Hermes Pipeline
31 lines
2.0 KiB
JSON
31 lines
2.0 KiB
JSON
{
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"name": "graph-based-node-workflow",
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"version": "1.0.0",
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"goal": "Create and execute typed node graphs for AI/robotics workflows by defining nodes with inputs/outputs and connecting them with edges, then cooking the graph to run the workflow.",
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"inputs": [
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"bn.Graph() - the graph container for the workflow",
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"Node definitions (Literal, Text, LLMAgent, Concat, Output, etc.) with specified inputs and outputs",
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"Edge connections mapping from_port to to_port between nodes"
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],
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"steps": [
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"Step 1: Initialize a bn.Graph() instance to serve as the workflow container",
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"Step 2: Define individual nodes with their required inputs and outputs (e.g., Literal for data, LLMAgent for inference, Concat for combining, Output for final results)",
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"Step 3: Create edges connecting nodes by specifying source from_port and destination to_port for each data flow",
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"Step 4: Execute the graph by calling g.cook() to process the defined workflow and produce results"
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],
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"outputs": [
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"Executed workflow results stored in the graph's output nodes",
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"Cooked graph ready for inspection, replay, or deployment",
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"Potential error states if node dependencies are missing or ports don't match"
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],
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"failure_modes": [
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"Missing node dependencies causing undefined variable errors",
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"Port mismatch in edge connections leading to no data flow",
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"Incomplete graph definition causing cook() to fail",
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"Model API key missing or invalid for LLMAgent nodes"
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],
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"confidence": 0.95,
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"explanation": "This workflow pattern is reusable across different AI/robotics applications because it provides a standardized way to compose complex pipelines from typed nodes. The pattern can be adapted to various use cases like research pipelines, agent workflows, or robotics control graphs by simply adding/removing nodes and edges while maintaining the same graph-cooking execution model.",
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"source_repo": "https://github.com/temiroff/Blacknode.git",
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"score": 1.0
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} |