RAG - Initial commit

rag/requirements.txt

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+sentence_transformers
+transformers
+numpy
+loguru

rag/src/config/config.py

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+import os
+
+cur_dir = os.path.dirname(os.path.abspath(__file__))                # config
+src_dir = os.path.dirname(cur_dir)                                  # src
+base_dir = os.path.dirname(src_dir)                                 # base
+
+# model
+model_dir = os.path.join(base_dir, 'model')                         # model
+embedding_path = os.path.join(model_dir, 'gte-small-zh')            # embedding
+llm_path = os.path.join(model_dir, 'pythia-14m')                    # llm
+
+# data
+data_dir = os.path.join(base_dir, 'data')                           # data
+knowledge_json_path = os.path.join(data_dir, 'knowledge.json')      # json
+knowledge_pkl_path = os.path.join(data_dir, 'knowledge.pkl')        # pickle
+
+# log
+log_dir = os.path.join(base_dir, 'log')                             # log
+log_path = os.path.join(log_dir, 'log.log')                         # file

rag/src/main.py

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+import os
+import json
+import pickle
+import numpy as np
+from typing import Tuple
+from sentence_transformers import SentenceTransformer
+
+from config.config import knowledge_json_path, knowledge_pkl_path
+from util.encode import load_embedding, encode_qa
+
+
+"""
+读取知识库
+"""
+def load_knowledge() -> Tuple[list, list]:
+    # 如果 pkl 不存在，则先编码存储
+    if not os.path.exists(knowledge_pkl_path):
+        encode_qa(knowledge_json_path, knowledge_pkl_path)
+
+    # 加载 json 和 pkl
+    with open(knowledge_json_path, 'r', encoding='utf-8') as f1, open(knowledge_pkl_path, 'rb') as f2:
+        knowledge = json.load(f1)
+        encoded_knowledge = pickle.load(f2)
+    return knowledge, encoded_knowledge
+
+
+"""
+召回 top_k 个相关的文本段
+"""
+def find_top_k(
+    emb: SentenceTransformer,
+    query: str,
+    knowledge: list,
+    encoded_knowledge: list,
+    k=3
+) -> list[str]:
+    # 编码 query
+    query_embedding = emb.encode(query)
+
+    # 查找 top_k
+    scores = query_embedding @ encoded_knowledge.T
+    # 使用 argpartition 找出每行第 k 个大的值的索引，第 k 个位置左侧都是比它大的值，右侧都是比它小的值
+    top_k_indices = np.argpartition(scores, -k)[-k:]
+    # 由于 argpartition 不保证顺序，我们需要对提取出的 k 个索引进行排序
+    top_k_values_sorted_indices = np.argsort(scores[top_k_indices])[::-1]
+    top_k_indices = top_k_indices[top_k_values_sorted_indices]
+
+    # 返回
+    contents = [knowledge[index] for index in top_k_indices]
+    return contents
+    
+
+def main():
+    emb = load_embedding()
+    knowledge, encoded_knowledge = load_knowledge()
+    query = "认知心理学研究哪些心理活动？"
+    contents = find_top_k(emb, query, knowledge, encoded_knowledge, 2)
+    print('召回的 top-k 条相关内容如下：')
+    print(json.dumps(contents, ensure_ascii=False, indent=2))
+    # 这里我没实现 LLM 部分，如果有 LLM
+    ## 1. 读取 LLM
+    ## 2. 将 contents 拼接为 prompt，传给 LLM，作为 {已知内容}
+    ## 3. 要求 LLM 根据已知内容回复
+
+
+if __name__ == '__main__':
+    main()

rag/src/util/encode.py

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+import json
+import pickle
+from loguru import logger
+from sentence_transformers import SentenceTransformer
+
+from config.config import embedding_path
+
+
+"""
+加载向量模型
+"""
+def load_embedding() -> SentenceTransformer:
+    logger.info('Loading embedding...')
+    emb = SentenceTransformer(embedding_path)
+    logger.info('Embedding loaded.')
+    return emb
+
+
+"""
+文本编码
+"""
+def encode_raw_corpus(file_path: str, store_path: str) -> None:
+    emb = load_embedding()
+    with open(file_path, 'r', encoding='utf-8') as f:
+        read_lines = f.readlines()
+    
+    """
+    对文本分割（例如：按句子分割）
+    """
+    lines = []
+    # 分割好后的存入 lines 中
+
+    # 编码（转换为向量）
+    encoded_knowledge = emb.encode(lines)
+    with open(store_path, 'wb') as f:
+        pickle.dump(encoded_knowledge, f)
+
+
+"""
+QA 对编码
+暂时只实现了加载 json，csv和txt先没写
+"""
+def encode_qa(file_path: str, store_path: str) -> None:
+    emb = load_embedding()
+    with open(file_path, 'r', encoding='utf-8') as f:
+        qa_list = json.load(f)
+    
+    # 将 QA 对拼起来作为完整一句来编码，也可以只编码 Q
+    lines = []
+    for qa in qa_list:
+        question = qa['question']
+        answer = qa['answer']
+        lines.append(question + answer)
+
+    encoded_knowledge = emb.encode(lines)
+    with open(store_path, 'wb') as f:
+        pickle.dump(encoded_knowledge, f)