loading_content.py
"""
Loading Content: All Source Types
==================================
Knowledge supports loading content from many sources: local files, URLs,
raw text, topics (Wikipedia/ArXiv), and batch operations.
This example demonstrates each source type. In production, you'll typically
use one or two of these patterns.
Steps:
1. From a local file path
2. From a URL
3. From raw text
4. From topics (Wikipedia, ArXiv)
5. Batch loading from multiple sources
Note: All examples use async methods (ainsert, ainsert_many).
Sync equivalents (insert, insert_many) are also available.
"""
import asyncio
from agno.agent import Agent
from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.knowledge.reader.wikipedia_reader import WikipediaReader
# Also available: from agno.knowledge.reader.arxiv_reader import ArxivReader
from agno.models.openai import OpenAIResponses
from agno.vectordb.qdrant import Qdrant
from agno.vectordb.search import SearchType
# ---------------------------------------------------------------------------
# Setup
# ---------------------------------------------------------------------------
qdrant_url = "http://localhost:6333"
knowledge = Knowledge(
vector_db=Qdrant(
collection="loading_content",
url=qdrant_url,
search_type=SearchType.hybrid,
embedder=OpenAIEmbedder(id="text-embedding-3-small"),
),
)
agent = Agent(
model=OpenAIResponses(id="gpt-5.2"),
knowledge=knowledge,
search_knowledge=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Demo
# ---------------------------------------------------------------------------
if __name__ == "__main__":
async def main():
# --- 1. From a local file path ---
print("\n" + "=" * 60)
print("SOURCE 1: Local file")
print("=" * 60 + "\n")
await knowledge.ainsert(
name="CV",
path="cookbook/07_knowledge/testing_resources/cv_1.pdf",
metadata={"source": "local_file"},
)
agent.print_response("What skills does Jordan Mitchell have?", stream=True)
# --- 2. From a URL ---
print("\n" + "=" * 60)
print("SOURCE 2: URL")
print("=" * 60 + "\n")
await knowledge.ainsert(
name="Recipes",
url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf",
metadata={"source": "url"},
)
agent.print_response("What Thai recipes do you know about?", stream=True)
# --- 3. From raw text ---
print("\n" + "=" * 60)
print("SOURCE 3: Raw text")
print("=" * 60 + "\n")
await knowledge.ainsert(
name="Company Info",
text_content="Acme Corp was founded in 2020. They build AI tools for developers.",
metadata={"source": "text"},
)
agent.print_response("What does Acme Corp do?", stream=True)
# --- 4. From topics (Wikipedia + ArXiv) ---
print("\n" + "=" * 60)
print("SOURCE 4: Topics (Wikipedia)")
print("=" * 60 + "\n")
await knowledge.ainsert(
topics=["Retrieval-Augmented Generation"],
reader=WikipediaReader(),
)
agent.print_response("What is RAG?", stream=True)
# --- 5. Batch loading from multiple sources ---
print("\n" + "=" * 60)
print("SOURCE 5: Batch loading (insert_many)")
print("=" * 60 + "\n")
await knowledge.ainsert_many(
[
{
"name": "Doc 1",
"text_content": "Python is a programming language.",
"metadata": {"topic": "programming"},
},
{
"name": "Doc 2",
"text_content": "TypeScript adds types to JavaScript.",
"metadata": {"topic": "programming"},
},
]
)
agent.print_response("Compare Python and TypeScript", stream=True)
asyncio.run(main())
Run the Example
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"