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Mendon Kissling
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1 changed files with 64 additions and 63 deletions
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@ -5,14 +5,69 @@ slug: /reference/configuration
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OpenRAG supports multiple configuration methods with the following priority, from highest to lowest:
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1. [Environment variables](#environment-variables) - Environment variables in the `.env` control Langflow authentication, Oauth settings, and the required OpenAI API key.
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2. [Configuration file (`config.yaml`)](#configuration-file) - The `config.yaml` file is generated with values input during [Application onboarding](/install#application-onboarding). If the same value is available in `.env` and `config.yaml`, the value in `.env` takes precedence.
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1. [Configuration file (`config.yaml`)](#configuration-file) - The `config.yaml` file is generated with values input during [Application onboarding](/install#application-onboarding), and configure the [OpenRAG configuration variables](#openrag-config-variables). These values configure OpenRAG application behavior.
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2. [Environment variables](#environment-variables) - Environment variables control how OpenRAG connects to services. Environment variables in the `.env` control underlying services such as Langflow authentication, Oauth settings, and OpenSearch security.
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3. [Langflow runtime overrides](#langflow-runtime-overrides)
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4. [Default or fallback values](#default-values-and-fallbacks)
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## Configuration file (`config.yaml) {#configuration-file}
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The `config.yaml` file controls what OpenRAG _does_, including language model and embedding model provider, Docling ingestion settings, and API keys.
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The `config.yaml` file overrides values in the `.env` if the variable is present in both files.
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```yaml
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config.yaml:
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provider:
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model_provider: openai
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api_key: ${PROVIDER_API_KEY} # optional: can be literal instead
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endpoint: https://api.example.com # optional: only for Ollama or IBM providers
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project_id: my-project # optional: only for IBM providers
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knowledge:
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embedding_model: text-embedding-3-small
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chunk_size: 1000
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chunk_overlap: 200
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ocr: true
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picture_descriptions: false
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agent:
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llm_model: gpt-4o-mini
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system_prompt: "You are a helpful AI assistant..."
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```
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## OpenRAG configuration variables {#openrag-config-variables}
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The OpenRAG configuration variables are generated during [Application onboarding](/install#application-onboarding). These values configure the OpenRAG application behavior.
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### Provider settings
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| Variable | Description | Default |
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| -------------------- | ---------------------------------------- | -------- |
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| `MODEL_PROVIDER` | Model provider (openai, anthropic, etc.) | `openai` |
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| `PROVIDER_API_KEY` | API key for the model provider. | |
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| `PROVIDER_ENDPOINT` | Custom provider endpoint. Only used for IBM or Ollama providers. | |
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| `PROVIDER_PROJECT_ID`| Project ID for providers. Only required for the IBM watsonx.ai provider. | |
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| `OPENAI_API_KEY` | OpenAI API key. | |
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### Knowledge settings
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| Variable | Description | Default |
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| ------------------------------ | --------------------------------------- | ------------------------ |
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| `EMBEDDING_MODEL` | Embedding model for vector search. | `text-embedding-3-small` |
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| `CHUNK_SIZE` | Text chunk size for document processing. | `1000` |
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| `CHUNK_OVERLAP` | Overlap between chunks. | `200` |
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| `OCR_ENABLED` | Enable OCR for image processing. | `true` |
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| `PICTURE_DESCRIPTIONS_ENABLED` | Enable picture descriptions. | `false` |
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### Agent settings
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| Variable | Description | Default |
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| --------------- | --------------------------------- | ------------------------ |
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| `LLM_MODEL` | Language model for the chat agent. | `gpt-4o-mini` |
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| `SYSTEM_PROMPT` | System prompt for the agent. | "You are a helpful AI assistant with access to a knowledge base. Answer questions based on the provided context." |
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## Environment variables
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Environment variables override configuration file settings.
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You can create a `.env` file in the project root to set these variables, or set them in the TUI, which will create a `.env` file for you.
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## Required variables
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@ -23,18 +78,18 @@ You can create a `.env` file in the project root to set these variables, or set
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| `OPENSEARCH_PASSWORD` | Password for OpenSearch admin user |
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| `LANGFLOW_SUPERUSER` | Langflow admin username |
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| `LANGFLOW_SUPERUSER_PASSWORD` | Langflow admin password |
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| `LANGFLOW_CHAT_FLOW_ID` | ID of your Langflow chat flow |
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| `LANGFLOW_INGEST_FLOW_ID` | ID of your Langflow ingestion flow |
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| `NUDGES_FLOW_ID` | ID of your Langflow nudges/suggestions flow |
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| `LANGFLOW_CHAT_FLOW_ID` | This value is pre-filled. The default value is found in [.env.example](https://github.com/langflow-ai/openrag/blob/main/.env.example). |
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| `LANGFLOW_INGEST_FLOW_ID` | This value is pre-filled. The default value is found in [.env.example](https://github.com/langflow-ai/openrag/blob/main/.env.example). |
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| `NUDGES_FLOW_ID` | This value is pre-filled. The default value is found in [.env.example](https://github.com/langflow-ai/openrag/blob/main/.env.example). |
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## Ingestion configuration
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| Variable | Description |
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| ------------------------------ | ------------------------------------------------------ |
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| `DISABLE_INGEST_WITH_LANGFLOW` | Disable Langflow ingestion pipeline (default: `false`) |
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| `DISABLE_INGEST_WITH_LANGFLOW` | Disable Langflow ingestion pipeline. Default: `false`. |
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- `false` or unset: Uses Langflow pipeline (upload → ingest → delete)
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- `true`: Uses traditional OpenRAG processor for document ingestion
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- `false` or unset: Uses Langflow pipeline (upload → ingest → delete).
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- `true`: Uses traditional OpenRAG processor for document ingestion.
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## Optional variables
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@ -58,60 +113,6 @@ You can create a `.env` file in the project root to set these variables, or set
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| `LANGFLOW_ENABLE_SUPERUSER_CLI` | Enable superuser CLI (default: `False`) |
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| `OPENRAG_DOCUMENTS_PATHS` | Document paths for ingestion (default: `./documents`) |
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## OpenRAG configuration variables {#openrag-config-variables}
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### Provider settings
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| Variable | Description | Default |
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| -------------------- | ---------------------------------------- | -------- |
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| `MODEL_PROVIDER` | Model provider (openai, anthropic, etc.) | `openai` |
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| `PROVIDER_API_KEY` | API key for the model provider | |
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| `PROVIDER_ENDPOINT` | Custom provider endpoint (e.g., Watson) | |
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| `PROVIDER_PROJECT_ID`| Project ID for providers (e.g., Watson) | |
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| `OPENAI_API_KEY` | OpenAI API key (backward compatibility) | |
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### Knowledge settings
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| Variable | Description | Default |
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| ------------------------------ | --------------------------------------- | ------------------------ |
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| `EMBEDDING_MODEL` | Embedding model for vector search | `text-embedding-3-small` |
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| `CHUNK_SIZE` | Text chunk size for document processing | `1000` |
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| `CHUNK_OVERLAP` | Overlap between chunks | `200` |
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| `OCR_ENABLED` | Enable OCR for image processing | `true` |
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| `PICTURE_DESCRIPTIONS_ENABLED` | Enable picture descriptions | `false` |
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### Agent settings
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| Variable | Description | Default |
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| --------------- | --------------------------------- | ------------------------ |
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| `LLM_MODEL` | Language model for the chat agent | `gpt-4o-mini` |
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| `SYSTEM_PROMPT` | System prompt for the agent | "You are a helpful AI assistant with access to a knowledge base. Answer questions based on the provided context." |
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## Configuration file (`config.yaml) {#configuration-file}
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The `config.yaml` file created during [Application onboarding](/install#application-onboarding) can control the variables in [OpenRAG configuration variables](#openrag-config-variables), but is overridden by the `.env` if the variable is present both files.
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The `config.yaml` file controls application configuration, including language model and embedding model provider, Docling ingestion settings, and API keys.
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```yaml
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config.yaml:
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provider:
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model_provider: openai
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api_key: ${PROVIDER_API_KEY} # optional: can be literal instead
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endpoint: https://api.example.com
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project_id: my-project
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knowledge:
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embedding_model: text-embedding-3-small
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chunk_size: 1000
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chunk_overlap: 200
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ocr: true
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picture_descriptions: false
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agent:
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llm_model: gpt-4o-mini
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system_prompt: "You are a helpful AI assistant..."
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```
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## Langflow runtime overrides
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Langflow runtime overrides allow you to modify component settings at runtime without changing the base configuration.
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