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.
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:
# 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.
