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https://github.com/crewAIInc/crewAI.git
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Clean up docs
This commit is contained in:
Brandon Hancock
@@ -23,14 +23,11 @@ Flows allow you to create structured, event-driven workflows. They provide a sea
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Let's create a simple Flow where you will use OpenAI to generate a random city in one task and then use that city to generate a fun fact in another task.
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, listen, start
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from dotenv import load_dotenv
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from litellm import completion
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load_dotenv()
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class ExampleFlow(Flow):
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model = "gpt-4o-mini"
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@@ -70,13 +67,11 @@ class ExampleFlow(Flow):
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return fun_fact
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async def main():
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flow = ExampleFlow()
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result = await flow.kickoff()
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print(f"Generated fun fact: {result}")
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flow = ExampleFlow()
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result = flow.kickoff()
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asyncio.run(main())
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print(f"Generated fun fact: {result}")
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```
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In the above example, we have created a simple Flow that generates a random city using OpenAI and then generates a fun fact about that city. The Flow consists of two tasks: `generate_city` and `generate_fun_fact`. The `generate_city` task is the starting point of the Flow, and the `generate_fun_fact` task listens for the output of the `generate_city` task.
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@@ -124,26 +119,23 @@ Here's how you can access the final output:
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<CodeGroup>
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, listen, start
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class OutputExampleFlow(Flow):
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@start()
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def first_method(self):
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return "Output from first_method"
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@start()
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def first_method(self):
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return "Output from first_method"
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@listen(first_method)
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def second_method(self, first_output):
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return f"Second method received: {first_output}"
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async def main():
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flow = OutputExampleFlow()
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final_output = await flow.kickoff()
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final_output = flow.kickoff()
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print("---- Final Output ----")
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print(final_output)
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asyncio.run(main())
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````
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``` text Output
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@@ -165,7 +157,6 @@ Here's an example of how to update and access the state:
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<CodeGroup>
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, listen, start
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from pydantic import BaseModel
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@@ -186,14 +177,11 @@ class StateExampleFlow(Flow[ExampleState]):
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self.state.counter += 1
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return self.state.message
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async def main():
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flow = StateExampleFlow()
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final_output = await flow.kickoff()
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print(f"Final Output: {final_output}")
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print("Final State:")
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print(flow.state)
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asyncio.run(main())
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flow = StateExampleFlow()
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final_output = flow.kickoff()
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print(f"Final Output: {final_output}")
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print("Final State:")
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print(flow.state)
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```
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```text Output
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@@ -221,8 +209,6 @@ In unstructured state management, all state is stored in the `state` attribute o
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This approach offers flexibility, enabling developers to add or modify state attributes on the fly without defining a strict schema.
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, listen, start
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class UntructuredExampleFlow(Flow):
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@@ -245,12 +231,8 @@ class UntructuredExampleFlow(Flow):
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print(f"State after third_method: {self.state}")
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async def main():
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flow = UntructuredExampleFlow()
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await flow.kickoff()
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asyncio.run(main())
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flow = UntructuredExampleFlow()
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flow.kickoff()
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```
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**Key Points:**
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@@ -264,8 +246,6 @@ Structured state management leverages predefined schemas to ensure consistency a
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By using models like Pydantic's `BaseModel`, developers can define the exact shape of the state, enabling better validation and auto-completion in development environments.
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, listen, start
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from pydantic import BaseModel
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@@ -294,12 +274,8 @@ class StructuredExampleFlow(Flow[ExampleState]):
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print(f"State after third_method: {self.state}")
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async def main():
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flow = StructuredExampleFlow()
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await flow.kickoff()
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asyncio.run(main())
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flow = StructuredExampleFlow()
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flow.kickoff()
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```
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**Key Points:**
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@@ -332,7 +308,6 @@ The `or_` function in Flows allows you to listen to multiple methods and trigger
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<CodeGroup>
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, listen, or_, start
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class OrExampleFlow(Flow):
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@@ -350,12 +325,9 @@ class OrExampleFlow(Flow):
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print(f"Logger: {result}")
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async def main():
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flow = OrExampleFlow()
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await flow.kickoff()
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asyncio.run(main())
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flow = OrExampleFlow()
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flow.kickoff()
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```
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```text Output
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@@ -375,7 +347,6 @@ The `and_` function in Flows allows you to listen to multiple methods and trigge
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<CodeGroup>
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```python Code
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import asyncio
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from crewai.flow.flow import Flow, and_, listen, start
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class AndExampleFlow(Flow):
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@@ -393,13 +364,8 @@ class AndExampleFlow(Flow):
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print("---- Logger ----")
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print(self.state)
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async def main():
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flow = AndExampleFlow()
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await flow.kickoff()
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asyncio.run(main())
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flow = AndExampleFlow()
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flow.kickoff()
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```
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```text Output
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@@ -420,7 +386,6 @@ You can specify different routes based on the output of the method, allowing you
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<CodeGroup>
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```python Code
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import asyncio
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import random
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from crewai.flow.flow import Flow, listen, router, start
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from pydantic import BaseModel
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@@ -452,12 +417,8 @@ class RouterFlow(Flow[ExampleState]):
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print("Fourth method running")
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async def main():
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flow = RouterFlow()
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await flow.kickoff()
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asyncio.run(main())
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flow = RouterFlow()
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flow.kickoff()
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```
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```text Output
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@@ -525,7 +486,6 @@ Here's an example of how you can connect the `poem_crew` in the `main.py` file:
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```python Code
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#!/usr/bin/env python
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import asyncio
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from random import randint
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from pydantic import BaseModel
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@@ -541,7 +501,6 @@ class PoemFlow(Flow[PoemState]):
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@start()
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def generate_sentence_count(self):
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print("Generating sentence count")
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# Generate a number between 1 and 5
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self.state.sentence_count = randint(1, 5)
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@listen(generate_sentence_count)
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@@ -558,22 +517,17 @@ class PoemFlow(Flow[PoemState]):
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with open("poem.txt", "w") as f:
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f.write(self.state.poem)
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async def kickoff():
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def kickoff():
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poem_flow = PoemFlow()
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await poem_flow.kickoff()
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poem_flow.kickoff()
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def plot():
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poem_flow = PoemFlow()
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poem_flow.plot()
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def main():
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asyncio.run(kickoff())
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if __name__ == "__main__":
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main()
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kickoff()
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```
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In this example, the `PoemFlow` class defines a flow that generates a sentence count, uses the `PoemCrew` to generate a poem, and then saves the poem to a file. The flow is kicked off by calling the `kickoff()` method.
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