Embedding database for RAG and semantic search.

Skill metadata


Source	Optional — install with `mibyan 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

The following is the complete skill definition that Mibyan loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

Chroma - Open-Source Embedding Database

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

When to use Chroma

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

Installation

# Python
pip install chromadb

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

Basic usage (Python)

import chromadb

# 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

1. Create collection

# 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

# 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)

# 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

# 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

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

6. Delete documents

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

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    where={"source": "outdated"}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

Persistent storage

# 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

Default (Sentence Transformers)

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

OpenAI

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

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

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

# 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

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

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

# 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

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

# Connect to server
import chromadb
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

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

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

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

تطبيق ديسكتوب

البداية

استخدام مبيان

الميزات

منصات المراسلة

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الأدلة والدروس

دليل المطوّر

المرجع

Chroma — Embedding database for RAG and semantic search

Skill metadata

Reference: full SKILL.md

Chroma - Open-Source Embedding Database

When to use Chroma

Quick start

Installation

Basic usage (Python)

Core operations

1. Create collection

2. Add documents

3. Query (similarity search)

4. Get documents

5. Update documents

6. Delete documents

Persistent storage

Embedding functions

Default (Sentence Transformers)

OpenAI

HuggingFace

Custom embedding function

Metadata filtering

LangChain integration

LlamaIndex integration

Server mode

Best practices

Performance

Resources

​Skill metadata

​Reference: full SKILL.md

​Chroma - Open-Source Embedding Database

​When to use Chroma

​Quick start

​Installation

​Basic usage (Python)

​Core operations

​1. Create collection

​2. Add documents

​3. Query (similarity search)

​4. Get documents

​5. Update documents

​6. Delete documents

​Persistent storage

​Embedding functions

​Default (Sentence Transformers)

​OpenAI

​HuggingFace

​Custom embedding function

​Metadata filtering

​LangChain integration

​LlamaIndex integration

​Server mode

​Best practices

​Performance

​Resources

Skill metadata

Reference: full SKILL.md

Chroma - Open-Source Embedding Database

When to use Chroma

Quick start

Installation

Basic usage (Python)

Core operations

1. Create collection

2. Add documents

3. Query (similarity search)

4. Get documents

5. Update documents

6. Delete documents

Persistent storage

Embedding functions

Default (Sentence Transformers)

OpenAI

HuggingFace

Custom embedding function

Metadata filtering

LangChain integration

LlamaIndex integration

Server mode

Best practices

Performance

Resources