Recipe RAG Image Agent

"""Example: Multi-Modal RAG & Image Agent

An agent that uses Llama 4 for multi-modal RAG and OpenAITools to create a visual, step-by-step image manual for a recipe.

Run: `pip install openai agno groq cohere` to install the dependencies
"""

from pathlib import Path

from agno.agent import Agent
from agno.knowledge.embedder.cohere import CohereEmbedder
from agno.knowledge.knowledge import Knowledge
from agno.models.groq import Groq
from agno.tools.openai import OpenAITools
from agno.utils.media import download_image
from agno.vectordb.pgvector import PgVector

knowledge_base = Knowledge(
    vector_db=PgVector(
        db_url="postgresql+psycopg://ai:ai@localhost:5532/ai",
        table_name="embed_vision_documents",
        embedder=CohereEmbedder(
            id="embed-v4.0",
        ),
    ),
)

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

agent = Agent(
    name="EmbedVisionRAGAgent",
    model=Groq(id="meta-llama/llama-4-scout-17b-16e-instruct"),
    tools=[OpenAITools()],
    knowledge=knowledge_base,
    instructions=[
        "You are a specialized recipe assistant.",
        "When asked for a recipe:",
        "1. Search the knowledge base to retrieve the relevant recipe details.",
        "2. Analyze the retrieved recipe steps carefully.",
        "3. Use the `generate_image` tool to create a visual, step-by-step image manual for the recipe.",
        "4. Present the recipe text clearly and mention that you have generated an accompanying image manual. Add instructions while generating the image.",
    ],
    markdown=True,
    debug_mode=True,
)

response = agent.print_response(
    "What is the recipe for a Thai curry?",
)
if response.images:
    download_image(response.images[0].url, Path("tmp/recipe_image.png"))