Hermes Pipeline
106 lines
3.5 KiB
Markdown
106 lines
3.5 KiB
Markdown
---
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name: research-pipeline
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version: 1.0.0
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description: Fetch a Wikipedia page, summarise it using an AI agent, and write the
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summary to a file.
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inputs:
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- URL of the Wikipedia page
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steps:
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- 'Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key)
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and blacknode'
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- 'Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`'
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- 'Step 3: Create a Graph instance `g`'
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- 'Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)'
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- 'Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect
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it to the Literal node'
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- 'Step 6: Add an LLMAgent node with system prompt ''You are a technical writer. Summarise
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the text in 3 bullet points.'' and model NIM_MODEL (`summarise`), connecting its
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input to the output of `fetcher`'
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- 'Step 7: Add a FileWrite node to write the summary to a file named ''summary.txt''
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(`writer`), connecting its input to the output of `summarise`'
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- 'Step 8: Cook the graph starting from the writer node and print the path where the
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summary is written'
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outputs:
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- Path to the summary file
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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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# research-pipeline
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Fetch a Wikipedia page, summarise it using an AI agent, and write the summary to a file.
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## Setup
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**Dependencies:**
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```text
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pip install anthropic>=0.25 docker>=7.1 openai>=1.0
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```
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**Setup steps:**
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1. Ensure NVIDIA NIM API key is set in the environment or editor UI
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1. Install required dependencies using `pip install -r requirements.txt`
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## Key Files
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- `examples/research_pipeline.py - Contains the research pipeline workflow`
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## Steps
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1. Step 1: Import necessary modules from _bootstrap (NIM_MODEL, require_nim_api_key) and blacknode
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2. Step 2: Require NVIDIA NIM API key using `require_nim_api_key()`
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3. Step 3: Create a Graph instance `g`
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4. Step 4: Add a Literal node for the URL of the Wikipedia page (`url`)
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5. Step 5: Add an HTTPGet node to fetch the content from the URL (`fetcher`) and connect it to the Literal node
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6. Step 6: Add an LLMAgent node with system prompt 'You are a technical writer. Summarise the text in 3 bullet points.' and model NIM_MODEL (`summarise`), connecting its input to the output of `fetcher`
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7. Step 7: Add a FileWrite node to write the summary to a file named 'summary.txt' (`writer`), connecting its input to the output of `summarise`
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8. Step 8: Cook the graph starting from the writer node and print the path where the summary is written
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## Implementation Details
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```python
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from _bootstrap import NIM_MODEL, require_nim_api_key
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import blacknode as bn
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```
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```python
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g = bn.Graph()
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url = g.node('Literal', value='https://en.wikipedia.org/w/api.php?action=query&prop=extracts&exintro=1&explaintext=1&titles=Houdini_(software)&format=json&formatversion=2&origin=*')
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fetcher = g.node('HTTPGet')
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summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL)
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writer = g.node('FileWrite', path='summary.txt')
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```
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```python
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url.out('value') >> fetcher.inp('url')
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fetcher.out('text') >> summarise.inp('prompt')
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summarise.out('text') >> writer.inp('text')
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```
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```python
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result = g.cook(writer, 'path')
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print(f'Summary written to: {result}')
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```
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## Inputs
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- URL of the Wikipedia page
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## Outputs
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- Path to the summary file
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## Failure Modes
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- If the URL is invalid, HTTPGet will fail; if NIM API key is missing, LLMAgent will not function properly
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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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