Chroma
本笔记本介绍了如何开始使用 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 基于 Apache 2.0 许可。请在 此页面 查看
Chroma的完整文档，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"
凭据
您可以使用 Chroma 向量存储而无需任何凭据，只需安装上面的软件包就足够了！
如果您想获得一流的模型调用自动跟踪，您还可以通过取消注释以下代码来设置您的 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
下面是一个基本初始化，包括使用目录在本地保存数据。
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
)
从客户端初始化
您还可以从 Chroma 客户端初始化，如果您想更容易地访问底层数据库，这将特别有用。
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，我们可以通过添加和删除不同的项来与之交互。
将项添加到向量存储
我们可以使用 add_documents 函数将项添加到向量存储。
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
现在我们已经向向量存储添加了文档，我们可以使用 update_documents 函数更新现有文档。
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
我们还可以按如下方式从向量存储中删除项
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按如下方式完成
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，您可以运行
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
您还可以按向量搜索
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
本笔记本中未涵盖各种其他搜索方法，例如 MMR 搜索或按向量搜索。有关 AstraDBVectorStore 可用的完整搜索功能列表，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，以便在链中更轻松地使用。有关您可以传递的不同搜索类型和 kwargs 的更多信息，请访问 此处 的 API 参考。
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
有关如何将此向量存储用于检索增强生成 (RAG) 的指南，请参阅以下部分
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.ac.cn/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html