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from langchain_community.document_loaders import TextLoader
from langchain_community.docstore.document import Document
from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.retrievers import BM25Retriever
from langchain_community.llms import OpenAI
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.schema import AIMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import os
def split_with_source(text, source):
splitter = CharacterTextSplitter(
separator = "\n",
chunk_size = 512,
chunk_overlap = 128,
add_start_index = True,
)
documents = splitter.create_documents([text])
# print(documents)
for doc in documents:
doc.metadata["source"] = source
# print(doc.metadata)
return documents
def count_files_in_folder(folder_path):
# Kiểm tra xem đường dẫn thư mục có tồn tại không
if not os.path.isdir(folder_path):
print("Đường dẫn không hợp lệ.")
return None
# Sử dụng os.listdir() để lấy danh sách các tập tin và thư mục trong thư mục
files = os.listdir(folder_path)
# Đếm số lượng tập tin trong danh sách
file_count = len(files)
return file_count
def get_document_from_raw_text():
documents = [Document(page_content="", metadata={'source': 0})]
files = os.listdir(os.path.join(os.getcwd(), "raw_data"))
# print(files)
for i in files:
file_path = i
with open(os.path.join(os.path.join(os.getcwd(), "raw_data"),file_path), 'r', encoding="utf-8") as file:
# Xử lý bằng text_spliter
# Tiền xử lý văn bản
content = file.read().replace('\n\n', "\n")
# content = ''.join(content.split('.'))
new_doc = content
texts = split_with_source(new_doc, i)
documents = documents + texts
##Xử lý mỗi khi xuống dòng
# for line in file:
# # Loại bỏ khoảng trắng thừa và ký tự xuống dòng ở đầu và cuối mỗi dòng
# line = line.strip()
# documents.append(Document(page_content=line, metadata={"source": i}))
# print(documents)
return documents
def load_the_embedding_retrieve(is_ready = False, k = 3, model= 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'):
embeddings = HuggingFaceEmbeddings(model_name=model)
if is_ready:
retriever = Chroma(persist_directory=os.path.join(os.getcwd(), "Data"), embedding_function=embeddings).as_retriever(
search_kwargs={"k": k}
)
else:
documents = get_document_from_raw_text()
print(type(documents))
retriever = Chroma.from_documents(documents, embeddings).as_retriever(
search_kwargs={"k": k}
)
return retriever
def load_the_bm25_retrieve(k = 3):
documents = get_document_from_raw_text()
bm25_retriever = BM25Retriever.from_documents(documents)
bm25_retriever.k = k
return bm25_retriever
def get_qachain(llm_name = "gpt-3.5-turbo-0125", chain_type = "stuff", retriever = None, return_source_documents = True):
llm = ChatOpenAI(temperature=0,
model_name=llm_name)
return RetrievalQA.from_chain_type(llm=llm,
chain_type=chain_type,
retriever=retriever,
return_source_documents=return_source_documents)
def summarize_messages(demo_ephemeral_chat_history, llm):
stored_messages = demo_ephemeral_chat_history.messages
if len(stored_messages) == 0:
return False
summarization_prompt = ChatPromptTemplate.from_messages(
[
MessagesPlaceholder(variable_name="chat_history"),
(
"user", os.environ['SUMARY_MESSAGE_PROMPT'],
),
]
)
summarization_chain = summarization_prompt | llm
summary_message = summarization_chain.invoke({"chat_history": stored_messages})
demo_ephemeral_chat_history.clear()
demo_ephemeral_chat_history.add_message(summary_message)
return demo_ephemeral_chat_history
def get_question_from_summarize(summary, question, llm):
new_qa_prompt = ChatPromptTemplate.from_messages([
("system", os.environ['NEW_QUESTION_PROMPT']),
("human",
'''
Sumary: {summary}
Question: {question}
Output:
'''
)
]
)
new_qa_chain = new_qa_prompt | llm
return new_qa_chain.invoke({'summary': summary, 'question': question}).content
def get_final_answer(question, context, prompt, llm):
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", prompt),
("human", '''
Context: {context}
Question: {question}
Output:
'''),
]
)
answer_chain = qa_prompt | llm
answer = answer_chain.invoke({'question': question, 'context': context})
return answer.content
def process_llm_response(llm_response):
print(llm_response['result'])
print('\n\nSources:')
for source in llm_response["source_documents"]:
print(source.metadata['source'])
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