chroma

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 6c04b84d00a4 · 31 Oct 2026Report an issue or request a re-scan

• Publisherorchestra-research GitHub organizationOwner of the repository this skill is read from
• Source folderorchestra-research/ai-research-skills/15-rag/chromaThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict6c04b84d00a4A new audit runs when the source changes
• Repository statusNot archivedLast push 20 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for…

---
name: chroma
description: Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [RAG, Chroma, Vector Database, Embeddings, Semantic Search, Open Source, Self-Hosted, Document Retrieval, Metadata Filtering]
dependencies: [chromadb, sentence-transformers]
---

# 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

```bash
# Python
pip install chromadb

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

### Basic usage (Python)

```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

```python
# 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

```python
# 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)

```python
# 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

```python
# 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

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

### 6. Delete documents

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

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

## Persistent storage

```python
# 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)

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

### OpenAI

```python
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

```python
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

```python
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

```python
# Exact match
results = collection.query(
    query_texts=["query"],
    where={"category": "tutoria

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install chromadb
npm install chromadb @chroma-core/default-embed

git clone https://github.com/orchestra-research/ai-research-skills
git -C ai-research-skills checkout 6c04b84d00a4

Works in

1. 2026-10-31
   ✓SAFE B 89 / 100

   first audit · source 6c04b84d00a4

Questions about chroma

• github.com/orchestra-research/ai-research-skills/tree/main/15-ra — the audited source at commit 6c04b84d00a4 and every finding aboveObserved · 31 Oct 2026

Spotted something wrong? Report a correction · Methodology