Faiss
Facebook AI 相似性搜索 (FAISS) 是一个用于高效相似性搜索和密集向量聚类的库。它包含算法，这些算法可以在任何大小的向量集中搜索，直到那些可能不适合 RAM 的向量集。它还包含用于评估和参数调整的支持代码。
参见 FAISS 库 论文。
您可以在 此页面 上找到 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。它将展示特定于此集成的功能。浏览完后，探索 相关的用例页面 可能会有所帮助，以了解如何将此向量存储用作更大链的一部分。
设置
该集成位于 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它们：
请注意，您也可以安装 faiss-gpu，如果您想使用支持 GPU 的版本。
pip install -qU langchain-community faiss-cpu
如果您希望获得一流的模型调用自动跟踪，您还可以通过取消下面的注释来设置您的 LangSmith API 密钥
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
初始化
- OpenAI
- HuggingFace
- 假嵌入
pip install -qU langchain-openai
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
pip install -qU langchain-huggingface
from langchain_huggingface import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings(model="sentence-transformers/all-mpnet-base-v2")
pip install -qU langchain-core
from langchain_core.embeddings import FakeEmbeddings
embeddings = FakeEmbeddings(size=4096)
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 参考：InMemoryDocstore | FAISS
管理向量存储
向向量存储添加项
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 参考：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
vector_store.delete(ids=[uuids[-1]])
True
查询向量存储
创建向量存储并添加相关文档后，您很可能希望在链或代理运行期间查询它。
直接查询
相似性搜索
使用对元数据的过滤执行简单的相似性搜索，可以按如下方式完成
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'}]
带分数的相似性搜索
您还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
还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
您还可以将向量存储转换为检索器，以便在您的链中更轻松地使用它。
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.')]
用于检索增强生成的使用情况
有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参见以下部分
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它时都重新创建它。
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
您还可以合并两个 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，请访问 API 参考：https://python.langchain.ac.cn/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html