rag.py

from langchain.embeddings import HuggingFaceEmbeddings
from langchain.prompts import PromptTemplate
from utility import load_data, process_data, CustomRetriever, CustomRetriever1


data1 = load_data('raw_data/sv')
data2 = load_data('raw_data/thacsi')
data3 = load_data('raw_data/tiensi')
data = data1 + data2 + data3

# Embedding model
embedding = HuggingFaceEmbeddings(
    model_name="VoVanPhuc/sup-SimCSE-VietNamese-phobert-base",
    model_kwargs={"device": "cpu"}
)

# The splitter to use to create smaller chunks
from langchain_text_splitters import RecursiveCharacterTextSplitter

child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)

#####################################################################

vectorstore1, retriever1 = process_data(data, child_text_splitter, embedding, "data")
vectorstore2, retriever2 = process_data(data2, child_text_splitter, embedding, "data2")
vectorstore3, retriever3 = process_data(data3, child_text_splitter, embedding, "data3")

##############################################################################

ANYSCALE_API_BASE = "credential-1711634141163"
ANYSCALE_API_KEY = "esecret_chitz7splr5ut6vfvqpn72itd3"
ANYSCALE_MODEL_NAME = "meta-llama/Meta-Llama-3-8B-Instruct"
# ANYSCALE_MODEL_NAME = "meta-llama/Llama-3-8b-chat-hf"
# ANYSCALE_MODEL_NAME = "google/gemma-7b-it"
# ANYSCALE_MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.1"
# ANYSCALE_MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"

import os

os.environ["ANYSCALE_API_BASE"] = ANYSCALE_API_BASE
os.environ["ANYSCALE_API_KEY"] = ANYSCALE_API_KEY

from langchain.chains import LLMChain
from langchain_community.llms import Anyscale
from langchain_core.prompts import PromptTemplate
from langchain_community.chat_models import ChatAnyscale

# llm = Anyscale(model_name=ANYSCALE_MODEL_NAME)
llm= ChatAnyscale(model_name=ANYSCALE_MODEL_NAME, temperature=0)

#####################################################################

from langchain_openai.llms.azure import AzureOpenAI
llm_openai = AzureOpenAI(
    deployment_name="gpt-35-turbo-instruct",
    # deployment_name="gpt-35-turbo-16k",
    api_key = 'c90c0e7fb1894a898c56123580a6ee3e',
    api_version = "2023-09-15-preview",
    azure_endpoint = "https://bkchatbot.openai.azure.com/",
    temperature=0.0,
    max_tokens=500
)

##########################################################################

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)

# Build prompt
from langchain.prompts import PromptTemplate
template ="""
Trả lời câu hỏi dựa trên những quy định được cung cấp.
Không cần ghi chú và trích dẫn nguồn thông tin đã tham khảo trong câu trả lời.
Câu trả lời nên bắt đầu bằng: "Theo quy định của Trường ĐH Bách Khoa Tp.HCM, ..."
Nếu trong quy văn bản không có thông tin cho câu trả lời, vui lòng thông báo: "Xin lỗi, tôi không có thông tin cho câu hỏi này!"

Quy định: {context}

Câu hỏi: {question}

Câu trả lời:
"""
QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template)

#############################################################################

from langchain_core.runnables import RunnableParallel

rag_chain_from_docs = (
    RunnablePassthrough.assign(context=(lambda x: format_docs(x["context"])))
    | QA_CHAIN_PROMPT
    | llm
    | StrOutputParser()
)

###############################################################################

from langchain.prompts import ChatPromptTemplate

# Multi Query: Different Perspectives
template = """
### Hãy tạo ra thêm các truy vấn tìm kiếm tương đương ngữ nghĩa với một câu hỏi ban đầu.
Kết quả hiển thị dạng list gồm câu hỏi ban đầu và 2 câu hỏi thay thế.

### Câu hỏi ban đầu: {question}
### Kết quả:

"""
prompt_perspectives = ChatPromptTemplate.from_template(template)

from langchain_core.output_parsers import StrOutputParser
# from langchain_openai import ChatOpenAI

generate_queries = (
    prompt_perspectives
    | llm_openai
    | StrOutputParser()
    | (lambda x: x.split("\n"))
)

#########################################################################################

from langchain.retrievers import BM25Retriever, EnsembleRetriever

# initialize the bm25 retriever and chroma retriever
bm25_retriever1 = BM25Retriever.from_documents(data, k=25)
ensemble_retriever1 = EnsembleRetriever(retrievers=[bm25_retriever1, retriever1], weights=[0.5, 0.5])

bm25_retriever2 = BM25Retriever.from_documents(data2, k=25)
ensemble_retriever2 = EnsembleRetriever(retrievers=[bm25_retriever2, retriever2], weights=[0.5, 0.5])

bm25_retriever3 = BM25Retriever.from_documents(data3, k=25)
ensemble_retriever3 = EnsembleRetriever(retrievers=[bm25_retriever3, retriever3], weights=[0.5, 0.5])

#########################################################################################

custom_retriever1 = CustomRetriever1(retriever = ensemble_retriever1)
custom_retriever2 = CustomRetriever1(retriever = ensemble_retriever2)
custom_retriever3 = CustomRetriever1(retriever = ensemble_retriever3)

multiq_chain1 = generate_queries | custom_retriever1
multiq_chain2 = generate_queries | custom_retriever2
multiq_chain3 = generate_queries | custom_retriever3

rag_chain_with_source1 = RunnableParallel(
    {"context": multiq_chain1, "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)

rag_chain_with_source2 = RunnableParallel(
    {"context": multiq_chain2 , "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)

rag_chain_with_source3 = RunnableParallel(
    {"context": multiq_chain3, "question": RunnablePassthrough()}
).assign(answer=rag_chain_from_docs)

############################################################################################

from flashtext import KeywordProcessor
keyword_processor = KeywordProcessor()
# keyword_processor.add_keyword(<unclean name>, <standardised name>)
keyword_processor.add_keyword('thạc sĩ')
keyword_processor.add_keyword('học viên')
keyword_processor.add_keyword('nghiên cứu sinh')
keyword_processor.add_keyword('tiến sĩ')

################################################################################

rag_chain = [rag_chain_with_source1, rag_chain_with_source2, rag_chain_with_source3]

###################################################################################

def rag(question: str) -> str:

    keywords_found = keyword_processor.extract_keywords(question)
    if 'thạc sĩ' in keywords_found or 'học viên' in keywords_found:
      response = rag_chain[1].invoke(question)
    elif 'nghiên cứu sinh' in keywords_found or 'tiến sĩ' in keywords_found:
      response = rag_chain[2].invoke(question)
    else:
      response = rag_chain[0].invoke(question)
    
    return response['answer']

###################################################################################


# # Run chain
# from langchain.chains import RetrievalQA

# qa_chain = RetrievalQA.from_chain_type(llm,
#                                        verbose=False,
#                                        # retriever=vectordb.as_retriever(),
#                                        retriever=custom_retriever,
#                                        return_source_documents=True,
#                                        chain_type_kwargs={"prompt": QA_CHAIN_PROMPT})

# def remove_special_characters(text):
#     text = text.replace('].', '')
#     text = text.replace('/.', '')
#     text = text.replace('/.-', '')
#     text = text.replace('-', '')
#     return text

# def rag(question: str) -> str:
#     # call QA chain
#     response = qa_chain({"query": question})

#     return remove_special_characters(response["result"])