https://github.com/firstbatchxyz/dria-workflows
Workflow creation for Dria Agents
https://github.com/firstbatchxyz/dria-workflows
Last synced: over 1 year ago
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Workflow creation for Dria Agents
- Host: GitHub
- URL: https://github.com/firstbatchxyz/dria-workflows
- Owner: firstbatchxyz
- Created: 2024-09-04T16:06:52.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-11-27T10:38:02.000Z (over 1 year ago)
- Last Synced: 2025-04-07T18:52:48.205Z (over 1 year ago)
- Language: Python
- Size: 149 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Dria Workflows
Dria Workflows enables the creation of workflows for Dria Agents.
## Installation
You can install Dria Workflows using pip:
```bash
pip install dria_workflows
````
## Usage Example
Here's a simple example of how to use Dria Workflows:
```python
import logging
from dria_workflows import WorkflowBuilder, Operator, Write, Edge, validate_workflow_json
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
builder = WorkflowBuilder()
# Add a step to your workflow
builder.generative_step(id="write_poem", prompt="Write a poem as if you are Kahlil Gibran", operator=Operator.GENERATION, outputs=[Write.new("poem")])
# Define the flow of your workflow
flow = [Edge(source="write_poem", target="_end")]
builder.flow(flow)
# Set the return value of your workflow
builder.set_return_value("poem")
# Build your workflow
workflow = builder.build()
# Validate your workflow
validate_workflow_json(workflow.model_dump_json(indent=2, exclude_unset=True, exclude_none=True))
# Save workflow
workflow.save("poem_workflow.json")
if __name__ == "__main__":
main()
```
# Here is a more complex workflow
```python
import logging
from dria_workflows import WorkflowBuilder, ConditionBuilder, Operator, Write, GetAll, Read, Push, Edge, Expression, validate_workflow_json
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# Give a starting memory as input
builder = WorkflowBuilder(memory={"topic_1":"Linear Algebra", "topic_2":"CUDA"})
# Add steps to your workflow
builder.generative_step(id="create_query", prompt="Write down a search query related to following topics: {{topic_1}} and {{topic_2}}. If any, avoid asking questions asked before: {{history}}", operator=Operator.GENERATION, inputs=[GetAll.new("history", False)], outputs=[Write.new("search_query")])
builder.generative_step(id="search", prompt="{{search_query}}", operator=Operator.FUNCTION_CALLING, outputs=[Write.new("result"), Push.new("history")])
builder.generative_step(id="evaluate", prompt="Evaluate if search result is related and high quality to given question by saying Yes or No. Question: {{search_query}} , Search Result: {{result}}. Only output Yes or No and nothing else.", operator=Operator.GENERATION, outputs=[Write.new("is_valid")])
# Define the flow of your workflow
flow = [
Edge(source="create_query", target="search"),
Edge(source="search", target="evaluate"),
Edge(source="evaluate", target="_end", condition=ConditionBuilder.build(expected="Yes", target_if_not="create_query", expression=Expression.CONTAINS, input=Read.new("is_valid", True))),
]
builder.flow(flow)
# Set the return value of your workflow
builder.set_return_value("result")
# Build your workflow
workflow = builder.build()
validate_workflow_json(workflow.model_dump_json(indent=2, exclude_unset=True, exclude_none=True))
workflow.save("search_workflow.json")
if __name__ == "__main__":
main()
```
Detailed docs soon.
[andthattoo](https://x.com/andthatto)