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Chroma

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.

Setup​

To access Chroma vector stores you'll need to install the langchain-chroma integration package.

pip install -qU "langchain-chroma>=0.1.2"

Credentials​

You can use the Chroma vector store without any credentials, simply installing the package above is enough!

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"

Initialization​

Basic Initialization​

Below is a basic initialization, including the use of a directory to save the data locally.

pip install -qU langchain-openai
import getpass

    os.environ["OPENAI_API_KEY"] = getpass.getpass()

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 neccesary
)

Initialization from client​

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

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,
)

Manage vector store​

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

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 chocalate 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 Reference:Document
['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']

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 chocalate 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_1]
)

Delete items from vector store​

We can also delete items from our vector store as follows:

vector_store.delete(ids=uuids[-1])

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.

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}]")
* 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'}]

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}]")
* [SIM=1.726390] The stock market is down 500 points today due to fears of a recession. [{'source': 'news'}]

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}]")
* I had chocalate chip pancakes and fried eggs for breakfast this morning. [{'source': 'tweet'}]

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.

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"})
[Document(metadata={'source': 'news'}, page_content='Robbers broke into the city bank and stole $1 million in cash.')]

Chain usage​

The code below shows how to use the vector store as a retriever in a simple RAG chain:

pip install -qU langchain-openai
import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o-mini")
from langchain import hub
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough

prompt = hub.pull("rlm/rag-prompt")


def format_docs(docs):
    return "\n\n".join(doc.page_content for doc in docs)


rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

rag_chain.invoke("What is LangGraph used for?")
'LangGraph is used for building stateful, agentic applications. It provides a framework that supports the development of such applications efficiently.'

API reference​

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

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