LLM Configuration
DocRouter routes all LLM calls through LiteLLM. Providers are defined in code (packages/python/analytiq_data/llm/providers.py), seeded into MongoDB on startup, and configured per deployment. See the Platform page for a summary of supported clouds and providers.
After install (Docker Compose or Kubernetes), add provider keys here. Cloud-specific setup: AWS, GCP, Azure.
Supported providers (summary)
| Provider | Auth | Env variable | Default enabled |
|---|---|---|---|
| OpenAI | API key | OPENAI_API_KEY |
Yes |
| Anthropic | API key | ANTHROPIC_API_KEY |
Yes |
| Gemini (Google AI API) | API key | GEMINI_API_KEY |
Yes |
| Mistral | API key | MISTRAL_API_KEY |
Yes |
| Groq | API key | GROQ_API_KEY |
Yes |
| xAI | API key | XAI_API_KEY |
Yes |
| OpenRouter | API key | OPENROUTER_API_KEY |
Yes |
| Azure OpenAI | API key | AZURE_OPENAI_API_KEY |
No |
| Microsoft Foundry | Service principal | AZURE_TENANT_ID, AZURE_CLIENT_ID, AZURE_CLIENT_SECRET, AZURE_API_BASE |
No |
| AWS Bedrock | AWS user keys | (same as AWS) | No |
| Google Vertex AI | GCP service account JSON | GCP setup UI | No |
How to add API keys
Option 1 — .env at install time
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
MISTRAL_API_KEY=...
GEMINI_API_KEY=...
GROQ_API_KEY=...
On first startup, setup_llm_providers copies non-empty env values into encrypted llm_providers.token fields.
Option 2 — Admin UI (preferred after install)
- Sign in as admin.
- Go to Account → Development → LLM Manager (
/settings/account/development/llm-manager). - Open a provider (e.g. OpenAI, Mistral).
- Paste the API key, enable the provider, and select which models are enabled.
- Use Test on a model to confirm connectivity.
API: PUT /v0/account/llm/provider/{provider_name} with { "token": "...", "enabled": true, ... }.
Bedrock — no separate API key; enable the provider and ensure the AWS user has bedrock:InvokeModel and models are enabled in the Bedrock console. See AWS Configuration.
Vertex AI — do not set a token on the LLM provider; use GCP Configuration.
Microsoft Foundry — do not set a token on the azure_ai provider; use Azure Configuration.
Recommended models for document extraction
For speed and cost on production document workflows:
| Use case | Recommended model | Provider |
|---|---|---|
| Document processing | gemini/gemini-3-flash-preview |
Vertex AI or Gemini API |
| Document Agent | claude-opus-4-6 |
AWS Bedrock or Anthropic |
| Chat With Knowledge Base | openai-5.2 | OpenAI |
See knowledge_base/prompts.md in the doc-router repository for model-selection guidance used by built-in agents.
Credential storage
| Credential | Storage | Admin UI |
|---|---|---|
| OpenAI, Mistral, etc. | llm_providers.token (encrypted) |
Account → Development → LLM Manager |
| AWS (for Bedrock) | cloud_config type: "aws" |
Account → Development → AWS setup |
| GCP (for Vertex AI) | cloud_config type: "gcp" |
Account → Development → GCP setup |
| Azure Foundry SP | cloud_config type: "azure" |
Account → Development → Azure setup |