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Faiss

Facebook AI Similarity Search (FAISS) 是一个用于高效相似性搜索和密集向量聚类的库。它包含可以在任何大小的向量集中进行搜索的算法，甚至可能不适合放入RAM的向量集。它还包括用于评估和参数调整的支持代码。

参见 The FAISS Library 论文。

你可以在这个页面找到FAISS的文档。

本笔记本展示了如何使用与FAISS向量数据库相关的功能。它将展示此集成特有的功能。在阅读完本笔记本后，探索相关用例页面可能会有所帮助，以了解如何将此向量存储作为更大链条的一部分使用。

设置​

集成位于langchain-community包中。我们还需要安装faiss包本身。我们可以通过以下方式安装这些包：

请注意，如果您想使用支持GPU的版本，您也可以安装faiss-gpu

pip install -qU langchain-community faiss-cpu

如果你想获得最佳的模型调用自动追踪功能，你也可以通过取消下面的注释来设置你的LangSmith API密钥：

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

初始化​

pip install -qU langchain-openai
import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
import faiss
from langchain_community.docstore.in_memory import InMemoryDocstore
from langchain_community.vectorstores import FAISS

index = faiss.IndexFlatL2(len(embeddings.embed_query("hello world")))

vector_store = FAISS(
    embedding_function=embeddings,
    index=index,
    docstore=InMemoryDocstore(),
    index_to_docstore_id={},
)
API Reference:InMemoryDocstore | FAISS

管理向量存储​

添加项目到向量存储​

from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocalate chip pancakes and scrambled eggs for breakfast this morning.",
    metadata={"source": "tweet"},
)

document_2 = Document(
    page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
    metadata={"source": "news"},
)

document_3 = Document(
    page_content="Building an exciting new project with LangChain - come check it out!",
    metadata={"source": "tweet"},
)

document_4 = Document(
    page_content="Robbers broke into the city bank and stole $1 million in cash.",
    metadata={"source": "news"},
)

document_5 = Document(
    page_content="Wow! That was an amazing movie. I can't wait to see it again.",
    metadata={"source": "tweet"},
)

document_6 = Document(
    page_content="Is the new iPhone worth the price? Read this review to find out.",
    metadata={"source": "website"},
)

document_7 = Document(
    page_content="The top 10 soccer players in the world right now.",
    metadata={"source": "website"},
)

document_8 = Document(
    page_content="LangGraph is the best framework for building stateful, agentic applications!",
    metadata={"source": "tweet"},
)

document_9 = Document(
    page_content="The stock market is down 500 points today due to fears of a recession.",
    metadata={"source": "news"},
)

document_10 = Document(
    page_content="I have a bad feeling I am going to get deleted :(",
    metadata={"source": "tweet"},
)

documents = [
    document_1,
    document_2,
    document_3,
    document_4,
    document_5,
    document_6,
    document_7,
    document_8,
    document_9,
    document_10,
]
uuids = [str(uuid4()) for _ in range(len(documents))]

vector_store.add_documents(documents=documents, ids=uuids)
API Reference:Document
['22f5ce99-cd6f-4e0c-8dab-664128307c72',
 'dc3f061b-5f88-4fa1-a966-413550c51891',
 'd33d890b-baad-47f7-b7c1-175f5f7b4e59',
 '6e6c01d2-6020-4a7b-95da-ef43d43f01b5',
 'e677223d-ad75-4c1a-bef6-b5912bd1de03',
 '47e2a168-6462-4ed2-b1d9-d9edfd7391d6',
 '1e4d66d6-e155-4891-9212-f7be97f36c6a',
 'c0663096-e1a5-4665-b245-1c2e6c4fb653',
 '8297474a-7f7c-4006-9865-398c1781b1bc',
 '44e4be03-0a8d-4316-b3c4-f35f4bb2b532']

从向量存储中删除项目​

vector_store.delete(ids=[uuids[-1]])
True

查询向量存储​

一旦您的向量存储已创建并且相关文档已添加，您很可能希望在链或代理运行期间查询它。

直接查询​

执行一个简单的相似性搜索并过滤元数据可以如下进行：

results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": "tweet"},
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]

一些MongoDB查询和投影操作符支持更高级的元数据过滤。当前支持的操作符列表如下：

  • $eq (等于)
  • $neq (不等于)
  • $gt (大于)
  • $lt (小于)
  • $gte (大于或等于)
  • $lte (小于或等于)
  • $in (列表中的成员)
  • $nin (不在列表中)
  • $and (所有条件必须匹配)
  • $or (任何条件必须匹配)
  • $not (条件的否定)

使用高级元数据过滤执行上述相同的相似性搜索可以如下进行：

results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": {"$eq": "tweet"}},
)
for res in results:
    print(f"* {res.page_content} [{res.metadata}]")
* Building an exciting new project with LangChain - come check it out! [{'source': 'tweet'}]
* LangGraph is the best framework for building stateful, agentic applications! [{'source': 'tweet'}]

带分数的相似性搜索​

你也可以使用分数进行搜索：

results = vector_store.similarity_search_with_score(
    "Will it be hot tomorrow?", k=1, filter={"source": "news"}
)
for res, score in results:
    print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
* [SIM=0.893688] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]

其他搜索方法​

有多种其他方法可以搜索FAISS向量存储。有关这些方法的完整列表，请参阅API参考

通过转换为检索器进行查询​

你也可以将向量存储转换为检索器，以便在你的链中更轻松地使用。

retriever = vector_store.as_retriever(search_type="mmr", search_kwargs={"k": 1})
retriever.invoke("Stealing from the bank is a crime", filter={"source": "news"})
[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]

检索增强生成的使用​

有关如何使用此向量存储进行检索增强生成（RAG）的指南，请参阅以下部分：

保存和加载​

您还可以保存和加载FAISS索引。这很有用，因此您不必每次使用时都重新创建它。

vector_store.save_local("faiss_index")

new_vector_store = FAISS.load_local(
    "faiss_index", embeddings, allow_dangerous_deserialization=True
)

docs = new_vector_store.similarity_search("qux")
docs[0]
Document(metadata={'source': 'tweet'}, page_content='Building an exciting new project with LangChain - come check it out!')

合并​

你也可以合并两个FAISS向量存储

db1 = FAISS.from_texts(["foo"], embeddings)
db2 = FAISS.from_texts(["bar"], embeddings)

db1.docstore._dict
{'b752e805-350e-4cf5-ba54-0883d46a3a44': Document(page_content='foo')}
db2.docstore._dict
{'08192d92-746d-4cd1-b681-bdfba411f459': Document(page_content='bar')}
db1.merge_from(db2)
db1.docstore._dict
{'b752e805-350e-4cf5-ba54-0883d46a3a44': Document(page_content='foo'),
 '08192d92-746d-4cd1-b681-bdfba411f459': Document(page_content='bar')}

API 参考​

有关所有FAISS向量存储功能和配置的详细文档，请参阅API参考：https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html

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