update rag/src/data_processing.py & main,py
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zealot52099
parent
a119fdb507
commit
fdf05f480c
@ -1,114 +1,155 @@
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import json
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import pickle
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import faiss
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import pickle
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import os
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from loguru import logger
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from sentence_transformers import SentenceTransformer
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from langchain_community.vectorstores import FAISS
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from config.config import embedding_path, doc_dir, qa_dir, knowledge_pkl_path, data_dir, base_dir, vector_db_dir
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import os
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import faiss
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import platform
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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from langchain_community.document_loaders import DirectoryLoader, TextLoader, JSONLoader
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from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter
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from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter, RecursiveJsonSplitter
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from BCEmbedding import EmbeddingModel, RerankerModel
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from util.pipeline import EmoLLMRAG
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import pickle
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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import streamlit as st
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from openxlab.model import download
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from langchain.document_loaders.pdf import PyPDFDirectoryLoader
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from langchain.document_loaders import UnstructuredFileLoader,DirectoryLoader
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from langchain_community.llms import Cohere
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from langchain.retrievers import ContextualCompressionRetriever
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from langchain.retrievers.document_compressors import FlashrankRerank
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from langchain_core.documents.base import Document
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from FlagEmbedding import FlagReranker
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class Data_process():
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def __init__(self):
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self.vector_db_dir = vector_db_dir
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self.doc_dir = doc_dir
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self.qa_dir = qa_dir
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self.knowledge_pkl_path = knowledge_pkl_path
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self.chunk_size: int=1000
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self.chunk_overlap: int=100
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'''
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1）根据QA对/TXT 文本生成 embedding
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2）调用 langchain FAISS 接口构建 vector DB
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3）存储到 openxlab.dataset 中，方便后续调用
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4）提供 embedding 的接口函数，方便后续调用
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5）提供 rerank 的接口函数，方便后续调用
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'''
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def load_embedding_model(self, model_name="BAAI/bge-small-zh-v1.5", device='cpu', normalize_embeddings=True):
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"""
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加载嵌入模型。
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"""
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加载向量模型
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"""
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def load_embedding_model():
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参数:
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- model_name: 模型名称，字符串类型，默认为"BAAI/bge-small-zh-v1.5"。
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- device: 指定模型加载的设备，'cpu' 或 'cuda'，默认为'cpu'。
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- normalize_embeddings: 是否标准化嵌入向量，布尔类型，默认为 True。
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"""
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logger.info('Loading embedding model...')
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# model = EmbeddingModel(model_name_or_path="huggingface/bce-embedding-base_v1")
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model = EmbeddingModel(model_name_or_path="maidalun1020/bce-embedding-base_v1")
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try:
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embeddings = HuggingFaceBgeEmbeddings(
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model_name=model_name,
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model_kwargs={'device': device},
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encode_kwargs={'normalize_embeddings': normalize_embeddings}
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)
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except Exception as e:
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logger.error(f'Failed to load embedding model: {e}')
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return None
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logger.info('Embedding model loaded.')
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return model
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return embeddings
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def load_rerank_model():
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logger.info('Loading rerank_model...')
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model = RerankerModel(model_name_or_path="maidalun1020/bce-reranker-base_v1")
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# model = RerankerModel(model_name_or_path="huggingface/bce-reranker-base_v1")
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logger.info('Rerank model loaded.')
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return model
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def load_rerank_model(self, model_name='BAAI/bge-reranker-large'):
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"""
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加载重排名模型。
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参数:
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- model_name (str): 模型的名称。默认为 'BAAI/bge-reranker-large'。
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返回:
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- FlagReranker 实例。
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异常:
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- ValueError: 如果模型名称不在批准的模型列表中。
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- Exception: 如果模型加载过程中发生任何其他错误。
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"""
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try:
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reranker_model = FlagReranker(model_name, use_fp16=True)
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except Exception as e:
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logger.error(f'Failed to load rerank model: {e}')
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raise
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return reranker_model
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def extract_text_from_json(self, obj, content=None):
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"""
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抽取json中的文本，用于向量库构建
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参数:
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- obj: dict,list,str
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- content: str
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返回:
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- content: str
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"""
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if isinstance(obj, dict):
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for key, value in obj.items():
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try:
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self.extract_text_from_json(value, content)
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except Exception as e:
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print(f"Error processing value: {e}")
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elif isinstance(obj, list):
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for index, item in enumerate(obj):
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try:
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self.extract_text_from_json(item, content)
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except Exception as e:
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print(f"Error processing item: {e}")
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elif isinstance(obj, str):
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content += obj
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return content
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def split_document(self, data_path, chunk_size=500, chunk_overlap=100):
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"""
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切分data_path文件夹下的所有txt文件
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参数:
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- data_path: str
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- chunk_size: int
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- chunk_overlap: int
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返回：
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- split_docs: list
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"""
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def split_document(data_path, chunk_size=1000, chunk_overlap=100):
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# text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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text_spliter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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split_docs = []
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logger.info(f'Loading txt files from {data_path}')
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if os.path.isdir(data_path):
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# 如果是文件夹，则遍历读取
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for root, dirs, files in os.walk(data_path):
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for file in files:
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if file.endswith('.txt'):
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file_path = os.path.join(root, file)
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# logger.info(f'splitting file {file_path}')
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text_loader = TextLoader(file_path, encoding='utf-8')
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text = text_loader.load()
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splits = text_spliter.split_documents(text)
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# logger.info(f"splits type {type(splits[0])}")
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# logger.info(f'splits size {len(splits)}')
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split_docs += splits
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loader = DirectoryLoader(data_path, glob="**/*.txt",show_progress=True)
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docs = loader.load()
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split_docs = text_spliter.split_documents(docs)
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elif data_path.endswith('.txt'):
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file_path = os.path.join(root, data_path)
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# logger.info(f'splitting file {file_path}')
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file_path = data_path
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logger.info(f'splitting file {file_path}')
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text_loader = TextLoader(file_path, encoding='utf-8')
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text = text_loader.load()
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splits = text_spliter.split_documents(text)
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# logger.info(f"splits type {type(splits[0])}")
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# logger.info(f'splits size {len(splits)}')
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split_docs = splits
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logger.info(f'split_docs size {len(split_docs)}')
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return split_docs
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##TODO 1、读取system prompt 2、限制序列长度
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def split_conversation(path):
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'''
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data format:
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[
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{
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"conversation": [
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{
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"input": Q1
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"output": A1
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},
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{
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"input": Q2
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"output": A2
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},
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]
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},
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]
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'''
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qa_pairs = []
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def split_conversation(self, path):
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"""
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按conversation块切分path文件夹下的所有json文件
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##TODO 限制序列长度
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"""
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# json_spliter = RecursiveJsonSplitter(max_chunk_size=500)
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logger.info(f'Loading json files from {path}')
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if os.path.isfile(path):
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with open(path, 'r', encoding='utf-8') as file:
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data = json.load(file)
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for conversation in data:
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for dialog in conversation['conversation']:
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# input_text = dialog['input']
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# output_text = dialog['output']
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# if len(input_text) > max_length or len(output_text) > max_length:
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# continue
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qa_pairs.append(dialog)
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elif os.path.isdir(path):
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# 如果是文件夹，则遍历读取
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split_qa = []
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if os.path.isdir(path):
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# loader = DirectoryLoader(path, glob="**/*.json",show_progress=True)
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# jsons = loader.load()
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for root, dirs, files in os.walk(path):
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for file in files:
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if file.endswith('.json'):
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@ -116,147 +157,114 @@ def split_conversation(path):
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logger.info(f'splitting file {file_path}')
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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print(data)
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for conversation in data:
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for dialog in conversation['conversation']:
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qa_pairs.append(dialog)
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return qa_pairs
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# for dialog in conversation['conversation']:
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##按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
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# content = self.extract_text_from_json(dialog,'')
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# split_qa.append(Document(page_content = content))
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#按conversation块切分
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content = self.extract_text_from_json(conversation['conversation'], '')
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split_qa.append(Document(page_content = content))
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# logger.info(f'split_qa size====={len(split_qa)}')
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return split_qa
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# 加载本地索引
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def load_index_and_knowledge():
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current_os = platform.system()
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split_doc = []
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split_qa = []
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#读取知识库
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def load_knowledge(self, knowledge_pkl_path):
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'''
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读取或创建知识.pkl
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'''
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if not os.path.exists(knowledge_pkl_path):
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split_doc = split_document(doc_dir)
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split_qa = split_conversation(qa_dir)
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# logger.info(f'split_qa size:{len(split_qa)}')
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# logger.info(f'type of split_qa:{type(split_qa[0])}')
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# logger.info(f'split_doc size:{len(split_doc)}')
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# logger.info(f'type of doc:{type(split_doc[0])}')
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split_doc = self.split_document(doc_dir)
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split_qa = self.split_conversation(qa_dir)
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knowledge_chunks = split_doc + split_qa
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with open(knowledge_pkl_path, 'wb') as file:
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pickle.dump(knowledge_chunks, file)
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else:
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with open(knowledge_pkl_path , 'rb') as f:
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knowledge_chunks = pickle.load(f)
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return knowledge_chunks
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#读取vector DB
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if not os.path.exists(vector_db_dir):
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def create_vector_db(self, emb_model):
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'''
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创建并保存向量库
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'''
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logger.info(f'Creating index...')
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emb_model = load_embedding_model()
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if not split_doc:
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split_doc = split_document(doc_dir)
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if not split_qa:
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split_qa = split_conversation(qa_dir)
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# 创建索引,windows不支持faiss-gpu
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if current_os == 'Linux':
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index = create_index_gpu(split_doc, split_qa, emb_model, vector_db_dir)
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split_doc = self.split_document(self.doc_dir)
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split_qa = self.split_conversation(self.qa_dir)
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# logger.info(f'split_doc == {len(split_doc)}')
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# logger.info(f'split_qa == {len(split_qa)}')
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# logger.info(f'split_doc type == {type(split_doc[0])}')
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# logger.info(f'split_qa type== {type(split_qa[0])}')
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db = FAISS.from_documents(split_doc + split_qa, emb_model)
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db.save_local(vector_db_dir)
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return db
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def load_vector_db(self, knowledge_pkl_path=knowledge_pkl_path, doc_dir=doc_dir, qa_dir=qa_dir):
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'''
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读取向量库
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'''
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# current_os = platform.system()
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emb_model = self.load_embedding_model()
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if not os.path.exists(vector_db_dir) or not os.listdir(vector_db_dir):
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db = self.create_vector_db(emb_model)
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else:
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index = create_index_cpu(split_doc, split_qa, emb_model, vector_db_dir)
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else:
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if current_os == 'Linux':
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res = faiss.StandardGpuResources()
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index = faiss.index_cpu_to_gpu(res, 0, index, vector_db_dir)
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else:
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index = faiss.read_index(vector_db_dir)
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return index, knowledge_chunks
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db = FAISS.load_local(vector_db_dir, emb_model, allow_dangerous_deserialization=True)
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return db
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def create_index_cpu(split_doc, split_qa, emb_model, knowledge_pkl_path, dimension = 768, question_only=False):
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# 假设BCE嵌入的维度是768，根据你选择的模型可能不同
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faiss_index_cpu = faiss.IndexFlatIP(dimension) # 创建一个使用内积的FAISS索引
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# 将问答对转换为向量并添加到FAISS索引中
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for doc in split_doc:
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# type_of_docs = type(split_doc)
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text = f"{doc.page_content}"
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vector = emb_model.encode([text])
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faiss_index_cpu.add(vector)
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for qa in split_qa:
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#仅对Q对进行编码
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text = f"{qa['input']}"
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vector = emb_model.encode([text])
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faiss_index_cpu.add(vector)
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faiss.write_index(faiss_index_cpu, knowledge_pkl_path)
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return faiss_index_cpu
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def retrieve(self, query, vector_db, k=5):
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'''
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基于query对向量库进行检索
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'''
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retriever = vector_db.as_retriever(search_kwargs={"k": k})
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docs = retriever.invoke(query)
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return docs, retriever
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def create_index_gpu(split_doc, split_qa, emb_model, knowledge_pkl_path, dimension = 768, question_only=False):
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res = faiss.StandardGpuResources()
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index = faiss.IndexFlatIP(dimension)
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faiss_index_gpu = faiss.index_cpu_to_gpu(res, 0, index)
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for doc in split_doc:
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# type_of_docs = type(split_doc)
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text = f"{doc.page_content}"
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vector = emb_model.encode([text])
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faiss_index_gpu.add(vector)
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for qa in split_qa:
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#仅对Q对进行编码
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text = f"{qa['input']}"
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vector = emb_model.encode([text])
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faiss_index_gpu.add(vector)
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faiss.write_index(faiss_index_gpu, knowledge_pkl_path)
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return faiss_index_gpu
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##FlashrankRerank效果一般
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# def rerank(self, query, retriever):
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# compressor = FlashrankRerank()
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# compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
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# compressed_docs = compression_retriever.get_relevant_documents(query)
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# return compressed_docs
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# 根据query搜索相似文本
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def find_top_k(query, faiss_index, k=5):
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emb_model = load_embedding_model()
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emb_query = emb_model.encode([query])
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distances, indices = faiss_index.search(emb_query, k)
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return distances, indices
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def rerank(query, indices, knowledge_chunks):
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def rerank(self, query, docs):
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reranker = self.load_rerank_model()
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passages = []
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for index in indices[0]:
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content = knowledge_chunks[index]
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'''
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txt: 'langchain_core.documents.base.Document'
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json: dict
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'''
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# logger.info(f'retrieved content:{content}')
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# logger.info(f'type of content:{type(content)}')
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if type(content) == dict:
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content = content["input"] + '\n' + content["output"]
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else:
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content = content.page_content
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passages.append(content)
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for doc in docs:
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passages.append(str(doc.page_content))
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scores = reranker.compute_score([[query, passage] for passage in passages])
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sorted_pairs = sorted(zip(passages, scores), key=lambda x: x[1], reverse=True)
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sorted_passages, sorted_scores = zip(*sorted_pairs)
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return sorted_passages, sorted_scores
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model = load_rerank_model()
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rerank_results = model.rerank(query, passages)
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return rerank_results
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@st.cache_resource
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def load_model():
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model = (
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AutoModelForCausalLM.from_pretrained("model", trust_remote_code=True)
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.to(torch.bfloat16)
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.cuda()
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)
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tokenizer = AutoTokenizer.from_pretrained("model", trust_remote_code=True)
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return model, tokenizer
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if __name__ == "__main__":
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logger.info(data_dir)
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if not os.path.exists(data_dir):
|
||||
os.mkdir(data_dir)
|
||||
faiss_index, knowledge_chunks = load_index_and_knowledge()
|
||||
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 = "她要阻挠姐姐的婚姻，即使她自己的尸体在房门跟前"
|
||||
# query = "肯定的。我最近睡眠很差，总是做噩梦。而且我吃得也不好，体重一直在下降"
|
||||
# query = "序言 （一） 变态心理学是心理学本科生的必修课程之一，教材更新的问题一直在困扰着我们。"
|
||||
query = "心理咨询师，我觉得我的胸闷症状越来越严重了，这让我很害怕"
|
||||
distances, indices = find_top_k(query, faiss_index, 5)
|
||||
logger.info(f'distances==={distances}')
|
||||
logger.info(f'indices==={indices}')
|
||||
|
||||
|
||||
# rerank无法返回id，先实现按整个问答对排序
|
||||
rerank_results = rerank(query, indices, knowledge_chunks)
|
||||
for passage, score in zip(rerank_results['rerank_passages'], rerank_results['rerank_scores']):
|
||||
print(str(score)+'\n')
|
||||
print(passage+'\n')
|
||||
|
||||
# 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"
|
||||
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])
|
||||
@ -13,9 +13,8 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
import torch
|
||||
import streamlit as st
|
||||
from openxlab.model import download
|
||||
from data_processing import load_index_and_knowledge, create_index_cpu, create_index_gpu, find_top_k, rerank
|
||||
from config.config import embedding_path, doc_dir, qa_dir, knowledge_pkl_path, data_dir
|
||||
|
||||
from data_processing import Data_process
|
||||
'''
|
||||
1）构建完整的 RAG pipeline。输入为用户 query，输出为 answer
|
||||
2）调用 embedding 提供的接口对 query 向量化
|
||||
@ -42,30 +41,23 @@ def load_model():
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||
return model, tokenizer
|
||||
|
||||
def get_prompt():
|
||||
pass
|
||||
|
||||
def get_prompt_template():
|
||||
pass
|
||||
|
||||
def main(query, system_prompt):
|
||||
model, tokenizer = load_model()
|
||||
model = model.eval()
|
||||
def main(query, system_prompt=''):
|
||||
logger.info(data_dir)
|
||||
if not os.path.exists(data_dir):
|
||||
os.mkdir(data_dir)
|
||||
# 下载基于 FAISS 预构建的 vector DB 以及原始知识库
|
||||
faiss_index, knowledge_chunks = load_index_and_knowledge()
|
||||
distances, indices = find_top_k(query, faiss_index, 5)
|
||||
rerank_results = rerank(query, indices, knowledge_chunks)
|
||||
messages = [(system_prompt, rerank_results['rerank_passages'][0])]
|
||||
logger.info(f'messages:{messages}')
|
||||
response, history = model.chat(tokenizer, query, history=messages)
|
||||
messages.append((query, response))
|
||||
print(f"robot >>> {response}")
|
||||
dp = Data_process()
|
||||
vector_db = dp.load_vector_db()
|
||||
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(doc)
|
||||
passages,scores = dp.rerank(query, docs)
|
||||
logger.info("After reranking===============================")
|
||||
for i in range(len(scores)):
|
||||
logger.info(passages[i])
|
||||
logger.info(f'score: {str(scores[i])}')
|
||||
|
||||
if __name__ == '__main__':
|
||||
# query = '你好'
|
||||
query = "心理咨询师，我觉得我的胸闷症状越来越严重了，这让我很害怕"
|
||||
#TODO system_prompt = get_prompt()
|
||||
system_prompt = "你是一个由aJupyter、Farewell、jujimeizuo、Smiling&Weeping研发（排名按字母顺序排序，不分先后）、散步提供技术支持、上海人工智能实验室提供支持开发的心理健康大模型。现在你是一个心理专家，我有一些心理问题，请你用专业的知识帮我解决。"
|
||||
main(query, system_prompt)
|
||||
if __name__ == "__main__":
|
||||
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"
|
||||
main(query)
|
||||
Loading…
Reference in New Issue
Block a user