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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。它包含在任意大小的向量集合中进行搜索的算法，甚至适用于可能无法放入 RAM 的大型集合。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。
您可以在此页面找到 FAISS 文档。 此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。了解之后，探索相关用例页面以学习如何将此向量存储用作更大链的一部分可能会很有用。

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设置

集成位于 langchain-community 包中。我们还需要安装 faiss 包本身。我们可以使用以下命令安装这些包： 请注意，如果您想使用 GPU 加速版本，也可以安装 faiss-gpu 
pip install -qU langchain-community faiss-cpu
如果您希望获得最佳的模型调用自动跟踪，您还可以通过取消注释以下内容来设置您的 LangSmith API 密钥： 
os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

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初始化

# | output: false
# | echo: false
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={},
)

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管理向量存储

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向向量存储添加项目

from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
    page_content="I had chocolate 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)

['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']

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从向量存储删除项目

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

True

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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对其进行查询。

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直接查询

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相似性搜索

执行简单的相似性搜索并根据元数据进行过滤，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 (在列表中)
  • $nin (不在列表中)
  • $and (所有条件必须匹配)
  • $or (任何条件必须匹配)
  • $not (条件的否定)
执行上述相同的相似性搜索并使用高级元数据过滤，可以如下进行： 
results = vector_store.similarity_search(
    "LangChain provides abstractions to make working with LLMs easy",
    k=2,
    filter={"source": {"$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'}]

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带分数的相似性搜索

您也可以带分数进行搜索： 
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'}]

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其他搜索方法

有多种其他方式可以搜索 FAISS 向量存储。

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通过转换为检索器进行查询

您也可以将向量存储转换为检索器，以便在链中更轻松地使用。 
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.')]

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用于检索增强生成

有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分：

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保存和加载

您也可以保存和加载 FAISS 索引。这很有用，这样您就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!')

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合并

您也可以合并两个 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')}
在 GitHub 上编辑此页面 或 提交问题。
连接这些文档 到 Claude、VSCode 等，通过 MCP 获取实时答案。