Fix public asset urls

README.md

@@ -138,7 +138,7 @@ agent = Agent(
         "Prefer the information in your knowledge base over the web results."
     ],
     knowledge=PDFUrlKnowledgeBase(
-        urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
         vector_db=LanceDb(
             uri="tmp/lancedb",
             table_name="recipes",

cookbook/agent_concepts/hybrid_search/lancedb/agent.py

@@ -16,7 +16,7 @@
 
 # Knowledge Base
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/hybrid_search/pgvector/agent.py

@@ -5,7 +5,7 @@
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="recipes", db_url=db_url, search_type=SearchType.hybrid
     ),

cookbook/agent_concepts/hybrid_search/pinecone/agent.py

@@ -25,7 +25,7 @@
 )
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/knowledge/chunking/agentic_chunking.py

@@ -6,7 +6,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes_agentic_chunking", db_url=db_url),
     chunking_strategy=AgenticChunking(),
 )

cookbook/agent_concepts/knowledge/chunking/default.py

@@ -5,7 +5,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run

cookbook/agent_concepts/knowledge/chunking/document_chunking.py

@@ -6,7 +6,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes_document_chunking", db_url=db_url),
     chunking_strategy=DocumentChunking(),
 )

cookbook/agent_concepts/knowledge/chunking/fixed_size_chunking.py

@@ -6,7 +6,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes_fixed_size_chunking", db_url=db_url),
     chunking_strategy=FixedSizeChunking(),
 )

cookbook/agent_concepts/knowledge/chunking/recursive_chunking.py

@@ -6,7 +6,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes_recursive_chunking", db_url=db_url),
     chunking_strategy=RecursiveChunking(),
 )

cookbook/agent_concepts/knowledge/chunking/semantic_chunking.py

@@ -6,7 +6,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes_semantic_chunking", db_url=db_url),
     chunking_strategy=SemanticChunking(similarity_threshold=0.5),
 )

cookbook/agent_concepts/knowledge/combined_kb.py

@@ -21,7 +21,7 @@
 
 # Create PDF URL knowledge base
 pdf_url_kb = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="pdf_documents",
         db_url=db_url,

cookbook/agent_concepts/knowledge/csv_url_kb.py

@@ -5,7 +5,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = CSVUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/csvs/employees.csv"],
+    urls=["https://agno-public.s3.amazonaws.com/csvs/employees.csv"],
     vector_db=PgVector(table_name="csv_documents", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run

cookbook/agent_concepts/knowledge/pdf_url_kb.py

@@ -5,7 +5,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run

cookbook/agent_concepts/knowledge/s3_pdf_kb.py

@@ -5,7 +5,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = S3PDFKnowledgeBase(
-    bucket_name="phi-public",
+    bucket_name="agno-public",
     key="recipes/ThaiRecipes.pdf",
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )

cookbook/agent_concepts/knowledge/s3_text_kb.py

@@ -5,7 +5,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = S3TextKnowledgeBase(
-    bucket_name="phi-public",
+    bucket_name="agno-public",
     key="recipes/recipes.docx",
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )

cookbook/agent_concepts/memory/04_persistent_memory_postgres.py

@@ -8,7 +8,7 @@
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="recipes", db_url=db_url, search_type=SearchType.hybrid
     ),

cookbook/agent_concepts/rag/agentic_rag_agent_ui.py

@@ -15,7 +15,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 # Create a knowledge base of PDFs from URLs
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     # Use PgVector as the vector database and store embeddings in the `ai.recipes` table
     vector_db=PgVector(
         table_name="recipes",

cookbook/agent_concepts/rag/agentic_rag_lancedb.py

@@ -11,7 +11,7 @@
 
 # Create a knowledge base of PDFs from URLs
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     # Use LanceDB as the vector database and store embeddings in the `recipes` table
     vector_db=LanceDb(
         table_name="recipes",

cookbook/agent_concepts/rag/agentic_rag_pgvector.py

@@ -13,7 +13,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 # Create a knowledge base of PDFs from URLs
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     # Use PgVector as the vector database and store embeddings in the `ai.recipes` table
     vector_db=PgVector(
         table_name="recipes",

cookbook/agent_concepts/rag/agentic_rag_with_reranking.py

@@ -12,7 +12,7 @@
 
 # Create a knowledge base of PDFs from URLs
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     # Use LanceDB as the vector database and store embeddings in the `recipes` table
     vector_db=LanceDb(
         table_name="recipes",

cookbook/agent_concepts/rag/rag_with_lance_db_and_sqlite.py

@@ -25,7 +25,7 @@
 
 # Create a knowledge base from a PDF URL using LanceDb for vector storage and OllamaEmbedder for embedding
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/rag/traditional_rag_lancedb.py

@@ -11,7 +11,7 @@
 
 # Create a knowledge base of PDFs from URLs
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     # Use LanceDB as the vector database and store embeddings in the `recipes` table
     vector_db=LanceDb(
         table_name="recipes",

cookbook/agent_concepts/rag/traditional_rag_pgvector.py

@@ -13,7 +13,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 # Create a knowledge base of PDFs from URLs
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     # Use PgVector as the vector database and store embeddings in the `ai.recipes` table
     vector_db=PgVector(
         table_name="recipes",

cookbook/agent_concepts/tools/duckdb_tools.py

@@ -4,7 +4,7 @@
 agent = Agent(
     tools=[DuckDbTools()],
     show_tool_calls=True,
-    instructions="Use this file for Movies data: https://phidata-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
+    instructions="Use this file for Movies data: https://agno-public.s3.amazonaws.com/demo_data/IMDB-Movie-Data.csv",
 )
 agent.print_response(
     "What is the average rating of movies?", markdown=True, stream=False

cookbook/agent_concepts/vector_dbs/cassandra_db.py

@@ -22,7 +22,7 @@
 )
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=CassandraDb(
         table_name="recipes",
         keyspace="testkeyspace",

cookbook/agent_concepts/vector_dbs/chroma_db.py

@@ -9,7 +9,7 @@
 
 # Create knowledge base
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/vector_dbs/clickhouse.py

@@ -6,7 +6,7 @@
 agent = Agent(
     storage=SqliteAgentStorage(table_name="recipe_agent"),
     knowledge=PDFUrlKnowledgeBase(
-        urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
         vector_db=ClickhouseDb(
             table_name="recipe_documents",
             host="localhost",

cookbook/agent_concepts/vector_dbs/lance_db.py

@@ -13,7 +13,7 @@
 
 # Create knowledge base
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/vector_dbs/milvus.py

@@ -16,7 +16,7 @@
 )
 # Create knowledge base
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/vector_dbs/mongodb.py

@@ -15,7 +15,7 @@
 mdb_connection_string = "mongodb://ai:ai@localhost:27017/ai?authSource=admin"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=MongoDBVector(
         collection_name="recipes",
         db_url=mdb_connection_string,

cookbook/agent_concepts/vector_dbs/pg_vector.py

@@ -7,7 +7,7 @@
 vector_db = PgVector(table_name="recipes", db_url=db_url)
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 knowledge_base.load(recreate=False)  # Comment out after first run

cookbook/agent_concepts/vector_dbs/pinecone_db.py

@@ -16,7 +16,7 @@
 )
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/vector_dbs/qdrant_db.py

@@ -7,7 +7,7 @@
 vector_db = Qdrant(collection=COLLECTION_NAME, url="http://localhost:6333")
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=vector_db,
 )
 

cookbook/agent_concepts/vector_dbs/singlestore.py

@@ -21,7 +21,7 @@
 db_engine = create_engine(db_url)
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=S2VectorDb(
         collection="recipes",
         db_engine=db_engine,

cookbook/getting_started/03_agent_with_knowledge.py

@@ -70,7 +70,7 @@
         "- Be encouraging and supportive of home cooks at all skill levels"
     ),
     knowledge=PDFUrlKnowledgeBase(
-        urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
         vector_db=LanceDb(
             uri="tmp/lancedb",
             table_name="recipe_knowledge",

cookbook/getting_started/04_agent_with_storage.py

@@ -27,7 +27,7 @@
 from agno.vectordb.lancedb import LanceDb, SearchType
 
 agent_knowledge = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=LanceDb(
         uri="tmp/lancedb",
         table_name="recipe_knowledge",

cookbook/getting_started/readme_examples.py

@@ -41,7 +41,7 @@
         "Prefer the information in your knowledge base over the web results.",
     ],
     knowledge=PDFUrlKnowledgeBase(
-        urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
         vector_db=LanceDb(
             uri="tmp/lancedb",
             table_name="recipes",

cookbook/models/anthropic/knowledge.py

@@ -9,7 +9,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="recipes",
         db_url=db_url,

cookbook/models/aws/bedrock_claude/knowledge.py

@@ -8,7 +8,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=True)  # Comment out after first run

cookbook/models/azure/openai/knowledge.py

@@ -9,7 +9,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="recipes",
         db_url=db_url,

cookbook/models/cohere/knowledge.py

@@ -8,7 +8,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run

cookbook/models/google/gemini/knowledge.py

@@ -9,7 +9,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="recipes",
         db_url=db_url,

cookbook/models/groq/knowledge.py

@@ -8,7 +8,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run

cookbook/models/ollama/knowledge.py

@@ -9,7 +9,7 @@
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
-    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
     vector_db=PgVector(
         table_name="recipes",
         db_url=db_url,