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data_processing.py
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import json
import pickle
import os
from loguru import logger
from langchain_community.vectorstores import FAISS
from rag.src.config.config import (
embedding_path,
embedding_model_name,
doc_dir, qa_dir,
knowledge_pkl_path,
data_dir,
vector_db_dir,
rerank_path,
rerank_model_name,
chunk_size,
chunk_overlap
)
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents.base import Document
from FlagEmbedding import FlagReranker
class Data_process():
def __init__(self):
self.chunk_size: int=chunk_size
self.chunk_overlap: int=chunk_overlap
def load_embedding_model(self, model_name=embedding_model_name, device='cpu', normalize_embeddings=True):
"""
加载嵌入模型。
参数:
- model_name: 模型名称，字符串类型，默认为"BAAI/bge-small-zh-v1.5"。
- device: 指定模型加载的设备，'cpu' 或 'cuda'，默认为'cpu'。
- normalize_embeddings: 是否标准化嵌入向量，布尔类型，默认为 True。
"""
if not os.path.exists(embedding_path):
os.makedirs(embedding_path, exist_ok=True)
embedding_model_path = os.path.join(embedding_path,model_name.split('/')[1] + '.pkl')
logger.info('Loading embedding model...')
if os.path.exists(embedding_model_path):
try:
with open(embedding_model_path , 'rb') as f:
embeddings = pickle.load(f)
logger.info('Embedding model loaded.')
return embeddings
except Exception as e:
logger.error(f'Failed to load embedding model from {embedding_model_path}')
try:
embeddings = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs={'device': device},
encode_kwargs={'normalize_embeddings': normalize_embeddings})
logger.info('Embedding model loaded.')
with open(embedding_model_path, 'wb') as file:
pickle.dump(embeddings, file)
except Exception as e:
logger.error(f'Failed to load embedding model: {e}')
return None
return embeddings
def load_rerank_model(self, model_name=rerank_model_name):
"""
加载重排名模型。
参数:
- model_name (str): 模型的名称。默认为 'BAAI/bge-reranker-large'。
返回:
- FlagReranker 实例。
异常:
- ValueError: 如果模型名称不在批准的模型列表中。
- Exception: 如果模型加载过程中发生任何其他错误。
"""
if not os.path.exists(rerank_path):
os.makedirs(rerank_path, exist_ok=True)
rerank_model_path = os.path.join(rerank_path, model_name.split('/')[1] + '.pkl')
logger.info('Loading rerank model...')
if os.path.exists(rerank_model_path):
try:
with open(rerank_model_path , 'rb') as f:
reranker_model = pickle.load(f)
logger.info('Rerank model loaded.')
return reranker_model
except Exception as e:
logger.error(f'Failed to load embedding model from {rerank_model_path}')
try:
reranker_model = FlagReranker(model_name, use_fp16=True)
logger.info('Rerank model loaded.')
with open(rerank_model_path, 'wb') as file:
pickle.dump(reranker_model, file)
except Exception as e:
logger.error(f'Failed to load rerank model: {e}')
raise
return reranker_model
def extract_text_from_json(self, obj, content=None):
"""
抽取json中的文本，用于向量库构建
参数:
- obj: dict,list,str
- content: str
返回:
- content: str
"""
if isinstance(obj, dict):
for key, value in obj.items():
try:
content = self.extract_text_from_json(value, content)
except Exception as e:
print(f"Error processing value: {e}")
elif isinstance(obj, list):
for index, item in enumerate(obj):
try:
content = self.extract_text_from_json(item, content)
except Exception as e:
print(f"Error processing item: {e}")
elif isinstance(obj, str):
content += obj
return content
def split_document(self, data_path):
"""
切分data_path文件夹下的所有txt文件
参数:
- data_path: str
- chunk_size: int
- chunk_overlap: int
返回：
- split_docs: list
"""
# text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
text_spliter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap)
split_docs = []
logger.info(f'Loading txt files from {data_path}')
if os.path.isdir(data_path):
loader = DirectoryLoader(data_path, glob="**/*.txt",show_progress=True)
docs = loader.load()
split_docs = text_spliter.split_documents(docs)
elif data_path.endswith('.txt'):
file_path = data_path
logger.info(f'splitting file {file_path}')
text_loader = TextLoader(file_path, encoding='utf-8')
text = text_loader.load()
splits = text_spliter.split_documents(text)
split_docs = splits
logger.info(f'split_docs size {len(split_docs)}')
return split_docs
def split_conversation(self, path):
"""
按conversation块切分path文件夹下的所有json文件
##TODO 限制序列长度
"""
# json_spliter = RecursiveJsonSplitter(max_chunk_size=500)
logger.info(f'Loading json files from {path}')
split_qa = []
if os.path.isdir(path):
# loader = DirectoryLoader(path, glob="**/*.json",show_progress=True)
# jsons = loader.load()
for root, dirs, files in os.walk(path):
for file in files:
if file.endswith('.json'):
file_path = os.path.join(root, file)
logger.info(f'splitting file {file_path}')
with open(file_path, 'r', encoding='utf-8') as f:
for line in f.readlines():
content = self.extract_text_from_json(line,'')
split_qa.append(Document(page_content = content))
#data = json.load(f)
#for conversation in data:
# #for dialog in conversation['conversation']:
# # #按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
# # content = self.extract_text_from_json(dialog,'')
# # split_qa.append(Document(page_content = content))
# #按conversation块切分
# content = self.extract_text_from_json(conversation['conversation'], '')
# #logger.info(f'content====={content}')
# split_qa.append(Document(page_content = content))
# logger.info(f'split_qa size====={len(split_qa)}')
return split_qa
def create_vector_db(self, emb_model):
'''
创建并保存向量库
'''
logger.info(f'Creating index...')
split_doc = self.split_document(doc_dir)
split_qa = self.split_conversation(qa_dir)
# logger.info(f'split_doc == {len(split_doc)}')
# logger.info(f'split_qa == {len(split_qa)}')
# logger.info(f'split_doc type == {type(split_doc[0])}')
# logger.info(f'split_qa type== {type(split_qa[0])}')
db = FAISS.from_documents(split_doc + split_qa, emb_model)
db.save_local(vector_db_dir)
return db
def load_vector_db(self, knowledge_pkl_path=knowledge_pkl_path, doc_dir=doc_dir, qa_dir=qa_dir):
'''
读取向量库
'''
# current_os = platform.system()
emb_model = self.load_embedding_model()
if not os.path.exists(vector_db_dir) or not os.listdir(vector_db_dir):
db = self.create_vector_db(emb_model)
else:
db = FAISS.load_local(vector_db_dir, emb_model)
return db
if __name__ == "__main__":
logger.info(data_dir)
if not os.path.exists(data_dir):
os.mkdir(data_dir)
dp = Data_process()
# faiss_index, knowledge_chunks = dp.load_index_and_knowledge(knowledge_pkl_path='')
vector_db = dp.load_vector_db()
# 按照query进行查询
# query = "儿童心理学说明-内容提要-目录 《儿童心理学》1993年修订版说明 《儿童心理学》是1961年初全国高等学校文科教材会议指定朱智贤教授编 写的。1962年初版，1979年再版。"
# query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想，我现在感到非常孤独、累和迷茫。您能给我提供一些建议吗？"
# query = "这在一定程度上限制了其思维能力，特别是辩证 逻辑思维能力的发展。随着年龄的增长，初中三年级学生逐步克服了依赖性"
# query = "我现在处于高三阶段，感到非常迷茫和害怕。我觉得自己从出生以来就是多余的，没有必要存在于这个世界。无论是在家庭、学校、朋友还是老师面前，我都感到被否定。我非常难过，对高考充满期望但成绩却不理想"
# query = "我现在心情非常差，有什么解决办法吗？"
query = "我最近总感觉胸口很闷，但医生检查过说身体没问题。可我就是觉得喘不过气来，尤其是看到那些旧照片，想起过去的日子"
docs, retriever = dp.retrieve(query, vector_db, k=10)
logger.info(f'Query: {query}')
logger.info("Retrieve results:")
for i, doc in enumerate(docs):
logger.info(str(i) + '\n')
logger.info(doc)
# print(f'get num of docs:{len(docs)}')
# print(docs)
passages,scores = dp.rerank(query, docs)
logger.info("After reranking...")
for i in range(len(scores)):
logger.info(str(scores[i]) + '\n')
logger.info(passages[i])