Chroma¶

Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.

     

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Chroma is fully-typed, fully-tested and fully-documented.

Install Chroma with:

pip install chromadb

Chroma runs in various modes. See below for examples of each integrated with LlamaIndex.

• in-memory - in a python script or jupyter notebook
• in-memory with persistance - in a script or notebook and save/load to disk
• in a docker container - as a server running your local machine or in the cloud

Like any other database, you can:

• .add
• .get
• .update
• .upsert
• .delete
• .peek
• and .query runs the similarity search.

View full docs at docs.

Basic Example¶

In this basic example, we take the Paul Graham essay, split it into chunks, embed it using an open-source embedding model, load it into Chroma, and then query it.

If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

In [ ]:

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

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

In [ ]:

!pip install llama-index

!pip install llama-index

Creating a Chroma Index¶

In [ ]:

# !pip install llama-index chromadb --quiet
# !pip install chromadb
# !pip install sentence-transformers
# !pip install pydantic==1.10.11

# !pip install llama-index chromadb --quiet # !pip install chromadb # !pip install sentence-transformers # !pip install pydantic==1.10.11

In [ ]:

# 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

# 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

In [ ]:

# 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"]

# 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"]

Download Data

In [ ]:

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

!mkdir -p 'data/paul_graham/' !wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

In [ ]:

# 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>"))

# 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"{response}"))

/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'

The author worked on writing and programming growing up. They wrote short stories and tried writing programs on an IBM 1401 computer. Later, they got a microcomputer and started programming more extensively.

Basic Example (including saving to disk)¶

Extending the previous example, if you want to save to disk, simply initialize the Chroma client and pass the directory where you want the data to be saved to.

Caution: Chroma makes a best-effort to automatically save data to disk, however multiple in-memory clients can stomp each other's work. As a best practice, only have one client per path running at any given time.

In [ ]:

# 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>"))

# 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"{response}"))

The author worked on writing and programming growing up. They wrote short stories and tried writing programs on an IBM 1401 computer. Later, they got a microcomputer and started programming games and a word processor.

Basic Example (using the Docker Container)¶

You can also run the Chroma Server in a Docker container separately, create a Client to connect to it, and then pass that to LlamaIndex.

Here is how to clone, build, and run the Docker Image:

git clone [email protected]:chroma-core/chroma.git
docker-compose up -d --build

In [ ]:

# 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
)

# 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 )

In [ ]:

# 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>"))

# 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"{response}"))

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.

Update and Delete¶

While building toward a real application, you want to go beyond adding data, and also update and delete data.

Chroma has users provide ids to simplify the bookkeeping here. ids can be the name of the file, or a combined has like filename_paragraphNumber, etc.

Here is a basic example showing how to do various operations:

In [ ]:

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())

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