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This notebook covers how to get started with the Chroma vector store.
Chroma is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0. View the full docs of Chroma at this page, and find the API reference for the LangChain integration at this page.
Chroma CloudChroma Cloud powers serverless vector and full-text search. It’s extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.Get started with Chroma Cloud

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Setup

To access Chroma vector stores you’ll need to install the langchain-chroma integration package. 
pip install -qU "langchain-chroma>=0.1.2"

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Credentials

You can use the Chroma vector store without any credentials, simply installing the package above is enough! If you are a Chroma Cloud user, set your CHROMA_TENANT, CHROMA_DATABASE, and CHROMA_API_KEY environment variables. When you install the chromadb package you also get access to the Chroma CLI, which can set these for you. First, login via the CLI, and then use the connect command: 
chroma db connect [db_name] --env-file
If you want to get best in-class automated tracing of your model calls you can also set your LangSmith API key by uncommenting below: 
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
os.environ["LANGSMITH_TRACING"] = "true"

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Initialization

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Basic Initialization

Below is a basic initialization, including the use of a directory to save the data locally. 
# | output: false
# | echo: false
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")

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Running Locally (In-Memory)

You can get a Chroma server running in memory by simply instantiating a Chroma instance with a collection name and your embeddings provider: 
from langchain_chroma import Chroma

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
)
If you don’t need data persistence, this is a great option for experimenting while building your AI application with LangChain.

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Running Locally (with Data Persistence)

You can provide the persist_directory argument to save your data across multiple runs of your program: 
from langchain_chroma import Chroma

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    persist_directory="./chroma_langchain_db",
)

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Connecting to a Chroma Server

If you have a Chroma server running locally, or you have deployed one yourself, you can connect to it by providing the host argument. For example, you can start a Chroma server running locally with chroma run, and then connect it with host='localhost': 
from langchain_chroma import Chroma

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    host="localhost",
)
For other deployments you can use the port, ssl, and headers arguments to customize your connection.

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Chroma Cloud

Chroma Cloud users can also build with LangChain. Provide your Chroma instance with your Chroma Cloud API key, tenant, and DB name: 
from langchain_chroma import Chroma

vector_store = Chroma(
    collection_name="example_collection",
    embedding_function=embeddings,
    chroma_cloud_api_key=os.getenv("CHROMA_API_KEY"),
    tenant=os.getenv("CHROMA_TENANT"),
    database=os.getenv("CHROMA_DATABASE"),
)

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Initialization from client

You can also initialize from a Chroma client, which is particularly useful if you want easier access to the underlying database.

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Running Locally (In-Memory)

import chromadb

client = chromadb.Client()

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Running Locally (with Data Persistence)

import chromadb

client = chromadb.PersistentClient(path="./chroma_langchain_db")

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Connecting to a Chroma Server

For example, if you are running a Chroma server locally (using chroma run): 
import chromadb

client = chromadb.HttpClient(host="localhost", port=8000, ssl=False)

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Chroma Cloud

After setting your CHROMA_API_KEY, CHROMA_TENANT, and CHROMA_DATABASE, you can simply instantiate: 
import chromadb

client = chromadb.CloudClient()

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Access your Chroma DB

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

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Create a Chroma Vectorstore

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

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Manage vector store

Once you have created your vector store, we can interact with it by adding and deleting different items.

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Add items to vector store

We can add items to our vector store by using the add_documents function. 
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)

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Update items in vector store

Now that we have added documents to our vector store, we can update existing documents by using the update_documents function. 
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]
)

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Delete items from vector store

We can also delete items from our vector store as follows: 
vector_store.delete(ids=uuids[-1])

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Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

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Query directly

Performing a simple similarity search can be done as follows: 
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}]")

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Similarity search with score

If you want to execute a similarity search and receive the corresponding scores you can run: 
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}]")

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Search by vector

You can also search by vector: 
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}]")

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Other search methods

There are a variety of other search methods that are not covered in this notebook, such as MMR search or searching by vector. For a full list of the search abilities available for AstraDBVectorStore check out the API reference.

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Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains. For more information on the different search types and kwargs you can pass, please visit the API reference here. 
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"})

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Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

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API reference

For detailed documentation of all Chroma vector store features and configurations head to the API reference: python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html
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