使用 Hugging Face 工具进行 RAG

定义

首先我们来定义什么是 RAG：检索增强生成（Retrieval-Augmented Generation）。它是一种自然语言处理（NLP）技术，通过整合外部知识源（如数据库或搜索引擎）来提高语言模型的性能。其基本思想是根据输入查询从外部源检索相关信息。

工具

本博客需要以下库

pip install -q datasets sentence-transformers faiss-cpu accelerate

嵌入原始数据集

这是一个非常慢的过程，因此我们建议您选择 GPU

这是必要的一步，也是我们列表中最慢的一步，我们建议您嵌入数据集并将其保存/推送到 Hub，以避免每次都执行此操作。

让我们从加载原始数据集开始

from datasets import load_dataset

dataset = load_dataset("not-lain/wikipedia")

dataset # Let's checkout our dataset
>>> DatasetDict({
    train: Dataset({
        features: ['id', 'url', 'title', 'text'],
        num_rows: 3000
    })
})

然后我们加载嵌入模型，我将选择 mixedbread-ai/mxbai-embed-large-v1

from sentence_transformers import SentenceTransformer
ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")

现在让我们嵌入数据集

def embed(batch):
    """
    adds a column to the dataset called 'embeddings'
    """
    # or you can combine multiple columns here
    # For example the title and the text
    information = batch["text"]
    return {"embeddings" : ST.encode(information)}

dataset = dataset.map(embed,batched=True,batch_size=16)

建议您保存数据集，以避免每次都重复此步骤

为了保持所有用户的原始数据集完整，我将把嵌入后的数据集推送到一个新的分支，这可以使用 `revision` 参数轻松实现

dataset.push_to_hub("not-lain/wikipedia", revision="embedded")

搜索数据集

您可以从 Hub 调用数据集

from datasets import load_dataset

dataset = load_dataset("not-lain/wikipedia",revision = "embedded")

然后使用我们创建的 `embeddings` 列添加 Faiss 索引。

data = dataset["train"]
data = data.add_faiss_index("embeddings")

让我们定义一个搜索函数

def search(query: str, k: int = 3 ):
    """a function that embeds a new query and returns the most probable results"""
    embedded_query = ST.encode(query) # embed new query
    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
        k=k # get only top k results
    )
    return scores, retrieved_examples

# search for word anarchy and get the best 4 matching values from the dataset
scores , result = search("anarchy", 4 ) 
result['title']
>>> ['Anarchism', 'Anarcho-capitalism', 'Community', 'Capitalism']

print(result["text"][0])
>>>"Anarchism is a political philosophy and movement that is skeptical of all justifications for authority and (...)"

RAG 聊天机器人

以下是一个 RAG 聊天机器人的草稿

embed (only once)
│
└── new query
    │
    └── retrieve
        │
        └─── format prompt
            │
            └── GenAI
                │
                └── generate response

现在让我们在嵌入后将所有内容整合到一个新会话中。

pip install -q datasets sentence-transformers faiss-cpu accelerate bitsandbytes

from sentence_transformers import SentenceTransformer
from datasets import load_dataset

ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")

dataset = load_dataset("not-lain/wikipedia",revision = "embedded")

data = dataset["train"]
data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset

def search(query: str, k: int = 3 ):
    """a function that embeds a new query and returns the most probable results"""
    embedded_query = ST.encode(query) # embed new query
    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
        k=k # get only top k results
    )
    return scores, retrieved_examples

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

# use quantization to lower GPU usage
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=bnb_config
)
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

我们建议您设置一个系统提示，以引导大型语言模型 (LLM) 生成响应。

SYS_PROMPT = """You are an assistant for answering questions.
You are given the extracted parts of a long document and a question. Provide a conversational answer.
If you don't know the answer, just say "I do not know." Don't make up an answer."""

def format_prompt(prompt,retrieved_documents,k):
  """using the retrieved documents we will prompt the model to generate our responses"""
  PROMPT = f"Question:{prompt}\nContext:"
  for idx in range(k) :
    PROMPT+= f"{retrieved_documents['text'][idx]}\n"
  return PROMPT

def generate(formatted_prompt):
  formatted_prompt = formatted_prompt[:2000] # to avoid GPU OOM
  messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
  # tell the model to generate
  input_ids = tokenizer.apply_chat_template(
      messages,
      add_generation_prompt=True,
      return_tensors="pt"
  ).to(model.device)
  outputs = model.generate(
      input_ids,
      max_new_tokens=1024,
      eos_token_id=terminators,
      do_sample=True,
      temperature=0.6,
      top_p=0.9,
  )
  response = outputs[0][input_ids.shape[-1]:]
  return tokenizer.decode(response, skip_special_tokens=True)

def rag_chatbot(prompt:str,k:int=2):
  scores , retrieved_documents = search(prompt, k)
  formatted_prompt = format_prompt(prompt,retrieved_documents,k)
  return generate(formatted_prompt)

rag_chatbot("what's anarchy ?", k = 2)
>>>"So, anarchism is a political philosophy that questions the need for authority and hierarchy, and (...)"

演示

您可以在此处找到一个演示应用程序来试用该应用程序。

致谢

in loving memory of Rayner V. Giuret, a friend, a brother, and an idol to all of us at LowRes.
Your legacy lives on in our hearts and minds. Thanks for everything.

Rest in peace, Rayner.