AgentOS Excel Analyst

Query Excel spreadsheet data loaded into PgVector knowledge through an AgentOS agent.

agentos_excel_analyst.py

"""
Agentos Excel Analyst
=====================

Demonstrates agentos excel analyst.
"""

from pathlib import Path

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.excel_reader import ExcelReader
from agno.models.openai import OpenAIChat
from agno.os import AgentOS
from agno.vectordb.pgvector import PgVector

# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------

db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"

db = PostgresDb(db_url=db_url)

excel_knowledge = Knowledge(
    name="Excel Products",
    contents_db=db,  # Required for UI to show knowledge
    vector_db=PgVector(
        db_url=db_url,
        table_name="agentos_excel_knowledge",
    ),
)

excel_agent = Agent(
    name="Excel Data Agent",
    model=OpenAIChat(id="gpt-4o-mini"),
    db=db,  # For session storage
    knowledge=excel_knowledge,
    search_knowledge=True,
    markdown=True,
    instructions=[
        "You are a data analyst assistant with access to Excel spreadsheet data.",
        "Search the knowledge base to answer questions about the data.",
        "Provide specific numbers and details when available.",
    ],
)

# Create AgentOS app
agent_os = AgentOS(
    description="Excel Knowledge API - Query Excel data via REST",
    agents=[excel_agent],
)

app = agent_os.get_app()

# ---------------------------------------------------------------------------
# Run Example
# ---------------------------------------------------------------------------

if __name__ == "__main__":
    repo_root = Path(__file__).parent.parent.parent.parent
    sample_file = (
        repo_root / "cookbook/07_knowledge/testing_resources/sample_products.xlsx"
    )

    if sample_file.exists():
        print("Loading sample products data...")
        excel_knowledge.insert(
            path=str(sample_file),
            reader=ExcelReader(),
            skip_if_exists=True,
        )

    print("\nStarting AgentOS server...")
    print("Test at: http://localhost:7777/")
    print("\nExample queries:")
    print("  - What electronics products are in stock?")
    print("  - What is the price of the Bluetooth speaker?")

    agent_os.serve(app="agentos_excel_analyst:app", reload=True)

Run the Example

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U "agno[os]" "psycopg[binary]" openai openpyxl pgvector xlrd
   
3. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
4. Run PgVector

   macOS / Linux

   docker run -d \
     -e POSTGRES_DB=ai \
     -e POSTGRES_USER=ai \
     -e POSTGRES_PASSWORD=ai \
     -e PGDATA=/var/lib/postgresql \
     -v pgvolume:/var/lib/postgresql \
     -p 5532:5432 \
     --name pgvector \
     agnohq/pgvector:18
   

   docker run -d `
     -e POSTGRES_DB=ai `
     -e POSTGRES_USER=ai `
     -e POSTGRES_PASSWORD=ai `
     -e PGDATA=/var/lib/postgresql `
     -v pgvolume:/var/lib/postgresql `
     -p 5532:5432 `
     --name pgvector `
     agnohq/pgvector:18
   
5. Clone Agno

   Clone the repository and run the remaining commands from its root:

   git clone https://github.com/agno-agi/agno.git
   cd agno
   
6. Run the example

   Run the example from the repository root:

   python cookbook/05_agent_os/knowledge/agentos_excel_analyst.py
   

Full source: cookbook/05_agent_os/knowledge/agentos_excel_analyst.py