Hermes Pipeline
109 lines
4.2 KiB
Markdown
109 lines
4.2 KiB
Markdown
---
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name: graph-based-node-orchestration
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version: 1.0.0
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description: Build a typed node graph that processes data through a sequence of operations
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and produces a final result
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inputs:
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- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
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- blacknode package with Graph, Node, and cook functionality
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- Python script defining node types with inputs/outputs and connecting them via edges
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steps:
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- Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available
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via require_nim_api_key()
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- Create a bn.Graph() instance to hold all nodes and their connections
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- Define individual nodes with specific input/output parameters (e.g., Text node with
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'value' input, LLMAgent node with model parameter, Output node with 'value' output)
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- Connect nodes together using edge definitions (from_port -> to_port) to establish
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data flow
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- Execute the graph using g.cook() to run the pipeline and process data through the
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node chain
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- Extract the final result from the output node to complete the workflow
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outputs:
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- A fully constructed graph with typed nodes and defined connections
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- Executed result (e.g., processed text, summary, or other output) from the final
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node
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- A reusable pattern that can be adapted to different models, hardware, or task types
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tags: []
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metadata:
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source_repo: https://github.com/temiroff/Blacknode.git
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extracted_at: ''
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confidence: 0.95
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---
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# graph-based-node-orchestration
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Build a typed node graph that processes data through a sequence of operations and produces a final result
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## Setup
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**Dependencies:**
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```text
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pip install blacknode >= 0.3.0 Python >= 3.11 NVIDIA NIM API key (optional but recommended) Anthropic, OpenAI, or other LLM models
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```
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**Setup steps:**
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1. Clone the repository and install dependencies: pip install -e .
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1. Set NVIDIA_API_KEY or other required API keys in .env
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1. Run the example script: python examples/hello_agent.py
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1. For production, configure hardware pairing and deploy via the blacknode CLI
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## Key Files
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- `examples/converted_nvidia_nim.py - Full graph with Model, Text, LLMAgent, Output nodes`
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- `examples/hello_agent.py - Minimal agent example connecting Literal → LLMAgent → Print`
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- `blacknode/core - Graph and node implementation (internal)`
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## Steps
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1. Import required modules (blacknode as bn, NIM_MODEL) and ensure API keys are available via require_nim_api_key()
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2. Create a bn.Graph() instance to hold all nodes and their connections
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3. Define individual nodes with specific input/output parameters (e.g., Text node with 'value' input, LLMAgent node with model parameter, Output node with 'value' output)
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4. Connect nodes together using edge definitions (from_port -> to_port) to establish data flow
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5. Execute the graph using g.cook() to run the pipeline and process data through the node chain
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6. Extract the final result from the output node to complete the workflow
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## Implementation Details
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```python
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g = bn.Graph()
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```
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```python
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model = g.node('Model', **{'value': 'nim:meta/llama-3.1-8b-instruct'})
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```
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```python
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agent = g.node('LLMAgent', **{model})
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```
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```python
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result = g.cook(output, 'value')
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```
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## Inputs
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- NIM_MODEL constant (e.g., 'nim:meta/llama-3.1-8b-instruct') for the LLM model
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- blacknode package with Graph, Node, and cook functionality
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- Python script defining node types with inputs/outputs and connecting them via edges
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## Outputs
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- A fully constructed graph with typed nodes and defined connections
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- Executed result (e.g., processed text, summary, or other output) from the final node
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- A reusable pattern that can be adapted to different models, hardware, or task types
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## Failure Modes
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- Missing or invalid API key (NIM_API_KEY, OPENAI_API_KEY) causing require_nim_api_key() to fail
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- Type mismatch in node connections (e.g., expecting 'value' but receiving 'text') breaking the data flow
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- Model not available or unreachable (NIM_MODEL not found) causing the LLMAgent node to fail
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- Graph execution error due to incorrect edge configuration or circular dependencies
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- Resource exhaustion (e.g., GPU memory) when processing large inputs in the pipeline
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## Source
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Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
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Confidence: 0.95
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