From d0663208e3c34acbcef35f67eb5f395dc23c354a Mon Sep 17 00:00:00 2001
From: Anooyman <875734078@qq.com>
Date: Wed, 27 Mar 2024 21:11:06 +0800
Subject: [PATCH] Update RAG

---
 rag/README.md              |  21 +++++-
 rag/src/config/config.py   |   4 ++
 rag/src/data_processing.py | 133 +++++++++----------------------------
 rag/src/main.py            |   3 +-
 4 files changed, 56 insertions(+), 105 deletions(-)

diff --git a/rag/README.md b/rag/README.md
index d969c57..15af25c 100644
--- a/rag/README.md
+++ b/rag/README.md
@@ -35,6 +35,24 @@ pip3 install -r requirements.txt
 - txt数据：放入到 src.data.txt 目录下
 - json 数据：放入到 src.data.json 目录下
 
+JSON 数据格式如下
+```python
+[
+    {
+        "conversation": [
+            {
+                "input": "（微微皱眉）艾薇，我觉得我在学校的学习压力好大啊，虽然我真的很喜欢我的专业，但最近总是担心自己无法达到自己的期望，这让我有点焦虑。",
+                "output": "（微笑）😊 我完全理解你的感受，亲爱的。学习是我们生活的一部分，尤其是在我们热爱的专业里，期待自己做得更好是再正常不过了。你知道吗？这种对成功的渴望其实是一种积极的动力源呢！但是，当它开始影响到我们的心理健康时，我们就需要学会调整和应对了。你可以先试着跟我分享一下，具体是什么样的学习任务或者目标让你感到压力山大呢？"
+            },
+            {
+                "input": "主要是下个月的一场重要考试，我总觉得自己复习得不够充分，而且害怕失败...",
+                "output": "💖 噢，明白了。面对重大考试的紧张和担忧是很普遍的现象。首先，我要肯定你对自己的高标准要求，这是你对自己负责的表现。不过，我们可以尝试制定一个更为实际可行的学习计划，将大目标分解成小目标，每天完成一部分，这样既能提升自信心又能减轻焦虑感。同时，也要记得适时放松，合理安排休息时间哦！比如学习45分钟后，可以适当休息10分钟，做些自己喜欢的事情，劳逸结合才是长久之计呢！💪📚\n另外，也可以尝试一些深呼吸、冥想等放松技巧来缓解焦虑情绪。如果你愿意的话，下次咨询我们可以一起练习，看看哪种方式最适合帮助你应对压力。现在，让我们一步步来，先从细化学习计划开始，你觉得怎么样呢？🌸"
+            }
+        ]
+    },
+] 
+```
+
 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
@@ -91,6 +109,7 @@ 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
+- 经过清洗的对话: 每一个对话作为一个样本进行 embedding
 - 经过筛选的TXT文本
 	- 直接对TXT文本生成embedding (基于token长度进行切分)
 	- 过滤目录等无关信息后对TXT文本生成embedding (基于token长度进行切分)
@@ -115,7 +134,7 @@ LangChain 是一个开源框架，用于构建基于大型语言模型（LLM）
 Faiss是一个用于高效相似性搜索和密集向量聚类的库。它包含的算法可以搜索任意大小的向量集。由于langchain已经整合过FAISS，因此本项目中不在基于原生文档开发[FAISS in Langchain](https://python.langchain.com/docs/integrations/vectorstores/faiss)
 
 
-### [RAGAS](https://github.com/explodinggradients/ragas)
+### [RAGAS](https://github.com/explodinggradients/ragas) (TODO)
 
 RAG的经典评估框架，通过以下三个方面进行评估:
 
diff --git a/rag/src/config/config.py b/rag/src/config/config.py
index 673c5b5..3a1a6a9 100644
--- a/rag/src/config/config.py
+++ b/rag/src/config/config.py
@@ -25,6 +25,10 @@ qa_dir = os.path.join(data_dir, 'json')
 log_dir = os.path.join(base_dir, 'log')                             # log
 log_path = os.path.join(log_dir, 'log.log')                         # file
 
+# txt embedding 切分参数     
+chunk_size=1000
+chunk_overlap=100
+
 # vector DB
 vector_db_dir = os.path.join(data_dir, 'vector_db')
 
diff --git a/rag/src/data_processing.py b/rag/src/data_processing.py
index 0b94e3d..d894faa 100644
--- a/rag/src/data_processing.py
+++ b/rag/src/data_processing.py
@@ -4,7 +4,18 @@ import os
 
 from loguru import logger
 from langchain_community.vectorstores import FAISS
-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 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,
+    chunk_size,
+    chunk_overlap
+)
 from langchain.embeddings import HuggingFaceBgeEmbeddings
 from langchain_community.document_loaders import DirectoryLoader, TextLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
@@ -15,8 +26,9 @@ from FlagEmbedding import FlagReranker
 class Data_process():
 
     def __init__(self):
-        self.chunk_size: int=1000
-        self.chunk_overlap: int=100    
+
+        self.chunk_size: int=chunk_size
+        self.chunk_overlap: int=chunk_overlap
         
     def load_embedding_model(self, model_name=embedding_model_name, device='cpu', normalize_embeddings=True):
         """
@@ -53,7 +65,6 @@ class Data_process():
         return embeddings
     
     def load_rerank_model(self, model_name=rerank_model_name):
-
         """
         加载重排名模型。
         
@@ -117,10 +128,8 @@ class Data_process():
         elif isinstance(obj, str):
             content += obj
         return content
-    
 
     def split_document(self, data_path):
-
         """
         切分data_path文件夹下的所有txt文件
         
@@ -132,8 +141,6 @@ class Data_process():
         返回：
         - split_docs: list
         """
-        
-        
         # text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
         text_spliter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap) 
         split_docs = []
@@ -151,7 +158,6 @@ class Data_process():
             split_docs = splits
         logger.info(f'split_docs size {len(split_docs)}')
         return split_docs
-
   
     def split_conversation(self, path):
         """
@@ -171,43 +177,29 @@ class Data_process():
                         file_path = os.path.join(root, file)
                         logger.info(f'splitting file {file_path}')
                         with open(file_path, 'r', encoding='utf-8') as f:
-                            data = json.load(f)
-                            # print(data)
-                            for conversation in data:
-                                # for dialog in conversation['conversation']:
-                                    ##按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
-                                    # content = self.extract_text_from_json(dialog,'')
-                                    # 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}')
-                                split_qa.append(Document(page_content = content))    
+                            for line in f.readlines():
+                                content = self.extract_text_from_json(line,'')
+                                split_qa.append(Document(page_content = content))
+
+                            #data = json.load(f)
+                            #for conversation in data:
+                            #    #for dialog in conversation['conversation']:
+                            #    #    #按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
+                            #    #    content = self.extract_text_from_json(dialog,'')
+                            #    #    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}')
+                            #    split_qa.append(Document(page_content = content))    
             # logger.info(f'split_qa size====={len(split_qa)}')
         return split_qa
 
-
-    def load_knowledge(self, knowledge_pkl_path):
-        '''
-        读取或创建知识.pkl
-        '''
-        if not os.path.exists(knowledge_pkl_path):
-            split_doc = self.split_document(doc_dir)
-            split_qa = self.split_conversation(qa_dir)
-            knowledge_chunks = split_doc + split_qa
-            with open(knowledge_pkl_path, 'wb') as file:
-                pickle.dump(knowledge_chunks, file)
-        else:
-            with open(knowledge_pkl_path , 'rb') as f:
-                knowledge_chunks = pickle.load(f)
-        return knowledge_chunks
-      
- 
     def create_vector_db(self, emb_model):
         '''
         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)}')
@@ -217,7 +209,6 @@ class Data_process():
         db.save_local(vector_db_dir)
         return db
         
-  
     def load_vector_db(self, knowledge_pkl_path=knowledge_pkl_path, doc_dir=doc_dir, qa_dir=qa_dir):
         '''
         读取向量库
@@ -230,66 +221,6 @@ class Data_process():
             db = FAISS.load_local(vector_db_dir, emb_model, allow_dangerous_deserialization=True)
         return db
     
- 
-    def retrieve(self, query, vector_db, k=5):
-        '''
-        基于query对向量库进行检索
-        '''
-        retriever = vector_db.as_retriever(search_kwargs={"k": k})
-        docs = retriever.invoke(query)
-        return docs, retriever
-    
-    ##FlashrankRerank效果一般
-    # def rerank(self, query, retriever):
-    #     compressor = FlashrankRerank()
-    #     compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
-    #     compressed_docs = compression_retriever.get_relevant_documents(query)
-    #     return compressed_docs
-
-    def rerank(self, query, docs): 
-        reranker = self.load_rerank_model()
-        passages = []
-        for doc in docs:
-            passages.append(str(doc.page_content))
-        scores = reranker.compute_score([[query, passage] for passage in passages])
-        sorted_pairs = sorted(zip(passages, scores), key=lambda x: x[1], reverse=True)
-        sorted_passages, sorted_scores = zip(*sorted_pairs)
-        return sorted_passages, sorted_scores
-
-
-# def create_prompt(question, context):
-#     from langchain.prompts import PromptTemplate
-#     prompt_template = f"""请基于以下内容回答问题：
-
-#     {context}
-
-#     问题: {question}
-#     回答:"""
-#     prompt = PromptTemplate(
-#         template=prompt_template, input_variables=["context", "question"]
-#     )
-#     logger.info(f'Prompt: {prompt}')
-#     return prompt
-    
-def create_prompt(question, context):
-    prompt = f"""请基于以下内容: {context} 给出问题答案。问题如下: {question}。回答:"""
-    logger.info(f'Prompt: {prompt}')
-    return prompt
-        
-def test_zhipu(prompt):
-    from zhipuai import ZhipuAI
-    api_key = "" # 填写您自己的APIKey
-    if api_key == "":
-        raise ValueError("请填写api_key")
-    client = ZhipuAI(api_key=api_key) 
-    response = client.chat.completions.create(
-    model="glm-4",  # 填写需要调用的模型名称
-    messages=[
-        {"role": "user", "content": prompt[:100]}
-    ],
-)
-    print(response.choices[0].message)
-
 if __name__ == "__main__":
     logger.info(data_dir)
     if not os.path.exists(data_dir):
@@ -316,6 +247,4 @@ if __name__ == "__main__":
     logger.info("After reranking...")
     for i in range(len(scores)):
         logger.info(str(scores[i]) + '\n')
-        logger.info(passages[i])
-    prompt = create_prompt(query, passages[0])
-    test_zhipu(prompt) ## 如果显示'Server disconnected without sending a response.'可能是由于上下文窗口限制
\ No newline at end of file
+        logger.info(passages[i])
\ No newline at end of file
diff --git a/rag/src/main.py b/rag/src/main.py
index f324f50..6f926a2 100644
--- a/rag/src/main.py
+++ b/rag/src/main.py
@@ -13,8 +13,7 @@ from loguru import logger
 
 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
+        我现在经常会被别人催眠，做一些我不愿意做的事情，是什么原因？
     """
 
     """