Anooyman
parent
a67596a215
commit
25184d894c
@ -35,6 +35,24 @@ pip3 install -r requirements.txt
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- txt数据：放入到 src.data.txt 目录下
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- json 数据：放入到 src.data.json 目录下
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JSON 数据格式如下
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```python
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[
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{
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"conversation": [
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{
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"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。",
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"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？"
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},
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{
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"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...",
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"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？🌸"
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}
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]
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},
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]
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```
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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
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如果已经有 vector DB 则会直接加载对应数据库
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@ -91,6 +109,7 @@ python main.py
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## **数据集**
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- 经过清洗的QA对: 每一个QA对作为一个样本进行 embedding
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- 经过清洗的对话: 每一个对话作为一个样本进行 embedding
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- 经过筛选的TXT文本
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- 直接对TXT文本生成embedding (基于token长度进行切分)
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- 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)
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@ -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）
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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)
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### [RAGAS](https://github.com/explodinggradients/ragas)
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### [RAGAS](https://github.com/explodinggradients/ragas) (TODO)
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RAG的经典评估框架，通过以下三个方面进行评估:
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@ -25,6 +25,10 @@ qa_dir = os.path.join(data_dir, 'json')
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log_dir = os.path.join(base_dir, 'log') # log
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log_path = os.path.join(log_dir, 'log.log') # file
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# txt embedding 切分参数
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chunk_size=1000
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chunk_overlap=100
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# vector DB
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vector_db_dir = os.path.join(data_dir, 'vector_db')
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@ -4,7 +4,18 @@ import os
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from loguru import logger
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from langchain_community.vectorstores import FAISS
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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
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from config.config import (
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embedding_path,
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embedding_model_name,
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doc_dir, qa_dir,
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knowledge_pkl_path,
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data_dir,
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vector_db_dir,
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rerank_path,
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rerank_model_name,
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chunk_size,
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chunk_overlap
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)
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from langchain.embeddings import HuggingFaceBgeEmbeddings
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from langchain_community.document_loaders import DirectoryLoader, TextLoader
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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@ -15,8 +26,9 @@ from FlagEmbedding import FlagReranker
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class Data_process():
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def __init__(self):
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self.chunk_size: int=1000
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self.chunk_overlap: int=100
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self.chunk_size: int=chunk_size
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self.chunk_overlap: int=chunk_overlap
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def load_embedding_model(self, model_name=embedding_model_name, device='cpu', normalize_embeddings=True):
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"""
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@ -53,7 +65,6 @@ class Data_process():
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return embeddings
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def load_rerank_model(self, model_name=rerank_model_name):
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"""
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加载重排名模型。
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@ -117,10 +128,8 @@ class Data_process():
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elif isinstance(obj, str):
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content += obj
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return content
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def split_document(self, data_path):
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"""
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切分data_path文件夹下的所有txt文件
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@ -132,8 +141,6 @@ class Data_process():
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返回：
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- split_docs: list
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"""
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# text_spliter = CharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
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text_spliter = RecursiveCharacterTextSplitter(chunk_size=self.chunk_size, chunk_overlap=self.chunk_overlap)
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split_docs = []
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@ -151,7 +158,6 @@ class Data_process():
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split_docs = splits
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logger.info(f'split_docs size {len(split_docs)}')
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return split_docs
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def split_conversation(self, path):
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"""
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@ -171,43 +177,29 @@ class Data_process():
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file_path = os.path.join(root, file)
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logger.info(f'splitting file {file_path}')
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with open(file_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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# print(data)
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for conversation in data:
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# for dialog in conversation['conversation']:
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##按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
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# content = self.extract_text_from_json(dialog,'')
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# split_qa.append(Document(page_content = content))
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#按conversation块切分
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content = self.extract_text_from_json(conversation['conversation'], '')
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#logger.info(f'content====={content}')
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split_qa.append(Document(page_content = content))
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for line in f.readlines():
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content = self.extract_text_from_json(line,'')
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split_qa.append(Document(page_content = content))
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#data = json.load(f)
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#for conversation in data:
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# #for dialog in conversation['conversation']:
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# # #按qa对切分,将每一轮qa转换为langchain_core.documents.base.Document
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# # content = self.extract_text_from_json(dialog,'')
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# # split_qa.append(Document(page_content = content))
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# #按conversation块切分
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# content = self.extract_text_from_json(conversation['conversation'], '')
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# #logger.info(f'content====={content}')
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# split_qa.append(Document(page_content = content))
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# logger.info(f'split_qa size====={len(split_qa)}')
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return split_qa
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def load_knowledge(self, knowledge_pkl_path):
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'''
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读取或创建知识.pkl
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'''
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if not os.path.exists(knowledge_pkl_path):
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split_doc = self.split_document(doc_dir)
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split_qa = self.split_conversation(qa_dir)
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knowledge_chunks = split_doc + split_qa
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with open(knowledge_pkl_path, 'wb') as file:
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pickle.dump(knowledge_chunks, file)
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else:
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with open(knowledge_pkl_path , 'rb') as f:
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knowledge_chunks = pickle.load(f)
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return knowledge_chunks
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def create_vector_db(self, emb_model):
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'''
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创建并保存向量库
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'''
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logger.info(f'Creating index...')
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split_doc = self.split_document(doc_dir)
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#split_doc = self.split_document(doc_dir)
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split_qa = self.split_conversation(qa_dir)
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# logger.info(f'split_doc == {len(split_doc)}')
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# logger.info(f'split_qa == {len(split_qa)}')
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@ -217,7 +209,6 @@ class Data_process():
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db.save_local(vector_db_dir)
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return db
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def load_vector_db(self, knowledge_pkl_path=knowledge_pkl_path, doc_dir=doc_dir, qa_dir=qa_dir):
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'''
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读取向量库
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@ -230,66 +221,6 @@ class Data_process():
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db = FAISS.load_local(vector_db_dir, emb_model, allow_dangerous_deserialization=True)
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return db
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def retrieve(self, query, vector_db, k=5):
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'''
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基于query对向量库进行检索
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'''
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retriever = vector_db.as_retriever(search_kwargs={"k": k})
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docs = retriever.invoke(query)
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return docs, retriever
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##FlashrankRerank效果一般
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# def rerank(self, query, retriever):
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# compressor = FlashrankRerank()
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# compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=retriever)
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# compressed_docs = compression_retriever.get_relevant_documents(query)
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# return compressed_docs
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def rerank(self, query, docs):
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reranker = self.load_rerank_model()
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passages = []
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for doc in docs:
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passages.append(str(doc.page_content))
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scores = reranker.compute_score([[query, passage] for passage in passages])
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sorted_pairs = sorted(zip(passages, scores), key=lambda x: x[1], reverse=True)
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sorted_passages, sorted_scores = zip(*sorted_pairs)
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return sorted_passages, sorted_scores
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# def create_prompt(question, context):
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# from langchain.prompts import PromptTemplate
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# prompt_template = f"""请基于以下内容回答问题：
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# {context}
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# 问题: {question}
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# 回答:"""
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# prompt = PromptTemplate(
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# template=prompt_template, input_variables=["context", "question"]
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# )
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# logger.info(f'Prompt: {prompt}')
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# return prompt
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def create_prompt(question, context):
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prompt = f"""请基于以下内容: {context} 给出问题答案。问题如下: {question}。回答:"""
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logger.info(f'Prompt: {prompt}')
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return prompt
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def test_zhipu(prompt):
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from zhipuai import ZhipuAI
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api_key = "" # 填写您自己的APIKey
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if api_key == "":
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raise ValueError("请填写api_key")
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client = ZhipuAI(api_key=api_key)
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response = client.chat.completions.create(
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model="glm-4", # 填写需要调用的模型名称
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messages=[
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{"role": "user", "content": prompt[:100]}
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],
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)
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print(response.choices[0].message)
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if __name__ == "__main__":
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logger.info(data_dir)
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if not os.path.exists(data_dir):
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@ -316,6 +247,4 @@ if __name__ == "__main__":
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logger.info("After reranking...")
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for i in range(len(scores)):
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logger.info(str(scores[i]) + '\n')
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logger.info(passages[i])
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prompt = create_prompt(query, passages[0])
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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
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logger.info(passages[i])
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@ -13,8 +13,7 @@ from loguru import logger
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if __name__ == "__main__":
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query = """
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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。
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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
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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？
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"""
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"""
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Block a user