Update RAG pipeline

rag/src/config/config.py

@@ -33,5 +33,5 @@ prompt_template = """
 	{system_prompt}
 	根据下面检索回来的信息，回答问题。
 	{content}
-	问题：{question}
+	问题：{query}
 """

rag/src/data_processing.py

@@ -12,7 +12,7 @@ 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 util.pipeline import EmoLLMRAG
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from langchain.document_loaders.pdf import PyPDFDirectoryLoader
 from langchain.document_loaders import UnstructuredFileLoader,DirectoryLoader
@@ -254,7 +254,8 @@ if __name__ == "__main__":
     # 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？"
     docs, retriever = dp.retrieve(query, vector_db, k=10)
     logger.info(f'Query: {query}')
     logger.info("Retrieve results:")

rag/src/main.py

@@ -1,20 +1,17 @@
 import os
-import json
+import time
-import pickle
+import jwt
-import numpy as np
-from typing import Tuple
-from sentence_transformers import SentenceTransformer
 
-from config.config import knowledge_json_path, knowledge_pkl_path, model_repo, model_dir, base_dir
+from config.config import base_dir, data_dir
-from util.encode import load_embedding, encode_qa
+from data_processing import Data_process
-from util.pipeline import EmoLLMRAG
+from pipeline import EmoLLMRAG
+
+from langchain_openai import ChatOpenAI
 from loguru import logger
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 import streamlit as st
 from openxlab.model import download
-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
@@ -24,21 +21,45 @@ from data_processing import Data_process
 	6）拼接 prompt 并调用模型返回结果
 
 '''
-# download(
+def get_glm(temprature):
-#     model_repo=model_repo, 
+    llm = ChatOpenAI(
-#     output='model'
+        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_dir, trust_remote_code=True)
+        AutoModelForCausalLM.from_pretrained('model', trust_remote_code=True)
         .to(torch.bfloat16)
         .cuda()
     )
-    tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+    tokenizer = AutoTokenizer.from_pretrained('model', trust_remote_code=True)
     return model, tokenizer
 
 def main(query, system_prompt=''):
@@ -61,3 +82,8 @@ def 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)
+    #model = get_glm(0.7)
+    #rag_obj = EmoLLMRAG(model, 3)
+    #res = rag_obj.main(query)
+    #logger.info(res)
+

rag/src/pipeline.py

@@ -2,9 +2,8 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import PromptTemplate
 from transformers.utils import logging
 
-from data_processing import DataProcessing
+from data_processing import Data_process
-from config.config import retrieval_num, select_num, system_prompt, prompt_template
+from config.config import system_prompt, prompt_template 
-
 logger = logging.get_logger(__name__)
 
 
@@ -28,10 +27,8 @@ class EmoLLMRAG(object):
 
         """
         self.model = model
+        self.data_processing_obj = Data_process()
         self.vectorstores = self._load_vector_db()
-        self.system_prompt = self._get_system_prompt()
-        self.prompt_template = self._get_prompt_template()
-        self.data_processing_obj = DataProcessing()
         self.system_prompt = system_prompt
         self.prompt_template = prompt_template
         self.retrieval_num = retrieval_num
@@ -43,8 +40,6 @@ class EmoLLMRAG(object):
             调用 embedding 模块给出接口 load vector DB
         """
         vectorstores = self.data_processing_obj.load_vector_db()
-        if not vectorstores:
-            vectorstores = self.data_processing_obj.load_index_and_knowledge()
 
         return vectorstores 
 
@@ -57,13 +52,17 @@ class EmoLLMRAG(object):
         content = ''
         documents = self.vectorstores.similarity_search(query, k=self.retrieval_num)
 
-        # 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)
-
         for doc in documents:
             content += 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 = ''
+            for doc in documents:
+                content += doc
+        logger.info(f'Retrieval data: {content}')
         return content
     
     def generate_answer(self, query, content) -> str: