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
Chromaat this page, and find the API reference for the LangChain integration at this page.
Chroma 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.
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 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"
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
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")
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.
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",
)
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.
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"),
)
Initialization from client
You can also initialize from a Chroma client, which is particularly useful if you want easier access to the underlying database.
Running Locally (In-Memory)
import chromadb
client = chromadb.Client()
Running Locally (with Data Persistence)
import chromadb
client = chromadb.PersistentClient(path="./chroma_langchain_db")
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)
Chroma Cloud
After setting your CHROMA_API_KEY, CHROMA_TENANT, and CHROMA_DATABASE, you can simply instantiate:
import chromadb
client = chromadb.CloudClient()
Access your Chroma DB
collection = client.get_or_create_collection("collection_name")
collection.add(ids=["1", "2", "3"], documents=["a", "b", "c"])
Create a Chroma Vectorstore
vector_store_from_client = Chroma(
client=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 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)
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]
)
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
Similarity search
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}]")
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}]")
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}]")
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"})
Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
API reference
For detailed documentation of all Chroma vector store features and configurations head to the API reference: https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html
Related
- Vector store conceptual guide
- Vector store how-to guides