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LlamaIndex Python Documentation
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Chroma

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许可证发布。

Discord    License    Integration Tests

Chroma是完全类型化、完全测试和完全文档化的。

使用以下命令安装 Chroma：

Terminal window
pip install chromadb

Chroma 可在多种模式下运行。以下展示了与 LlamaIndex 集成的各种模式示例。

  • in-memory - 在 Python 脚本或 Jupyter 笔记本中
  • in-memory with persistence - 在脚本或笔记本中，并保存/加载到磁盘
  • in a docker container - 作为在您本地机器或云端运行的服务器

与任何其他数据库一样，您可以：

  • .add
  • .get
  • .update
  • .upsert
  • .delete
  • .peek
  • 并且 .query 执行相似性搜索。

查看完整文档请访问文档。

基础示例

Section titled “Basic Example”

在这个基础示例中，我们获取Paul Graham的文章，将其分割成多个片段，使用开源嵌入模型进行嵌入处理，加载到Chroma中，然后进行查询。

如果您在 Colab 上打开这个笔记本，您可能需要安装 LlamaIndex 🦙。

%pip install llama-index-vector-stores-chroma
%pip install llama-index-embeddings-huggingface
!pip install llama-index

创建 Chroma 索引

Section titled “Creating a Chroma Index”
# !pip install llama-index chromadb --quiet
# !pip install chromadb
# !pip install sentence-transformers
# !pip install pydantic==1.10.11
# import
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from IPython.display import Markdown, display
import chromadb
# set up OpenAI
import os
import getpass


os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai


openai.api_key = os.environ["OPENAI_API_KEY"]

下载数据

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
# create client and a new collection
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")


# define embedding function
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")


# load documents
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()


# set up ChromaVectorStore and load in data
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)


# Query Data
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
/Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
/Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
  warn("The installed version of bitsandbytes was compiled without GPU support. "




'NoneType' object has no attribute 'cadam32bit_grad_fp32'

作者从小就开始从事写作和编程。他们撰写短篇小说，并尝试在IBM 1401计算机上编写程序。后来，他们获得了一台微型计算机，并开始更广泛地进行编程。

基础示例（包含保存到磁盘）

Section titled “Basic Example (including saving to disk)”

扩展之前的示例，如果您想要保存到磁盘，只需初始化 Chroma 客户端并传入您希望数据保存的目录路径。

Caution: Chroma会尽最大努力自动将数据保存到磁盘，但多个内存中的客户端可能会相互覆盖工作内容。最佳实践是，在任何给定时间，每个路径只运行一个客户端。

# save to disk


db = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db.get_or_create_collection("quickstart")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)


index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)


# load from disk
db2 = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db2.get_or_create_collection("quickstart")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
index = VectorStoreIndex.from_vector_store(
    vector_store,
    embed_model=embed_model,
)


# Query Data from the persisted index
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))

作者从小就开始从事写作和编程。他们撰写短篇故事，并尝试在IBM 1401计算机上编写程序。后来，他们获得了一台微型计算机，并开始编写游戏和文字处理软件。

基础示例（使用Docker容器）

Section titled “Basic Example (using the Docker Container)”

你也可以在Docker容器中单独运行Chroma服务器，创建一个客户端连接它，然后将其传递给LlamaIndex。

以下是克隆、构建和运行 Docker 镜像的方法：

git clone git@github.com:chroma-core/chroma.git
docker-compose up -d --build
# create the chroma client and add our data
import chromadb


remote_db = chromadb.HttpClient()
chroma_collection = remote_db.get_or_create_collection("quickstart")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)


index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)
# Query Data from the Chroma Docker index
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
Growing up, the author wrote short stories, programmed on an IBM 1401, and wrote programs on a TRS-80 microcomputer. He also took painting classes at Harvard and worked as a de facto studio assistant for a painter. He also tried to start a company to put art galleries online, and wrote software to build online stores.

更新与删除

Section titled “Update and Delete”

在构建实际应用程序的过程中，您不仅需要添加数据，还需要更新和删除数据。

Chroma让用户提供ids来简化此处的簿记工作。ids可以是文件名，也可以是类似filename_paragraphNumber的组合哈希值等。

以下是一个基础示例，展示如何进行各种操作：

doc_to_update = chroma_collection.get(limit=1)
doc_to_update["metadatas"][0] = {
    **doc_to_update["metadatas"][0],
    **{"author": "Paul Graham"},
}
chroma_collection.update(
    ids=[doc_to_update["ids"][0]], metadatas=[doc_to_update["metadatas"][0]]
)
updated_doc = chroma_collection.get(limit=1)
print(updated_doc["metadatas"][0])


# delete the last document
print("count before", chroma_collection.count())
chroma_collection.delete(ids=[doc_to_update["ids"][0]])
print("count after", chroma_collection.count())
{'_node_content': '{"id_": "be08c8bc-f43e-4a71-ba64-e525921a8319", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "2cbecdbb-0840-48b2-8151-00119da0995b", "node_type": null, "metadata": {}, "hash": "4c702b4df575421e1d1af4b1fd50511b226e0c9863dbfffeccb8b689b8448f35"}, "3": {"node_id": "6a75604a-fa76-4193-8f52-c72a7b18b154", "node_type": null, "metadata": {}, "hash": "d6c408ee1fbca650fb669214e6f32ffe363b658201d31c204e85a72edb71772f"}}, "hash": "b4d0b960aa09e693f9dc0d50ef46a3d0bf5a8fb3ac9f3e4bcf438e326d17e0d8", "text": "", "start_char_idx": 0, "end_char_idx": 4050, "text_template": "{metadata_str}\\n\\n{content}", "metadata_template": "{key}: {value}", "metadata_seperator": "\\n"}', 'author': 'Paul Graham', 'doc_id': '2cbecdbb-0840-48b2-8151-00119da0995b', 'document_id': '2cbecdbb-0840-48b2-8151-00119da0995b', 'ref_doc_id': '2cbecdbb-0840-48b2-8151-00119da0995b'}
count before 20
count after 19