Faiss
Facebook AI Similarity Search (FAISS) 是一個用於有效相似性搜尋和密集向量叢集的程式庫。它包含在任何大小的向量集中搜尋的演算法，甚至可以搜尋那些可能不適合 RAM 的向量集。它還包括用於評估和參數調整的支援程式碼。
請參閱 FAISS 程式庫 論文。
您可以在 此頁面 找到 FAISS 文件。
此筆記本展示如何使用與 FAISS 向量資料庫相關的功能。它將展示特定於此整合的功能。瀏覽完畢後，探索相關的使用案例頁面，以了解如何將此向量儲存庫用作較大鏈的一部分可能會很有用。
設定
此整合位於 langchain-community 套件中。我們還需要安裝 faiss 套件本身。我們可以使用以下命令安裝它們
請注意，如果您想使用啟用 GPU 的版本，也可以安裝 faiss-gpu
pip install -qU langchain-community faiss-cpu
如果您想要獲得一流的自動化追蹤模型呼叫，您也可以透過取消註釋以下內容來設定您的 LangSmith API 金鑰
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
初始化
選擇嵌入模型
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 參考：InMemoryDocstore | FAISS
管理向量儲存庫
將項目新增至向量儲存庫
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 參考：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']
從向量儲存庫刪除項目
vector_store.delete(ids=[uuids[-1]])
True
查詢向量儲存庫
一旦建立了您的向量儲存庫並新增了相關文件，您很可能會希望在鏈或代理程式的運行過程中查詢它。
直接查詢
相似性搜尋
可以使用以下方式執行基於中繼資料篩選的簡單相似性搜尋
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'}]
帶分數的相似性搜尋
您也可以使用分數進行搜尋
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'}]
其他搜尋方法
有多種其他方法可以搜尋 FAISS 向量儲存庫。有關這些方法的完整清單，請參閱 API 參考
透過轉變為檢索器來查詢
您也可以將向量儲存庫轉換為檢索器，以便在鏈中更輕鬆地使用。
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.')]
用於檢索增強生成的用法
有關如何將此向量儲存庫用於檢索增強生成 (RAG) 的指南，請參閱以下各節
儲存和載入
您也可以儲存和載入 FAISS 索引。這很有用，因此您不必每次使用它時都重新建立它。
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!')
合併
您還可以合併兩個 FAISS 向量儲存庫
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 參考
有關所有 FAISS 向量儲存庫功能和配置的詳細文件，請前往 API 參考：https://langchain-python.dev.org.tw/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html