Add RAG into internlm2

rag/README.md

@@ -56,6 +56,12 @@ JSON 数据格式如下
 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
+**注意**: 可以直接从 xlab 下载对应 DB（请在rag文件目录下执行对应 code）
+```python
+# https://openxlab.org.cn/models/detail/Anooyman/EmoLLMRAGTXT/tree/main
+git lfs install
+git clone https://code.openxlab.org.cn/Anooyman/EmoLLMRAGTXT.git
+```
 
 
 ### 配置 config 文件

rag/src/config/config.py

@@ -20,6 +20,7 @@ knowledge_json_path = os.path.join(data_dir, 'knowledge.json')      # json
 knowledge_pkl_path = os.path.join(data_dir, 'knowledge.pkl')        # pkl
 doc_dir = os.path.join(data_dir, 'txt')   
 qa_dir = os.path.join(data_dir, 'json')   
+cloud_vector_db_dir = os.path.join(base_dir, 'EmoLLMRAGTXT')
 
 # log
 log_dir = os.path.join(base_dir, 'log')                             # log
@@ -30,13 +31,13 @@ chunk_size=1000
 chunk_overlap=100
 
 # vector DB
-vector_db_dir = os.path.join(data_dir, 'vector_db')
+vector_db_dir = os.path.join(cloud_vector_db_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
+retrieval_num = 3
 
 # LLM key
 glm_key = ''

rag/src/data_processing.py

@@ -4,7 +4,7 @@ import os
 
 from loguru import logger
 from langchain_community.vectorstores import FAISS
-from config.config import (
+from rag.src.config.config import (
     embedding_path,
     embedding_model_name,
     doc_dir, qa_dir,
@@ -19,7 +19,6 @@ from config.config import (
 from langchain.embeddings import HuggingFaceBgeEmbeddings
 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
 
@@ -199,7 +198,7 @@ class Data_process():
         创建并保存向量库
         '''
         logger.info(f'Creating index...')
-        #split_doc = self.split_document(doc_dir)
+        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)}')
@@ -218,7 +217,7 @@ class Data_process():
         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, allow_dangerous_deserialization=True)
+            db = FAISS.load_local(vector_db_dir, emb_model)
         return db
     
 if __name__ == "__main__":

rag/src/pipeline.py

@@ -2,8 +2,8 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import PromptTemplate
 from transformers.utils import logging
 
-from data_processing import Data_process
+from rag.src.data_processing import Data_process
-from config.config import prompt_template 
+from rag.src.config.config import prompt_template 
 logger = logging.get_logger(__name__)
 
 
@@ -48,19 +48,19 @@ class EmoLLMRAG(object):
             ouput: 检索后并且 rerank 的内容        
         """
     
-        content = ''
+        content = []
         documents = self.vectorstores.similarity_search(query, k=self.retrieval_num)
 
         for doc in documents:
-            content += doc.page_content
+            content.append(doc.page_content)
 
         # 如果需要rerank，调用接口对 documents 进行 rerank
         if self.rerank_flag:
             documents, _ = self.data_processing_obj.rerank(documents, self.select_num)
 
-            content = ''
+            content = []
             for doc in documents:
-                content += doc
+                content.append(doc)
         logger.info(f'Retrieval data: {content}')
         return content
     

web_internlm2.py

@@ -12,6 +12,7 @@ import copy
 import os
 import warnings
 from dataclasses import asdict, dataclass
+from rag.src.pipeline import EmoLLMRAG
 from typing import Callable, List, Optional
 
 import streamlit as st
@@ -188,8 +189,9 @@ robot_prompt = "<|im_start|>assistant\n{robot}<|im_end|>\n"
 cur_query_prompt = "<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n"
 
 
-def combine_history(prompt):
+def combine_history(prompt, retrieval_content=''):
     messages = st.session_state.messages
+    prompt = f"你需要根据以下从书本中检索到的专业知识:`{retrieval_content}`。从一个心理专家的专业角度来回答后续提问：{prompt}"
     meta_instruction = (
         "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。"
     )
@@ -211,6 +213,7 @@ def main():
     # torch.cuda.empty_cache()
     print("load model begin.")
     model, tokenizer = load_model()
+    rag_obj = EmoLLMRAG(model)
     print("load model end.")
 
     user_avator = "assets/user.png"
@@ -232,9 +235,12 @@ def main():
     # Accept user input
     if prompt := st.chat_input("What is up?"):
         # Display user message in chat message container
+        retrieval_content = rag_obj.get_retrieval_content(prompt)
         with st.chat_message("user", avatar=user_avator):
             st.markdown(prompt)
-        real_prompt = combine_history(prompt)
+            #st.markdown(retrieval_content)
+
+        real_prompt = combine_history(prompt, retrieval_content)
         # Add user message to chat history
         st.session_state.messages.append({"role": "user", "content": prompt, "avatar": user_avator})