Weaviate

本笔记本介绍如何在 LangChain 中使用 langchain-weaviate 包开始使用 Weaviate 向量存储。

Weaviate 是一个开源向量数据库。它允许您存储数据对象和来自您喜欢的机器学习模型的向量嵌入，并无缝扩展到数十亿个数据对象。

要使用此集成，您需要有一个正在运行的 Weaviate 数据库实例。

最低版本

此模块需要 Weaviate 1.23.7 或更高版本。我们建议您使用 Weaviate 的最新版本。

连接到 Weaviate

在本笔记本中，我们假设您在 http://localhost:8080 上运行了一个本地的 Weaviate 实例，并且端口 50051 已开放以支持 gRPC 流量。因此，我们将通过以下方式连接到 Weaviate：

weaviate_client = weaviate.connect_to_local()

其他部署选项

Weaviate 可以通过多种方式进行部署，例如使用 Weaviate Cloud Services (WCS)、Docker 或 Kubernetes。

如果您的 Weaviate 实例以其他方式部署，在这里阅读更多信息关于连接到 Weaviate 的不同方式。您可以使用不同的 辅助函数 或 创建自定义实例。

请注意，您需要一个 v4 客户端 API，这将创建一个 weaviate.WeaviateClient 对象。

认证

一些 Weaviate 实例，例如运行在 WCS 上的实例，启用了认证，例如 API 密钥和/或用户名+密码认证。

有关更多信息，请阅读 客户端认证指南，以及 深入的认证配置页面。

安装

# install package
# %pip install -Uqq langchain-weaviate
# %pip install openai tiktoken langchain

环境设置

此笔记本通过 OpenAIEmbeddings 使用 OpenAI API。我们建议获取一个 OpenAI API 密钥，并将其作为环境变量导出，名称为 OPENAI_API_KEY。

完成后，您的 OpenAI API 密钥将自动读取。如果您对环境变量不熟悉，可以在 这里 或 本指南 中了解更多信息。

使用方法

按相似性查找对象

以下是如何通过与查询相似的对象进行查找的示例，从数据导入到查询 Weaviate 实例。

第一步：数据导入

首先，我们将创建数据以添加到 Weaviate，通过加载和分块长文本文件的内容。

from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.
  warn_deprecated(

现在，我们可以导入数据。

为此，连接到 Weaviate 实例并使用生成的 weaviate_client 对象。例如，我们可以如下所示导入文档：

import weaviate
from langchain_weaviate.vectorstores import WeaviateVectorStore

weaviate_client = weaviate.connect_to_local()
db = WeaviateVectorStore.from_documents(docs, embeddings, client=weaviate_client)

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)

第2步：执行搜索

我们现在可以执行相似性搜索。这将根据存储在Weaviate中的嵌入和从查询文本生成的等效嵌入，返回与查询文本最相似的文档。

query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)

# Print the first 100 characters of each result
for i, doc in enumerate(docs):
    print(f"\nDocument {i+1}:")
    print(doc.page_content[:100] + "...")

Document 1:
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Ac...

Document 2:
And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of ...

Document 3:
Vice President Harris and I ran for office with a new economic vision for America. 

Invest in Ameri...

Document 4:
A former top litigator in private practice. A former federal public defender. And from a family of p...

您还可以添加过滤器，这将根据过滤条件包含或排除结果。（请参见更多过滤示例.）

from weaviate.classes.query import Filter

for filter_str in ["blah.txt", "state_of_the_union.txt"]:
    search_filter = Filter.by_property("source").equal(filter_str)
    filtered_search_results = db.similarity_search(query, filters=search_filter)
    print(len(filtered_search_results))
    if filter_str == "state_of_the_union.txt":
        assert len(filtered_search_results) > 0  # There should be at least one result
    else:
        assert len(filtered_search_results) == 0  # There should be no results

0
4

还可以提供k，这是返回结果数量的上限。

search_filter = Filter.by_property("source").equal("state_of_the_union.txt")
filtered_search_results = db.similarity_search(query, filters=search_filter, k=3)
assert len(filtered_search_results) <= 3

量化结果相似性

您可以选择性地检索相关性“分数”。这是一个相对分数，表示特定搜索结果在搜索结果池中的优劣。

请注意，这是相对分数，意味着它不应被用来确定相关性的阈值。然而，它可以用于比较整个搜索结果集中不同搜索结果的相关性。

docs = db.similarity_search_with_score("country", k=5)

for doc in docs:
    print(f"{doc[1]:.3f}", ":", doc[0].page_content[:100] + "...")

0.935 : For that purpose we’ve mobilized American ground forces, air squadrons, and ship deployments to prot...
0.500 : And built the strongest, freest, and most prosperous nation the world has ever known. 

Now is the h...
0.462 : If you travel 20 miles east of Columbus, Ohio, you’ll find 1,000 empty acres of land. 

It won’t loo...
0.450 : And my report is this: the State of the Union is strong—because you, the American people, are strong...
0.442 : Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Ac...

搜索机制

similarity_search 使用 Weaviate 的 混合搜索。

混合搜索结合了向量搜索和关键词搜索，alpha 是向量搜索的权重。similarity_search 函数允许您作为 kwargs 传递额外参数。有关可用参数，请参见此 参考文档。

因此，您可以通过添加 alpha=0 来执行纯关键词搜索，如下所示：

docs = db.similarity_search(query, alpha=0)
docs[0]

Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': 'state_of_the_union.txt'})

持久性

通过 langchain-weaviate 添加的任何数据将根据其配置在 Weaviate 中持久化。

例如，WCS 实例被配置为无限期持久化数据，而 Docker 实例可以设置为在卷中持久化数据。了解更多关于 Weaviate 的持久性。

多租户

多租户 允许您在单个 Weaviate 实例中拥有大量隔离的数据集合，且它们具有相同的集合配置。这对于多用户环境非常有利，例如构建 SaaS 应用程序，其中每个最终用户将拥有自己隔离的数据集合。

要使用多租户，向向量存储提供 tenant 参数是必要的。

因此，在添加任何数据时，请提供如下所示的 tenant 参数。

db_with_mt = WeaviateVectorStore.from_documents(
    docs, embeddings, client=weaviate_client, tenant="Foo"
)

2024-Mar-26 03:40 PM - langchain_weaviate.vectorstores - INFO - Tenant Foo does not exist in index LangChain_30b9273d43b3492db4fb2aba2e0d6871. Creating tenant.

在执行查询时，也请提供 tenant 参数。

db_with_mt.similarity_search(query, tenant="Foo")

检索器选项

Weaviate 也可以用作检索器

最大边际相关性搜索 (MMR)

除了在检索器对象中使用 similaritysearch，您还可以使用 mmr。

retriever = db.as_retriever(search_type="mmr")
retriever.invoke(query)[0]

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)

Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': 'state_of_the_union.txt'})

与 LangChain 一起使用

大型语言模型 (LLMs) 的一个已知局限性是它们的训练数据可能过时，或者不包括您所需的特定领域知识。

请看下面的示例：

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
llm.predict("What did the president say about Justice Breyer")

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.openai.ChatOpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.
  warn_deprecated(
/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `predict` was deprecated in LangChain 0.1.7 and will be removed in 0.2.0. Use invoke instead.
  warn_deprecated(
/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)

"I'm sorry, I cannot provide real-time information as my responses are generated based on a mixture of licensed data, data created by human trainers, and publicly available data. The last update was in October 2021."

向量存储通过提供存储和检索相关信息的方法来补充 LLM。这使您能够结合 LLM 和向量存储的优势，利用 LLM 的推理和语言能力以及向量存储检索相关信息的能力。

结合 LLM 和向量存储的两个著名应用是：

• 问答
• 检索增强生成 (RAG)

问答与来源

在langchain中，问答可以通过使用向量存储来增强。让我们看看如何做到这一点。

本节使用RetrievalQAWithSourcesChain，该链从索引中查找文档。

首先，我们将文本再次分块并导入到Weaviate向量存储中。

from langchain.chains import RetrievalQAWithSourcesChain
from langchain_openai import OpenAI

with open("state_of_the_union.txt") as f:
    state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)

docsearch = WeaviateVectorStore.from_texts(
    texts,
    embeddings,
    client=weaviate_client,
    metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))],
)

现在我们可以构建链，并指定检索器：

chain = RetrievalQAWithSourcesChain.from_chain_type(
    OpenAI(temperature=0), chain_type="stuff", retriever=docsearch.as_retriever()
)

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.llms.openai.OpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.
  warn_deprecated(

并运行链，询问问题：

chain(
    {"question": "What did the president say about Justice Breyer"},
    return_only_outputs=True,
)

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.
  warn_deprecated(
/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)
/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)

{'answer': ' The president thanked Justice Stephen Breyer for his service and announced his nomination of Judge Ketanji Brown Jackson to the Supreme Court.\n',
 'sources': '31-pl'}

检索增强生成

将 LLM 和向量存储结合起来的另一个非常流行的应用是检索增强生成（RAG）。这是一种使用检索器从向量存储中查找相关信息的技术，然后使用 LLM 根据检索到的数据和提示提供输出。

我们从一个类似的设置开始：

with open("state_of_the_union.txt") as f:
    state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(state_of_the_union)

docsearch = WeaviateVectorStore.from_texts(
    texts,
    embeddings,
    client=weaviate_client,
    metadatas=[{"source": f"{i}-pl"} for i in range(len(texts))],
)

retriever = docsearch.as_retriever()

我们需要为 RAG 模型构建一个模板，以便检索到的信息可以填充到模板中。

from langchain_core.prompts import ChatPromptTemplate

template = """You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.
Question: {question}
Context: {context}
Answer:
"""
prompt = ChatPromptTemplate.from_template(template)

print(prompt)

input_variables=['context', 'question'] messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['context', 'question'], template="You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\nQuestion: {question}\nContext: {context}\nAnswer:\n"))]

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

运行该单元后，我们得到一个非常相似的输出。

from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain.invoke("What did the president say about Justice Breyer")

/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)
/workspaces/langchain-weaviate/.venv/lib/python3.12/site-packages/pydantic/main.py:1024: PydanticDeprecatedSince20: The `dict` method is deprecated; use `model_dump` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.6/migration/
  warnings.warn('The `dict` method is deprecated; use `model_dump` instead.', category=PydanticDeprecatedSince20)

"The president honored Justice Stephen Breyer for his service to the country as an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. The president also mentioned nominating Circuit Court of Appeals Judge Ketanji Brown Jackson to continue Justice Breyer's legacy of excellence. The president expressed gratitude towards Justice Breyer and highlighted the importance of nominating someone to serve on the United States Supreme Court."

但请注意，由于模板的构建由您决定，您可以根据需要进行自定义。

总结与资源

Weaviate 是一个可扩展的、适合生产环境的向量存储。

此集成允许 Weaviate 与 LangChain 一起使用，以增强大型语言模型的能力，提供强大的数据存储。它的可扩展性和适合生产环境的特性使其成为您的 LangChain 应用程序的优秀向量存储选择，并将缩短您的生产时间。

相关

• 向量存储 概念指南
• 向量存储 操作指南