import { Callout } from 'nextra/components'

DocsGPT Settings

DocsGPT is highly configurable, allowing you to tailor it to your specific needs and preferences. You can control various aspects of the application, from choosing the Large Language Model (LLM) provider to selecting embedding models and vector stores.

This document will guide you through the basic settings you can configure in DocsGPT. These settings determine how DocsGPT interacts with LLMs and processes your data.

Configuration Methods

There are two primary ways to configure DocsGPT settings:

1. Configuration via .env file (Recommended)

The easiest and recommended way to configure basic settings is by using a .env file. This file should be located in the root directory of your DocsGPT project (the same directory where setup.sh is located).

Example .env file structure:

LLM_PROVIDER=openai
API_KEY=YOUR_OPENAI_API_KEY
LLM_NAME=gpt-4o

2. Configuration via settings.py file (Advanced)

For more advanced configurations or if you prefer to manage settings directly in code, you can modify the settings.py file. This file is located in the application/core directory of your DocsGPT project.

While modifying settings.py offers more flexibility, it's generally recommended to use the .env file for basic settings and reserve settings.py for more complex adjustments or when you need to configure settings programmatically.

Location of settings.py: application/core/settings.py

Basic Settings Explained

Here are some of the most fundamental settings you'll likely want to configure:

• LLM_PROVIDER: This setting determines which Large Language Model (LLM) provider DocsGPT will use. It tells DocsGPT which API to interact with.

  • Common values:
    • docsgpt: Use the DocsGPT Public API Endpoint (simple and free, as offered in setup.sh option 1).
    • openai: Use OpenAI's API (requires an API key).
    • google: Use Google's Vertex AI or Gemini models.
    • anthropic: Use Anthropic's Claude models.
    • groq: Use Groq's models.
    • huggingface: Use HuggingFace Inference API.
    • azure_openai: Use Azure OpenAI Service.
    • openai (when using local inference engines like Ollama, Llama.cpp, TGI, etc.): This signals DocsGPT to use an OpenAI-compatible API format, even if the actual LLM is running locally.

• LLM_NAME: Specifies the specific model to use from the chosen LLM provider. The available models depend on the LLM_PROVIDER you've selected.

  • Examples:
    • For LLM_PROVIDER=openai: gpt-4o
    • For LLM_PROVIDER=google: gemini-2.0-flash
    • For local models (e.g., Ollama): llama3.2:1b (or any model name available in your setup).

• EMBEDDINGS_NAME: This setting defines which embedding model DocsGPT will use to generate vector embeddings for your documents. Embeddings are numerical representations of text that allow DocsGPT to understand the semantic meaning of your documents for efficient search and retrieval.

  • Default value: huggingface_sentence-transformers/all-mpnet-base-v2 (a good general-purpose embedding model).
  • Other options: You can explore other embedding models from Hugging Face Sentence Transformers or other providers if needed.

• API_KEY: Required for most cloud-based LLM providers. This is your authentication key to access the LLM provider's API. You'll need to obtain this key from your chosen provider's platform.

• OPENAI_BASE_URL: Specifically used when LLM_PROVIDER is set to openai but you are connecting to a local inference engine (like Ollama, Llama.cpp, etc.) that exposes an OpenAI-compatible API. This setting tells DocsGPT where to find your local LLM server.

• STT_PROVIDER: Selects the speech-to-text provider used for microphone transcription in chat and for audio file ingestion through the parser pipeline.

Configuration Examples

Let's look at some concrete examples of how to configure these settings in your .env file.

Example for Cloud API Provider (OpenAI)

To use OpenAI's gpt-4o model, you would configure your .env file like this:

LLM_PROVIDER=openai
API_KEY=YOUR_OPENAI_API_KEY  # Replace with your actual OpenAI API key
LLM_NAME=gpt-4o

Make sure to replace YOUR_OPENAI_API_KEY with your actual OpenAI API key.

Example for Local Deployment

To use a local Ollama server with the llama3.2:1b model, you would configure your .env file like this:

LLM_PROVIDER=openai # Using OpenAI compatible API format for local models
API_KEY=None      # API Key is not needed for local Ollama
LLM_NAME=llama3.2:1b
OPENAI_BASE_URL=http://host.docker.internal:11434/v1 # Default Ollama API URL within Docker
EMBEDDINGS_NAME=huggingface_sentence-transformers/all-mpnet-base-v2 # You can also run embeddings locally if needed

In this case, even though you are using Ollama locally, LLM_PROVIDER is set to openai because Ollama (and many other local inference engines) are designed to be API-compatible with OpenAI. OPENAI_BASE_URL points DocsGPT to the local Ollama server.

Adding Custom Models (MODELS_CONFIG_DIR)

DocsGPT ships with a built-in catalog of models for the providers it supports out of the box (OpenAI, Anthropic, Google, Groq, OpenRouter, Novita, Azure OpenAI, Hugging Face, DocsGPT). To add your own models without forking the repo — for example, a Mistral or Together account, a self-hosted vLLM endpoint, or any other OpenAI-compatible API — point MODELS_CONFIG_DIR at a directory of YAML files.

MODELS_CONFIG_DIR=/etc/docsgpt/models
MISTRAL_API_KEY=sk-...

A minimal YAML for one provider:

# /etc/docsgpt/models/mistral.yaml
provider: openai_compatible
display_provider: mistral
api_key_env: MISTRAL_API_KEY
base_url: https://api.mistral.ai/v1
defaults:
  supports_tools: true
  context_window: 128000
models:
  - id: mistral-large-latest
    display_name: Mistral Large
  - id: mistral-small-latest
    display_name: Mistral Small

After restart, those models appear in /api/models and are selectable in the UI. A working template lives at application/core/models/examples/mistral.yaml.example.

What you can do:

• Add new openai_compatible providers (Mistral, Together, Fireworks, Ollama, vLLM, ...) — one YAML per provider, each with its own api_key_env and base_url.
• Extend an existing provider's catalog by dropping a YAML with the same provider: value as the built-in (e.g. provider: anthropic with extra models).
• Override a built-in model's capabilities by re-declaring the same id — later wins, override is logged at WARNING.

What you cannot do via MODELS_CONFIG_DIR: add a brand-new non-OpenAI provider. That requires a Python plugin under application/llm/providers/. See application/core/models/README.md for the full schema reference.

Docker

Mount the directory and set the env var:

# docker-compose.yml
services:
  app:
    image: arc53/docsgpt
    environment:
      MODELS_CONFIG_DIR: /etc/docsgpt/models
      MISTRAL_API_KEY: ${MISTRAL_API_KEY}
    volumes:
      - ./my-models:/etc/docsgpt/models:ro

Misconfiguration

If MODELS_CONFIG_DIR is set but the path doesn't exist (or isn't a directory), the app logs a WARNING at boot and continues with just the built-in catalog — it does not fail to start. If a YAML declares an unknown provider name or has a schema error, the app does fail to start, with the offending file path in the message.

Speech-to-Text Settings

DocsGPT can transcribe audio in two places:

• Voice input in the chat.
• Audio file ingestion. Uploaded .wav, .mp3, .m4a, .ogg, and .webm files are transcribed first and then passed through the normal parser, chunking, embedding, and indexing pipeline.

The settings below control speech-to-text behaviour for both voice input and audio file ingestion.

Example: OpenAI Speech-to-Text

STT_PROVIDER=openai
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
OPENAI_STT_MODEL=gpt-4o-mini-transcribe
STT_LANGUAGE=
STT_MAX_FILE_SIZE_MB=50
STT_ENABLE_TIMESTAMPS=false
STT_ENABLE_DIARIZATION=false

If you already use API_KEY for OpenAI, DocsGPT can reuse that key for transcription. Set OPENAI_API_KEY only when you want a dedicated key.

Example: Local faster_whisper

STT_PROVIDER=faster_whisper
STT_LANGUAGE=en
STT_ENABLE_TIMESTAMPS=true
STT_ENABLE_DIARIZATION=false

faster_whisper is an optional backend dependency. Install it in the Python environment used by the DocsGPT API and worker before selecting this provider.

Authentication Settings

DocsGPT includes a JWT (JSON Web Token) based authentication feature for managing sessions or securing local deployments while allowing access.

AUTH_TYPE Overview

The AUTH_TYPE setting in your .env file or settings.py determines the authentication method used by DocsGPT. This allows you to control how users authenticate with your DocsGPT instance.

How to Configure

Add the following to your .env file (or set in settings.py):

# No authentication (default)
AUTH_TYPE=None

# OR: Simple JWT (shared token)
AUTH_TYPE=simple_jwt
JWT_SECRET_KEY=your_secret_key_here

# OR: Session JWT (per-user/session tokens)
AUTH_TYPE=session_jwt
JWT_SECRET_KEY=your_secret_key_here

• If AUTH_TYPE is set to simple_jwt or session_jwt, a JWT_SECRET_KEY is required.
• If JWT_SECRET_KEY is not set, DocsGPT will generate one and store it in .jwt_secret_key in the project root.

How Each Method Works

• None: No authentication. All API and UI access is open.
• simple_jwt:
  • A single JWT token is generated at startup and printed to the console.
  • Use this token in the Authorization header for all API requests:
    httpCopy

    Authorization: Bearer <SIMPLE_JWT_TOKEN>
    

  • The frontend will prompt for this token if not already set.
• session_jwt:
  • Clients can request a new token from /api/generate_token.
  • Use the received token in the Authorization header for subsequent requests.
  • Each user/session gets a unique token.

Security Notes

• Always keep your JWT_SECRET_KEY secure and private.
• If you set it manually, use a strong, random string.
• If not set, DocsGPT will generate a secure key and persist it in .jwt_secret_key.

Checking Current Auth Type

• Use the /api/config endpoint to check the current auth_type and whether authentication is required.

Frontend Token Input for simple_jwt

If you have configured AUTH_TYPE=simple_jwt, the DocsGPT frontend will prompt you to enter the JWT token if it's not already set or is invalid. Paste the SIMPLE_JWT_TOKEN (printed to your console when the backend starts) into this field to access the application.

S3 Storage Backend

By default DocsGPT stores files locally. Set STORAGE_TYPE=s3 to use Amazon S3 instead.

The S3 credentials use SAGEMAKER_* variable names because they are shared with the SageMaker integration.

STORAGE_TYPE=s3
S3_BUCKET_NAME=your-bucket-name
SAGEMAKER_ACCESS_KEY=your-aws-access-key-id
SAGEMAKER_SECRET_KEY=your-aws-secret-access-key
SAGEMAKER_REGION=us-east-1

Your IAM user needs these permissions on the bucket: s3:PutObject, s3:GetObject, s3:DeleteObject, s3:ListBucket, s3:HeadObject.

User-Data Storage (Postgres)

DocsGPT stores user data — conversations, agents, prompts, sources, attachments, workflows, logs, and token usage — in PostgreSQL. The backend connects via a single setting:

Example:

POSTGRES_URI=postgresql://docsgpt:docsgpt@localhost:5432/docsgpt
# Append ?sslmode=require for managed providers that enforce SSL.

With the defaults, the app applies the schema automatically on first boot. To run it explicitly instead (e.g., in CI/CD or a k8s Job):

python scripts/db/init_postgres.py

The default Docker Compose file bundles a postgres service, and the app auto-bootstraps the database on boot, so containerized deployments need no manual migration step. See PostgreSQL for User Data for the recommended production flow (both flags false, migrations gated by CI/CD).

Exploring More Settings

These are just the basic settings to get you started. The settings.py file contains many more advanced options that you can explore to further customize DocsGPT, such as:

• Vector store configuration (VECTOR_STORE, Qdrant, Milvus, LanceDB settings) If you're looking for an easy way to set up a vector store with pgvector, try Neon.
• Retriever settings (RETRIEVERS_ENABLED)
• Cache settings (CACHE_REDIS_URL)
• And many more!

For a complete list of available settings and their descriptions, refer to the settings.py file in application/core. Remember to restart your Docker containers after making changes to your .env file or settings.py for the changes to take effect.