> ## Documentation Index
> Fetch the complete documentation index at: https://phidatainc-agui.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Contents Database
> Track and manage the content you've added to your knowledge base.
Contents Database is an optional component that tracks what you've added to your knowledge base. While the vector database stores embeddings for search, Contents Database stores metadata about each piece of content: what it is, when you added it, and its processing status.
```python theme={null}
from agno.knowledge.knowledge import Knowledge
from agno.db.postgres import PostgresDb
from agno.vectordb.pgvector import PgVector
knowledge = Knowledge(
vector_db=PgVector(table_name="vectors", db_url=db_url),
contents_db=PostgresDb(db_url=db_url), # Enables content tracking
)
```
## Why Use Content DB
**Without Content DB**, you can search your knowledge base but can't see what's in it or manage individual pieces of content.
**With Content DB**, you get:
* **Visibility**: See all content that's been added, track processing status, view metadata
* **Management**: Delete specific content and automatically clean up associated vectors
* **Updates**: Edit names, descriptions, and metadata without rebuilding the knowledge base
* **Filtering**: Use [agentic filtering](/knowledge/concepts/filters/overview) to filter search results by metadata
Contents DB is required for [agentic filtering](/knowledge/concepts/filters/overview) and the [AgentOS Knowledge UI](/agent-os/features/knowledge-management).
## Setup
Agno supports multiple database backends:
```python theme={null}
from agno.db.postgres import PostgresDb
contents_db = PostgresDb(
db_url="postgresql+psycopg://user:pass@localhost:5432/db",
knowledge_table="knowledge_contents" # Optional custom table name
)
```
```python theme={null}
from agno.db.sqlite import SqliteDb
contents_db = SqliteDb(db_file="knowledge.db")
```
```python theme={null}
from agno.db.mongo import MongoDb
contents_db = MongoDb(
uri="mongodb://localhost:27017",
database="agno_db"
)
```
```python theme={null}
from agno.db.in_memory import InMemoryDb
contents_db = InMemoryDb() # For testing only
```
Other supported backends: [PostgreSQL](/database/providers/postgres/overview) (recommended for production), [SQLite](/database/providers/sqlite/overview) (development), [MySQL](/database/providers/mysql/overview), [MongoDB](/database/providers/mongo/overview), [Redis](/database/providers/redis/overview), [DynamoDB](/database/providers/dynamodb/overview), [Firestore](/database/providers/firestore/overview).
## Managing Content
### Add Content with Metadata
```python theme={null}
knowledge.insert(
name="Product Manual",
path="docs/manual.pdf",
metadata={"department": "engineering", "version": "2.1"}
)
```
### List Content
```python theme={null}
contents, total_count = knowledge.get_content(
limit=20,
page=1,
sort_by="created_at",
sort_order="desc"
)
for content in contents:
print(content.name, content.status, content.created_at)
```
### Get Content by ID
```python theme={null}
content = knowledge.get_content_by_id(content_id)
print(content.name) # Content name
print(content.description) # Description
print(content.metadata) # Custom metadata
print(content.file_type) # File type (.pdf, .txt, etc.)
print(content.size) # File size in bytes
print(content.status) # Processing status
print(content.created_at) # When it was added
print(content.updated_at) # Last modification
```
### Delete Content
Deleting content automatically:
1. Removes the content metadata from Content DB
2. Deletes associated vectors from the vector database
3. Maintains consistency between both databases
```python theme={null}
# Delete specific content
knowledge.remove_content_by_id(content_id)
# Delete all content
knowledge.remove_all_content()
```
### Filter by Metadata
```python theme={null}
# Get available filter keys
valid_filters = knowledge.get_filters()
# Search with filters
results = knowledge.search(
query="technical documentation",
filters={"department": "engineering"}
)
```
## Schema
Content DB stores the following fields for each piece of content:
| Field | Type | Description |
| ---------------- | ---- | ------------------------------------------ |
| `id` | str | Unique identifier |
| `name` | str | Content name |
| `description` | str | Content description |
| `metadata` | dict | Custom metadata |
| `type` | str | Content type |
| `size` | int | File size in bytes |
| `linked_to` | str | ID of linked content |
| `access_count` | int | Number of times accessed |
| `status` | str | Processing status |
| `status_message` | str | Status details |
| `created_at` | int | Created timestamp |
| `updated_at` | int | Updated timestamp |
| `external_id` | str | External ID for integrations like LightRAG |
## AgentOS Integration
Content DB is required for the AgentOS Knowledge UI. With it, the web interface provides:
* **Content Browser**: View all uploaded content with metadata
* **Upload Interface**: Add new content through the web UI
* **Status Monitoring**: Real-time processing status updates
* **Metadata Editor**: Update content metadata through forms
* **Search and Filtering**: Find content by metadata attributes
* **Bulk Operations**: Manage multiple content items at once
```python theme={null}
from agno.os import AgentOS
from agno.agent import Agent
knowledge = Knowledge(
vector_db=PgVector(table_name="vectors", db_url=db_url),
contents_db=PostgresDb(db_url=db_url),
)
agent = Agent(name="Knowledge Agent", knowledge=knowledge)
app = AgentOS(
id="knowledge-demo",
agents=[agent],
)
```
See [AgentOS Knowledge Management](/agent-os/features/knowledge-management) for more details.
## Next Steps
Understand the embedding storage layer
Filter search results by metadata
Manage knowledge through the web UI
Database configuration guides