# LightRAG Helm Chart This is the Helm chart for LightRAG, used to deploy LightRAG services on a Kubernetes cluster. There are two recommended deployment methods for LightRAG: 1. **Lightweight Deployment**: Using built-in lightweight storage, suitable for testing and small-scale usage 2. **Full Deployment**: Using external databases (such as PostgreSQL and Neo4J), suitable for production environments and large-scale usage ## Prerequisites Make sure the following tools are installed and configured: * **Kubernetes cluster** * A running Kubernetes cluster is required. * For local development or demos you can use [Minikube](https://minikube.sigs.k8s.io/docs/start/) (needs ≥ 2 CPUs, ≥ 4 GB RAM, and Docker/VM-driver support). * Any standard cloud or on-premises Kubernetes cluster (EKS, GKE, AKS, etc.) also works. * **kubectl** * The Kubernetes command-line interface. * Follow the official guide: [Install and Set Up kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl). * **Helm** (v3.x+) * Kubernetes package manager used by the scripts below. * Install it via the official instructions: [Installing Helm](https://helm.sh/docs/intro/install/). ## Lightweight Deployment (No External Databases Required) Uses built-in lightweight storage components with no need to configure external databases: ```bash helm upgrade --install lightrag ./lightrag \ --namespace rag \ --set-string env.LIGHTRAG_KV_STORAGE=JsonKVStorage \ --set-string env.LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage \ --set-string env.LIGHTRAG_GRAPH_STORAGE=NetworkXStorage \ --set-string env.LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage \ --set-string env.LLM_BINDING=openai \ --set-string env.LLM_MODEL=gpt-4o-mini \ --set-string env.LLM_BINDING_HOST=$OPENAI_API_BASE \ --set-string env.LLM_BINDING_API_KEY=$OPENAI_API_KEY \ --set-string env.EMBEDDING_BINDING=openai \ --set-string env.EMBEDDING_MODEL=text-embedding-ada-002 \ --set-string env.EMBEDDING_DIM=1536 \ --set-string env.EMBEDDING_BINDING_API_KEY=$OPENAI_API_KEY ``` You can refer to: [install_lightrag_dev.sh](install_lightrag_dev.sh) You can use it directly like this: ```bash export OPENAI_API_BASE= export OPENAI_API_KEY= bash ./install_lightrag_dev.sh ``` Then you can Access the application ```bash 1. Run this port-forward command in your terminal: kubectl --namespace rag port-forward svc/lightrag-dev 9621:9621 2. While the command is running, open your browser and navigate to: http://localhost:9621 ``` ## Full Deployment (Using External Databases) ### 1. Install Databases > You can skip this step if you've already prepared databases. Detailed information can be found in: [README.md](databases%2FREADME.md). We recommend KubeBlocks for database deployment. KubeBlocks is a cloud-native database operator that makes it easy to run any database on Kubernetes at production scale. FastGPT also use KubeBlocks for their database infrastructure. First, install KubeBlocks and KubeBlocks-Addons (skip if already installed): ```bash bash ./databases/01-prepare.sh ``` Then install the required databases. By default, this will install PostgreSQL and Neo4J, but you can modify [00-config.sh](databases%2F00-config.sh) to select different databases based on your needs. KubeBlocks supports various databases including MongoDB, Qdrant, Redis, and more. ```bash bash ./databases/02-install-database.sh ``` When the script completes, confirm that the clusters are up. It may take a few minutes for all the clusters to become ready, especially if this is the first time running the script as Kubernetes needs to pull container images from registries. You can monitor the progress using the following commands: ```bash kubectl get clusters -n rag NAME CLUSTER-DEFINITION TERMINATION-POLICY STATUS AGE neo4j-cluster Delete Running 39s pg-cluster postgresql Delete Creating 42s ``` You can see all the Database `Pods` created by KubeBlocks. Initially, you might see pods in `ContainerCreating` or `Pending` status - this is normal while images are being pulled and containers are starting up. Wait until all pods show `Running` status: ```bash kubectl get po -n rag NAME READY STATUS RESTARTS AGE neo4j-cluster-neo4j-0 1/1 Running 0 58s pg-cluster-postgresql-0 4/4 Running 0 59s pg-cluster-postgresql-1 4/4 Running 0 59s ``` ### 2. Install LightRAG LightRAG and its databases are deployed within the same Kubernetes cluster, making configuration straightforward. When using KubeBlocks to provide PostgreSQL and Neo4J database services, the `install_lightrag.sh` script can automatically retrieve all database connection information (host, port, user, password), eliminating the need to manually set database credentials. You only need to run [install_lightrag.sh](install_lightrag.sh) like this: ```bash export OPENAI_API_BASE= export OPENAI_API_KEY= bash ./install_lightrag.sh ``` The above commands automatically extract the database passwords from Kubernetes secrets, eliminating the need to manually set these credentials. After deployment, you can access the application: ```bash 1. Run this port-forward command in your terminal: kubectl --namespace rag port-forward svc/lightrag 9621:9621 2. While the command is running, open your browser and navigate to: http://localhost:9621 ``` ## Configuration ### Modifying Resource Configuration You can configure LightRAG's resource usage by modifying the `values.yaml` file: ```yaml replicaCount: 1 # Number of replicas, can be increased as needed resources: limits: cpu: 1000m # CPU limit, can be adjusted as needed memory: 2Gi # Memory limit, can be adjusted as needed requests: cpu: 500m # CPU request, can be adjusted as needed memory: 1Gi # Memory request, can be adjusted as needed ``` ### Modifying Persistent Storage ```yaml persistence: enabled: true ragStorage: size: 10Gi # RAG storage size, can be adjusted as needed inputs: size: 5Gi # Input data storage size, can be adjusted as needed ``` ### Configuring Environment Variables The `env` section in the `values.yaml` file contains all environment configurations for LightRAG, similar to a `.env` file. When using helm upgrade or helm install commands, you can override these with the --set flag. ```yaml env: HOST: 0.0.0.0 PORT: 9621 WEBUI_TITLE: Graph RAG Engine WEBUI_DESCRIPTION: Simple and Fast Graph Based RAG System # LLM Configuration LLM_BINDING: openai # LLM service provider LLM_MODEL: gpt-4o-mini # LLM model LLM_BINDING_HOST: # API base URL (optional) LLM_BINDING_API_KEY: # API key # Embedding Configuration EMBEDDING_BINDING: openai # Embedding service provider EMBEDDING_MODEL: text-embedding-ada-002 # Embedding model EMBEDDING_DIM: 1536 # Embedding dimension EMBEDDING_BINDING_API_KEY: # API key # Storage Configuration LIGHTRAG_KV_STORAGE: PGKVStorage # Key-value storage type LIGHTRAG_VECTOR_STORAGE: PGVectorStorage # Vector storage type LIGHTRAG_GRAPH_STORAGE: Neo4JStorage # Graph storage type LIGHTRAG_DOC_STATUS_STORAGE: PGDocStatusStorage # Document status storage type ``` ## Notes - Ensure all necessary environment variables (API keys and database passwords) are set before deployment - For security reasons, it's recommended to pass sensitive information using environment variables rather than writing them directly in scripts or values files - Lightweight deployment is suitable for testing and small-scale usage, but data persistence and performance may be limited - Full deployment (PostgreSQL + Neo4J) is recommended for production environments and large-scale usage - For more customized configurations, please refer to the official LightRAG documentation