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Chroma

本笔记本介绍如何开始使用 Chroma 向量存储。

Chroma 是一个以AI为原生的开源向量数据库,专注于开发者的生产力和幸福感。Chroma 采用 Apache 2.0 许可证。查看 Chroma 的完整文档 此页面,并在 此页面 找到 LangChain 集成的 API 参考。

设置

要访问 Chroma 向量存储,您需要安装 langchain-chroma 集成包。

pip install -qU "langchain-chroma>=0.1.2"

凭证

您可以在没有任何凭证的情况下使用 Chroma 向量存储,只需安装上述软件包即可!

如果您想获得最佳的自动跟踪模型调用,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["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

初始化

基本初始化

以下是基本初始化,包括使用目录将数据本地保存。

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")
<!--IMPORTS:[{"imported": "Chroma", "source": "langchain_chroma", "docs": "https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html", "title": "Chroma"}]-->
from langchain_chroma import Chroma

vector_store = Chroma(
collection_name="example_collection",
embedding_function=embeddings,
persist_directory="./chroma_langchain_db", # Where to save data locally, remove if not necessary
)

从客户端初始化

您还可以从 Chroma 客户端初始化,这在您想更轻松地访问底层数据库时特别有用。

import chromadb

persistent_client = chromadb.PersistentClient()
collection = persistent_client.get_or_create_collection("collection_name")
collection.add(ids=["1", "2", "3"], documents=["a", "b", "c"])

vector_store_from_client = Chroma(
client=persistent_client,
collection_name="collection_name",
embedding_function=embeddings,
)

管理向量存储

一旦您创建了向量存储,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

我们可以通过使用 add_documents 函数将项目添加到我们的向量存储。

<!--IMPORTS:[{"imported": "Document", "source": "langchain_core.documents", "docs": "https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html", "title": "Chroma"}]-->
from uuid import uuid4

from langchain_core.documents import Document

document_1 = Document(
page_content="I had chocolate chip pancakes and scrambled eggs for breakfast this morning.",
metadata={"source": "tweet"},
id=1,
)

document_2 = Document(
page_content="The weather forecast for tomorrow is cloudy and overcast, with a high of 62 degrees.",
metadata={"source": "news"},
id=2,
)

document_3 = Document(
page_content="Building an exciting new project with LangChain - come check it out!",
metadata={"source": "tweet"},
id=3,
)

document_4 = Document(
page_content="Robbers broke into the city bank and stole $1 million in cash.",
metadata={"source": "news"},
id=4,
)

document_5 = Document(
page_content="Wow! That was an amazing movie. I can't wait to see it again.",
metadata={"source": "tweet"},
id=5,
)

document_6 = Document(
page_content="Is the new iPhone worth the price? Read this review to find out.",
metadata={"source": "website"},
id=6,
)

document_7 = Document(
page_content="The top 10 soccer players in the world right now.",
metadata={"source": "website"},
id=7,
)

document_8 = Document(
page_content="LangGraph is the best framework for building stateful, agentic applications!",
metadata={"source": "tweet"},
id=8,
)

document_9 = Document(
page_content="The stock market is down 500 points today due to fears of a recession.",
metadata={"source": "news"},
id=9,
)

document_10 = Document(
page_content="I have a bad feeling I am going to get deleted :(",
metadata={"source": "tweet"},
id=10,
)

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)
['f22ed484-6db3-4b76-adb1-18a777426cd6',
'e0d5bab4-6453-4511-9a37-023d9d288faa',
'877d76b8-3580-4d9e-a13f-eed0fa3d134a',
'26eaccab-81ce-4c0a-8e76-bf542647df18',
'bcaa8239-7986-4050-bf40-e14fb7dab997',
'cdc44b38-a83f-4e49-b249-7765b334e09d',
'a7a35354-2687-4bc2-8242-3849a4d18d34',
'8780caf1-d946-4f27-a707-67d037e9e1d8',
'dec6af2a-7326-408f-893d-7d7d717dfda9',
'3b18e210-bb59-47a0-8e17-c8e51176ea5e']

更新向量存储中的项目

现在我们已经将文档添加到我们的向量存储中,我们可以通过使用 update_documents 函数来更新现有文档。

updated_document_1 = Document(
page_content="I had chocolate chip pancakes and fried eggs for breakfast this morning.",
metadata={"source": "tweet"},
id=1,
)

updated_document_2 = Document(
page_content="The weather forecast for tomorrow is sunny and warm, with a high of 82 degrees.",
metadata={"source": "news"},
id=2,
)

vector_store.update_document(document_id=uuids[0], document=updated_document_1)
# You can also update multiple documents at once
vector_store.update_documents(
ids=uuids[:2], documents=[updated_document_1, updated_document_2]
)

从向量存储中删除项目

我们也可以按如下方式从我们的向量存储中删除项目:

vector_store.delete(ids=uuids[-1])

查询向量存储

一旦您的向量存储被创建并且相关文档已被添加,您很可能希望在运行链或代理期间查询它。

直接查询

相似性搜索

执行简单的相似性搜索可以如下进行:

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

如果您想执行相似性搜索并接收相应的分数,可以运行:

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=1.726390] The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]

按向量搜索

您也可以按向量搜索:

results = vector_store.similarity_search_by_vector(
embedding=embeddings.embed_query("I love green eggs and ham!"), k=1
)
for doc in results:
print(f"* {doc.page_content} [{doc.metadata}]")
* I had chocalate chip pancakes and fried eggs for breakfast this morning. [{'source': 'tweet'}]

其他搜索方法

还有多种其他搜索方法未在此笔记本中涵盖,例如MMR搜索或按向量搜索。有关AstraDBVectorStore可用搜索能力的完整列表,请查看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。有关不同搜索类型和您可以传递的kwargs的更多信息,请访问API参考这里

retriever = vector_store.as_retriever(
search_type="mmr", search_kwargs={"k": 1, "fetch_k": 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.')]

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:

API 参考

有关所有 Chroma 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/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html

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