增加了langchain对本地MiniCPM的支持,增加了多文件小显存rag的demo

demo/langchain_demo.py

@@ -0,0 +1,393 @@
+"""
+你只需要最少6g显存(足够)的显卡就能在消费级显卡上体验流畅的rag。
+
+使用方法：
+1. 运行pull_request/rag/langchain_demo.py
+2. 上传pdf/txt文件(同一目录下可传多个)
+3. 输入问题。
+
+极低显存(4g)使用方法：
+1. 根据MiniCPM/quantize/readme.md进行量化，推荐量化MiniCPM-1B-sft-bf16
+2. 将cpm_model_path修改为量化后模型地址
+3. 保证encode_model_device设置为cpu
+"""
+
+
+from langchain.document_loaders import PyPDFLoader, TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+from langchain.embeddings.huggingface import HuggingFaceBgeEmbeddings
+from argparse import ArgumentParser
+from langchain.llms.base import LLM
+from typing import Any, List, Optional
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+from langchain.prompts import PromptTemplate
+from pydantic.v1 import Field
+import re
+import gradio as gr
+
+parser = ArgumentParser()
+# 大语言模型参数设置
+parser.add_argument(
+    "--cpm_model_path",
+    type=str,
+    default="openbmb/MiniCPM-1B-sft-bf16",
+)
+parser.add_argument(
+    "--cpm_device", type=str, default="cuda:0", choices=["auto", "cuda:0"]
+)
+parser.add_argument("--backend", type=str, default="torch", choices=["torch", "vllm"])
+
+# 嵌入模型参数设置
+parser.add_argument(
+    "--encode_model", type=str, default="BAAI/bge-base-zh"
+)
+parser.add_argument(
+    "--encode_model_device", type=str, default="cpu", choices=["cpu", "cuda:0"]
+)
+parser.add_argument("--query_instruction", type=str, default="")
+parser.add_argument(
+    "--file_path", type=str, default="/root/ld/pull_request/rag/红楼梦.pdf"
+)
+
+# 生成参数
+parser.add_argument("--top_k", type=int, default=3)
+parser.add_argument("--top_p", type=float, default=0.7)
+parser.add_argument("--temperature", type=float, default=0.7)
+parser.add_argument("--max_new_tokens", type=int, default=4096)
+parser.add_argument("--repetition_penalty", type=float, default=1.02)
+
+# retriever参数设置
+parser.add_argument("--embed_top_k", type=int, default=5)
+parser.add_argument("--chunk_size", type=int, default=256)
+parser.add_argument("--chunk_overlap", type=int, default=50)
+args = parser.parse_args()
+
+
+def clean_text(text):
+    """
+    清理文本，去除中英文字符、数字及常见标点。
+    
+    参数:
+    text (str): 需要清理的原始文本。
+    
+    返回:
+    str: 清理后的文本。
+    """
+    # 定义需要去除的字符模式：中文、英文、数字、常见标点
+    pattern = r'[\u4e00-\u9fa5]|[A-Za-z0-9]|[.,;!?()"\']'
+
+    # 使用正则表达式替换这些字符为空字符串
+    cleaned_text = re.sub(pattern, "", text)
+
+    # 去除多余的空格
+    cleaned_text = re.sub(r"\s+", " ", cleaned_text)
+
+    return cleaned_text
+
+
+class MiniCPM_LLM(LLM):
+    tokenizer: Any = Field(default=None)
+    model: Any = Field(default=None)
+
+    def __init__(self, model_path: str):
+        """
+        继承langchain的MiniCPM模型
+        
+        参数:
+        model_path (str): 需要加载的MiniCPM模型路径。
+
+        返回:
+        self.model: 加载的MiniCPM模型。
+        self.tokenizer: 加载的MiniCPM模型的tokenizer。
+        """
+        super().__init__()
+        if args.backend == "vllm":
+            from vllm import LLM
+
+            self.model = LLM(
+                model=model_path, trust_remote_code=True, enforce_eager=True
+            )
+        else:
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                model_path, trust_remote_code=True
+            )
+            self.model = AutoModelForCausalLM.from_pretrained(
+                model_path, trust_remote_code=True, torch_dtype=torch.float16
+            ).to(args.cpm_device)
+            self.model = self.model.eval()
+
+    def _call(self, prompt, stop: Optional[List[str]] = None):
+        """
+        langchain.llm的调用
+        
+        参数:
+        prompt (str): 传入的prompt文本
+
+        返回:
+        responds (str): 模型在prompt下生成的文本
+        """
+        if args.backend == "torch":
+            inputs = self.tokenizer("<用户>{}".format(prompt), return_tensors="pt")
+            inputs = inputs.to(args.cpm_device)
+            # Generate
+            generate_ids = self.model.generate(
+                inputs.input_ids,
+                max_length=args.max_new_tokens,
+                temperature=args.temperature,
+                top_p=args.top_p,
+                repetition_penalty=args.repetition_penalty,
+            )
+            responds = self.tokenizer.batch_decode(
+                generate_ids,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False,
+            )[0]
+            # responds, history = self.model.chat(self.tokenizer, prompt, temperature=args.temperature, top_p=args.top_p, repetition_penalty=1.02)
+        else:
+            from vllm import SamplingParams
+
+            params_dict = {
+                "n": 1,
+                "best_of": 1,
+                "presence_penalty": args.repetition_penalty,
+                "frequency_penalty": 0.0,
+                "temperature": args.temperature,
+                "top_p": args.top_p,
+                "top_k": args.top_k,
+                "use_beam_search": False,
+                "length_penalty": 1,
+                "early_stopping": False,
+                "stop": None,
+                "stop_token_ids": None,
+                "ignore_eos": False,
+                "max_tokens": args.max_new_tokens,
+                "logprobs": None,
+                "prompt_logprobs": None,
+                "skip_special_tokens": True,
+            }
+            sampling_params = SamplingParams(**params_dict)
+            prompt = "<用户>{}<AI>".format(prompt)
+            responds = self.model.generate(prompt, sampling_params)
+            responds = responds[0].outputs[0].text
+
+        return responds
+
+    @property
+    def _llm_type(self) -> str:
+        return "MiniCPM_LLM"
+
+
+# 加载PDF和TXT文件
+def load_documents(file_paths):
+    """
+        加载文本和pdf文件中的字符串，并进行简单的清洗
+        
+        参数:
+        file_paths (str or list): 传入的文件地址或者文件列表
+
+        返回:
+        documents (list): 读取的文本列表
+    """
+    files_list = []
+    if type(file_paths) == list:
+        files_list = file_paths
+    else:
+        files_list = [file_paths]
+    documents = []
+    for file_path in files_list:
+        if file_path.endswith(".pdf"):
+            loader = PyPDFLoader(file_path)
+        elif file_path.endswith(".txt"):
+            loader = TextLoader(file_path)
+        else:
+            raise ValueError("Unsupported file type")
+        doc = loader.load()
+        doc[0].page_content = clean_text(doc[0].page_content)
+        documents.extend(doc)
+
+    return documents
+
+
+def load_models():
+    """
+    加载模型和embedding模型
+    
+    返回:
+    llm: MiniCPM模型
+    embedding_models: embedding模型
+    """
+    llm = MiniCPM_LLM(model_path=args.cpm_model_path)
+    embedding_models = HuggingFaceBgeEmbeddings(
+        model_name=args.encode_model,
+        model_kwargs={"device": args.encode_model_device},  # 或者 'cuda' 如果你有GPU
+        encode_kwargs={
+            "normalize_embeddings": True,  # 是否归一化嵌入
+            "show_progress_bar": True,  # 是否显示进度条
+            "convert_to_numpy": True,  # 是否将输出转换为numpy数组
+            "batch_size": 8,  # 批处理大小'
+        },
+        query_instruction=args.query_instruction,
+    )
+    return llm, embedding_models
+
+
+# 分割并嵌入文档
+def embed_documents(documents, embedding_models):
+    """
+    对文档进行分割和嵌入
+    
+    参数:
+    documents (list): 读取的文本列表
+    embedding_models: embedding模型
+
+    返回:
+    vectorstore:向量数据库
+    """
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
+    )
+    texts = text_splitter.split_documents(documents)
+    vectorstore = Chroma.from_documents(texts, embedding_models)
+    return vectorstore
+
+
+def create_prompt_template():
+    """
+    创建自定义的prompt模板
+    
+    返回:
+    PROMPT:自定义的prompt模板
+    """
+    custom_prompt_template = """请使用以下内容片段对问题进行最终回复，如果内容中没有提到的信息不要瞎猜，严格按照内容进行回答，不要编造答案，如果无法从内容中找到答案，请回答“片段中未提及，无法回答”，不要编造答案。
+    Context:
+    {context}
+
+    Question: {question}
+    FINAL ANSWER:"""
+    PROMPT = PromptTemplate(
+        template=custom_prompt_template, input_variables=["context", "question"]
+    )
+    return PROMPT
+
+
+# 创建RAG链
+def create_rag_chain(llm, prompt):
+    # qa=load_qa_with_sources_chain(llm, chain_type="stuff")
+    qa = prompt | llm
+    return qa
+
+
+def analysis_links(docs):
+    """
+    分析链接
+    
+    参数:
+    docs (list): 读取的文本列表
+    
+    返回:
+    links_string:相关文档引用字符串，docname page content
+
+    示例:
+    >>> docs = [
+    ...     {'source': 'Document1', 'page': 1, 'content': 'This is the first document.'},
+    ...     {'source': 'Document2', 'page': 2, 'content': 'This is the second document.'}
+    ... ]
+    >>> extract_links(docs)
+    'Document1 page:1 \n\nThis is the first document.\nDocument2 page:2 \n\nThis is the second document.'
+    """
+    links_string = ""
+    for i in docs:
+        i.metadata["source"] = i.metadata["source"].split("/")[-1]
+        i.metadata["content"] = i.page_content
+        links_string += f"{i.metadata['source']} page:{i.metadata['page']}\n\n{i.metadata['content']}\n\n"
+    return links_string
+
+
+# 主函数
+def main():
+    # 加载文档
+    documents = load_documents(args.file_path)
+
+    # 嵌入文档
+    vectorstore = embed_documents(documents, embedding_models)
+
+    # 自建prompt模版
+    Prompt = create_prompt_template()
+
+    # 创建RAG链
+    rag_chain = create_rag_chain(llm, Prompt)
+
+    # 用户查询
+    while True:
+        query = input("请输入查询：")
+        if query == "exit":
+            break
+        docs = vectorstore.similarity_search(query, k=args.embed_top_k)
+        all_links = analysis_links(docs)
+        final_result = rag_chain.invoke({"context": all_links, "question": query})
+        # result = rag_chain({"input_documents": docs, "question": query}, return_only_outputs=True)
+        print(final_result)
+
+
+exist_file = None
+
+
+def process_query(file, query):
+    global exist_file, documents, vectorstore, rag_chain
+
+    if file != exist_file:
+
+        # 加载文档
+        documents = load_documents(file if isinstance(file, list) else file.name)
+
+        # 嵌入文档
+        vectorstore = embed_documents(documents, embedding_models)
+
+        # 自建prompt模版
+        Prompt = create_prompt_template()
+
+        # 创建RAG链
+        rag_chain = create_rag_chain(llm, Prompt)
+
+        exist_file = file
+
+    # 搜索并获取结果
+    docs = vectorstore.similarity_search(query, k=args.embed_top_k)
+    all_links = analysis_links(docs)
+    final_result = rag_chain.invoke({"context": all_links, "question": query})
+    # result = rag_chain({"input_documents": docs, "question": query}, return_only_outputs=False)
+    print(final_result)
+    final_result = final_result.split("FINAL ANSWER:")[-1]
+    return final_result, all_links
+
+
+if __name__ == "__main__":
+
+    llm, embedding_models = load_models()
+
+    # 如果不需要web界面可以直接运行main函数
+    #main()
+
+    with gr.Blocks(css="#textbox { height: 380%; }") as demo:
+        with gr.Row():
+            with gr.Column():
+                link_content = gr.Textbox(label="link_content", lines=30, max_lines=40)
+            with gr.Column():
+                file_input = gr.File(label="upload_files", file_count="multiple")
+                final_anser = gr.Textbox(label="final_anser", lines=5, max_lines=10)
+                query_input = gr.Textbox(
+                    label="User",
+                    placeholder="Input your query here!",
+                    lines=5,
+                    max_lines=10,
+                )
+                submit_button = gr.Button("Submit")
+        submit_button.click(
+            fn=process_query,
+            inputs=[file_input, query_input],
+            outputs=[final_anser, link_content],
+        )
+    demo.launch(share=True, show_error=True)