> ## Documentation Index
> Fetch the complete documentation index at: https://docs.agno.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Readers: CSV, JSON, Field-Labeled CSV

> Readers for structured data formats.

Readers for structured data formats. CSV and JSON files are processed row-by-row or as complete documents.

```python data.py theme={null}
"""
Data Readers: CSV, JSON, Field-Labeled CSV
============================================
Readers for structured data formats. CSV and JSON files are processed
row-by-row or as complete documents.

Supported data formats:
- CSV: Standard comma-separated values
- JSON: JSON files and arrays
- Field-Labeled CSV: CSV with column names as labels in output

See also: 01_documents.py for PDF/DOCX, 03_web.py for web sources.
"""

import asyncio

from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.csv_reader import CSVReader
from agno.knowledge.reader.json_reader import JSONReader
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType

# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------

qdrant_url = "http://localhost:6333"

knowledge = Knowledge(
    vector_db=Qdrant(
        collection="data_readers",
        url=qdrant_url,
        search_type=SearchType.hybrid,
        embedder=OpenAIEmbedder(id="text-embedding-3-small"),
    ),
)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    knowledge=knowledge,
    search_knowledge=True,
    markdown=True,
)

# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------

if __name__ == "__main__":

    async def main():
        # --- CSV: structured tabular data ---
        print("\n" + "=" * 60)
        print("READER: CSV")
        print("=" * 60 + "\n")

        # CSVReader reads each row as a separate document
        await knowledge.ainsert(
            name="Sample Data",
            text_content="name,role,department\nAlice,Engineer,Platform\nBob,Designer,Product\nCarol,Manager,Engineering",
            reader=CSVReader(),
        )
        agent.print_response("Who works in engineering?", stream=True)

        # --- JSON: structured data ---
        print("\n" + "=" * 60)
        print("READER: JSON")
        print("=" * 60 + "\n")

        await knowledge.ainsert(
            name="Config",
            text_content='{"app": "acme", "version": "2.0", "features": ["auth", "billing", "analytics"]}',
            reader=JSONReader(),
        )
        agent.print_response("What features does the app have?", stream=True)

    asyncio.run(main())
```

## Run the Example

<Steps>
  <Snippet file="create-venv-step.mdx" />

  <Step title="Install dependencies">
    ```bash theme={null}
    uv pip install -U agno aiofiles fastembed openai qdrant-client
    ```
  </Step>

  <Step title="Export your OpenAI API key">
    <CodeGroup>
      ```bash Mac/Linux theme={null}
      export OPENAI_API_KEY="your_openai_api_key_here"
      ```

      ```bash Windows theme={null}
      $Env:OPENAI_API_KEY="your_openai_api_key_here"
      ```
    </CodeGroup>
  </Step>

  <Step title="Run Qdrant">
    ```bash theme={null}
    docker run -d --name qdrant -p 6333:6333 qdrant/qdrant:latest
    ```
  </Step>

  <Step title="Run the example">
    Save the code above as `data.py`, then run:

    ```bash theme={null}
    python data.py
    ```
  </Step>
</Steps>

Full source: [cookbook/07\_knowledge/05\_integrations/readers/02\_data.py](https://github.com/agno-agi/agno/blob/main/cookbook/07_knowledge/05_integrations/readers/02_data.py)