sie-chroma

SIE integration for ChromaDB.

Installation

pip install sie-chroma

Features

SIEEmbeddingFunction: Custom embedding function for ChromaDB collections

Quick Start

Basic Usage

import chromadb
from sie_chroma import SIEEmbeddingFunction

# Create SIE embedding function
embedding_function = SIEEmbeddingFunction(
    base_url="http://localhost:8080",
    model="BAAI/bge-m3",
)

# Create ChromaDB client and collection
client = chromadb.Client()
collection = client.create_collection(
    name="my_collection",
    embedding_function=embedding_function,
)

# Add documents (embeddings are generated automatically)
collection.add(
    documents=[
        "Machine learning enables pattern recognition.",
        "Deep learning uses neural networks.",
        "Natural language processing analyzes text.",
    ],
    ids=["doc1", "doc2", "doc3"],
)

# Query the collection
results = collection.query(
    query_texts=["What is deep learning?"],
    n_results=2,
)
print(results["documents"])

With Persistent Storage

import chromadb
from sie_chroma import SIEEmbeddingFunction

# Persistent client
client = chromadb.PersistentClient(path="./chroma_data")

embedding_function = SIEEmbeddingFunction(
    base_url="http://localhost:8080",
    model="BAAI/bge-m3",
)

# Get or create collection
collection = client.get_or_create_collection(
    name="research_papers",
    embedding_function=embedding_function,
)

# Add documents with metadata
collection.add(
    documents=["Paper about transformers...", "Study on attention mechanisms..."],
    metadatas=[{"year": 2023}, {"year": 2024}],
    ids=["paper1", "paper2"],
)

# Query with metadata filtering
results = collection.query(
    query_texts=["attention in neural networks"],
    n_results=5,
    where={"year": {"$gte": 2023}},
)

With LangChain or LlamaIndex

The SIEEmbeddingFunction works with ChromaDB's LangChain and LlamaIndex integrations:

# LangChain
from langchain_chroma import Chroma
from sie_chroma import SIEEmbeddingFunction

embedding_function = SIEEmbeddingFunction(model="BAAI/bge-m3")
vectorstore = Chroma(
    collection_name="docs",
    embedding_function=embedding_function,  # Works directly!
)

# LlamaIndex
from llama_index.vector_stores.chroma import ChromaVectorStore

# SIE can also be used via LlamaIndex's SIEEmbedding

SIE Server

Start the SIE server before using this integration:

mise run serve -d cpu -p 8080

Testing

# Unit tests (no server required)
pytest

# Integration tests (requires running server)
pytest -m integration

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sie_chroma-0.6.29.tar.gz (11.1 kB view details)

Uploaded Aug 7, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

sie_chroma-0.6.29-py3-none-any.whl (4.2 kB view details)

Uploaded Aug 7, 2026 Python 3

File details

Details for the file sie_chroma-0.6.29.tar.gz.

File metadata

Download URL: sie_chroma-0.6.29.tar.gz
Upload date: Aug 7, 2026
Size: 11.1 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for sie_chroma-0.6.29.tar.gz
Algorithm	Hash digest
SHA256	`48d1b7d80d26c60eb2b46c648413fc153baf3f54c6977d8ecb0a0f2817971c80`
MD5	`f9912c875b5d0f1d01d8a93b7e44913d`
BLAKE2b-256	`36b8b94da05e3dbffcc305d112da38f6bf73b74986137a2f7785c31b588c0efa`

See more details on using hashes here.

Provenance

The following attestation bundles were made for sie_chroma-0.6.29.tar.gz:

Publisher: release-python.yml on superlinked/sie-internal

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: sie_chroma-0.6.29.tar.gz
- Subject digest: 48d1b7d80d26c60eb2b46c648413fc153baf3f54c6977d8ecb0a0f2817971c80
- Sigstore transparency entry: 2365289301
- Sigstore integration time: Aug 7, 2026
Source repository:
- Permalink: superlinked/sie-internal@3d72933ab5b6f5affee1355c2f3d789214d43375
- Branch / Tag: refs/tags/v0.6.29
- Owner: https://github.com/superlinked
- Access: private
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: self-hosted
- Publication workflow: release-python.yml@3d72933ab5b6f5affee1355c2f3d789214d43375
- Trigger Event: push