Configuration Reference

Configuration Reference

Complete EdgeQuake Configuration Options

EdgeQuake is configured through environment variables and a models.toml file. This reference covers all available options.

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Configuration Sources

┌─────────────────────────────────────────────────────────────────┐
│                   CONFIGURATION PRIORITY                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. Environment Variables (highest priority)                    │
│     │                                                           │
│  2. models.toml (for LLM/embedding configuration)               │
│     │   - EDGEQUAKE_MODELS_CONFIG env var path                  │
│     │   - ./models.toml (current directory)                     │
│     │   - ~/.edgequake/models.toml                              │
│     │   - Built-in defaults                                     │
│     │                                                           │
│  3. Compile-time defaults (lowest priority)                     │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

---

Environment Variables

Core Settings

Variable	Type	Default	Description
HOST	String	0.0.0.0	Server bind address
PORT	Integer	8080	Server port
RUST_LOG	String	edgequake=debug	Log level filter
WORKER_THREADS	Integer	CPU count	Background task workers

Database

Variable	Type	Default	Description
DATABASE_URL	String	None	PostgreSQL connection string

Connection String Format:

postgresql://user:password@host:port/database?sslmode=require

Examples:

# Local development
DATABASE_URL="postgresql://edgequake:edgequake_secret@localhost:5432/edgequake"

# Production with SSL
DATABASE_URL="postgresql://edgequake:pass@db.example.com:5432/edgequake?sslmode=require"

# With connection pooling
DATABASE_URL="postgresql://edgequake:pass@pgbouncer:6432/edgequake"

LLM Providers

OpenAI

Variable	Type	Default	Description
OPENAI_API_KEY	String	None	OpenAI API key (required for OpenAI)
OPENAI_BASE_URL	String	https://api.openai.com/v1	API endpoint
OPENAI_ORG_ID	String	None	Organization ID (optional)

Ollama

Variable	Type	Default	Description
OLLAMA_HOST	String	http://localhost:11434	Ollama server URL
OLLAMA_MODEL	String	gemma3:latest	Default LLM model
OLLAMA_EMBEDDING_MODEL	String	nomic-embed-text	Default embedding model

LM Studio

Variable	Type	Default	Description
LM_STUDIO_BASE_URL	String	http://localhost:1234	LM Studio server URL

Anthropic

Variable	Type	Default	Description
ANTHROPIC_API_KEY	String	None	Anthropic API key (required)
ANTHROPIC_BASE_URL	String	https://api.anthropic.com	API endpoint

Google Gemini

Variable	Type	Default	Description
GEMINI_API_KEY	String	None	Google AI API key
GEMINI_BASE_URL	String	https://generativelanguage.googleapis.com	API endpoint

xAI (Grok)

Variable	Type	Default	Description
XAI_API_KEY	String	None	xAI API key
XAI_BASE_URL	String	https://api.x.ai/v1	API endpoint

OpenRouter

Variable	Type	Default	Description
OPENROUTER_API_KEY	String	None	OpenRouter API key (required)
OPENROUTER_BASE_URL	String	https://openrouter.ai/api	API endpoint

Azure OpenAI

Variable	Type	Default	Description
AZURE_OPENAI_API_KEY	String	None	Azure OpenAI key (required)
AZURE_OPENAI_ENDPOINT	String	None	Azure resource endpoint
AZURE_OPENAI_API_VERSION	String	2024-02-15-preview	API version

Models Configuration

Variable	Type	Default	Description
EDGEQUAKE_MODELS_CONFIG	String	None	Path to custom models.toml
EDGEQUAKE_LLM_PROVIDER	String	ollama	Default LLM provider
EDGEQUAKE_EMBEDDING_PROVIDER	String	ollama	Default embedding provider

---

models.toml Reference

The models.toml file configures LLM providers and model cards.

Location Priority

1. EDGEQUAKE_MODELS_CONFIG environment variable
2. ./models.toml (current working directory)
3. ~/.edgequake/models.toml (user home)
4. Built-in defaults

Structure

# Default provider selection
[defaults]
llm_provider = "ollama"              # or "openai", "lm_studio"
llm_model = "gemma3:12b"
embedding_provider = "ollama"
embedding_model = "embeddinggemma"

# Provider definitions
[[providers]]
name = "openai"
display_name = "OpenAI"
type = "openai"
api_base = "https://api.openai.com/v1"
api_key_env = "OPENAI_API_KEY"
enabled = true
priority = 10
description = "OpenAI GPT models"

# Model definitions within provider
[[providers.models]]
name = "gpt-4o-mini"
display_name = "GPT-4o Mini"
model_type = "llm"                   # or "embedding"
description = "Cost-effective model"
deprecated = false
tags = ["recommended", "fast"]

[providers.models.capabilities]
context_length = 128000
max_output_tokens = 16384
supports_vision = true
supports_function_calling = true
supports_json_mode = true
supports_streaming = true
supports_system_message = true
embedding_dimension = 0              # 0 for LLMs, >0 for embeddings

[providers.models.cost]
input_per_1k = 0.00015
output_per_1k = 0.0006
embedding_per_1k = 0.0
image_per_unit = 0.0

Provider Types

Type	Description	API Key Variable
openai	OpenAI API compatible	OPENAI_API_KEY
anthropic	Anthropic Claude models	ANTHROPIC_API_KEY
gemini	Google Gemini models	GEMINI_API_KEY
xai	xAI Grok models	XAI_API_KEY
openrouter	OpenRouter aggregator	OPENROUTER_API_KEY
azure	Azure OpenAI	AZURE_OPENAI_API_KEY
ollama	Ollama local models	None (local)
lm_studio	LM Studio local	None (local)
mock	Testing without costs	None

Model Types

Type	Purpose	Key Capability
llm	Text generation	context_length, max_output_tokens
embedding	Vector embeddings	embedding_dimension

---

Provider Configuration Examples

OpenAI (Production)

[[providers]]
name = "openai"
display_name = "OpenAI"
type = "openai"
api_base = "https://api.openai.com/v1"
api_key_env = "OPENAI_API_KEY"
enabled = true
priority = 10

[[providers.models]]
name = "gpt-4o-mini"
display_name = "GPT-4o Mini"
model_type = "llm"
tags = ["recommended"]

[providers.models.capabilities]
context_length = 128000
max_output_tokens = 16384
supports_vision = true
supports_function_calling = true
supports_json_mode = true
supports_streaming = true

[[providers.models]]
name = "text-embedding-3-small"
display_name = "Text Embedding 3 Small"
model_type = "embedding"
tags = ["recommended"]

[providers.models.capabilities]
context_length = 8191
embedding_dimension = 1536

Ollama (Local Development)

[[providers]]
name = "ollama"
display_name = "Ollama"
type = "ollama"
api_base = "http://localhost:11434"
enabled = true
priority = 20

[[providers.models]]
name = "gemma3:12b"
display_name = "Gemma 3 12B"
model_type = "llm"
tags = ["recommended", "local"]

[providers.models.capabilities]
context_length = 128000
max_output_tokens = 8192
supports_vision = true
supports_streaming = true

[[providers.models]]
name = "nomic-embed-text"
display_name = "Nomic Embed Text"
model_type = "embedding"

[providers.models.capabilities]
context_length = 8192
embedding_dimension = 768

Azure OpenAI

[[providers]]
name = "azure-openai"
display_name = "Azure OpenAI"
type = "openai"  # Uses OpenAI-compatible API
api_base = "https://your-resource.openai.azure.com"
api_key_env = "AZURE_OPENAI_API_KEY"
enabled = true
priority = 5

[[providers.models]]
name = "gpt-4o-mini"  # Your deployment name
display_name = "Azure GPT-4o Mini"
model_type = "llm"

[providers.models.capabilities]
context_length = 128000
max_output_tokens = 16384
supports_function_calling = true
supports_json_mode = true
supports_streaming = true

Anthropic Claude

[[providers]]
name = "anthropic"
display_name = "Anthropic"
type = "anthropic"
api_base = "https://api.anthropic.com"
api_key_env = "ANTHROPIC_API_KEY"
enabled = true
priority = 8

[[providers.models]]
name = "claude-sonnet-4-5-20250929"
display_name = "Claude Sonnet 4.5"
model_type = "llm"
tags = ["recommended", "fast"]

[providers.models.capabilities]
context_length = 200000
max_output_tokens = 128000
supports_vision = true
supports_streaming = true
supports_system_message = true

[providers.models.cost]
input_per_1k = 0.003
output_per_1k = 0.015

Google Gemini

[[providers]]
name = "gemini"
display_name = "Google Gemini"
type = "gemini"
api_base = "https://generativelanguage.googleapis.com"
api_key_env = "GEMINI_API_KEY"
enabled = true
priority = 9

[[providers.models]]
name = "gemini-2.5-flash"
display_name = "Gemini 2.5 Flash"
model_type = "llm"
tags = ["recommended", "fast", "thinking"]

[providers.models.capabilities]
context_length = 1000000
max_output_tokens = 8192
supports_vision = true
supports_streaming = true

[providers.models.cost]
input_per_1k = 0.00015
output_per_1k = 0.0006

[[providers.models]]
name = "gemini-embedding-001"
display_name = "Gemini Embedding"
model_type = "embedding"

[providers.models.capabilities]
context_length = 10000
embedding_dimension = 3072

[providers.models.cost]
input_per_1k = 0.00015

xAI (Grok)

[[providers]]
name = "xai"
display_name = "xAI"
type = "xai"
api_base = "https://api.x.ai/v1"
api_key_env = "XAI_API_KEY"
enabled = true
priority = 7

[[providers.models]]
name = "grok-4-1-fast"
display_name = "Grok 4.1 Fast"
model_type = "llm"
tags = ["recommended", "fast", "large-context"]

[providers.models.capabilities]
context_length = 2000000
max_output_tokens = 16384
supports_vision = false
supports_streaming = true

[providers.models.cost]
input_per_1k = 0.0002
output_per_1k = 0.0005

OpenRouter

[[providers]]
name = "openrouter"
display_name = "OpenRouter"
type = "openrouter"
api_base = "https://openrouter.ai/api"
api_key_env = "OPENROUTER_API_KEY"
enabled = true
priority = 6

[[providers.models]]
name = "openai/gpt-4o-mini"
display_name = "OpenRouter GPT-4o Mini"
model_type = "llm"
tags = ["recommended"]

[providers.models.capabilities]
context_length = 128000
max_output_tokens = 16384
supports_vision = true
supports_streaming = true

[providers.models.cost]
input_per_1k = 0.00015
output_per_1k = 0.0006

---

Runtime Provider Switching

EdgeQuake supports switching providers at runtime via API:

# Get current providers
curl http://localhost:8080/api/v1/providers

# Get available models for a provider
curl http://localhost:8080/api/v1/providers/openai/models

# Query with specific provider (per-request)
curl -X POST http://localhost:8080/api/v1/query \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What is quantum computing?",
    "mode": "hybrid",
    "llm_provider": "openai",
    "llm_model": "gpt-4o-mini"
  }'

---

Workspace-Level Configuration

Each workspace can have its own LLM/embedding configuration:

# Create workspace with custom providers
curl -X POST http://localhost:8080/api/v1/workspaces \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Production Workspace",
    "llm_provider": "openai",
    "llm_model": "gpt-4o",
    "embedding_provider": "openai",
    "embedding_model": "text-embedding-3-large"
  }'

Workspace configuration overrides server defaults for all operations within that workspace.

---

Logging Configuration

The RUST_LOG environment variable controls logging:

# Debug all EdgeQuake components
RUST_LOG="edgequake=debug"

# Production logging
RUST_LOG="edgequake=info,tower_http=info"

# Verbose debugging
RUST_LOG="edgequake=trace,sqlx=debug,tower_http=debug"

# Specific component debugging
RUST_LOG="edgequake_pipeline=debug,edgequake_query=debug"

Log Levels

Level	Use Case
error	Errors only
warn	Errors + warnings
info	Standard production
debug	Development debugging
trace	Detailed tracing

---

Performance Tuning

Worker Threads

# Set worker count (default: CPU count)
WORKER_THREADS=8

Workers handle background document processing. More workers = faster ingestion but higher memory.

Connection Pool (PostgreSQL)

Connection pooling is built into SQLx. For high-load scenarios, use an external pooler:

# Use PgBouncer
DATABASE_URL="postgresql://user:pass@pgbouncer:6432/edgequake?application_name=edgequake"

Query Tuning

Setting	Via API	Default	Description
max_chunks	Per query	10	Max chunks retrieved
max_entities	Per query	20	Max entities retrieved
temperature	Per query	0.7	LLM temperature
max_tokens	Per query	4096	Max response tokens

---

Example Configurations

Development (Minimal)

# Just run with defaults (in-memory, mock LLM if no key)
cargo run

Development with Ollama

export OLLAMA_HOST="http://localhost:11434"
export OLLAMA_MODEL="gemma3:12b"
cargo run

Development with PostgreSQL

export DATABASE_URL="postgresql://edgequake:edgequake_secret@localhost:5432/edgequake"
export OPENAI_API_KEY="sk-..."
cargo run

Production

export DATABASE_URL="postgresql://edgequake:$DB_PASS@db.example.com:5432/edgequake?sslmode=require"
export OPENAI_API_KEY="$OPENAI_KEY"
export RUST_LOG="edgequake=info,tower_http=info"
export HOST="0.0.0.0"
export PORT="8080"
export WORKER_THREADS="8"
./edgequake

---

Validation

EdgeQuake validates configuration at startup:

╔══════════════════════════════════════════════════════════════╗
║                                                              ║
║   ⚡ EdgeQuake v0.1.0                                         ║
║                                                              ║
║   🐘 Storage: POSTGRESQL (persistent)
║   🌐 Server:  http://0.0.0.0:8080
║   📚 Swagger: http://0.0.0.0:8080/swagger-ui/
║                                                              ║
╚══════════════════════════════════════════════════════════════╝

Validation errors are logged with actionable messages:

ERROR: DATABASE_URL is invalid: invalid connection string
HINT: Format: postgresql://user:password@host:port/database

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See Also

• Deployment Guide - Production deployment
• Monitoring Guide - Observability setup
• REST API Reference - API documentation
• LLM Provider Docs - Provider integration