Pinecone
Pinecone 是一个功能广泛的向量数据库。
此笔记本演示了如何使用与 Pinecone 向量数据库相关的功能。
设置
要使用 PineconeVectorStore，您首先需要安装合作伙伴包，以及本笔记本中使用的其他包。
%pip install -qU langchain-pinecone pinecone-notebooks
迁移说明：如果您从 langchain_community.vectorstores 的 Pinecone 实现迁移，则可能需要在安装 langchain-pinecone 之前删除 pinecone-client v2 依赖项，langchain-pinecone 依赖于 pinecone-client v3。
凭据
创建一个新的 Pinecone 帐户，或登录您现有的帐户，并创建一个 API 密钥以在本笔记本中使用。
import getpass
import os
import time
from pinecone import Pinecone, ServerlessSpec
if not os.getenv("PINECONE_API_KEY"):
os.environ["PINECONE_API_KEY"] = getpass.getpass("Enter your Pinecone API key: ")
pinecone_api_key = os.environ.get("PINECONE_API_KEY")
pc = Pinecone(api_key=pinecone_api_key)
如果您希望获得模型调用的自动跟踪，还可以通过取消下面的注释来设置您的 LangSmith API 密钥。
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
初始化
在初始化我们的向量存储之前，让我们连接到一个 Pinecone 索引。如果不存在名为 index_name 的索引，则将创建一个。
import time
index_name = "langchain-test-index" # change if desired
existing_indexes = [index_info["name"] for index_info in pc.list_indexes()]
if index_name not in existing_indexes:
pc.create_index(
name=index_name,
dimension=3072,
metric="cosine",
spec=ServerlessSpec(cloud="aws", region="us-east-1"),
)
while not pc.describe_index(index_name).status["ready"]:
time.sleep(1)
index = pc.Index(index_name)
现在我们的 Pinecone 索引已设置，我们可以初始化我们的向量存储。
- OpenAI
- HuggingFace
- 伪造嵌入
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)
from langchain_pinecone import PineconeVectorStore
vector_store = PineconeVectorStore(index=index, embedding=embeddings)
API 参考：PineconeVectorStore
管理向量存储
创建向量存储后，我们可以通过添加和删除不同的项目与之交互。
将项目添加到向量存储
我们可以使用 add_documents 函数将项目添加到我们的向量存储。
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 参考：Document
['167b8681-5974-467f-adcb-6e987a18df01',
'd16010fd-41f8-4d49-9c22-c66d5555a3fe',
'ffcacfb3-2bc2-44c3-a039-c2256a905c0e',
'cf3bfc9f-5dc7-4f5e-bb41-edb957394126',
'e99b07eb-fdff-4cb9-baa8-619fd8efeed3',
'68c93033-a24f-40bd-8492-92fa26b631a4',
'b27a4ecb-b505-4c5d-89ff-526e3d103558',
'4868a9e6-e6fb-4079-b400-4a1dfbf0d4c4',
'921c0e9c-0550-4eb5-9a6c-ed44410788b2',
'c446fc23-64e8-47e7-8c19-ecf985e9411e']
从向量存储中删除项目
vector_store.delete(ids=[uuids[-1]])
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
可以按如下方式执行简单的相似性搜索。
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'}]
带有分数的相似性搜索
您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=0.553187] The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees. [{'source': 'news'}]
其他搜索方法
还有更多在本笔记本中未列出的搜索方法（例如 MMR），要查找所有方法，请务必阅读 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，以便在链中更容易使用。
retriever = vector_store.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={"k": 1, "score_threshold": 0.5},
)
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。
API 参考
有关 ModuleNameVectorStore 所有功能和配置的详细文档，请前往 API 参考：https://python.langchain.ac.cn/v0.2/api_reference/pinecone/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html