# Edge Python API
API to connect to dRISK Edge.
### Useful Edge Links
Some useful links for new edge users:
- Log in to edge: [demo.drisk.ai](https://demo.drisk.ai/)
- Documentation: [demo.drisk.ai/docs](https://demo.drisk.ai/docs/)
## Installation
```
pip install drisk_api
```
## Basic Usage
The API supports the basic building blocs for Create/Read/Update/Delete operations on the graph. For example:
```python
from drisk_api import GraphClient
token = "<edge_auth_token>"
# create or conntect to a graph
new_graph = GraphClient.create_graph("a graph", token)
graph = GraphClient("graph_id", token)
# make a new node
node_id = graph.create_node(label="a node")
# get a node
node = graph.get_node(node_id)
# get the successors of the node
successors = graph.get_successors(node_id)
# update the node
graph.update_node(node_id, label="new label", size=3)
# add edges in batch
other_id = graph.create_node(label="another node")
with graph.batch():
graph.create_edge(node_id, other, weight=5.)
```
## More Examples
We can use these building blocks to create whatever graphs we are most interested in. Below are some examples:
### Wikepedia Crawler
In this example we will scrape the main url links for a given wikipedia page and create a graph out of it.
Most of the code will be leveraging the [wikipedia api](https://pypi.org/project/wikipedia/) and is not particularly important.
What is more interesting is how we can use the `api` to convert the corresponding information into a graph to then explore it in edge.
First load the relevant module
```python
import wikipedia
from wikipedia import PageError, DisambiguationError, search, WikipediaPage
from tqdm import tqdm
from drisk_api import GraphClient
```
Let's define some helper functions that will help us create a graph of wikipedia urls for a given page.
The main function to pay attention to is `wiki_scraper` which will find the 'most important' links in a
given page and add them to the graph, linking back to the original page.
It will do this recursively for each node until a terminal condition is reached (e.g. a max recursion depth).
```python
def find_page(title):
"""Find the wikipedia page."""
results, suggestion = search(title, results=1, suggestion=True)
try:
title = results[0] or suggestion
page = WikipediaPage(title, redirect=True, preload=False)
except IndexError:
raise PageError(title)
return page
def top_links(links, text, top_n):
"""Find most important links in a wikipedia page."""
link_occurrences = {}
for link in links:
link_occurrences[link] = text.lower().count(link.lower())
sorted_links = sorted(link_occurrences.items(), key=lambda x: x[1], reverse=True)
top_n_relevant_links = [link for link, count in sorted_links[:top_n]]
return top_n_relevant_links
def wiki_scraper(
graph,
page_node,
page_name,
string_cache,
visited_pages,
max_depth=3,
current_depth=0,
max_links=10,
first_depth_max_links=100,
):
try:
page = find_page(title=page_name)
except (DisambiguationError, PageError) as e:
return
# add the url to the page_node (and make sure label is right)
graph.update_node(page_node, label=page_name, url=page.url)
if page_name in visited_pages or current_depth >= max_depth:
return
links = top_links(page.links, page.content, first_depth_max_links if current_depth == 0 else max_links)
if current_depth == 0:
tqdm_bar = tqdm(total=len(links), desc="wiki scraping")
for link in links:
if current_depth == 0:
tqdm_bar.update(1)
# see if we have already visted the page
new_page_node = None
if link in string_cache:
new_page_node = string_cache[link]
else:
# if we haven't add a new node and add to cache
new_page_node = graph.create_node(label=link)
string_cache[link] = new_page_node
# link this original page to the new one
graph.create_edge(page_node, new_page_node, 1.)
# repeat for new link
wiki_scraper(
graph,
new_page_node,
link,
string_cache,
visted_pages,
current_depth=current_depth + 1,
max_links=max_links,
first_depth_max_links=first_depth_max_links,
)
visited_pages.add(page_name)
```
Then we can connect to our graph (or make one):
```python
TOKEN = "<edge_auth_token>"
graph_id = "graph_id"
home_view = "view_id"
g = GraphClient(graph_id, TOKEN)
```
and run the scraper:
```python
page_name = "Napoleon"
string_cache = {}
visted_pages = set()
page_node = g.create_node(label=page_name)
g.add_nodes_to_view(home_view, [page_node], [(0., 0.)])
with g.batch():
wiki_scraper(
g,
page_node,
page_name,
string_cache,
visted_pages,
max_depth=3,
current_depth=0,
max_links=3,
first_depth_max_links=2,
)
```
We can then head to edge to interact with the graph:
<p align="center">
<img src="https://github.com/driskai/drisk_api/blob/main/docs/images/Napoleon-graph.png" width="80%">
</p>
)))
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"description": "# Edge Python API\nAPI to connect to dRISK Edge.\n\n### Useful Edge Links\nSome useful links for new edge users:\n\n- Log in to edge: [demo.drisk.ai](https://demo.drisk.ai/)\n- Documentation: [demo.drisk.ai/docs](https://demo.drisk.ai/docs/)\n\n\n\n## Installation\n```\npip install drisk_api\n```\n\n## Basic Usage\n\nThe API supports the basic building blocs for Create/Read/Update/Delete operations on the graph. For example:\n\n\n```python\nfrom drisk_api import GraphClient\n\ntoken = \"<edge_auth_token>\"\n\n# create or conntect to a graph\nnew_graph = GraphClient.create_graph(\"a graph\", token)\ngraph = GraphClient(\"graph_id\", token)\n\n# make a new node\nnode_id = graph.create_node(label=\"a node\")\n\n# get a node\nnode = graph.get_node(node_id)\n\n# get the successors of the node\nsuccessors = graph.get_successors(node_id)\n\n# update the node\ngraph.update_node(node_id, label=\"new label\", size=3)\n\n# add edges in batch\nother_id = graph.create_node(label=\"another node\")\nwith graph.batch():\n graph.create_edge(node_id, other, weight=5.)\n\n```\n\n## More Examples\n\nWe can use these building blocks to create whatever graphs we are most interested in. Below are some examples:\n\n\n### Wikepedia Crawler\n\nIn this example we will scrape the main url links for a given wikipedia page and create a graph out of it.\n\n\nMost of the code will be leveraging the [wikipedia api](https://pypi.org/project/wikipedia/) and is not particularly important.\nWhat is more interesting is how we can use the `api` to convert the corresponding information into a graph to then explore it in edge.\n\n\nFirst load the relevant module\n\n```python\nimport wikipedia\nfrom wikipedia import PageError, DisambiguationError, search, WikipediaPage\nfrom tqdm import tqdm\nfrom drisk_api import GraphClient\n```\n\nLet's define some helper functions that will help us create a graph of wikipedia urls for a given page.\nThe main function to pay attention to is `wiki_scraper` which will find the 'most important' links in a\ngiven page and add them to the graph, linking back to the original page.\nIt will do this recursively for each node until a terminal condition is reached (e.g. a max recursion depth).\n\n\n```python\n\ndef find_page(title):\n \"\"\"Find the wikipedia page.\"\"\"\n results, suggestion = search(title, results=1, suggestion=True)\n try:\n title = results[0] or suggestion\n page = WikipediaPage(title, redirect=True, preload=False)\n except IndexError:\n raise PageError(title)\n return page\n\n\ndef top_links(links, text, top_n):\n \"\"\"Find most important links in a wikipedia page.\"\"\"\n link_occurrences = {}\n for link in links:\n link_occurrences[link] = text.lower().count(link.lower())\n\n sorted_links = sorted(link_occurrences.items(), key=lambda x: x[1], reverse=True)\n\n top_n_relevant_links = [link for link, count in sorted_links[:top_n]]\n\n return top_n_relevant_links\n\n\n\ndef wiki_scraper(\n graph,\n page_node,\n page_name,\n string_cache,\n visited_pages,\n max_depth=3,\n current_depth=0,\n max_links=10,\n first_depth_max_links=100,\n):\n try:\n page = find_page(title=page_name)\n except (DisambiguationError, PageError) as e:\n return\n\n # add the url to the page_node (and make sure label is right)\n graph.update_node(page_node, label=page_name, url=page.url)\n\n if page_name in visited_pages or current_depth >= max_depth:\n return\n\n links = top_links(page.links, page.content, first_depth_max_links if current_depth == 0 else max_links)\n\n if current_depth == 0:\n tqdm_bar = tqdm(total=len(links), desc=\"wiki scraping\")\n\n for link in links:\n if current_depth == 0:\n tqdm_bar.update(1)\n\n # see if we have already visted the page\n new_page_node = None\n if link in string_cache:\n new_page_node = string_cache[link]\n else:\n # if we haven't add a new node and add to cache\n new_page_node = graph.create_node(label=link)\n string_cache[link] = new_page_node\n\n # link this original page to the new one\n graph.create_edge(page_node, new_page_node, 1.)\n\n # repeat for new link\n wiki_scraper(\n graph,\n new_page_node,\n link,\n string_cache,\n visted_pages,\n current_depth=current_depth + 1,\n max_links=max_links,\n first_depth_max_links=first_depth_max_links,\n )\n\n visited_pages.add(page_name)\n\n```\n\nThen we can connect to our graph (or make one):\n\n```python\nTOKEN = \"<edge_auth_token>\"\ngraph_id = \"graph_id\"\nhome_view = \"view_id\"\ng = GraphClient(graph_id, TOKEN)\n```\n\nand run the scraper:\n\n```python\n\npage_name = \"Napoleon\"\nstring_cache = {}\nvisted_pages = set()\n\npage_node = g.create_node(label=page_name)\ng.add_nodes_to_view(home_view, [page_node], [(0., 0.)])\n\nwith g.batch():\n wiki_scraper(\n g,\n page_node,\n page_name,\n string_cache,\n visted_pages,\n max_depth=3,\n current_depth=0,\n max_links=3,\n first_depth_max_links=2,\n )\n\n```\n\nWe can then head to edge to interact with the graph:\n\n<p align=\"center\">\n<img src=\"https://github.com/driskai/drisk_api/blob/main/docs/images/Napoleon-graph.png\" width=\"80%\">\n</p>\n\n)))\n\n",
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