chroma

Embedding database for RAG and semantic search.

  • RAG
  • Chroma
  • Vector Database
  • Embeddings
  • Semantic Search
  • Open Source
  • Self-Hosted
  • Document Retrieval
  • Metadata Filtering

Declared platforms: linux · macos · windows

Install
npx skills add 'https://github.com/NousResearch/hermes-agent/tree/main/optional-skills/mlops/chroma'
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main · 24fd22bScanned 2026-09-15

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references/integration.md

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Chroma Integration Guide

Integration with LangChain, LlamaIndex, and frameworks.

LangChain

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings

vectorstore = Chroma.from_documents(
    documents=docs,
    embedding=OpenAIEmbeddings(),
    persist_directory="./chroma_db"
)

# Query
results = vectorstore.similarity_search("query", k=3)

# As retriever
retriever = vectorstore.as_retriever()

LlamaIndex

from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb

db = chromadb.PersistentClient(path="./chroma_db")
collection = db.get_or_create_collection("docs")

vector_store = ChromaVectorStore(chroma_collection=collection)

Resources