update

rag/README.md

@@ -8,6 +8,61 @@
 - 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
+
+```
+
+```python
+cd rag
+
+pip3 install -r requirements.txt
+```
+
+## **使用指南** 
+
+### 配置 config 文件
+
+根据需要改写 config.config 文件：
+
+```python
+# 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 +120,3 @@ 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
 
 
-
-
-
-
-
-
-
-
-

rag/requirements.txt

@@ -2,5 +2,10 @@ 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

rag/src/config/config.py

@@ -23,13 +23,18 @@ 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}
 	根据下面检索回来的信息，回答问题。

rag/src/data_processing.py

@@ -1,24 +1,14 @@
 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 langchain.embeddings import HuggingFaceBgeEmbeddings
-from langchain_community.document_loaders import DirectoryLoader, TextLoader, JSONLoader
+from langchain_community.document_loaders import DirectoryLoader, TextLoader
-from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter, RecursiveJsonSplitter
+from langchain_text_splitters import RecursiveCharacterTextSplitter
-from BCEmbedding import EmbeddingModel, RerankerModel
+from langchain.document_loaders import DirectoryLoader
-# 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_core.documents.base import Document
 from FlagEmbedding import FlagReranker
 

rag/src/main.py

@@ -1,17 +1,9 @@
 import os
-import time
+from config.config import data_dir
-import jwt
-
-from config.config import base_dir, data_dir
 from data_processing import Data_process
 from pipeline import EmoLLMRAG
-
+from util.llm import get_glm
-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
 '''
 	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
@@ -21,46 +13,6 @@ 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)
@@ -81,9 +33,28 @@ 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)
+    输入:
-    #res = rag_obj.main(query)
+        model_name='glm-4',
-    #logger.info(res)
+        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)
 

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

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"},
+    )