indexing"
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)
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args = parser.parse_args()
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if not os.path.exists(args.sql_file_path):
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os.makedirs(args.sql_file_path)
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colossal_api = ColossalAPI.get_api(args.model_name, args.model_path)
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llm = ColossalLLM(n=1, api=colossal_api)
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# Define the retriever
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information_retriever = CustomRetriever(k=3, sql_file_path=args.sql_file_path, verbose=True)
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# Setup embedding model locally
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embedding = HuggingFaceEmbeddings(
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model_name="moka-ai/m3e-base", model_kwargs={"device": "cpu"}, encode_kwargs={"normalize_embeddings": False}
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)
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# Define memory with summarization ability
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memory = ConversationBufferWithSummary(
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llm=llm, max_tokens=2000, llm_kwargs={"max_new_tokens": 50, "temperature": 0.6, "do_sample": True}
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)
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# Define the chain to preprocess the input
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# Disambiguate the input. e.g. "What is the capital of that country?" -> "What is the capital of France?"
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llm_chain_disambiguate = LLMChain(
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llm=llm, prompt=PROMPT_DISAMBIGUATE_EN, llm_kwargs={"max_new_tokens": 30, "temperature": 0.6, "do_sample": True}
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)
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def disambiguity(input):
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out = llm_chain_disambiguate.run(input=input, chat_history=memory.buffer, stop=["\n"])
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return out.split("\n")[0]
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# Load data to vector store
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print("Select files for constructing retriever")
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documents = []
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while True:
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file = input("Enter a file path or press Enter directory without input to exit:").strip()
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if file == "":
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break
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data_name = input("Enter a short description of the data:")
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separator = input(
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"Enter a separator to force separating text into chunks, if no separator is given, the default separator is '\\n\\n'. Note that"
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+ "we use neural text spliter to split texts into chunks, the seperator only serves as a delimiter to force split long passage into"
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+ " chunks before passing to the neural network. Press ENTER directly to skip:"
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)
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separator = separator if separator != "" else "\n\n"
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retriever_data = DocumentLoader([[file, data_name.replace(" ", "_")]]).all_data
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# Split
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=100, chunk_overlap=20)
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splits = text_splitter.split_documents(retriever_data)
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documents.extend(splits)
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# Create retriever
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information_retriever.add_documents(docs=documents, cleanup="incremental", mode="by_source", embedding=embedding)
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# Set document retrieval chain, we need this chain to calculate prompt length
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memory.initiate_document_retrieval_chain(
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llm,
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PROMPT_RETRIEVAL_QA_EN,
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information_retriever,
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chain_type_kwargs={
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"chat_history": "",
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},
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)
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# Define retrieval chain
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retrieval_chain = RetrievalQA.from_chain_type(
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llm=llm,
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verbose=False,
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chain_type="stuff",
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retriever=information_retriever,
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chain_type_kwargs={"prompt": PROMPT_RETRIEVAL_QA_EN, "memory": memory},
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llm_kwargs={"max_new_tokens": 50, "temperature": 0.75, "do_sample": True},
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)
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# Set disambiguity handler
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information_retriever.set_rephrase_handler(disambiguity)
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# Start conversation
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while True:
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user_input = input("User: ")
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if "END" == user_input:
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print("Agent: Happy to chat with you ：)")
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break
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agent_response = retrieval_chain.run(
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query=user_input,
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stop=["Human: "],
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rejection_trigger_keywords=EN_RETRIEVAL_QA_TRIGGER_KEYWORDS,
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rejection_answer=EN_RETRIEVAL_QA_REJECTION_ANSWER,
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)
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agent_response = agent_response.split("\n")[0]
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print(f"Agent: {agent_response}")
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