import asynciofrom pathlib import Pathfrom agno.agent import Agentfrom agno.knowledge.embedder.cohere import CohereEmbedderfrom agno.knowledge.knowledge import Knowledge# from agno.models.groq import Groqfrom agno.tools.openai import OpenAIToolsfrom agno.utils.media import download_imagefrom agno.vectordb.pgvector import PgVectorknowledge = Knowledge( vector_db=PgVector( db_url="postgresql+psycopg://ai:ai@localhost:5532/ai", table_name="embed_vision_documents", embedder=CohereEmbedder( id="embed-v4.0", ), ),)asyncio.run( knowledge.add_content_async( 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, 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,)agent.print_response( "What is the recipe for a Thai curry?",)response = agent.get_last_run_output()if response.images: download_image(response.images[0].url, Path("tmp/recipe_image.png"))