LangChain Embeddings¶

This page shows the current Chroma (1.5.3) and LangChain embedding integration patterns.

Use LangChain Embeddings With Chroma Collections¶

In Chroma 1.5.3, wrap a LangChain Embeddings implementation with ChromaLangchainEmbeddingFunction.

Query workaround in 1.5.3

If collection.query(query_texts=[...]) raises an error with wrapped LangChain embeddings, use query_embeddings=[lc_embeddings.embed_query("...")] instead.

Hugging FaceOpenAI

# pip install chromadb==1.5.3 langchain-core langchain-huggingface sentence-transformers
import chromadb
from chromadb.utils.embedding_functions import ChromaLangchainEmbeddingFunction
from langchain_huggingface import HuggingFaceEmbeddings

lc_embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-MiniLM-L6-v2"
)
chroma_ef = ChromaLangchainEmbeddingFunction(embedding_function=lc_embeddings)

client = chromadb.PersistentClient(path="./chroma-langchain-data")
collection = client.get_or_create_collection(
    name="my_collection",
    embedding_function=chroma_ef,
)
collection.add(ids=["1"], documents=["test document goes here"])

# pip install chromadb==1.5.3 langchain-core langchain-openai
import chromadb
from chromadb.utils.embedding_functions import ChromaLangchainEmbeddingFunction
from langchain_openai import OpenAIEmbeddings

lc_embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
chroma_ef = ChromaLangchainEmbeddingFunction(embedding_function=lc_embeddings)

client = chromadb.PersistentClient(path="./chroma-langchain-data")
collection = client.get_or_create_collection(
    name="my_collection",
    embedding_function=chroma_ef,
)
collection.add(ids=["1"], documents=["test document goes here"])

Use Chroma As A LangChain Vector Store¶

For LangChain vector stores, use the langchain-chroma package:

# pip install chromadb==1.5.3 langchain-core langchain-chroma langchain-openai
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

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

vector_store = Chroma(
    collection_name="docs_store",
    persist_directory="./chroma-langchain-store",
    embedding_function=embeddings,
)

vector_store.add_texts(
    texts=["foo", "bar", "baz"],
    metadatas=[{"topic": "a"}, {"topic": "b"}, {"topic": "c"}],
    ids=["1", "2", "3"],
)

results = vector_store.similarity_search("foo", k=2, filter={"topic": "a"})

Optional: Manual Adapter (Chroma -> LangChain)¶

If you already have a Chroma embedding function and want to use it in LangChain, you can adapt it to LangChain's Embeddings interface:

from chromadb.api.types import EmbeddingFunction
from langchain_core.embeddings import Embeddings


class ChromaEmbeddingsAdapter(Embeddings):
    def __init__(self, ef: EmbeddingFunction) -> None:
        self.ef = ef

    def embed_documents(self, texts: list[str]) -> list[list[float]]:
        return self.ef(texts)

    def embed_query(self, query: str) -> list[float]:
        return self.ef([query])[0]

Full Working Example¶

See examples/langchain/python/embeddings_example.py for a complete runnable script and dependency list. The example uses DeterministicFakeEmbedding so it runs without API keys.