chroma

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• SKILL.md
• integration.md · references/

SKILL.md (first lines)

# 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

From future1314/hermes-agent-private, MIT.

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