> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Knowledge Content Types

> Add knowledge content from local files, URLs, raw text, topics, and cloud storage.

Agno Knowledge uses `Content` as the record for each knowledge source.
Content can be added to knowledge from different sources.

| Content Origin | Description                                                                                                                |
| -------------- | -------------------------------------------------------------------------------------------------------------------------- |
| Path           | Local files or directories containing files                                                                                |
| URL            | Direct links to files or other sites                                                                                       |
| Text           | Raw text content                                                                                                           |
| Topic          | Search topics from repositories like arXiv or Wikipedia                                                                    |
| Remote Content | Content from [cloud storage providers](/knowledge/concepts/cloud-storage) like S3, GCS, SharePoint, GitHub, and Azure Blob |

Knowledge content needs to be read and chunked before it can be passed to the vector database for embedding, storage, and retrieval.
For supported file and URL types, Knowledge selects a reader from the file extension or content type. Raw text uses the text reader. Topic ingestion requires an explicit reader such as `ArxivReader` or `WikipediaReader`. Readers parse content from the origin and chunk it into smaller pieces that are then embedded and stored in the vector database.

To override the default reader or its settings, pass a reader when adding content. The example below creates a `PDFReader` with a custom `chunk_size`. Other parameters like `chunking_strategy` work the same way and control how content is ingested and processed.

```bash theme={null}
uv pip install -U agno chromadb openai pypdf
```

Set `OPENAI_API_KEY` before running the example. ChromaDB uses Agno's default OpenAI embedder when no embedder is supplied.

```python theme={null}
import asyncio

from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.pdf_reader import PDFReader
from agno.vectordb.chroma import ChromaDb

reader = PDFReader(
    chunk_size=1000,
)

knowledge_base = Knowledge(
    vector_db=ChromaDb(
        collection="pdf-content",
        path="tmp/chromadb",
        persistent_client=True,
    ),
)

asyncio.run(
    knowledge_base.ainsert(
        path="data/pdf",
        reader=reader
    )
)
```

See [Readers](/knowledge/concepts/readers/overview) for the available readers and their capabilities.

## Next Steps

<CardGroup cols={2}>
  <Card title="Search & Retrieval" icon="magnifying-glass" href="/knowledge/concepts/search-and-retrieval/overview">
    How agents search and find information in your knowledge base
  </Card>

  <Card title="Readers" icon="book-open" href="/knowledge/concepts/readers/overview">
    Explore content parsing and ingestion options in detail
  </Card>

  <Card title="Chunking Strategies" icon="scissors" href="/knowledge/concepts/chunking/overview">
    Optimize how content is broken down for better search results
  </Card>

  <Card title="Vector Databases" icon="database" href="/knowledge/concepts/vector-db">
    Choose the right storage solution for your knowledge base
  </Card>
</CardGroup>