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feast(agent server) Add populate db command (#7342)
* Add populate db command * Added in a reddit graph creation command
This commit is contained in:
Swifty
@@ -80,6 +80,32 @@ poetry run cli app
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poetry run cli stop
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```
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## Adding Test Data
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1. Start the server using 1 of the above methods
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2. Run the populate db command
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```
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poetry run cli test populate-db http://0.0.0.0:8000
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```
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This will add a graph, a graph execution and a cron schedule to run the graph every 5 mins
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### Reddit Graph
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There is a command to add the test reddit graph
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```
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poetry run cli test reddit http://0.0.0.0:8000
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```
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For help run:
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```
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poetry run cli test reddit --help
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```
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# Testing
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To run the tests
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@@ -101,6 +101,220 @@ def test():
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pass
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@test.command()
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@click.argument("server_address")
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@click.option(
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"--client-id", required=True, help="Reddit client ID", default="TODO_FILL_OUT_THIS"
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)
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@click.option(
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"--client-secret",
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required=True,
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help="Reddit client secret",
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default="TODO_FILL_OUT_THIS",
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)
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@click.option(
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"--username", required=True, help="Reddit username", default="TODO_FILL_OUT_THIS"
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)
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@click.option(
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"--password", required=True, help="Reddit password", default="TODO_FILL_OUT_THIS"
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)
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@click.option(
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"--user-agent",
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required=True,
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help="Reddit user agent",
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default="TODO_FILL_OUT_THIS",
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)
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def reddit(
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server_address: str,
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client_id: str,
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client_secret: str,
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username: str,
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password: str,
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user_agent: str,
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):
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"""
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Create an event graph
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"""
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import requests
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from autogpt_server.data.graph import Graph, Link, Node
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from autogpt_server.blocks.ai import LlmConfig, LlmCallBlock, LlmModel
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from autogpt_server.blocks.reddit import (
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RedditCredentials,
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RedditGetPostsBlock,
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RedditPostCommentBlock,
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)
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from autogpt_server.blocks.text import TextFormatterBlock, TextMatcherBlock
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reddit_creds = RedditCredentials(
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client_id=client_id,
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client_secret=client_secret,
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username=username,
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password=password,
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user_agent=user_agent,
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)
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openai_creds = LlmConfig(
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model=LlmModel.openai_gpt4,
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api_key="TODO_FILL_OUT_THIS",
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)
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# Hardcoded inputs
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reddit_get_post_input = {
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"creds": reddit_creds,
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"last_minutes": 60,
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"post_limit": 3,
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}
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text_formatter_input = {
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"format": """
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Based on the following post, write your marketing comment:
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* Post ID: {id}
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* Post Subreddit: {subreddit}
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* Post Title: {title}
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* Post Body: {body}""".strip(),
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}
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llm_call_input = {
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"sys_prompt": """
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You are an expert at marketing, and have been tasked with picking Reddit posts that are relevant to your product.
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The product you are marketing is: Auto-GPT an autonomous AI agent utilizing GPT model.
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You reply the post that you find it relevant to be replied with marketing text.
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Make sure to only comment on a relevant post.
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""",
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"config": openai_creds,
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"expected_format": {
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"post_id": "str, the reddit post id",
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"is_relevant": "bool, whether the post is relevant for marketing",
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"marketing_text": "str, marketing text, this is empty on irrelevant posts",
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},
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}
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text_matcher_input = {"match": "true", "case_sensitive": False}
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reddit_comment_input = {"creds": reddit_creds}
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# Nodes
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reddit_get_post_node = Node(
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block_id=RedditGetPostsBlock().id,
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input_default=reddit_get_post_input,
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)
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text_formatter_node = Node(
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block_id=TextFormatterBlock().id,
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input_default=text_formatter_input,
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)
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llm_call_node = Node(block_id=LlmCallBlock().id, input_default=llm_call_input)
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text_matcher_node = Node(
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block_id=TextMatcherBlock().id,
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input_default=text_matcher_input,
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)
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reddit_comment_node = Node(
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block_id=RedditPostCommentBlock().id,
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input_default=reddit_comment_input,
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)
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nodes = [
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reddit_get_post_node,
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text_formatter_node,
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llm_call_node,
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text_matcher_node,
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reddit_comment_node,
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]
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# Links
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links = [
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Link(reddit_get_post_node.id, text_formatter_node.id, "post", "named_texts"),
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Link(text_formatter_node.id, llm_call_node.id, "output", "usr_prompt"),
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Link(llm_call_node.id, text_matcher_node.id, "response", "data"),
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Link(llm_call_node.id, text_matcher_node.id, "response_#_is_relevant", "text"),
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Link(
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text_matcher_node.id,
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reddit_comment_node.id,
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"positive_#_post_id",
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"post_id",
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),
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Link(
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text_matcher_node.id,
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reddit_comment_node.id,
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"positive_#_marketing_text",
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"comment",
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),
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]
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# Create graph
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test_graph = Graph(
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name="RedditMarketingAgent",
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description="Reddit marketing agent",
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nodes=nodes,
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links=links,
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)
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url = f"{server_address}/graphs"
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headers = {"Content-Type": "application/json"}
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data = test_graph.model_dump_json()
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response = requests.post(url, headers=headers, data=data)
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graph_id = response.json()["id"]
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print(f"Graph created with ID: {graph_id}")
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@test.command()
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@click.argument("server_address")
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def populate_db(server_address: str):
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"""
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Create an event graph
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"""
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import requests
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from autogpt_server.blocks.sample import ParrotBlock, PrintingBlock
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from autogpt_server.blocks.text import TextFormatterBlock
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from autogpt_server.data import graph
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nodes = [
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graph.Node(block_id=ParrotBlock().id),
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graph.Node(block_id=ParrotBlock().id),
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graph.Node(
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block_id=TextFormatterBlock().id,
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input_default={
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"format": "{texts[0]},{texts[1]},{texts[2]}",
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"texts_$_3": "!!!",
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},
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),
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graph.Node(block_id=PrintingBlock().id),
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]
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links = [
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graph.Link(nodes[0].id, nodes[2].id, "output", "texts_$_1"),
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graph.Link(nodes[1].id, nodes[2].id, "output", "texts_$_2"),
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graph.Link(nodes[2].id, nodes[3].id, "output", "text"),
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]
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test_graph = graph.Graph(
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name="TestGraph",
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description="Test graph",
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nodes=nodes,
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links=links,
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)
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url = f"{server_address}/graphs"
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headers = {"Content-Type": "application/json"}
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data = test_graph.model_dump_json()
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response = requests.post(url, headers=headers, data=data)
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graph_id = response.json()["id"]
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if response.status_code == 200:
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execute_url = f"{server_address}/graphs/{response.json()['id']}/execute"
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text = "Hello, World!"
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input_data = {"input": text}
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response = requests.post(execute_url, headers=headers, json=input_data)
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schedule_url = f"{server_address}/graphs/{graph_id}/schedules"
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data = {
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"graph_id": graph_id,
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"cron": "*/5 * * * *",
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"input_data": {"input": "Hello, World!"},
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}
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response = requests.post(schedule_url, headers=headers, json=data)
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print("Database populated with: \n- graph\n- execution\n- schedule")
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@test.command()
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@click.argument("server_address")
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def graph(server_address: str):
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