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。它包含在任意大小的向量集合中进行搜索的算法,甚至可以处理可能不适合 RAM 的向量。它还包括用于评估和参数调优的支持代码。
请参见 FAISS 库 论文。
您可以在 此页面 找到 FAISS 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。它将展示与此集成特定的功能。完成后,探索 相关用例页面 可能会很有用,以了解如何将此向量存储作为更大链的一部分使用。
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:
请注意,如果您想使用启用 GPU 的版本,您也可以安装 faiss-gpu
。
pip install -qU langchain-community faiss-cpu
如果您想获得最佳的自动追踪模型调用,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["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
初始化
- OpenAI
- HuggingFace
- Fake Embedding
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)
<!--IMPORTS:[{"imported": "InMemoryDocstore", "source": "langchain_community.docstore.in_memory", "docs": "https://python.langchain.com/api_reference/community/docstore/langchain_community.docstore.in_memory.InMemoryDocstore.html", "title": "Faiss"}, {"imported": "FAISS", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html", "title": "Faiss"}]-->
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={},
)
管理向量存储
向向量存储添加项目
<!--IMPORTS:[{"imported": "Document", "source": "langchain_core.documents", "docs": "https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html", "title": "Faiss"}]-->
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)
['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
一旦您的向量存储创建完成并且相关文档已添加,您很可能希望在运行链或代理时查询它。
直接查询
相似性搜索
执行简单的相似性搜索并对元数 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'}]
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。
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用法
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,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!')
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.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html