The AI-native embedding database

Current Version: 0.6.3
(For up to date version: Main ChromaDB)

# Deploy and Host ChromaDB on Railway ChromaDB is the AI-native open-source embedding database that makes it easy to build AI applications with embeddings. ChromaDB is designed to be easy, fast and effecient and is optimized for managing high-dimensional vector data with built-in distance functions and metadata filtering. ## About Hosting ChromaDB Hosting ChromaDB provides you with a powerful embedding database which is capable of handling vector-data, similarity searches and metadata filtering at scale. ChromaDB excels at semantic search, similarity matching and retrieval-agumented generation (RAG) workflows. ChromaDB deployments on Railway benefit from scalable CPU, RAM and storage while supporting enterprise-grade network security through built-in token authentication and Railway's secure private network features. Railway provides automated deployment and comprehensive logging to support your AI application operations. ## Common Use Cases - **Retrieval-Augmented Generation (RAG)**: Store tokenized knowledge, search-results and other data that AI applications can use for better contextual understanding and personalized responses. - **Semantic Search Applications**: Powers search engines and applications that seek to find patterns that match not just similarity but meaning, leading to powerfull and relevant results. - **Recommendation Systems**: Build strong recommendation engines for e-commerce, content platforms, and social media that suggest items and media based on user preferences. - **AI Agent Memory**: Serve long-term persistent memory to AI agents, which helps agents remember past interactions, learned preferences and contextual information. ## Dependencies for ChromaDB Hosting - `chromadb`: The core ChromaDB Python package - `uvicorn`: ASGI server for running the ChromaDB HTTP server ### Deployment Dependencies - The official ChromaDB image: https://hub.docker.com/r/chromadb/chroma - The GitHub repo for ChromaDB: https://github.com/chroma-core/chroma ## Implementation Details ### Authentication Configuration This ChromaDB template uses token authentication by default as specified in the `CHROMA_SERVER_AUTHN_PROVIDER` service variable. A secure token is automatically generated and stored in the `CHROMA_SERVER_AUTHN_CREDENTIALS` service variable. The authentication system provides secure access to your vector database, ensuring that only authorized applications can read from or write to your collections. ### Network Configuration The template is configured to work with both public and private networking: - **Public Access**: Available through the `CHROMA_PUBLIC_URL` service variable for external connections - **Private Network**: Recommended for production use through the `CHROMA_PRIVATE_URL` service variable within Railway's private network To enable private network access, set `CHROMA_HOST_ADDR` to `::` on the ChromaDB service. ## Quick Start Guide Here's a super quick rundown of how to use this template. Assuming we have this template deployed and we have another NodeJS service next to it that we want to use to connect to ChromaDB. You can easily connect by doing the following: ### Setting Up Service Variables In your client application service, create the following service variables: ``` CHROMA_TOKEN="${{Chroma.CHROMA_SERVER_AUTHN_CREDENTIALS}}" CHROMA_URL="${{Chroma.CHROMA_PUBLIC_URL}}" ``` > **Note**: This assumes the ChromaDB service name wasn't changed from "Chroma". ### Creating a Client Connection In your application, add the following code to connect to ChromaDB: ```typescript import { ChromaClient } from "chromadb"; const chroma = new ChromaClient({ path: process.env.CHROMA_URL, auth: { provider: "token", credentials: process.env.CHROMA_TOKEN, tokenHeaderType: "AUTHORIZATION" } }); // Example: Create a collection and add embeddings const collection = await chroma.createCollection({ name: "my_collection" }); await collection.add({ ids: ["id1", "id2"], embeddings: [[1.2, 2.3, 4.5], [6.7, 8.9, 1.2]], metadatas: [{"source": "doc1"}, {"source": "doc2"}], documents: ["This is document 1", "This is document 2"] }); ``` > **Note**: Ensure you have the `chromadb` package installed in your application dependencies. ### Private Network Connection For production deployments, use Railway's private network for enhanced security: 1. Set `CHROMA_HOST_ADDR` to `::` in your ChromaDB service 2. Update your client service variables to use the private URL: ``` CHROMA_URL="${{Chroma.CHROMA_PRIVATE_URL}}" ``` ## Why Deploy ChromaDB on Railway? Railway is a singular platform to deploy your AI infrastructure stack. Railway will host your vector database so you don't have to deal with configuration complexities, while allowing you to scale vertically based on your embedding storage and query needs. By deploying ChromaDB on Railway, you are one step closer to supporting a complete AI application stack with minimal operational burden. Host your embedding database, AI platforms, application servers, and more on Railway to build powerful semantic search and RAG applications.