Epictetus
271f79610d
New skills: - blacknode-graph-workflow - multi-agent-workflow-execution - langgraph-agent-workflow - langgraph-multi-agent-router - three-tier-evaluation-pipeline Config: LLM pipeline uses LFM on llama.cpp (8080)
97 lines
3.1 KiB
Markdown
97 lines
3.1 KiB
Markdown
---
|
|
name: blacknode-graph-workflow
|
|
version: 1.0.0
|
|
description: Build and execute node-based AI workflows with LLM agents and processing
|
|
nodes
|
|
inputs:
|
|
- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
|
|
- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite,
|
|
etc.)
|
|
- Data sources (URLs, text content, or other inputs for the workflow)
|
|
steps:
|
|
- Initialize a blacknode.Graph instance to create the workflow structure
|
|
- Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent,
|
|
FileWrite)
|
|
- Define edges connecting nodes to establish data flow between them
|
|
- Execute the graph using cook() to run the workflow and generate outputs
|
|
outputs:
|
|
- Processed results from the final node (e.g., printed text, written files, or generated
|
|
data)
|
|
- Graph execution status and any errors encountered during execution
|
|
tags: []
|
|
metadata:
|
|
source_repo: https://github.com/temiroff/Blacknode.git
|
|
extracted_at: ''
|
|
confidence: 0.95
|
|
---
|
|
|
|
# blacknode-graph-workflow
|
|
|
|
Build and execute node-based AI workflows with LLM agents and processing nodes
|
|
|
|
## Setup
|
|
|
|
**Dependencies:**
|
|
|
|
```text
|
|
pip install blacknode (core package) anthropic>=0.25 openai>=1.0 petgraph (for graph operations)
|
|
```
|
|
|
|
**Setup steps:**
|
|
|
|
1. Install blacknode package: pip install blacknode
|
|
1. Configure model API keys (NIM_API_KEY, OPENAI_API_KEY, etc.) in .env or editor
|
|
1. Create a Graph instance and add nodes with inputs/outputs
|
|
1. Define node connections in g._edges list
|
|
1. Execute with g.cook() to run the workflow and capture results
|
|
|
|
## Key Files
|
|
|
|
- `blacknode/blacknode.py (Graph class implementation)`
|
|
- `examples/hello_agent.py (simple LLM agent workflow)`
|
|
- `examples/converted_nvidia_nim.py (NIM model workflow)`
|
|
|
|
## Steps
|
|
|
|
1. Initialize a blacknode.Graph instance to create the workflow structure
|
|
2. Add nodes to the graph with defined inputs and outputs (e.g., Literal, LLMAgent, FileWrite)
|
|
3. Define edges connecting nodes to establish data flow between them
|
|
4. Execute the graph using cook() to run the workflow and generate outputs
|
|
|
|
## Implementation Details
|
|
|
|
```python
|
|
g = bn.Graph()
|
|
```
|
|
|
|
```python
|
|
g._edges = [{'from': 'model', 'from_port': 'value', 'to': 'agent', 'to_port': 'model'}]
|
|
```
|
|
|
|
```python
|
|
result = g.cook(output_node, 'value')
|
|
```
|
|
|
|
## Inputs
|
|
|
|
- Model configuration (NIM_MODEL, API keys for NVIDIA NIM, OpenAI, Anthropic)
|
|
- Node definitions with specified inputs and outputs (Literal, LLMAgent, FileWrite, etc.)
|
|
- Data sources (URLs, text content, or other inputs for the workflow)
|
|
|
|
## Outputs
|
|
|
|
- Processed results from the final node (e.g., printed text, written files, or generated data)
|
|
- Graph execution status and any errors encountered during execution
|
|
|
|
## Failure Modes
|
|
|
|
- Missing or invalid model API key causing graph initialization failure
|
|
- Incorrect node connections or missing edge definitions leading to runtime errors
|
|
- Model not found or unavailable in the specified environment causing execution failure
|
|
- Graph edges not properly defined or mismatched causing cook() to fail
|
|
|
|
## Source
|
|
|
|
Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
|
|
Confidence: 0.95
|