---
name: research-pipeline
version: 1.0.0
description: Fetch a Wikipedia page, summarise its content using an AI agent, and
  write the summary to a file.
inputs:
- URL of the Wikipedia page
steps:
- 'Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap
  import NIM_MODEL, require_nim_api_key; import blacknode as bn`'
- 'Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the
  API key is set.'
- 'Step 3: Create a graph instance: Initialize `g = bn.Graph()`.'
- 'Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node(''Literal'',
  value=''URL of the Wikipedia page''); fetcher = g.node(''HTTPGet''); summarise =
  g.node(''LLMAgent'', system=''You are a technical writer. Summarise the text in
  3 bullet points.'', model=NIM_MODEL); writer = g.node(''FileWrite'', path=''summary.txt'')`'
- 'Step 5: Connect nodes with edges: `url.out(''value'') >> fetcher.inp(''url'');
  fetcher.out(''text'') >> summarise.inp(''prompt''); summarise.out(''text'') >> writer.inp(''text'')`'
- 'Step 6: Cook the graph to execute and get output: `result = g.cook(writer, ''path'');
  print(f''Summary written to: {result}'')`'
outputs:
- Path of the summary file
tags: []
metadata:
  source_repo: https://github.com/temiroff/Blacknode.git
  extracted_at: ''
  confidence: 0.95
---

# research-pipeline

Fetch a Wikipedia page, summarise its content using an AI agent, and write the summary to a file.

## Setup

**Dependencies:**

```text
pip install anthropic>=0.25 docker>=7.1 openai>=1.0
```

**Setup steps:**

1. Ensure NVIDIA NIM API key is set in the environment or editor
1. Install required dependencies: `pip install -r requirements.txt`

## Key Files

- `examples/research_pipeline.py - Contains the research pipeline workflow`

## Steps

1. Step 1: Import necessary modules from `_bootstrap` and `blacknode`: Run `from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn`
2. Step 2: Require NVIDIA NIM API key: Call `require_nim_api_key()` to ensure the API key is set.
3. Step 3: Create a graph instance: Initialize `g = bn.Graph()`.
4. Step 4: Add nodes for URL, HTTPGet, summarisation, and file writing: `url = g.node('Literal', value='URL of the Wikipedia page'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')`
5. Step 5: Connect nodes with edges: `url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')`
6. Step 6: Cook the graph to execute and get output: `result = g.cook(writer, 'path'); print(f'Summary written to: {result}')`

## Implementation Details

```python
from _bootstrap import NIM_MODEL, require_nim_api_key; import blacknode as bn
```

```python
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=*'); fetcher = g.node('HTTPGet'); summarise = g.node('LLMAgent', system='You are a technical writer. Summarise the text in 3 bullet points.', model=NIM_MODEL); writer = g.node('FileWrite', path='summary.txt')
```

```python
url.out('value') >> fetcher.inp('url'); fetcher.out('text') >> summarise.inp('prompt'); summarise.out('text') >> writer.inp('text')
```

## Inputs

- URL of the Wikipedia page

## Outputs

- Path of the summary file

## Failure Modes

- If the URL is invalid or unreachable, the HTTPGet node will fail; if the summarisation fails, the output text might be empty

## Source

Extracted from: [https://github.com/temiroff/Blacknode.git](https://github.com/temiroff/Blacknode.git)
Confidence: 0.95