SKILLoptionalHermes Optional Skills

Chroma

Name: Chroma
Author: Orchestra Research

Embedding database for RAG and semantic search

RAGChromaVector DatabaseEmbeddingsSemantic SearchOpen SourceSelf-HostedDocument Retrieval

Last registry verification2026-08-18v1.0.0Orchestra Research

Open original source ↗اقرأ بالعربية

Plain meaning

What does it add to Hermes?

Embedding database for RAG and semantic search

Chroma is a skill related to databases and data. It gives the agent a structured way to read or query data. Some tools may also write data, so clear limits matter.

This plain-language explanation is based on the publisher description. The original text remains visible for verification.

Use it when

Use it when your goal in databases and data is clear and you can limit it to the data and actions it actually needs.

Skip it when

Do not add it merely to experiment when Hermes already has a simpler path, or when you cannot review its source and permissions.

Who is it for?

Best for users who want a repeatable way of working inside Hermes.

Safe first test

Use a test database and a read-only account, then request a simple query that changes no records.

Original publisher description

Embedding database for RAG and semantic search

✓

Data source

This entry was indexed from Hermes Optional Skills. Our explanation interprets the type and domain without inventing a capability not present upstream.

Security review

The source is official or editorially reviewed, but you still need to review permissions and version compatibility.

Safe setup path

Inspect, install, then test.

01
Open the source
Match the publisher, license, and description to your need. Check the real update history.
02
Review permissions and secrets
Never paste a secret value into this site. Use environment-variable names and grant the smallest scope.
03
Copy setup only after review
The controls below copy text. They do not execute commands on your device.
04
Test with a non-sensitive task
Inspect the visible tools, then exclude write or delete tools you do not need.

Install command

Review the command, then copy it.

hermes skills install chroma

Hermes Belarabi does not execute this command. Installation happens on your device and remains subject to Hermes scanning and your review.

The full skill definition

Exactly what Hermes loads when this skill runs.

Reproduced from the official documentation. Read it before enabling the skill: this text becomes the agent's instructions.

Embedding database for RAG and semantic search.

Skill metadata

A lookup table. Do not read it all; find the row that applies to you.

Source	Optional — install with `hermes skills install official/mlops/chroma`
Path	`optional-skills/mlops/chroma`
Version	`1.0.0`
Author	Orchestra Research
License	MIT
Dependencies	`chromadb`, `sentence-transformers`
Platforms	linux, macos, windows
Tags	`RAG`, `Chroma`, `Vector Database`, `Embeddings`, `Semantic Search`, `Open Source`, `Self-Hosted`, `Document Retrieval`, `Metadata Filtering`

Reference: full SKILL.md

Explains the idea itself. Read it slowly; the later sections build on it.

The AI-native database for building LLM applications with memory.

When to use Chroma

Explains the idea itself. Read it slowly; the later sections build on it.

Use Chroma when:

Building RAG (retrieval-augmented generation) applications
Need local/self-hosted vector database
Want open-source solution (Apache 2.0)
Prototyping in notebooks
Semantic search over documents
Storing embeddings with metadata

Metrics:

24,300+ GitHub stars
1,900+ forks
v1.3.3 (stable, weekly releases)
Apache 2.0 license

Use alternatives instead:

Pinecone: Managed cloud, auto-scaling
FAISS: Pure similarity search, no metadata
Weaviate: Production ML-native database
Qdrant: High performance, Rust-based

Quick start

Ordered, practical steps. Run one and confirm it worked before moving on.

Installation

Shell5 lines

# Python
pip install chromadb

# JavaScript/TypeScript
npm install chromadb @chroma-core/default-embed

Basic usage (Python)

Python22 lines



# Create client
client = chromadb.Client()

# Create collection
collection = client.create_collection(name="my_collection")

# Add documents
collection.add(
    documents=["This is document 1", "This is document 2"],
    metadatas=[{"source": "doc1"}, {"source": "doc2"}],
    ids=["id1", "id2"]
)

# Query
results = collection.query(
    query_texts=["document about topic"],
    n_results=2
)

print(results)

Core operations

Explains the idea itself. Read it slowly; the later sections build on it.

1. Create collection

Python21 lines

# Simple collection
collection = client.create_collection("my_docs")

# With custom embedding function
from chromadb.utils import embedding_functions

openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-key",
    model_name="text-embedding-3-small"
)

collection = client.create_collection(
    name="my_docs",
    embedding_function=openai_ef
)

# Get existing collection
collection = client.get_collection("my_docs")

# Delete collection
client.delete_collection("my_docs")

2. Add documents

Python17 lines

# Add with auto-generated IDs
collection.add(
    documents=["Doc 1", "Doc 2", "Doc 3"],
    metadatas=[
        {"source": "web", "category": "tutorial"},
        {"source": "pdf", "page": 5},
        {"source": "api", "timestamp": "2025-01-01"}
    ],
    ids=["id1", "id2", "id3"]
)

# Add with custom embeddings
collection.add(
    embeddings=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    documents=["Doc 1", "Doc 2"],
    ids=["id1", "id2"]
)

3. Query (similarity search)

Python29 lines

# Basic query
results = collection.query(
    query_texts=["machine learning tutorial"],
    n_results=5
)

# Query with filters
results = collection.query(
    query_texts=["Python programming"],
    n_results=3,
    where={"source": "web"}
)

# Query with metadata filters
results = collection.query(
    query_texts=["advanced topics"],
    where={
        "$and": [
            {"category": "tutorial"},
            {"difficulty": {"$gte": 3}}
        ]
    }
)

# Access results
print(results["documents"])      # List of matching documents
print(results["metadatas"])      # Metadata for each doc
print(results["distances"])      # Similarity scores
print(results["ids"])            # Document IDs

4. Get documents

Python13 lines

# Get by IDs
docs = collection.get(
    ids=["id1", "id2"]
)

# Get with filters
docs = collection.get(
    where={"category": "tutorial"},
    limit=10
)

# Get all documents
docs = collection.get()

5. Update documents

Python6 lines

# Update document content
collection.update(
    ids=["id1"],
    documents=["Updated content"],
    metadatas=[{"source": "updated"}]
)

6. Delete documents

Python7 lines

# Delete by IDs
collection.delete(ids=["id1", "id2"])

# Delete with filter
collection.delete(
    where={"source": "outdated"}
)

Persistent storage

Explains the idea itself. Read it slowly; the later sections build on it.

Python10 lines

# Persist to disk
client = chromadb.PersistentClient(path="./chroma_db")

collection = client.create_collection("my_docs")
collection.add(documents=["Doc 1"], ids=["id1"])

# Data persisted automatically
# Reload later with same path
client = chromadb.PersistentClient(path="./chroma_db")
collection = client.get_collection("my_docs")

Embedding functions

Explains the idea itself. Read it slowly; the later sections build on it.

Default (Sentence Transformers)

Python3 lines

# Uses sentence-transformers by default
collection = client.create_collection("my_docs")
# Default model: all-MiniLM-L6-v2

OpenAI

Python11 lines

from chromadb.utils import embedding_functions

openai_ef = embedding_functions.OpenAIEmbeddingFunction(
    api_key="your-key",
    model_name="text-embedding-3-small"
)

collection = client.create_collection(
    name="openai_docs",
    embedding_function=openai_ef
)

HuggingFace

Python9 lines

huggingface_ef = embedding_functions.HuggingFaceEmbeddingFunction(
    api_key="your-key",
    model_name="sentence-transformers/all-mpnet-base-v2"
)

collection = client.create_collection(
    name="hf_docs",
    embedding_function=huggingface_ef
)

Custom embedding function

Python12 lines

from chromadb import Documents, EmbeddingFunction, Embeddings

class MyEmbeddingFunction(EmbeddingFunction):
    def __call__(self, input: Documents) -> Embeddings:
        # Your embedding logic
        return embeddings

my_ef = MyEmbeddingFunction()
collection = client.create_collection(
    name="custom_docs",
    embedding_function=my_ef
)

Metadata filtering

Explains the idea itself. Read it slowly; the later sections build on it.

Python28 lines

# Exact match
results = collection.query(
    query_texts=["query"],
    where={"category": "tutorial"}
)

# Comparison operators
results = collection.query(
    query_texts=["query"],
    where={"page": {"$gt": 10}}  # $gt, $gte, $lt, $lte, $ne
)

# Logical operators
results = collection.query(
    query_texts=["query"],
    where={
        "$and": [
            {"category": "tutorial"},
            {"difficulty": {"$lte": 3}}
        ]
    }  # Also: $or
)

# Contains
results = collection.query(
    query_texts=["query"],
    where={"tags": {"$in": ["python", "ml"]}}
)

LangChain integration

Explains the idea itself. Read it slowly; the later sections build on it.

Python20 lines

from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter

# Split documents
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
docs = text_splitter.split_documents(documents)

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

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

# As retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

LlamaIndex integration

Explains the idea itself. Read it slowly; the later sections build on it.

Python21 lines

from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import VectorStoreIndex, StorageContext


# Initialize Chroma
db = chromadb.PersistentClient(path="./chroma_db")
collection = db.get_or_create_collection("my_collection")

# Create vector store
vector_store = ChromaVectorStore(chroma_collection=collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Create index
index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context
)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is machine learning?")

Server mode

Explains the idea itself. Read it slowly; the later sections build on it.

Python15 lines

# Run Chroma server
# Terminal: chroma run --path ./chroma_db --port 8000

# Connect to server

from chromadb.config import Settings

client = chromadb.HttpClient(
    host="localhost",
    port=8000,
    settings=Settings(anonymized_telemetry=False)
)

# Use as normal
collection = client.get_or_create_collection("my_docs")

Best practices

Explains the idea itself. Read it slowly; the later sections build on it.

Use persistent client - Don't lose data on restart
Add metadata - Enables filtering and tracking
Batch operations - Add multiple docs at once
Choose right embedding model - Balance speed/quality
Use filters - Narrow search space
Unique IDs - Avoid collisions
Regular backups - Copy chroma_db directory
Monitor collection size - Scale up if needed
Test embedding functions - Ensure quality
Use server mode for production - Better for multi-user

Performance

Explains the idea itself. Read it slowly; the later sections build on it.

Operation	Latency	Notes
Add 100 docs	~1-3s	With embedding
Query (top 10)	~50-200ms	Depends on collection size
Metadata filter	~10-50ms	Fast with proper indexing

Resources

Explains the idea itself. Read it slowly; the later sections build on it.

GitHub: https://github.com/chroma-core/chroma ⭐ 24,300+
Docs: https://docs.trychroma.com
Discord: https://discord.gg/MMeYNTmh3x
Version: 1.3.3+
License: Apache 2.0