From a67596a215bcc5687bcc15c9d3ffe2829810fbf6 Mon Sep 17 00:00:00 2001
From: Anooyman <875734078@qq.com>
Date: Wed, 27 Mar 2024 21:13:26 +0800
Subject: [PATCH] Revert "Update RAG"

This reverts commit d0663208e3c34acbcef35f67eb5f395dc23c354a.
---
 rag/README.md              |  21 +-----
 rag/src/config/config.py   |   4 --
 rag/src/data_processing.py | 133 ++++++++++++++++++++++++++++---------
 rag/src/main.py            |   3 +-
 4 files changed, 105 insertions(+), 56 deletions(-)

diff --git a/rag/README.md b/rag/README.md
index 15af25c..d969c57 100644
--- a/rag/README.md
+++ b/rag/README.md
@@ -35,24 +35,6 @@ 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
@@ -109,7 +91,6 @@ 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)
@@ -134,7 +115,7 @@ 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) (TODO)
+### [RAGAS](https://github.com/explodinggradients/ragas)
 
 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 3a1a6a9..673c5b5 100644
--- a/rag/src/config/config.py
+++ b/rag/src/config/config.py
@@ -25,10 +25,6 @@ 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 d894faa..0b94e3d 100644
--- a/rag/src/data_processing.py
+++ b/rag/src/data_processing.py
@@ -4,18 +4,7 @@ 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,
-    chunk_size,
-    chunk_overlap
-)
+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
 from langchain_text_splitters import RecursiveCharacterTextSplitter
@@ -26,9 +15,8 @@ from FlagEmbedding import FlagReranker
 class Data_process():
 
     def __init__(self):
-
-        self.chunk_size: int=chunk_size
-        self.chunk_overlap: int=chunk_overlap
+        self.chunk_size: int=1000
+        self.chunk_overlap: int=100    
         
     def load_embedding_model(self, model_name=embedding_model_name, device='cpu', normalize_embeddings=True):
         """
@@ -65,6 +53,7 @@ class Data_process():
         return embeddings
     
     def load_rerank_model(self, model_name=rerank_model_name):
+
         """
         加载重排名模型。
         
@@ -128,8 +117,10 @@ 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
         
@@ -141,6 +132,8 @@ 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 = []
@@ -158,6 +151,7 @@ class Data_process():
             split_docs = splits
         logger.info(f'split_docs size {len(split_docs)}')
         return split_docs
+
   
     def split_conversation(self, path):
         """
@@ -177,29 +171,43 @@ 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:
-                            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))    
+                            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))    
             # 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)}')
@@ -209,6 +217,7 @@ 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
@@ -221,6 +230,66 @@ 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):
@@ -247,4 +316,6 @@ if __name__ == "__main__":
     logger.info("After reranking...")
     for i in range(len(scores)):
         logger.info(str(scores[i]) + '\n')
-        logger.info(passages[i])
\ No newline at end of file
+        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
diff --git a/rag/src/main.py b/rag/src/main.py
index 6f926a2..f324f50 100644
--- a/rag/src/main.py
+++ b/rag/src/main.py
@@ -13,7 +13,8 @@ from loguru import logger
 
 if __name__ == "__main__":
     query = """
-        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？
+        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
     """
 
     """