Knowledge
Document Knowledge Base
Examples
- Introduction
- Getting Started
- Agents
- Workflows
- Applications
Agent Concepts
- Multimodal
- RAG
- Knowledge
- Memory
- Teams
- Async
- Hybrid Search
- Storage
- Tools
- Vector Databases
- Embedders
Models
- Anthropic
- AWS Bedrock Claude
- Azure OpenAI
- Cohere
- DeepSeek
- Fireworks
- Gemini
- Groq
- Hugging Face
- Mistral
- NVIDIA
- Ollama
- OpenAI
- Together
- Vertex AI
- xAI
Knowledge
Document Knowledge Base
Code
from agno.agent import Agent
from agno.document.base import Document
from agno.knowledge.document import DocumentKnowledgeBase
from agno.vectordb.pgvector import PgVector
fun_facts = """
- Earth is the third planet from the Sun and the only known astronomical object to support life.
- Approximately 71% of Earth's surface is covered by water, with the Pacific Ocean being the largest.
- The Earth's atmosphere is composed mainly of nitrogen (78%) and oxygen (21%), with traces of other gases.
- Earth rotates on its axis once every 24 hours, leading to the cycle of day and night.
- The planet has one natural satellite, the Moon, which influences tides and stabilizes Earth's axial tilt.
- Earth's tectonic plates are constantly shifting, leading to geological activities like earthquakes and volcanic eruptions.
- The highest point on Earth is Mount Everest, standing at 8,848 meters (29,029 feet) above sea level.
- The deepest part of the ocean is the Mariana Trench, reaching depths of over 11,000 meters (36,000 feet).
- Earth has a diverse range of ecosystems, from rainforests and deserts to coral reefs and tundras.
- The planet's magnetic field protects life by deflecting harmful solar radiation and cosmic rays.
"""
# Load documents from the data/docs directory
documents = [Document(content=fun_facts)]
# Database connection URL
db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
# Create a knowledge base with the loaded documents
knowledge_base = DocumentKnowledgeBase(
documents=documents,
vector_db=PgVector(
table_name="documents",
db_url=db_url,
),
)
# Load the knowledge base
knowledge_base.load(recreate=False)
# Create an agent with the knowledge base
agent = Agent(
knowledge=knowledge_base,
)
# Ask the agent about the knowledge base
agent.print_response(
"Ask me about something from the knowledge base about earth", markdown=True
)
Usage
1
Create a virtual environment
Open the Terminal
and create a python virtual environment.
2
Install libraries
pip install -U sqlalchemy 'psycopg[binary]' pgvector agno
3
Run PgVector
docker run -d \
-e POSTGRES_DB=ai \
-e POSTGRES_USER=ai \
-e POSTGRES_PASSWORD=ai \
-e PGDATA=/var/lib/postgresql/data/pgdata \
-v pgvolume:/var/lib/postgresql/data \
-p 5532:5432 \
--name pgvector \
agnohq/pgvector:16
4
Run Agent