AI--向量的存储和检索

step1 Document

LangChain 实现了Document抽象，旨在表示文本单元和相关元数据。它具有两个属性：

• page_content：代表内容的字符串；
• metadata：包含任意元数据的字典。

  该metadata属性可以捕获有关文档来源、其与其他文档的关系以及其他信息的信息.单个Document对象通常代表较大文档的一部分。

  from langchain_core.documents import Document
  documents = [
      Document(
          page_content="Dogs are great companions, known for their loyalty and friendliness.",
          metadata = {"source": "mammal-pets-doc"},
      ),
       Document(
          page_content="Cats are independent pets that often enjoy their own space.",
          metadata={"source": "mammal-pets-doc"},
      ),
      Document(
          page_content="Goldfish are popular pets for beginners, requiring relatively simple care.",
          metadata={"source": "fish-pets-doc"},
      ),
      Document(
          page_content="Parrots are intelligent birds capable of mimicking human speech.",
          metadata={"source": "bird-pets-doc"},
      ),
      Document(
          page_content="Rabbits are social animals that need plenty of space to hop around.",
          metadata={"source": "mammal-pets-doc"},
      ),
  ]
  

  step2 向量检索

  向量检索是一种常见的存储和检索非结构化数据的方式，主要思路是存储文本的数据向量，给出一个查询，我们编码查询成同一个维度的数据向量，然后使用相似度去查找相关数据

  LangChain VectorStore对象包含用于将文本和Document对象添加到存储区以及使用各种相似度指标查询它们的方法。它们通常使用嵌入模型进行初始化，这些模型决定了如何将文本数据转换为数字向量。

  下面我是使用bce-embedding模型作为编码模型，地址下载

  from langchain.embeddings import HuggingFaceEmbeddings
  from langchain_community.vectorstores.utils import DistanceStrategy
  # init embedding model
  model_kwargs = {'device': 'cuda'}
  encode_kwargs = {'batch_size': 64, 'normalize_embeddings': True}
  embed_model = HuggingFaceEmbeddings(
      model_name=EMBEDDING_PATH,
      model_kwargs=model_kwargs,
      encode_kwargs=encode_kwargs
    )
  vetorstore = Chroma.from_documents(
      documents,
      embedding=embed_model,
  )
  vetorstore.similarity_search("cat")
  

  输出结果为：

  [Document(page_content=‘Cats are independent pets that often enjoy their own space.’, metadata={‘source’: ‘mammal-pets-doc’}),

  Document(page_content=‘Goldfish are popular pets for beginners, requiring relatively simple care.’, metadata={‘source’:

  ‘fish-pets-doc’}),

  Document(page_content=‘Dogs are great companions, known for their loyalty and friendliness.’, metadata={‘source’:‘mammal-pets-doc’}),

  Document(page_content=‘Parrots are intelligent> birds capable of mimicking human speech.’, metadata={‘source’:‘bird-pets-doc’})]

  搜索返回相似度分数

  vetorstore.similarity_search_with_score("cat")
  

  [(Document(page_content=‘Cats are independent pets that often enjoy their own space.’, metadata={‘source’: ‘mammal-pets-doc’}),

  0.9107884),

  (Document(page_content=‘Goldfish are popular pets for beginners, requiring relatively simple care.’, metadata={‘source’: ‘fish-pets-doc’}),

  1.3231826),

  (Document(page_content=‘Dogs are great companions, known for their loyalty and friendliness.’, metadata={‘source’: ‘mammal-pets-doc’}),

  1.4060305),

  (Document(page_content=‘Parrots are intelligent birds capable of mimicking human speech.’, metadata={‘source’: ‘bird-pets-doc’}),

  1.4284585),

  (Document(page_content=‘Rabbits are social animals that need plenty of space to hop around.’, metadata={‘source’: ‘mammal-pets-doc’}),

  1.4566814)]

  上面结果返回的score，越小表示越接近

  基于向量查询

  embedding = embed_model.embed_query("cat")
  vetorstore.similarity_search_by_vector(embedding)
  

  输出结果

  [Document(page_content=‘Cats are independent pets that often enjoy their own space.’, metadata={‘source’: ‘mammal-pets-doc’}),

  Document(page_content=‘Goldfish are popular pets for beginners, requiring relatively simple care.’, metadata={‘source’: ‘fish-pets-doc’}),

  Document(page_content=‘Dogs are great companions, known for their loyalty and friendliness.’, metadata={‘source’: ‘mammal-pets-doc’}),

  Document(page_content=‘Parrots are intelligent birds capable of mimicking human speech.’, metadata={‘source’: ‘bird-pets-doc’})]

  step3 检索

  LangChainVectorStore对象没有Runnable子类，因此不能立即集成到 LangChain 表达语言链中。

  LangChain Retrievers是 Runnable，因此它们实现了一组标准方法（例如同步和异步invoke操作batch）并且旨在纳入 LCEL 链。

  我们可以自己创建一个简单的版本，而无需子类化Retriever。如果我们选择要使用的方法检索文档，我们可以轻松创建一个可运行的程序。下面我们将围绕该similarity_search方法构建一个：

  from typing import List
  from langchain_core.documents import Document
  from langchain_core.runnables import RunnableLambda
  retriever = RunnableLambda(vetorstore.similarity_search).bind(k=1)
  print(retriever.invoke("cat"))
  print(retriever.batch(["cat","dog"]))
  

  输出结果

  [Document(page_content=‘Cats are independent pets that often enjoy their own space.’, metadata={‘source’: ‘mammal-pets-doc’})]

  [[Document(page_content=‘Cats are independent pets that often enjoy their own space.’, metadata={‘source’: ‘mammal-pets-doc’})], [Document(page_content=‘Dogs are great companions, known for their loyalty and friendliness.’, metadata={‘source’: ‘mammal-pets-doc’})]]

  Vectorstore 实现了as_retriever一个生成 Retriever 的方法，特别是VectorStoreRetriever。这些检索器包括特定的search_type属性search_kwargs，用于标识要调用的底层向量存储的哪些方法以及如何参数化它们。

  retriever = vetorstore.as_retriever(
      search_type="similarity",
      search_kwargs={"k": 1},
  )
  retriever.batch(["cat", "shark"])
  

  输出结果

  [[Document(page_content=‘Cats are independent pets that often enjoy their own space.’, metadata={‘source’: ‘mammal-pets-doc’})],

  [Document(page_content=‘Goldfish are popular pets for beginners, requiring relatively simple care.’, metadata={‘source’: ‘fish-pets-doc’})]]

  检索器可以轻松地合并到更复杂的应用程序中，例如检索增强生成（RAG）应用程序，

  from langchain_openai import ChatOpenAI
  from langchain_core.prompts import ChatPromptTemplate
  from langchain_core.runnables import RunnablePassthrough
  chat = ChatOpenAI()
  message = """
  Answer this question using the provided context only.
  {question}
  Context:
  {context}
  """
  retriever = vetorstore.as_retriever(
      search_type="similarity",
      search_kwargs={"k": 1},
  )
  prompt = ChatPromptTemplate.from_messages(
      [
          ("human",message),
      ]
  )
  rag_chat = {"context":retriever,"question":RunnablePassthrough()} | prompt |chat
  response = rag_chat.invoke("tell me about cats")
  print(response.content)
  

  输出结果

  Cats are independent pets that often enjoy their own space.