diff --git a/rag/README.md b/rag/README.md index 9c16408..d969c57 100644 --- a/rag/README.md +++ b/rag/README.md @@ -8,6 +8,86 @@ - 经典案例 - 客户背景知识 +## **环境准备** + +```python + +langchain==0.1.13 +langchain_community==0.0.29 +langchain_core==0.1.33 +langchain_openai==0.0.8 +langchain_text_splitters==0.0.1 +FlagEmbedding==1.2.8 +unstructured==0.12.6 +``` + +```python + +cd rag +pip3 install -r requirements.txt + +``` + +## **使用指南** + +### 准备数据 + +- txt数据：放入到 src.data.txt 目录下 +- json 数据：放入到 src.data.json 目录下 + +会根据准备的数据构建vector DB，最终会在 data 文件夹下产生名为 vector_db 的文件夹包含 index.faiss 和 index.pkl + +如果已经有 vector DB 则会直接加载对应数据库 + + +### 配置 config 文件 + +根据需要改写 config.config 文件： + +```python + +# 存放所有 model +model_dir = os.path.join(base_dir, 'model') + +# embedding model 路径以及 model name +embedding_path = os.path.join(model_dir, 'embedding_model') +embedding_model_name = 'BAAI/bge-small-zh-v1.5' + + +# rerank model 路径以及 model name +rerank_path = os.path.join(model_dir, 'rerank_model') +rerank_model_name = 'BAAI/bge-reranker-large' + + +# select num: 代表rerank 之后选取多少个 documents 进入 LLM +select_num = 3 + +# retrieval num： 代表从 vector db 中检索多少 documents。（retrieval num 应该大于等于 select num） +retrieval_num = 10 + +# 智谱 LLM 的 API key。目前 demo 仅支持智谱 AI api 作为最后生成 +glm_key = '' + +# Prompt template: 定义 +prompt_template = """ + 你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇，我有一些心理问题，请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决，回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n + + 根据下面检索回来的信息，回答问题。 + + {content} + + 问题：{query} +""" +``` + +### 调用 + +```python +cd rag/src +python main.py +``` + + ## **数据集** - 经过清洗的QA对: 每一个QA对作为一个样本进行 embedding @@ -65,12 +145,3 @@ RAG的经典评估框架，通过以下三个方面进行评估: - 增加多路检索以增加召回率。即根据用户输入生成多个类似的query进行检索 - - - - - - - - - diff --git a/rag/requirements.txt b/rag/requirements.txt index 15f915c..0dd7fe6 100644 --- a/rag/requirements.txt +++ b/rag/requirements.txt @@ -2,5 +2,11 @@ sentence_transformers transformers numpy loguru -langchain torch +langchain==0.1.13 +langchain_community==0.0.29 +langchain_core==0.1.33 +langchain_openai==0.0.8 +langchain_text_splitters==0.0.1 +FlagEmbedding==1.2.8 +unstructured==0.12.6 \ No newline at end of file diff --git a/rag/src/config/config.py b/rag/src/config/config.py index 366cf85..673c5b5 100644 --- a/rag/src/config/config.py +++ b/rag/src/config/config.py @@ -8,7 +8,10 @@ model_repo = 'ajupyter/EmoLLM_aiwei' # model model_dir = os.path.join(base_dir, 'model') # model embedding_path = os.path.join(model_dir, 'embedding_model') # embedding -rerank_path = os.path.join(model_dir, 'rerank_model') # embedding +embedding_model_name = 'BAAI/bge-small-zh-v1.5' +rerank_path = os.path.join(model_dir, 'rerank_model') # embedding +rerank_model_name = 'BAAI/bge-reranker-large' + llm_path = os.path.join(model_dir, 'pythia-14m') # llm # data @@ -23,15 +26,21 @@ log_dir = os.path.join(base_dir, 'log') # log log_path = os.path.join(log_dir, 'log.log') # file # vector DB -vector_db_dir = os.path.join(data_dir, 'vector_db.pkl') +vector_db_dir = os.path.join(data_dir, 'vector_db') +# RAG related +# select num: 代表rerank 之后选取多少个 documents 进入 LLM +# retrieval num： 代表从 vector db 中检索多少 documents。（retrieval num 应该大于等于 select num） select_num = 3 retrieval_num = 10 -system_prompt = """ - 你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇，我有一些心理问题，请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决，回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n -""" + +# LLM key +glm_key = '' + +# prompt prompt_template = """ - {system_prompt} + 你是一个拥有丰富心理学知识的温柔邻家温柔大姐姐艾薇，我有一些心理问题，请你用专业的知识和温柔、可爱、俏皮、的口吻帮我解决，回复中可以穿插一些可爱的Emoji表情符号或者文本符号。\n + 根据下面检索回来的信息，回答问题。 {content} 问题：{query} diff --git a/rag/src/data_processing.py b/rag/src/data_processing.py index edf1a37..0b94e3d 100644 --- a/rag/src/data_processing.py +++ b/rag/src/data_processing.py @@ -1,33 +1,24 @@ import json import pickle -import faiss -import pickle import os from loguru import logger -from sentence_transformers import SentenceTransformer from langchain_community.vectorstores import FAISS -from config.config import embedding_path, doc_dir, qa_dir, knowledge_pkl_path, data_dir, vector_db_dir, rerank_path +from 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 from langchain.embeddings import HuggingFaceBgeEmbeddings -from langchain_community.document_loaders import DirectoryLoader, TextLoader, JSONLoader -from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter, RecursiveJsonSplitter -from BCEmbedding import EmbeddingModel, RerankerModel -# from util.pipeline import EmoLLMRAG -from transformers import AutoTokenizer, AutoModelForCausalLM -from langchain.document_loaders.pdf import PyPDFDirectoryLoader -from langchain.document_loaders import UnstructuredFileLoader,DirectoryLoader -from langchain_community.llms import Cohere -from langchain.retrievers import ContextualCompressionRetriever -from langchain.retrievers.document_compressors import FlashrankRerank +from langchain_community.document_loaders import DirectoryLoader, TextLoader +from langchain_text_splitters import RecursiveCharacterTextSplitter +from langchain.document_loaders import DirectoryLoader from langchain_core.documents.base import Document from FlagEmbedding import FlagReranker class Data_process(): + def __init__(self): self.chunk_size: int=1000 self.chunk_overlap: int=100 - def load_embedding_model(self, model_name='BAAI/bge-small-zh-v1.5', device='cpu', normalize_embeddings=True): + def load_embedding_model(self, model_name=embedding_model_name, device='cpu', normalize_embeddings=True): """ 加载嵌入模型。 @@ -61,7 +52,8 @@ class Data_process(): return None return embeddings - def load_rerank_model(self, model_name='BAAI/bge-reranker-large'): + def load_rerank_model(self, model_name=rerank_model_name): + """ 加载重排名模型。 @@ -99,7 +91,6 @@ class Data_process(): 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 @@ -128,7 +119,8 @@ class Data_process(): return content - def split_document(self, data_path, chunk_size=500, chunk_overlap=100): + def split_document(self, data_path): + """ 切分data_path文件夹下的所有txt文件 @@ -143,7 +135,7 @@ class Data_process(): # text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) - text_spliter = RecursiveCharacterTextSplitter(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): @@ -188,7 +180,7 @@ class Data_process(): # 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}') + #logger.info(f'content====={content}') split_qa.append(Document(page_content = content)) # logger.info(f'split_qa size====={len(split_qa)}') return split_qa diff --git a/rag/src/main.py b/rag/src/main.py index abd6056..f324f50 100644 --- a/rag/src/main.py +++ b/rag/src/main.py @@ -1,17 +1,6 @@ -import os -import time -import jwt - -from config.config import base_dir, data_dir -from data_processing import Data_process from pipeline import EmoLLMRAG - -from langchain_openai import ChatOpenAI +from util.llm import get_glm from loguru import logger -from transformers import AutoTokenizer, AutoModelForCausalLM -import torch -import streamlit as st -from openxlab.model import download ''' 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 @@ -21,69 +10,34 @@ from openxlab.model import download 6）拼接 prompt 并调用模型返回结果 ''' -def get_glm(temprature): - llm = ChatOpenAI( - model_name="glm-4", - openai_api_base="https://open.bigmodel.cn/api/paas/v4", - openai_api_key=generate_token("api-key"), - streaming=False, - temperature=temprature - ) - return llm - -def generate_token(apikey: str, exp_seconds: int=100): - try: - id, secret = apikey.split(".") - except Exception as e: - raise Exception("invalid apikey", e) - - payload = { - "api_key": id, - "exp": int(round(time.time() * 1000)) + exp_seconds * 1000, - "timestamp": int(round(time.time() * 1000)), - } - - return jwt.encode( - payload, - secret, - algorithm="HS256", - headers={"alg": "HS256", "sign_type": "SIGN"}, - ) - -@st.cache_resource -def load_model(): - model_dir = os.path.join(base_dir,'../model') - logger.info(f'Loading model from {model_dir}') - model = ( - AutoModelForCausalLM.from_pretrained('model', trust_remote_code=True) - .to(torch.bfloat16) - .cuda() - ) - tokenizer = AutoTokenizer.from_pretrained('model', trust_remote_code=True) - return model, tokenizer - -def main(query, system_prompt=''): - logger.info(data_dir) - if not os.path.exists(data_dir): - os.mkdir(data_dir) - 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 = "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) - #model = get_glm(0.7) - #rag_obj = EmoLLMRAG(model, 3) - #res = rag_obj.main(query) - #logger.info(res) + 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 + """ + + """ + 输入: + model_name='glm-4', + api_base="https://open.bigmodel.cn/api/paas/v4", + temprature=0.7, + streaming=False, + 输出： + LLM Model + """ + model = get_glm() + + """ + 输入: + LLM model + retrieval_num=3 + rerank_flag=False + select_num-3 + """ + rag_obj = EmoLLMRAG(model) + + res = rag_obj.main(query) + + logger.info(res) diff --git a/rag/src/pipeline.py b/rag/src/pipeline.py index b81b26c..8f59f55 100644 --- a/rag/src/pipeline.py +++ b/rag/src/pipeline.py @@ -3,7 +3,7 @@ from langchain_core.prompts import PromptTemplate from transformers.utils import logging from data_processing import Data_process -from config.config import system_prompt, prompt_template +from config.config import prompt_template logger = logging.get_logger(__name__) @@ -16,7 +16,7 @@ class EmoLLMRAG(object): 4. 将 query 和检索回来的 content 传入 LLM 中 """ - def __init__(self, model, retrieval_num, rerank_flag=False, select_num=3) -> None: + def __init__(self, model, retrieval_num=3, rerank_flag=False, select_num=3) -> None: """ 输入 Model 进行初始化 @@ -29,7 +29,6 @@ class EmoLLMRAG(object): self.model = model self.data_processing_obj = Data_process() self.vectorstores = self._load_vector_db() - self.system_prompt = system_prompt self.prompt_template = prompt_template self.retrieval_num = retrieval_num self.rerank_flag = rerank_flag @@ -75,7 +74,7 @@ class EmoLLMRAG(object): # 第一版不涉及 history 信息，因此将 system prompt 直接纳入到 template 之中 prompt = PromptTemplate( template=self.prompt_template, - input_variables=["query", "content", "system_prompt"], + input_variables=["query", "content"], ) # 定义 chain @@ -87,7 +86,6 @@ class EmoLLMRAG(object): { "query": query, "content": content, - "system_prompt": self.system_prompt } ) return generation diff --git a/rag/src/util/llm.py b/rag/src/util/llm.py index e69de29..b254722 100644 --- a/rag/src/util/llm.py +++ b/rag/src/util/llm.py @@ -0,0 +1,44 @@ +import time +import jwt +from langchain_openai import ChatOpenAI +from config.config import glm_key + + +def get_glm( + model_name='glm-4', + api_base="https://open.bigmodel.cn/api/paas/v4", + temprature=0.7, + streaming=False, + ): + """ + + """ + + llm = ChatOpenAI( + model_name=model_name, + openai_api_base=api_base, + openai_api_key=generate_token(glm_key), + streaming=streaming, + temperature=temprature + ) + + return llm + +def generate_token(apikey: str, exp_seconds: int=100): + try: + id, secret = apikey.split(".") + except Exception as e: + raise Exception("invalid apikey", e) + + payload = { + "api_key": id, + "exp": int(round(time.time() * 1000)) + exp_seconds * 1000, + "timestamp": int(round(time.time() * 1000)), + } + + return jwt.encode( + payload, + secret, + algorithm="HS256", + headers={"alg": "HS256", "sign_type": "SIGN"}, + )