Orchestrate Data Ingestion with Prefect and Report Generation

Design a Prefect flow to orchestrate the data ingestion and preprocessing for your CRE analysis. This flow should fetch raw data (simulated from a file or API), clean it, and make it available for your AutoGen agents. Additionally, outline how the AutoGen system's final investment report can be automatically formatted and published (e.g., to a PDF or a web dashboard like Plotly Dash) once the agents complete their analysis. Show how Prefect would trigger the AutoGen analysis and collect its output.

```python
from prefect import flow, task
# import pandas as pd

# @task
# def fetch_raw_cre_data(source: str) -> pd.DataFrame:
#    # Simulate fetching data
#    return pd.DataFrame({'location': ['Austin', 'Seattle'], 'type': ['retail', 'office'], 'yield': [0.05, 0.04]})

# @task
# def preprocess_data(df: pd.DataFrame) -> dict:
#    # Simulate preprocessing
#    return df.set_index('location').to_dict('index')

# @flow
# def cre_analysis_pipeline(query: str):
#    raw_data = fetch_raw_cre_data("external_api")
#    processed_data = preprocess_data(raw_data)

#    # Trigger AutoGen here, passing processed_data as context or tool input
#    # final_report = autogen_manager.run_analysis(query, processed_data)

#    # publish_report(final_report)

# if __name__ == "__main__":
#    cre_analysis_pipeline(query="Analyze retail properties.")
```