trustgraph/docs/tech-specs/flow-blueprint-definition.md

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default	Flow Blueprint Definition Specification	Tech Specs

Flow Blueprint Definition Specification

Overview

A flow blueprint defines a complete dataflow pattern template in the TrustGraph system. When instantiated, it creates an interconnected network of processors that handle data ingestion, processing, storage, and querying as a unified system.

Structure

A flow blueprint definition consists of five main sections:

1. Class Section

Defines shared service processors that are instantiated once per flow blueprint. These processors handle requests from all flow instances of this class.

"class": {
  "service-name:{class}": {
    "request": "queue-pattern:{workspace}:{class}",
    "response": "queue-pattern:{workspace}:{class}",
    "settings": {
      "setting-name": "fixed-value",
      "parameterized-setting": "{parameter-name}"
    }
  }
}

Characteristics:

• Shared across all flow instances of the same class within a workspace
• Typically expensive or stateless services (LLMs, embedding models)
• Use {workspace} and {class} template variables for queue naming
• Settings can be fixed values or parameterized with {parameter-name} syntax
• Examples: embeddings:{workspace}:{class}, text-completion:{workspace}:{class}

2. Flow Section

Defines flow-specific processors that are instantiated for each individual flow instance. Each flow gets its own isolated set of these processors.

"flow": {
  "processor-name:{id}": {
    "input": "queue-pattern:{workspace}:{id}",
    "output": "queue-pattern:{workspace}:{id}",
    "settings": {
      "setting-name": "fixed-value",
      "parameterized-setting": "{parameter-name}"
    }
  }
}

Characteristics:

• Unique instance per flow
• Handle flow-specific data and state
• Use {workspace} and {id} template variables for queue naming
• Settings can be fixed values or parameterized with {parameter-name} syntax
• Examples: chunker:{workspace}:{id}, pdf-decoder:{workspace}:{id}

3. Interfaces Section

Defines the entry points and interaction contracts for the flow. These form the API surface for external systems and internal component communication.

Interfaces can take two forms:

Fire-and-Forget Pattern (single queue):

"interfaces": {
  "document-load": "persistent://tg/flow/{workspace}:document-load:{id}",
  "triples-store": "persistent://tg/flow/{workspace}:triples-store:{id}"
}

Request/Response Pattern (object with request/response fields):

"interfaces": {
  "embeddings": {
    "request": "non-persistent://tg/request/{workspace}:embeddings:{class}",
    "response": "non-persistent://tg/response/{workspace}:embeddings:{class}"
  }
}

Types of Interfaces:

• Entry Points: Where external systems inject data (document-load, agent)
• Service Interfaces: Request/response patterns for services (embeddings, text-completion)
• Data Interfaces: Fire-and-forget data flow connection points (triples-store, entity-contexts-load)

4. Parameters Section

Maps flow-specific parameter names to centrally-stored parameter definitions:

"parameters": {
  "model": "llm-model",
  "temp": "temperature",
  "chunk": "chunk-size"
}

Characteristics:

• Keys are parameter names used in processor settings (e.g., {model})
• Values reference parameter definitions stored in schema/config
• Enables reuse of common parameter definitions across flows
• Reduces duplication of parameter schemas

5. Metadata

Additional information about the flow blueprint:

"description": "Human-readable description",
"tags": ["capability-1", "capability-2"]

Template Variables

System Variables

{workspace}

• Replaced with the workspace identifier
• Isolates queue names between workspaces so that two workspaces starting the same flow do not share queues
• Must be included in all queue name patterns to ensure workspace isolation
• Example: ws-acme, ws-globex
• All blueprint templates must include {workspace} in queue name patterns

{id}

• Replaced with the unique flow instance identifier
• Creates isolated resources for each flow
• Example: flow-123, customer-A-flow

{class}

• Replaced with the flow blueprint name
• Creates shared resources across flows of the same class
• Example: standard-rag, enterprise-rag

Parameter Variables

{parameter-name}

• Custom parameters defined at flow launch time
• Parameter names match keys in the flow's parameters section
• Used in processor settings to customize behavior
• Examples: {model}, {temp}, {chunk}
• Replaced with values provided when launching the flow
• Validated against centrally-stored parameter definitions

Processor Settings

Settings provide configuration values to processors at instantiation time. They can be:

Fixed Settings

Direct values that don't change:

"settings": {
  "model": "gemma3:12b",
  "temperature": 0.7,
  "max_retries": 3
}

Parameterized Settings

Values that use parameters provided at flow launch:

"settings": {
  "model": "{model}",
  "temperature": "{temp}",
  "endpoint": "https://{region}.api.example.com"
}

Parameter names in settings correspond to keys in the flow's parameters section.

Settings Examples

LLM Processor with Parameters:

// In parameters section:
"parameters": {
  "model": "llm-model",
  "temp": "temperature",
  "tokens": "max-tokens",
  "key": "openai-api-key"
}

// In processor definition:
"text-completion:{class}": {
  "request": "non-persistent://tg/request/text-completion:{class}",
  "response": "non-persistent://tg/response/text-completion:{class}",
  "settings": {
    "model": "{model}",
    "temperature": "{temp}",
    "max_tokens": "{tokens}",
    "api_key": "{key}"
  }
}

Chunker with Fixed and Parameterized Settings:

// In parameters section:
"parameters": {
  "chunk": "chunk-size"
}

// In processor definition:
"chunker:{id}": {
  "input": "persistent://tg/flow/chunk:{id}",
  "output": "persistent://tg/flow/chunk-load:{id}",
  "settings": {
    "chunk_size": "{chunk}",
    "chunk_overlap": 100,
    "encoding": "utf-8"
  }
}

Queue Patterns (Pulsar)

Flow blueprintes use Apache Pulsar for messaging. Queue names follow the Pulsar format:

<persistence>://<tenant>/<namespace>/<topic>

Components:

• persistence: persistent or non-persistent (Pulsar persistence mode)
• tenant: tg for TrustGraph-supplied flow blueprint definitions
• namespace: Indicates the messaging pattern
  • flow: Fire-and-forget services
  • request: Request portion of request/response services
  • response: Response portion of request/response services
• topic: The specific queue/topic name with template variables

Persistent Queues

• Pattern: persistent://tg/flow/<topic>:{id}
• Used for fire-and-forget services and durable data flow
• Data persists in Pulsar storage across restarts
• Example: persistent://tg/flow/chunk-load:{id}

Non-Persistent Queues

• Pattern: non-persistent://tg/request/<topic>:{class} or non-persistent://tg/response/<topic>:{class}
• Used for request/response messaging patterns
• Ephemeral, not persisted to disk by Pulsar
• Lower latency, suitable for RPC-style communication
• Example: non-persistent://tg/request/embeddings:{class}

Dataflow Architecture

The flow blueprint creates a unified dataflow where:

1. Document Processing Pipeline: Flows from ingestion through transformation to storage
2. Query Services: Integrated processors that query the same data stores and services
3. Shared Services: Centralized processors that all flows can utilize
4. Storage Writers: Persist processed data to appropriate stores

All processors (both {id} and {class}) work together as a cohesive dataflow graph, not as separate systems.

Example Flow Instantiation

Given:

• Flow Instance ID: customer-A-flow
• Flow Blueprint: standard-rag
• Flow parameter mappings:
  • "model": "llm-model"
  • "temp": "temperature"
  • "chunk": "chunk-size"
• User-provided parameters:
  • model: gpt-4
  • temp: 0.5
  • chunk: 512

Template expansions:

• persistent://tg/flow/chunk-load:{id} → persistent://tg/flow/chunk-load:customer-A-flow
• non-persistent://tg/request/embeddings:{class} → non-persistent://tg/request/embeddings:standard-rag
• "model": "{model}" → "model": "gpt-4"
• "temperature": "{temp}" → "temperature": "0.5"
• "chunk_size": "{chunk}" → "chunk_size": "512"

This creates:

• Isolated document processing pipeline for customer-A-flow
• Shared embedding service for all standard-rag flows
• Complete dataflow from document ingestion through querying
• Processors configured with the provided parameter values

Benefits

1. Resource Efficiency: Expensive services are shared across flows
2. Flow Isolation: Each flow has its own data processing pipeline
3. Scalability: Can instantiate multiple flows from the same template
4. Modularity: Clear separation between shared and flow-specific components
5. Unified Architecture: Query and processing are part of the same dataflow