> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-docs-scavio-google-v2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

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

> Ingest inline CSV and JSON text with CSVReader and JSONReader into Qdrant hybrid search and query the rows with an agent.

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)