LlamaIndex Vector Database

from agno.agent import Agent
from agno.knowledge.knowledge import Knowledge
from agno.vectordb.llamaindex import LlamaIndexVectorDb
from llama_index.core import SimpleDirectoryReader, StorageContext, VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.retrievers import VectorIndexRetriever

# Build a LlamaIndex index over your documents
documents = SimpleDirectoryReader("data/paul_graham").load_data()
splitter = SentenceSplitter(chunk_size=1024)
nodes = splitter.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
index = VectorStoreIndex(nodes=nodes, storage_context=storage_context)

# Point Agno at the index through a retriever
knowledge = Knowledge(
    vector_db=LlamaIndexVectorDb(knowledge_retriever=VectorIndexRetriever(index))
)

agent = Agent(
    knowledge=knowledge,
    search_knowledge=True,
)
agent.print_response(
    "Explain what this text means: low end eats the high end", markdown=True
)