Chroma
这个笔记本介绍了如何使用Chroma向量存储。
Chroma 是一个专注于开发者生产力和幸福感的 AI 原生开源向量数据库。Chroma 使用 Apache 2.0 协议授权。您可以在 该页面 查看
Chroma的完整文档，同时在 该页面 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"
Credentials
您可以无需任何凭据使用Chroma向量存储，只需安装上方的包即可！
如果您想要获得最佳的模型调用自动化跟踪，您也可以通过取消注释下方代码来设置您的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。
选择 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")
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
)
初始化从客户端
您也可以从Chroma客户端初始化，这在您希望更方便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
创建向量存储后，我们可以对其进行交互，通过添加和删除不同的项。
添加项到向量存储
我们可以通过使用add_documents函数来向我们的向量存储中添加项目。
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)
API 参考:文档
['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
现在我们已经将文档添加到了向量存储中，可以通过使用update_documents函数来更新现有的文档。
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]
)
删除向量存储中的项
我们还可以按照以下方式从向量存储中删除项目：
vector_store.delete(ids=uuids[-1])
查询向量存储
一旦您的向量存储已经创建并添加了相关文档，您很可能在运行链或代理的过程中希望对其进行查询。
查询直接
相似度搜索
简单进行相似性搜索可以按照以下方式进行：
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'}]
相似性搜索（带分数）
如果您想要执行相似性搜索并接收相应的评分，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'}]
搜索向量
您也可以通过向量进行搜索:
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 chocolate chip pancakes and fried eggs for breakfast this morning. [{'source': 'tweet'}]
其他搜索方法
在本笔记本中没有涵盖其他各种搜索方法，例如MMR搜索或向量搜索。要查看AstraDBVectorStore可用的完整搜索功能列表，请参阅API参考。
查询通过转换为检索器
您还可以将向量存储转换为检索器，以便在链中更轻松地使用。有关不同搜索类型和可以传递的kwargs的更多信息，请访问API参考文档这里。
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.')]
使用检索增强生成
对于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
详细文档请参阅所有Chroma向量存储功能和配置：
https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html