Flowcharts and Graphs

Overview

Deeptrain's Flowcharts and Graphs module provides a specialized pipeline for converting visual structural data into LLM-readable formats. Unlike standard OCR, which treats text as a flat stream, this module identifies nodes, edges, and hierarchical relationships, transforming diagrams into structured schemas or descriptive representations that preserve the original logic.

Processing Visual Diagrams

To process a flowchart or graph, use the process_diagram interface. This method accepts various visual formats and outputs structured data that can be injected directly into your model's context or stored in your localized embedding database.

Supported Formats

• Input: PNG, JPG, PDF, SVG.
• Output: Mermaid.js syntax, JSON (Node-Link), or Semantic Textual Descriptions.

Basic Usage

from deeptrain import MultiModalConnector

# Initialize the connector
connector = MultiModalConnector(api_key="your_api_key")

# Process a local flowchart image
diagram_data = connector.flowcharts.process(
    source="./assets/workflow_v1.png",
    output_format="mermaid",
    detail_level="high"
)

print(diagram_data.content)

Configuration Parameters

The process method accepts several parameters to fine-tune how visual structures are interpreted:

| Parameter | Type | Description | | :--- | :--- | :--- | | source | str | Path to a local file or a URL to an image/PDF. | | output_format | str | The desired representation: "mermaid", "json", or "text". | | ocr_engine | str | (Optional) Specify a preferred engine for text extraction within nodes. | | detect_colors | bool | Whether to include color-coding logic in the resulting graph data. | | detail_level | str | "low" (summary) vs "high" (captures every sub-label and edge weight). |

Structured Output Examples

Mermaid.js Output

Ideal for developers who want to re-render the logic or pass it to models trained on code and markup.

JSON Node-Link Schema

Recommended for applications that need to perform programmatic graph analysis or custom RAG (Retrieval-Augmented Generation) logic.

{
  "nodes": [
    {"id": "n1", "label": "Start", "type": "process"},
    {"id": "n2", "label": "Is Authorized?", "type": "decision"}
  ],
  "edges": [
    {"source": "n1", "target": "n2", "label": "next"}
  ]
}

Integration with LLM Context

Once a flowchart is processed, the resulting data is typically passed to the LLM to provide environmental context or logic rules.

# Integrating with an agent's prompt
response = connector.agents.query(
    prompt="Based on this flowchart, what happens if the user is not authorized?",
    context=diagram_data.content  # The Mermaid or JSON output
)

Best Practices

• Image Clarity: For complex architectural diagrams, ensure a minimum resolution of 1024px on the shortest side to maintain node label accuracy.
• Contextual Anchoring: When using the "text" output format, Deeptrain provides a semantic narrative. This is often more effective for smaller context windows than raw JSON.
• Vector Storage: For large-scale graph datasets, use the Deeptrain localized embedding database to store the structured JSON output, allowing your agent to query specific sub-graphs via RAG.