add new rag method

examples/transformers/rag/embedding.py

@@ -20,18 +20,25 @@
 
 from langchain.embeddings.base import Embeddings
 
-from mindnlp.sentence import SentenceTransformer
+from sentence_transformers import SentenceTransformer
 
 
 class EmbeddingsFunAdapter(Embeddings):
-    def __init__(self, embed_model, mirror='huggingface'):
+    def __init__(self, embed_model):
         self.embed_model = embed_model
-        self.embedding_model = SentenceTransformer(model_name_or_path=self.embed_model, mirror=mirror)
+        self.embedding_model = SentenceTransformer(model_name_or_path=self.embed_model)
+
+    def encode_texts(self, texts: List[str]) -> List[List[float]]:
+        texts = [t.replace("\n", " ") for t in texts]
+        embeddings = self.embedding_model.encode(texts)
+        for i, embedding in enumerate(embeddings):
+            embeddings[i] = embedding.tolist()
+        return embeddings
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        embeddings = self.embedding_model.encode_texts(texts)
+        embeddings = self.encode_texts(texts)
         return embeddings
 
     def embed_query(self, text: str) -> List[float]:
-        embeddings = self.embedding_model.encode_texts([text])
+        embeddings = self.encode_texts([text])
         return embeddings[0]

examples/transformers/rag/newchat.py

@@ -0,0 +1,172 @@
+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+
+import argparse
+from langchain_community.vectorstores import FAISS
+from langchain_text_splitters import CharacterTextSplitter
+
+import mindnlp
+from embedding import EmbeddingsFunAdapter
+from text import TextLoader
+from threading import Thread
+
+import mindspore
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
+
+def load_knowledge_base(file_name):
+    print(f"正在加载知识库文件: {file_name}")
+    loader = TextLoader(file_name)
+    texts = loader.load()
+    text_splitter = CharacterTextSplitter(separator='\n', chunk_size=256, chunk_overlap=0)
+    split_docs = text_splitter.split_text(texts)
+    print(f"文档已切分为 {len(split_docs)} 个片段")
+
+    embeddings = EmbeddingsFunAdapter("Qwen/Qwen3-Embedding-0.6B")
+    faiss = FAISS.from_texts(split_docs, embeddings)
+    print("FAISS 向量数据库构建完成。")
+    return faiss
+
+
+def load_model_and_tokenizer():
+    print("正在加载模型")
+    tokenizer = AutoTokenizer.from_pretrained('deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', use_fast=False, mirror='modelscope', trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained('deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B', ms_dtype=mindspore.bfloat16,mirror='modelscope', device_map=0)
+
+
+    print("模型加载完成。")
+    return tokenizer, model
+
+
+def retrieve_knowledge(faiss, query):
+    docs = faiss.similarity_search(query, k=1)  
+    return docs[0].page_content
+
+def generate_answer(tokenizer, model, query, knowledge=None):
+    if knowledge:
+        input_text = knowledge + "\n\n" + query
+    else:
+        input_text = query
+
+    messages = [
+        {"role": "user", "content": input_text}
+    ]
+
+    # 使用 tokenizer.apply_chat_template 构建输入
+    try:
+        prompt = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+    except Exception as e:
+        print(f"⚠️  apply_chat_template 失败，使用手动拼接: {e}")
+        prompt = f"<|im_start|>user\n{input_text}<|im_end|>\n<|im_start|>assistant\n"
+
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=8192).to(model.device)
+
+    # 创建 streamer
+    streamer = TextIteratorStreamer(
+        tokenizer,
+        skip_prompt=True,           # 跳过输入部分
+        skip_special_tokens=True    # 不输出特殊 token
+    )
+
+    # 启动生成线程
+    def generate():
+        model.generate(
+            **inputs,
+            streamer=streamer,
+            max_new_tokens=512,
+            temperature=0.001,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    thread = Thread(target=generate)
+    thread.start()
+
+    # 实时输出生成的文本
+    print("回答: ", end="", flush=True)
+    generated_text = ""
+    for new_text in streamer:
+        print(new_text, end="", flush=True)
+        generated_text += new_text
+    print()  # 换行
+
+    return generated_text.strip()
+
+
+
+def rag_pipeline(faiss, tokenizer, model, query, use_rag=True):
+    if use_rag:
+        knowledge = retrieve_knowledge(faiss, query)
+        answer = generate_answer(tokenizer, model, query, knowledge)
+        return answer, knowledge
+    else:
+        answer = generate_answer(tokenizer, model, query, "")
+        return answer, ""
+
+
+def main():
+    parser = argparse.ArgumentParser(description="RAG Demo - Command Line Version")
+    parser.add_argument("filename", help="知识库文本文件路径")
+    args = parser.parse_args()
+
+    # 加载知识库和模型
+    faiss_db = load_knowledge_base(args.filename)
+    tokenizer, model = load_model_and_tokenizer()
+
+    print("\n" + "="*60)
+    print("RAG系统已准备就绪！")
+    print("输入 'quit' 或 'exit' 退出程序。")
+    print("="*60)
+
+    while True:
+        try:
+            # 获取用户输入
+            query = input("\n请输入您的问题: ").strip()
+            if query.lower() in ['quit', 'exit', 'bye']:
+                print("再见！")
+                break
+            if not query:
+                print("问题不能为空，请重新输入。")
+                continue
+
+            # 是否启用 RAG
+            use_rag_input = input("是否启用检索增强 (RAG)? [Y/n]: ").strip().lower()
+            use_rag = use_rag_input not in ['n', 'no', 'N', 'NO']
+
+            # RAG 流程
+            if use_rag:
+                print("正在检索知识库...")
+                knowledge = retrieve_knowledge(faiss_db, query)
+                print(f"检索到的知识:\n{knowledge}")
+                # print("生成中: ", end="", flush=True)
+                answer = generate_answer(tokenizer, model, query, knowledge)
+            else:
+                print("直接生成回答（无检索）...")
+                # print("生成中: ", end="", flush=True)
+                answer = generate_answer(tokenizer, model, query)
+
+        except KeyboardInterrupt:
+            print("\n\n程序被用户中断，再见！")
+            break
+
+
+if __name__ == "__main__":
+    main()

examples/transformers/rag/readme.md

@@ -3,7 +3,7 @@
 #### Install dependencies
 
 ```
-pip install mindnlp langchain langchain-community faiss-cpu
+pip install -r requirements.txt
 ```
 
 ### Download knowledge file
@@ -16,5 +16,5 @@ wget https://raw.githubusercontent.com/limchiahooi/nlp-chinese/master/%E8%A5%BF%
 ### Run RAG Demo
 
 ```
-streamlit run startup.py xiyouji.txt 
+python newchat.py xiyouji.txt 
 ```

examples/transformers/rag/requirements.txt

@@ -0,0 +1,7 @@
+protobuf==3.20.3
+streamlit
+langchain 
+langchain-community 
+faiss-cpu
+transformers==4.55.4
+sentence_transformers