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

# Knowledge

> Add semantic search and RAG capabilities to agents

> Build knowledge bases with vector storage, embeddings, and content ingestion for retrieval-augmented generation (RAG).

Knowledge in Agno is composed of several resources that work together:

```
Content (URLs, text)
  └─▶ Knowledge
        ├─▶ VectorDB (Qdrant)
        │     └─▶ Embedder (OpenAI)
        └─▶ Contents DB (Postgres, optional)
```

## `agno/knowledge`

The central knowledge base resource that connects a vector database with optional embedder and content storage.

### Config

| Field         | Type         | Required | Default | Description                                                         |
| ------------- | ------------ | -------- | ------- | ------------------------------------------------------------------- |
| `vector_db`   | `Dependency` | **Yes**  | —       | Reference to `agno/vectordb/qdrant`                                 |
| `contents_db` | `Dependency` | No       | —       | Reference to `agno/db/postgres` for content metadata storage        |
| `embedder`    | `Dependency` | No       | —       | Reference to `agno/knowledge/embedder/openai` for custom embeddings |
| `max_results` | `integer`    | No       | `10`    | Maximum search results to return                                    |

### Outputs

| Field              | Type           | Description                                          |
| ------------------ | -------------- | ---------------------------------------------------- |
| `pip_dependencies` | `list[string]` | Required Python packages                             |
| `spec`             | `object`       | Serialized knowledge spec for runtime reconstruction |

***

## `agno/vectordb/qdrant`

Configures a Qdrant vector database for storing embeddings.

### Config

| Field         | Type                                    | Required | Default    | Description                                   |
| ------------- | --------------------------------------- | -------- | ---------- | --------------------------------------------- |
| `url`         | `Field[string]`                         | **Yes**  | —          | Qdrant server URL. Supports FieldReferences   |
| `collection`  | `Field[string]`                         | **Yes**  | —          | Collection name. Supports FieldReferences     |
| `api_key`     | `Field[string]`                         | No       | —          | API key for authentication                    |
| `search_type` | `"vector"` \| `"keyword"` \| `"hybrid"` | No       | `"hybrid"` | Search strategy                               |
| `embedder`    | `Dependency`                            | No       | —          | Reference to `agno/knowledge/embedder/openai` |

### Outputs

| Field              | Type           | Description                                                        |
| ------------------ | -------------- | ------------------------------------------------------------------ |
| `spec`             | `object`       | Serialized Qdrant config for runtime reconstruction                |
| `pip_dependencies` | `list[string]` | Required packages (includes `fastembed` for hybrid/keyword search) |

***

## `agno/knowledge/embedder/openai`

Configures an OpenAI embedding model for generating vector embeddings.

### Config

| Field             | Type                    | Required | Default                    | Description                                                          |
| ----------------- | ----------------------- | -------- | -------------------------- | -------------------------------------------------------------------- |
| `id`              | `string`                | No       | `"text-embedding-3-small"` | Embedding model identifier                                           |
| `api_key`         | `Field[string]`         | **Yes**  | —                          | OpenAI API key                                                       |
| `dimensions`      | `integer`               | No       | —                          | Override embedding dimensions (only for `text-embedding-3-*` models) |
| `encoding_format` | `"float"` \| `"base64"` | No       | `"float"`                  | Response encoding format                                             |
| `organization`    | `string`                | No       | —                          | OpenAI organization ID                                               |
| `base_url`        | `string`                | No       | —                          | Custom base URL for OpenAI-compatible APIs                           |

### Outputs

| Field              | Type           | Description                                         |
| ------------------ | -------------- | --------------------------------------------------- |
| `pip_dependencies` | `list[string]` | Required Python packages                            |
| `spec`             | `object`       | Serialized embedder spec for runtime reconstruction |

***

## `agno/knowledge/content`

Represents a content source to ingest into a knowledge base. Supports URLs and inline text.

### Config

| Field          | Type                   | Required    | Default       | Description                                                                      |
| -------------- | ---------------------- | ----------- | ------------- | -------------------------------------------------------------------------------- |
| `knowledge`    | `Dependency`           | **Yes**     | —             | Reference to `agno/knowledge`                                                    |
| `url`          | `string`               | Conditional | —             | URL to fetch (website, PDF, remote file). Mutually exclusive with `text_content` |
| `text_content` | `string`               | Conditional | —             | Inline text content. Mutually exclusive with `url`                               |
| `name`         | `string`               | No          | Resource name | Content identifier                                                               |
| `description`  | `string`               | No          | —             | Content description                                                              |
| `metadata`     | `dict[string, string]` | No          | —             | Custom metadata key-value pairs                                                  |
| `topics`       | `list[string]`         | No          | —             | Topic tags for categorization                                                    |

### Outputs

| Field              | Type           | Description                                                      |
| ------------------ | -------------- | ---------------------------------------------------------------- |
| `spec`             | `object`       | Content specification                                            |
| `pip_dependencies` | `list[string]` | Reader packages (`pypdf`, `beautifulsoup4`, `python-docx`, etc.) |

## Example

A complete knowledge base setup:

```yaml theme={"theme":{"light":"min-light","dark":"min-dark"}}
# 1. Embedder
provider: agno
resource: knowledge/embedder/openai
name: embedder
config:
  id: text-embedding-3-small
  api_key:
    provider: pragma
    resource: secret
    name: openai-key
    field: outputs.OPENAI_API_KEY
---
# 2. Vector database
provider: agno
resource: vectordb/qdrant
name: vectors
config:
  url:
    provider: qdrant
    resource: database
    name: main
    field: outputs.url
  collection:
    provider: qdrant
    resource: collection
    name: docs
    field: outputs.name
  api_key:
    provider: qdrant
    resource: database
    name: main
    field: outputs.api_key
  search_type: hybrid
  embedder:
    provider: agno
    resource: knowledge/embedder/openai
    name: embedder
---
# 3. Knowledge base
provider: agno
resource: knowledge
name: docs-kb
config:
  vector_db:
    provider: agno
    resource: vectordb/qdrant
    name: vectors
  max_results: 5
---
# 4. Content source
provider: agno
resource: knowledge/content
name: user-guide
config:
  knowledge:
    provider: agno
    resource: knowledge
    name: docs-kb
  url: "https://docs.example.com/guide.pdf"
  topics:
    - documentation
    - user-guide
```

## Notes

* You must provide exactly one of `url` or `text_content` in content resources.
* Content supports URLs to PDFs, web pages, documents (DOCX, PPTX), arxiv papers, YouTube transcripts, and Wikipedia articles.
* The `search_type: hybrid` option combines vector and keyword search for better results but requires the `fastembed` package.
* Content resources are **stateful** — they insert into and delete from the vector database during lifecycle events.