sure/docs/hosting/ai.md

LLM Configuration Guide

This document explains how Sure uses Large Language Models (LLMs) for AI features and how to configure them for your deployment.

Overview

Sure includes an AI assistant that can help users understand their financial data by answering questions about accounts, transactions, income, expenses, net worth, and more. The assistant uses LLMs to process natural language queries and provide insights based on the user's financial data.

Caution

Only gpt-4.1 was ever supported prior to v0.6.5-alpha* builds!

👉 Help us by taking a structured approach to your issue reporting. 🙏

Architecture: Two AI Pipelines

Sure has two separate AI systems that operate independently. Understanding this is important because they have different configuration requirements.

1. Chat Assistant (conversational)

The interactive chat where users ask questions about their finances. Routes through one of two backends:

• Builtin (default): Uses the OpenAI-compatible provider configured via OPENAI_ACCESS_TOKEN / OPENAI_URI_BASE / OPENAI_MODEL. Calls Sure's function tools directly (get_accounts, get_transactions, etc.).
• External: Delegates the entire conversation to a remote AI agent. The agent calls back to Sure via MCP to access financial data. Set ASSISTANT_TYPE=external as a global override, or configure each family's assistant type in Settings.

2. Auto-Categorization and Merchant Detection (background)

Background jobs that classify transactions and detect merchants. These always use the OpenAI-compatible provider (OPENAI_ACCESS_TOKEN), regardless of what the chat assistant uses. They rely on structured function calling with JSON schemas, not conversational chat.

What this means in practice

Setting	Chat assistant	Auto-categorization
ASSISTANT_TYPE=builtin (default)	Uses OpenAI provider	Uses OpenAI provider
ASSISTANT_TYPE=external	Uses external agent	Still uses OpenAI provider

If you use an external agent for chat, you still need OPENAI_ACCESS_TOKEN set for auto-categorization and merchant detection to work. The two systems are fully independent.

Quickstart: OpenAI Token

The easiest way to get started with AI features in Sure is to use OpenAI:

1. Get an API key from OpenAI
2. Set the environment variable:

   OPENAI_ACCESS_TOKEN=sk-proj-...your-key-here...
   

3. (Re-)Start Sure (both web and worker services!) and the AI assistant will be available to use after you agree/allow via UI option

That's it! Sure will use OpenAI's with a default model (currently gpt-4.1) for all AI operations.

Local vs. Cloud Inference

Cloud Inference (Recommended for Most Users)

What it means: The LLM runs on remote servers (like OpenAI's infrastructure), and your app sends requests over the internet.

Pros	Cons
Zero setup - works immediately	Requires internet connection
Always uses the latest models	Data leaves your infrastructure (though transmitted securely)
No hardware requirements	Per-request costs
Scales automatically	Dependent on provider availability
Regular updates and improvements

When to use:

• You're new to LLMs
• You want the best performance without setup
• You don't have powerful hardware (GPU with large VRAM)
• You're okay with cloud-based processing
• You're running a managed instance

Local Inference (Self-Hosted)

What it means: The LLM runs on your own hardware using tools like Ollama, LM Studio, or LocalAI.

Pros	Cons
Complete data privacy - nothing leaves your network	Requires significant hardware (see below)
No per-request costs after initial setup	Setup and maintenance overhead
Works offline	Models may be less capable than latest cloud offerings
Full control over models and updates	You manage updates and improvements
Can be more cost-effective at scale	Performance depends on your hardware

Hardware Requirements:

The amount of VRAM (GPU memory) you need depends on the model size:

• Minimum (8GB VRAM): Can run 7B parameter models like llama3.2:7b or gemma2:7b

  • Works for basic chat functionality
  • May struggle with complex financial analysis

• Recommended (16GB+ VRAM): Can run 13B-14B parameter models like llama3.1:13b or qwen2.5:14b

  • Good balance of performance and hardware requirements
  • Handles most financial queries well

• Ideal (24GB+ VRAM): Can run 30B+ parameter models or run smaller models with higher precision

  • Best quality responses
  • Complex reasoning about financial data

CPU-only inference: Possible but extremely slow (10-100x slower). Not recommended for production use.

When to use:

• Privacy is critical (regulated industries, sensitive financial data)
• You have the required hardware
• You're comfortable with technical setup
• You want to minimize ongoing costs
• You need offline functionality

Cloud Providers

Sure supports any OpenAI-compatible API endpoint. Here are tested providers:

OpenAI (Primary Support)

OPENAI_ACCESS_TOKEN=sk-proj-...
# No other configuration needed

# Optional: Request timeout in seconds (default: 60)
# OPENAI_REQUEST_TIMEOUT=60

Recommended models:

• gpt-4.1 - Default, best balance of speed and quality
• gpt-5 - Latest model, highest quality (more expensive)
• gpt-4o-mini - Cheaper, good quality

Pricing: See OpenAI Pricing

Google Gemini (via OpenRouter)

OpenRouter provides access to many models including Gemini:

OPENAI_ACCESS_TOKEN=your-openrouter-api-key
OPENAI_URI_BASE=https://openrouter.ai/api/v1
OPENAI_MODEL=google/gemini-2.0-flash-exp

Why OpenRouter?

• Single API for multiple providers
• Competitive pricing
• Automatic fallbacks
• Usage tracking

Recommended Gemini models via OpenRouter:

• google/gemini-2.5-flash - Fast and capable
• google/gemini-2.5-pro - High quality, good for complex queries

Anthropic Claude (via OpenRouter)

OPENAI_ACCESS_TOKEN=your-openrouter-api-key
OPENAI_URI_BASE=https://openrouter.ai/api/v1
OPENAI_MODEL=anthropic/claude-3.5-sonnet

Recommended Claude models:

• anthropic/claude-sonnet-4.5 - Excellent reasoning, good with financial data
• anthropic/claude-haiku-4.5 - Fast and cost-effective

Other Providers

Any service offering an OpenAI-compatible API should work:

• Groq - Fast inference, free tier available
• Together AI - Various open models
• Anyscale - Llama models
• Replicate - Various models

Local LLM Setup (Ollama)

Ollama is the recommended tool for running LLMs locally.

Installation

1. Install Ollama:

   # macOS
   brew install ollama
   
   # Linux
   curl -fsSL https://ollama.com/install.sh | sh
   
   # Windows
   # Download from https://ollama.com/download
   

2. Start Ollama:

   ollama serve
   

3. Pull a model:

   # Smaller, faster (requires 8GB VRAM)
   ollama pull gemma2:7b
   
   # Balanced (requires 16GB VRAM)
   ollama pull llama3.1:13b
   
   # Larger, more capable (requires 24GB+ VRAM)
   ollama pull qwen2.5:32b
   

Configuration

Configure Sure to use Ollama:

# Dummy token (Ollama doesn't need authentication)
OPENAI_ACCESS_TOKEN=ollama-local

# Ollama API endpoint
OPENAI_URI_BASE=http://localhost:11434/v1

# Model you pulled
OPENAI_MODEL=llama3.1:13b

# Raise this for large-context local models so auto-categorize and merchant detection
# have enough prompt budget for categories + schemas before transaction rows are added.
LLM_CONTEXT_WINDOW=8192

# Slow local models often need a longer HTTP timeout once the prompt budget issue is fixed.
OPENAI_REQUEST_TIMEOUT=180

# Optional: enable debug logging in the AI chat
AI_DEBUG_MODE=true 

Important: When using Ollama or any custom provider:

• You must set OPENAI_MODEL - the system cannot default to gpt-4.1 as that model won't exist in Ollama
• The OPENAI_ACCESS_TOKEN can be any non-empty value (Ollama ignores it)
• If you don't set a model, chats will fail with a validation error
• Auto-categorization uses a conservative default LLM_CONTEXT_WINDOW=2048, so large category lists or schemas can exhaust the prompt budget before any transactions are sent
• If requests start timing out after raising LLM_CONTEXT_WINDOW, increase OPENAI_REQUEST_TIMEOUT too; these are separate limits

Docker Compose Example

services:
  sure:
    environment:
      - OPENAI_ACCESS_TOKEN=ollama-local
      - OPENAI_URI_BASE=http://ollama:11434/v1
      - OPENAI_MODEL=llama3.1:13b
      - LLM_CONTEXT_WINDOW=8192
      - OPENAI_REQUEST_TIMEOUT=180
      - AI_DEBUG_MODE=true # Optional: enable debug logging in the AI chat
    depends_on:
      - ollama
  
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    # Uncomment if you have an NVIDIA GPU
    # deploy:
    #   resources:
    #     reservations:
    #       devices:
    #         - driver: nvidia
    #           count: 1
    #           capabilities: [gpu]

volumes:
  ollama_data:

Model Recommendations

Caution

REMINDER: Only gpt-4.1 was ever supported prior to v0.6.5-alpha* builds!

👉 Help us by taking a structured approach to your testing of the models mentioned below. 🙏

For Chat Assistant

The AI assistant needs to understand financial context and perform function/tool calling:

Cloud:

• Best: gpt-4.1 or gpt-5 - Most reliable, best function calling
• Good: anthropic/claude-4.5-sonnet - Excellent reasoning
• Budget: google/gemini-2.5-flash - Fast and affordable

Local:

• Best: qwen3-30b - Strong function calling and reasoning (24GB+ VRAM, 14GB at 3bit quantised )
• Good: openai/gpt-oss-20b - Solid performance (12GB VRAM)
• Budget: qwen3-8b, llama3.1-8b - Minimal hardware (8GB VRAM), still supports tool calling

For Auto-Categorization

Transaction categorization doesn't require function calling:

Cloud:

• Best: Same as chat - gpt-4.1 or gpt-5
• Budget: gpt-4o-mini - Much cheaper, still very accurate

Local:

• Any model that works for chat will work for categorization
• This is less demanding than chat, so smaller models may suffice
• Some models don't support structured outputs, please validate when using.

For Merchant Detection

Similar requirements to categorization:

Cloud:

• Same recommendations as auto-categorization

Local:

• Same recommendations as auto-categorization

Configuration via Settings UI

For self-hosted deployments, you can configure AI settings through the web interface:

1. Go to Settings → Self-Hosting
2. Scroll to the AI Provider section
3. Configure:
   • OpenAI Access Token - Your API key
   • OpenAI URI Base - Custom endpoint (leave blank for OpenAI)
   • OpenAI Model - Model name (required for custom endpoints)

Note: Environment variables take precedence over UI settings. When an env var is set, the corresponding UI field is disabled.

External AI Assistant

Instead of using the built-in LLM (which calls OpenAI or a local model directly), you can delegate chat to an external AI agent. The agent receives the conversation, can call back to Sure's financial data via MCP, and streams a response.

This is useful when:

• You have a custom AI agent with domain knowledge, memory, or personality
• You want to use a non-OpenAI-compatible model (the agent translates)
• You want to keep LLM credentials and logic outside Sure entirely

Important

Set ASSISTANT_TYPE=external to route all users to the external agent. Without it, routing falls back to each family's assistant_type DB column (configurable per-family in the Settings UI), then defaults to "builtin". If you want a global override that applies to every family regardless of their UI setting, set the env var. If you only want specific families to use the external agent, skip the env var and configure it per-family in Settings.

Note

The external assistant handles chat only. Auto-categorization and merchant detection still use the OpenAI-compatible provider (OPENAI_ACCESS_TOKEN). See Architecture: Two AI Pipelines for details.

How It Works

1. User sends a message in the Sure chat UI
2. Sure sends the conversation to your agent's API endpoint (OpenAI chat completions format)
3. Your agent processes it using whatever LLM, tools, or context it needs
4. Your agent can call Sure's /mcp endpoint for financial data (accounts, transactions, balance sheet, holdings)
5. Your agent streams the response back to Sure via Server-Sent Events (SSE)

The agent's API must be OpenAI chat completions compatible: accept POST with a messages array, return SSE with delta.content chunks.

Configuration

Configure via the UI or environment variables:

Settings UI:

1. Go to Settings -> Self-Hosting
2. Set Assistant type to "External (remote agent)"
3. Enter the Endpoint URL and API Token from your agent provider
4. Optionally set an Agent ID if the provider hosts multiple agents

Environment variables:

ASSISTANT_TYPE=external                          # Global override (or set per-family in UI)
EXTERNAL_ASSISTANT_URL=https://your-agent/v1/chat/completions
EXTERNAL_ASSISTANT_TOKEN=your-api-token
EXTERNAL_ASSISTANT_AGENT_ID=main                 # Optional, defaults to "main"
EXTERNAL_ASSISTANT_SESSION_KEY=agent:main:main   # Optional, for session persistence
EXTERNAL_ASSISTANT_ALLOWED_EMAILS=user@example.com  # Optional, comma-separated allowlist

When environment variables are set, the corresponding UI fields are disabled (env takes precedence).

MCP Callback Endpoint

Sure exposes a Model Context Protocol (MCP) endpoint at /mcp so your external agent can call back and query financial data. This is how the agent accesses accounts, transactions, balance sheets, and other user data.

Protocol: JSON-RPC 2.0 over HTTP POST

Authentication: Bearer token via Authorization header

Environment variables:

MCP_API_TOKEN=your-secret-token    # Bearer token the agent sends to authenticate
MCP_USER_EMAIL=user@example.com    # Email of the Sure user the agent acts as

The agent must send requests to https://your-sure-instance/mcp with:

Authorization: Bearer <MCP_API_TOKEN>
Content-Type: application/json

Supported methods:

Method	Description
initialize	Handshake, returns server info and capabilities
tools/list	Lists available tools with names, descriptions, and input schemas
tools/call	Calls a specific tool by name with arguments

Available tools (exposed via tools/list):

Tool	Description
get_accounts	Retrieve account information
get_transactions	Query transaction history
get_holdings	Investment holdings data
get_balance_sheet	Current financial position
get_income_statement	Income and expenses
import_bank_statement	Import bank statement data
search_family_files	Search uploaded documents

Example: list tools

curl -X POST https://your-sure-instance/mcp \
  -H "Authorization: Bearer $MCP_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}'

Example: call a tool

curl -X POST https://your-sure-instance/mcp \
  -H "Authorization: Bearer $MCP_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"get_accounts","arguments":{}}}'

OpenClaw Gateway Example

OpenClaw is an AI agent gateway that exposes agents as OpenAI-compatible endpoints. If your agent runs behind OpenClaw, configure it like this:

ASSISTANT_TYPE=external
EXTERNAL_ASSISTANT_URL=http://your-openclaw-host:18789/v1/chat/completions
EXTERNAL_ASSISTANT_TOKEN=your-gateway-token
EXTERNAL_ASSISTANT_AGENT_ID=your-agent-name

OpenClaw setup requirements:

• The gateway must have chatCompletions.enabled: true in its config
• The agent's MCP config must point to Sure's /mcp endpoint with the correct MCP_API_TOKEN
• The URL format is always /v1/chat/completions (OpenAI-compatible)

Kubernetes in-cluster example (agent in a different namespace):

# URL uses Kubernetes DNS: <service>.<namespace>.svc.cluster.local:<port>
EXTERNAL_ASSISTANT_URL=http://my-agent.my-namespace.svc.cluster.local:18789/v1/chat/completions

Giving the agent a long-term memory of its own

An external agent can do more than answer questions about the current balance: with its own repository behind it, it can maintain a document-backed history of a family's wealth, where every figure traces back to the statement it came from.

That is a two-install shape — Sure as the system of record, the agent harness as the model and the compiler — rather than a feature inside Sure. Sure exposes a set of preview MCP tools for it (the Statement Vault, coverage gaps, and valuations that require a source citation).

See Wealth history with an external agent harness for which side owns what, and the blueprint it implements.

Security with Pipelock

When Pipelock is enabled (pipelock.enabled=true in Helm, or the pipelock service in Docker Compose), all traffic between Sure and the external agent is scanned:

• Outbound (Sure -> agent): routed through Pipelock's forward proxy via HTTPS_PROXY
• Inbound (agent -> Sure /mcp): routed through Pipelock's MCP reverse proxy (port 8889)

Pipelock scans for prompt injection, DLP violations, and tool poisoning. The external agent does not need Pipelock installed. Sure's Pipelock handles both directions.

If you need audit evidence, configure Pipelock's flight recorder as described in Pipelock signed action receipts. pipelock.enabled=true gives scanning; receipts require mounted evidence storage plus a receipt-signing key.

NO_PROXY behavior (Helm/Kubernetes only): The Helm chart's env template sets NO_PROXY to include .svc.cluster.local and other internal domains. This means in-cluster agent URLs (like http://agent.namespace.svc.cluster.local:18789) bypass the forward proxy and go directly. If your agent is in-cluster, its traffic won't be forward-proxy scanned (but MCP callbacks from the agent are still scanned by the reverse proxy). Docker Compose deployments use a different NO_PROXY set; check your compose file for the exact values.

mcpToolPolicy note: The Helm chart's pipelock.mcpToolPolicy.enabled defaults to false. Pipelock rejects an enabled tool policy with no rules, so the chart ships it off by default. To turn it on, define at least one rule and set enabled: true:

# Helm values
pipelock:
  mcpToolPolicy:
    enabled: true
    action: warn
    rules:
      - name: example
        toolPattern: "^shell$"
        action: block

See the Pipelock documentation for tool policy configuration details.

Network Policies (Kubernetes)

If you use Kubernetes NetworkPolicies (and you should), both Sure and the agent's namespace need rules to allow traffic in both directions.

Warning

Port number gotcha: Kubernetes network policies evaluate after kube-proxy DNAT. This means egress rules must use the pod's targetPort, not the service port. If your agent's Service maps port 18789 to targetPort 18790, the network policy must allow port 18790.

Sure namespace egress (Sure calling the agent):

# Allow Sure -> agent namespace
- to:
    - namespaceSelector:
        matchLabels:
          kubernetes.io/metadata.name: agent-namespace
  ports:
    - protocol: TCP
      port: 18790  # targetPort, not service port!

Sure namespace ingress (agent calling Sure's pipelock MCP reverse proxy):

# Allow agent -> Sure pipelock MCP reverse proxy
- from:
    - namespaceSelector:
        matchLabels:
          kubernetes.io/metadata.name: agent-namespace
  ports:
    - protocol: TCP
      port: 8889

Agent namespace needs the reverse: egress to Sure on port 8889, ingress from Sure on its listening port.

Access Control

Use EXTERNAL_ASSISTANT_ALLOWED_EMAILS to restrict which users can use the external assistant. When set, only users whose email matches the comma-separated list will see the AI chat. When blank, all users can access it.

Docker Compose Example

x-rails-env: &rails_env
  ASSISTANT_TYPE: external
  EXTERNAL_ASSISTANT_URL: https://your-agent/v1/chat/completions
  EXTERNAL_ASSISTANT_TOKEN: your-api-token
  MCP_API_TOKEN: your-mcp-token          # For agent callback
  MCP_USER_EMAIL: user@example.com        # User the agent acts as

Or configure the assistant via the Settings UI after startup (MCP env vars are still required for callback).

Assistant Architecture

Sure's AI assistant system uses a modular architecture that allows different assistant implementations to be plugged in based on configuration. This section explains the architecture for contributors who want to understand or extend the system.

Overview

The assistant system evolved from a monolithic class to a module-based architecture with a registry pattern. This allows Sure to support multiple assistant types (builtin, external) and makes it easy to add new implementations.

Key benefits:

• Extensible: Add new assistant types without modifying existing code
• Configurable: Choose assistant type per family or globally
• Isolated: Each implementation has its own logic and dependencies
• Testable: Implementations are independent and can be tested separately

Component Hierarchy

Assistant Module

The main entry point for all assistant operations. Located in app/models/assistant.rb.

Key methods:

Method	Description
.for_chat(chat)	Returns the appropriate assistant instance for a chat
.config_for(chat)	Returns configuration for builtin assistants
.available_types	Lists all registered assistant types
.function_classes	Returns all available function/tool classes

Example usage:

# Get an assistant for a chat
assistant = Assistant.for_chat(chat)

# Respond to a message
assistant.respond_to(message)

Assistant::Base

Abstract base class that all assistant implementations inherit from. Located in app/models/assistant/base.rb.

Contract:

• Must implement respond_to(message) instance method
• Includes Assistant::Broadcastable for real-time updates
• Receives the chat object in the initializer

Example implementation:

class Assistant::MyCustom < Assistant::Base
  def respond_to(message)
    # Your custom logic here
    assistant_message = AssistantMessage.new(chat: chat, content: "Response")
    assistant_message.save!
  end
end

Assistant::Builtin

The default implementation that uses the configured OpenAI-compatible LLM provider. Located in app/models/assistant/builtin.rb.

Features:

• Uses Assistant::Provided for LLM provider selection
• Uses Assistant::Configurable for system prompts and function configuration
• Supports function calling via Assistant::FunctionToolCaller
• Streams responses in real-time

Key methods:

Method	Description
.for_chat(chat)	Creates a new builtin assistant with config
#respond_to(message)	Processes a message using the LLM

Assistant::External

Implementation for delegating chat to a remote AI agent. Located in app/models/assistant/external.rb.

Features:

• Sends conversation to external agent via OpenAI-compatible API
• Agent calls back to Sure's /mcp endpoint for financial data
• Supports access control via email allowlist
• Streams responses from the agent

Configuration:

config = Assistant::External.config
# => #<struct url="...", token="...", agent_id="...", session_key="...">

Registry Pattern

The Assistant module uses a registry to map type names to implementation classes:

REGISTRY = {
  "builtin" => Assistant::Builtin,
  "external" => Assistant::External
}.freeze

Type selection logic:

1. Check ENV["ASSISTANT_TYPE"] (global override)
2. Check chat.user.family.assistant_type (per-family setting)
3. Default to "builtin"

Example:

# Global override
ENV["ASSISTANT_TYPE"] = "external"
Assistant.for_chat(chat) # => Assistant::External instance

# Per-family setting
family.update(assistant_type: "external")
Assistant.for_chat(chat) # => Assistant::External instance

# Default
Assistant.for_chat(chat) # => Assistant::Builtin instance

Function Registry

The Assistant.function_classes method centralizes all available financial tools:

def self.function_classes
  [
    Function::GetTransactions,
    Function::GetAccounts,
    Function::GetHoldings,
    Function::GetBalanceSheet,
    Function::GetIncomeStatement,
    Function::ImportBankStatement,
    Function::SearchFamilyFiles
  ]
end

These functions are:

• Used by builtin assistants for LLM function calling
• Exposed via the MCP endpoint for external agents
• Defined in app/models/assistant/function/

Adding a New Assistant Type

To add a custom assistant implementation:

1. Create the implementation class

# app/models/assistant/my_custom.rb
class Assistant::MyCustom < Assistant::Base
  class << self
    def for_chat(chat)
      new(chat)
    end
  end

  def respond_to(message)
    # Your implementation here
    # Must create and save an AssistantMessage
    assistant_message = AssistantMessage.new(
      chat: chat,
      content: "My custom response"
    )
    assistant_message.save!
  end
end

2. Register the implementation

# app/models/assistant.rb
REGISTRY = {
  "builtin" => Assistant::Builtin,
  "external" => Assistant::External,
  "my_custom" => Assistant::MyCustom
}.freeze

3. Add validation

# app/models/family.rb
ASSISTANT_TYPES = %w[builtin external my_custom].freeze

4. Use the new type

# Global override
ASSISTANT_TYPE=my_custom

# Or set per-family in the database
family.update(assistant_type: "my_custom")

Integration Points

Pipelock Integration

For external assistants, Pipelock can scan traffic:

• Outbound: Sure -> agent (via HTTPS_PROXY)
• Inbound: Agent -> Sure /mcp (via MCP reverse proxy on port 8889)

See the External AI Assistant and Pipelock documentation for configuration.

OpenClaw/WebSocket Support

The Assistant::External implementation currently uses HTTP streaming. Future implementations could use WebSocket connections via OpenClaw or other gateways.

Example future implementation:

class Assistant::WebSocket < Assistant::Base
  def respond_to(message)
    # Connect via WebSocket
    # Stream bidirectional communication
    # Handle tool calls via MCP
  end
end

Register it in the REGISTRY and add to Family::ASSISTANT_TYPES to activate.

AI Cache Management

Sure caches AI-generated results (like auto-categorization and merchant detection) to avoid redundant API calls and costs. However, there are situations where you may want to clear this cache.

What is the AI Cache?

When AI rules process transactions, Sure stores:

• Enrichment records: Which attributes were set by AI (category, merchant, etc.)
• Attribute locks: Prevents rules from re-processing already-handled transactions

This caching means:

• Transactions won't be sent to the LLM repeatedly
• Your API costs are minimized
• Processing is faster on subsequent rule runs

When to Reset the AI Cache

You might want to reset the cache when:

1. Switching LLM models: Different models may produce better categorizations
2. Improving prompts: After system updates with better prompts
3. Fixing miscategorizations: When AI made systematic errors
4. Testing: During development or evaluation of AI features

Caution

Resetting the AI cache will cause all transactions to be re-processed by AI rules on the next run. This will incur API costs if using a cloud provider.

How to Reset the AI Cache

Via UI (Recommended):

1. Go to Settings → Rules
2. Click the menu button (three dots)
3. Select Reset AI cache
4. Confirm the action

The cache is cleared asynchronously in the background. You'll see a confirmation message when the process starts.

Automatic Reset: The AI cache is automatically cleared for all users when the OpenAI model setting is changed. This ensures that the new model processes transactions fresh.

What Happens When Cache is Reset

1. AI-locked attributes are unlocked: Transactions can be re-enriched
2. AI enrichment records are deleted: The history of AI changes is cleared
3. User edits are preserved: If you manually changed a category after AI set it, your change is kept

Cost Implications

Before resetting the cache, consider:

Scenario	Approximate Cost
100 transactions	$0.05-0.20
1,000 transactions	$0.50-2.00
10,000 transactions	$5.00-20.00

Costs vary by model. Use gpt-4o-mini for lower costs.

Tips to minimize costs:

• Use narrow rule filters before running AI actions
• Reset cache only when necessary
• Consider using local LLMs for bulk re-processing

Observability with Langfuse

Sure includes built-in support for Langfuse, an open-source LLM observability platform.

What is Langfuse?

Langfuse helps you:

• Track all LLM requests and responses
• Monitor costs per request
• Measure response latency
• Debug failed requests
• Analyze usage patterns
• Optimize prompts based on real data

Setup

1. Create a free account at Langfuse Cloud or self-host Langfuse

2. Get your API keys from the Langfuse dashboard

3. Configure Sure:

   LANGFUSE_PUBLIC_KEY=pk-lf-...
   LANGFUSE_SECRET_KEY=sk-lf-...
   LANGFUSE_HOST=https://cloud.langfuse.com  # or your self-hosted URL
   

4. Restart Sure

All LLM operations will now be logged to Langfuse, including:

• Chat messages and responses
• Auto-categorization requests
• Merchant detection
• Token usage and costs
• Response times

Langfuse Features in Sure

• Automatic tracing: Every LLM call is automatically traced
• Session tracking: Chat sessions are tracked with a unique session ID
• User anonymization: User IDs are hashed before sending to Langfuse
• Cost tracking: Token usage is logged for cost analysis
• Error tracking: Failed requests are logged with error details

Viewing Traces

1. Go to your Langfuse dashboard
2. Navigate to Traces
3. You'll see traces for:
   • openai.chat_response - Chat assistant interactions
   • openai.auto_categorize - Transaction categorization
   • openai.auto_detect_merchants - Merchant detection

Privacy Considerations

What's sent to Langfuse:

• Prompts and responses
• Model names
• Token counts
• Timestamps
• Session IDs
• Hashed user IDs (not actual user data)

What's NOT sent:

• User email addresses
• User names
• Unhashed user IDs
• Account credentials

For maximum privacy: Self-host Langfuse on your own infrastructure.

Testing and Evaluation

Manual Testing

Test your AI configuration:

1. Go to the Chat interface in Sure

2. Try these test prompts:

   • "Show me my total spending this month"
   • "What are my top 5 spending categories?"
   • "How much do I have in savings?"

3. Verify:

   • Responses are relevant
   • Function calls work (you should see "Analyzing your data..." briefly)
   • Numbers match your actual data

Automated Evaluation

Sure doesn't currently include automated evals, but you can build them using Langfuse:

1. Collect baseline responses: Run test prompts and save responses
2. Create evaluation dataset: Use Langfuse datasets feature
3. Run evaluations: Test new models/prompts against the dataset
4. Compare results: Use Langfuse's comparison tools

Benchmarking Models

To compare models for your use case:

1. Speed Test:

   • Send the same prompt to different models
   • Measure time to first token (TTFT)
   • Measure overall response time

2. Quality Test:

   • Create a set of 10-20 realistic financial questions
   • Get responses from each model
   • Manually rate accuracy and helpfulness

3. Cost Test:

   • Calculate cost per interaction based on token usage
   • Factor in your expected usage volume
   • Consider speed vs. cost tradeoffs

Example Evaluation Queries

Good test queries that exercise different capabilities:

• Simple retrieval: "What's my checking account balance?"
• Aggregation: "Total spending on restaurants last month?"
• Comparison: "Am I spending more or less than last year?"
• Analysis: "What are my biggest expenses this quarter?"
• Forecasting: "Based on my spending, when will I reach $10k savings?"

Cost Considerations

Cloud Costs

Typical costs for OpenAI (as of early 2025):

• gpt-4.1: ~$5-15 per 1M input tokens, ~$15-60 per 1M output tokens
• gpt-5: ~2-3x more expensive than gpt-4.1
• gpt-4o-mini: ~$0.15 per 1M input tokens (very cheap)

Typical usage:

• Chat message: 500-2000 tokens (input) + 100-500 tokens (output)
• Auto-categorization: 1000-3000 tokens per 25 transactions
• Cost per chat message: $0.01-0.05 for gpt-4.1

Optimization tips:

1. Use gpt-4o-mini for categorization
2. Use Langfuse to identify expensive prompts
3. Cache results when possible
4. Consider local LLMs for high-volume operations

Local Costs

One-time costs:

• GPU hardware: $500-2000+ depending on VRAM needs
• Setup time: 2-8 hours

Ongoing costs:

• Electricity: ~$0.10-0.50 per hour of GPU usage
• Maintenance: Occasional updates and monitoring

Break-even analysis:

If you process 10,000 messages/month:

• Cloud (gpt-4.1): ~$200-500/month
• Local (amortized): ~$50-100/month after hardware cost
• Break-even: 6-12 months depending on hardware cost

Recommendation: Start with cloud, switch to local if costs exceed $100-200/month.

Hybrid Approach

You can mix providers:

# Example: Use local for categorization, cloud for chat
# Categorization (high volume, lower complexity)
CATEGORIZATION_PROVIDER=ollama
CATEGORIZATION_MODEL=gemma2:7b

# Chat (lower volume, higher complexity)
CHAT_PROVIDER=openai
CHAT_MODEL=gpt-4.1

Note: Sure currently uses a single provider for all operations, but this could be customized.

Troubleshooting

"Messages is invalid" Error

Symptom: Cannot start a chat, see validation error

Cause: Using a custom provider (like Ollama) without setting OPENAI_MODEL

Fix:

# Make sure all three are set for custom providers
OPENAI_ACCESS_TOKEN=ollama-local  # Any non-empty value
OPENAI_URI_BASE=http://localhost:11434/v1
OPENAI_MODEL=your-model-name  # REQUIRED!

Model Not Found

Symptom: Error about model not being available

Cloud: Check that you're using a valid model name for your provider

Local: Make sure you've pulled the model:

ollama list  # See what's installed
ollama pull model-name  # Install a model

"Fixed prompt tokens exceed context budget"

Symptom: Auto-categorization or merchant detection fails immediately with an error like:

Fixed prompt tokens (2108) exceed context budget (1280)

Cause: Sure computes the usable prompt budget as:

context_window - max_response_tokens - system_prompt_reserve

The defaults are conservative:

• LLM_CONTEXT_WINDOW=2048
• LLM_MAX_RESPONSE_TOKENS=512
• LLM_SYSTEM_PROMPT_RESERVE=256

That leaves 1280 input tokens. On local or custom models, the fixed prompt can already exceed that budget once you include Sure's instructions, category taxonomy, and schema payloads.

Fix:

LLM_CONTEXT_WINDOW=8192

Then restart both web and worker so the new env var is loaded. If you are using Docker Compose, make sure your compose file forwards LLM_CONTEXT_WINDOW into the containers.

Slow Responses

Symptom: Long wait times for AI responses

Cloud:

• Switch to a faster model (e.g., gpt-4o-mini or gemini-2.0-flash-exp)
• Check your internet connection
• Verify provider status page

Local:

• Check GPU utilization (should be near 100% during inference)
• Try a smaller model
• Ensure you're using GPU, not CPU
• Check for thermal throttling
• If you see Net::ReadTimeout after fixing the context budget, raise OPENAI_REQUEST_TIMEOUT (for example 180)

No Provider Available

Symptom: "Provider not found" or similar error

Fix:

1. Check OPENAI_ACCESS_TOKEN is set
2. For custom providers, verify OPENAI_URI_BASE and OPENAI_MODEL
3. Restart Sure after changing environment variables
4. Check logs for specific error messages

"Failed to generate response" with External Assistant

Symptom: Chat shows "Failed to generate response" when expecting the external assistant

Check in order:

1. Is external routing active? Sure uses external mode when ASSISTANT_TYPE=external is set as an env var, OR when the family's assistant_type is set to "external" in Settings. Check what the pod sees:

   kubectl exec deploy/sure-web -c rails -- env | grep ASSISTANT_TYPE
   kubectl exec deploy/sure-worker -c sidekiq -- env | grep ASSISTANT_TYPE
   

   If the env var is unset, check the family setting in the database or Settings UI.

2. Can Sure reach the agent? Test from inside the worker pod (use sh -c so the env var expands inside the pod, not locally):

   kubectl exec deploy/sure-worker -c sidekiq -- \
     sh -c 'curl -s -o /dev/null -w "%{http_code}" \
     -H "Authorization: Bearer $EXTERNAL_ASSISTANT_TOKEN" \
     -H "Content-Type: application/json" \
     -d "{\"model\":\"test\",\"messages\":[{\"role\":\"user\",\"content\":\"ping\"}]}" \
     $EXTERNAL_ASSISTANT_URL'
   

   • Exit code 7 (connection refused): Network policy is blocking. Check egress rules, and remember to use the targetPort, not the service port.
   • HTTP 401/403: Token mismatch between Sure's EXTERNAL_ASSISTANT_TOKEN and the agent's expected token.
   • HTTP 404: Wrong URL path. Must be /v1/chat/completions.

3. Check worker logs for the actual error:

   kubectl logs deploy/sure-worker -c sidekiq --tail=50 | grep -i "external\|assistant\|error"
   

4. If using Pipelock: Check the Pipelock deployment logs. A crashed Pipelock proxy can block outbound requests:

   kubectl logs deploy/sure-pipelock --tail=20
   

High Costs

Symptom: Unexpected bills from cloud provider

Analysis:

1. Check Langfuse for usage patterns
2. Look for unusually long conversations
3. Check if you're using an expensive model

Optimization:

1. Switch to cheaper model for categorization
2. Consider local LLM for high-volume tasks
3. Implement rate limiting if needed
4. Review and optimize system prompts

Advanced Topics

Custom System Prompts

The builtin AI assistant uses a system prompt that defines its behavior. The prompt is defined in app/models/assistant/configurable.rb. This does not apply to external assistants, which manage their own prompts.

To customize:

1. Fork the repository
2. Edit the default_instructions method
3. Rebuild and deploy

What you can customize:

• Tone and personality
• Response format
• Rules and constraints
• Domain expertise

Function Calling

The assistant uses OpenAI's function calling (tool use) to access user data:

Available functions:

• get_transactions - Retrieve transaction history
• get_accounts - Get account information
• get_holdings - Investment holdings data
• get_balance_sheet - Current financial position
• get_income_statement - Income and expenses
• import_bank_statement - Import bank statement data
• search_family_files - Search uploaded documents

These are defined in app/models/assistant/function/.

Vector Store (Document Search)

Sure's AI assistant can search documents that have been uploaded to a family's vault. Under the hood, documents are indexed in a vector store so the assistant can retrieve relevant passages when answering questions (Retrieval-Augmented Generation).

How It Works

1. When a user uploads a document to their family vault, it is automatically pushed to the configured vector store.
2. When the assistant needs financial context from uploaded files, it calls the search_family_files function.
3. The vector store returns the most relevant passages, which the assistant uses to answer the question.

Supported Backends

Backend	Status	Best For	Requirements
OpenAI (default)	ready	Cloud deployments, zero setup	OPENAI_ACCESS_TOKEN
Pgvector	ready	Self-hosted, full data privacy	PostgreSQL with pgvector extension + embedding model
Qdrant	scaffolded	Self-hosted, dedicated vector DB	Running Qdrant instance

Configuration

OpenAI (Default)

No extra configuration is needed. If you already have OPENAI_ACCESS_TOKEN set for the AI assistant, document search works automatically. OpenAI manages chunking, embedding, and retrieval.

# Already set for AI chat - document search uses the same token
OPENAI_ACCESS_TOKEN=sk-proj-...

Pgvector (Self-Hosted)

Use PostgreSQL's pgvector extension for fully local document search. All data stays on your infrastructure.

Requirements:

• Use the pgvector/pgvector:pg16 Docker image instead of postgres:16 (drop-in replacement)
• An embedding model served via an OpenAI-compatible /v1/embeddings endpoint (e.g. Ollama with nomic-embed-text)
• Run the migration with VECTOR_STORE_PROVIDER=pgvector to create the vector_store_chunks table

# Required
VECTOR_STORE_PROVIDER=pgvector

# Embedding model configuration
EMBEDDING_MODEL=nomic-embed-text          # Default: nomic-embed-text
EMBEDDING_DIMENSIONS=1024                 # Default: 1024 (must match your model)
EMBEDDING_URI_BASE=http://ollama:11434/v1 # Falls back to OPENAI_URI_BASE if not set
EMBEDDING_ACCESS_TOKEN=                   # Falls back to OPENAI_ACCESS_TOKEN if not set

If you are using Ollama (as in compose.example.ai.yml), pull the embedding model:

docker compose exec ollama ollama pull nomic-embed-text

Qdrant (Self-Hosted)

Caution

Only OpenAI has been implemented!

Use a dedicated Qdrant vector database:

VECTOR_STORE_PROVIDER=qdrant
QDRANT_URL=http://localhost:6333   # Default if not set
QDRANT_API_KEY=your-api-key        # Optional, for authenticated instances

Docker Compose example:

services:
  sure:
    environment:
      - VECTOR_STORE_PROVIDER=qdrant
      - QDRANT_URL=http://qdrant:6333
    depends_on:
      - qdrant

  qdrant:
    image: qdrant/qdrant:latest
    ports:
      - "6333:6333"
    volumes:
      - qdrant_data:/qdrant/storage

volumes:
  qdrant_data:

Note: The Qdrant adapter is currently a skeleton. A future release will add full support including collection management and embedding configuration.

Verifying the Configuration

You can check whether a vector store is properly configured from the Rails console:

VectorStore.configured?          # => true / false
VectorStore.adapter              # => #<VectorStore::Openai:...>
VectorStore.adapter.class.name   # => "VectorStore::Openai"

Supported File Types

The following file extensions are supported for document upload and search:

.pdf, .txt, .md, .csv, .json, .xml, .html, .css, .js, .rb, .py, .docx, .pptx, .xlsx, .yaml, .yml, .log, .sh

Privacy Notes

• OpenAI backend: Document content is sent to OpenAI's API for indexing and search. The same privacy considerations as the AI chat apply.
• Pgvector / Qdrant backends: All data stays on your infrastructure. No external API calls are made for document search.

Multi-Model Setup

Currently not supported out of the box, but you could:

1. Create multiple provider instances
2. Add routing logic to select provider based on task
3. Update controllers to specify which provider to use

Rate Limiting

To prevent abuse or runaway costs:

1. Use Rack::Attack (already included)
2. Configure in config/initializers/rack_attack.rb
3. Limit requests per user or globally

Example:

# Limit chat creation to 10 per minute per user
throttle('chats/create', limit: 10, period: 1.minute) do |req|
  req.session[:user_id] if req.path == '/chats' && req.post?
end

Resources

• OpenAI Documentation
• Ollama Documentation
• OpenRouter Documentation
• Langfuse Documentation
• Sure GitHub Repository

Support

For issues with AI features:

1. Check this documentation first
2. Search existing GitHub issues
3. Open a new issue with:
   • Your configuration (redact API keys!)
   • Error messages
   • Steps to reproduce
   • Expected vs. actual behavior

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Last Updated: March 2026