​
代码

cookbook/apps/playground/upload_files.py
from agno.agent import Agent
from agno.knowledge.combined import CombinedKnowledgeBase
from agno.knowledge.csv import CSVKnowledgeBase
from agno.knowledge.docx import DocxKnowledgeBase
from agno.knowledge.json import JSONKnowledgeBase
from agno.knowledge.pdf import PDFKnowledgeBase
from agno.knowledge.text import TextKnowledgeBase
from agno.models.google.gemini import Gemini
from agno.models.openai import OpenAIChat
from agno.playground import Playground
from agno.storage.postgres import PostgresStorage
from agno.vectordb.pgvector import PgVector

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

knowledge_base = CombinedKnowledgeBase(
    sources=[
        PDFKnowledgeBase(
            vector_db=PgVector(table_name="recipes_pdf", db_url=db_url), path=""
        ),
        CSVKnowledgeBase(
            vector_db=PgVector(table_name="recipes_csv", db_url=db_url), path=""
        ),
        DocxKnowledgeBase(
            vector_db=PgVector(table_name="recipes_docx", db_url=db_url), path=""
        ),
        JSONKnowledgeBase(
            vector_db=PgVector(table_name="recipes_json", db_url=db_url), path=""
        ),
        TextKnowledgeBase(
            vector_db=PgVector(table_name="recipes_text", db_url=db_url), path=""
        ),
    ],
    vector_db=PgVector(table_name="recipes_combined", db_url=db_url),
)

file_agent = Agent(
    name="文件上传代理",
    agent_id="file-upload-agent",
    role="Answer questions about the uploaded files",
    model=OpenAIChat(id="gpt-4o-mini"),
    storage=PostgresStorage(
        table_name="agent_sessions", db_url=db_url, auto_upgrade_schema=True
    ),
    knowledge=knowledge_base,
    show_tool_calls=True,
    markdown=True,
)


audio_agent = Agent(
    name="音频理解代理",
    agent_id="audio-understanding-agent",
    role="Answer questions about audio files",
    model=OpenAIChat(id="gpt-4o-audio-preview"),
    storage=PostgresStorage(
        table_name="agent_sessions", db_url=db_url, auto_upgrade_schema=True
    ),
    add_history_to_messages=True,
    add_datetime_to_instructions=True,
    show_tool_calls=True,
    markdown=True,
)

video_agent = Agent(
    name="视频理解代理",
    model=Gemini(id="gemini-2.0-flash"),
    agent_id="video-understanding-agent",
    role="Answer questions about video files",
    storage=PostgresStorage(
        table_name="agent_sessions", db_url=db_url, auto_upgrade_schema=True
    ),
    add_history_to_messages=True,
    add_datetime_to_instructions=True,
    show_tool_calls=True,
    markdown=True,
)

playground = Playground(
    agents=[file_agent, audio_agent, video_agent],
    name="上传文件游乐场",
    description="上传文件并就文件内容提问",
    app_id="upload-files-playground",
)
app = playground.get_app()

if __name__ == "__main__":
    playground.serve(app="upload_files:app", reload=True)

​
用法

1

创建虚拟环境

打开 Terminal 并创建一个 python 虚拟环境。

python3 -m venv .venv
source .venv/bin/activate
2

设置您的 API 密钥

export OPENAI_API_KEY=xxx
export GOOGLE_API_KEY=xxx
3

安装库


pip install -U agno "uvicorn[standard]" openai google-generativeai psycopg-binary

pip install -U "agno[pdf,csv,docx,json,text]"
4

运行 Agent

python cookbook/apps/playground/upload_files.py