Agent with a knowledge base.

thai_recipe_agent.py

from agno.knowledge.embedder.openai import OpenAIEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.lancedb import LanceDb, SearchType
import nest_asyncio; nest_asyncio.apply()

embedder = OpenAIEmbedder(
    id="openai/embed/models/text-embedding-ada-002",
    api_key=os.environ["CLARIFAI_PAT"],
    base_url="https://api.clarifai.com/v2/ext/openai/v1"
)

vector_db = LanceDb(
    uri="tmp/lancedb",
    table_name="thai_recipes",
    search_type=SearchType.hybrid,
    embedder=embedder,
)

knowledge_base = Knowledge(vector_db=vector_db)
knowledge_base.add_content(
    url="https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"
)

agent = Agent(
    model=OpenAILike(
        id="openai/chat-completion/models/gpt-oss-120b",
        api_key=os.environ["CLARIFAI_PAT"],
        base_url="https://api.clarifai.com/v2/ext/openai/v1",
        max_tokens=4096,
    ),
    description="Thai cuisine expert powered by GPT-OSS 120B.",
    instructions=[
        "Search the knowledge base for Thai recipes first.",
        "Use web search only if needed.",
    ],
    knowledge=knowledge_base,
    tools=[DuckDuckGoTools()],
    markdown=True,
)

agent.print_response(
    "How do I make chicken and galangal in coconut milk soup?",
    markdown=True,
)