> ## Documentation Index
> Fetch the complete documentation index at: https://docs.phidata.com/llms.txt
> Use this file to discover all available pages before exploring further.

# LlamaIndex KnowledgeBase

<CodeGroup>
  ```python agent.py theme={null}
  """
  Import necessary modules
  pip install llama-index-core llama-index-readers-file llama-index-embeddings-openai phidata
  """

  from pathlib import Path
  from shutil import rmtree

  import httpx
  from phi.agent import Agent
  from phi.knowledge.llamaindex import LlamaIndexKnowledgeBase
  from llama_index.core import (
      SimpleDirectoryReader,
      StorageContext,
      VectorStoreIndex,
  )
  from llama_index.core.retrievers import VectorIndexRetriever
  from llama_index.core.node_parser import SentenceSplitter


  data_dir = Path(__file__).parent.parent.parent.joinpath("wip", "data", "paul_graham")
  if data_dir.is_dir():
      rmtree(path=data_dir, ignore_errors=True)
  data_dir.mkdir(parents=True, exist_ok=True)

  url = "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt"
  file_path = data_dir.joinpath("paul_graham_essay.txt")
  response = httpx.get(url)
  if response.status_code == 200:
      with open(file_path, "wb") as file:
          file.write(response.content)
      print(f"File downloaded and saved as {file_path}")
  else:
      print("Failed to download the file")


  documents = SimpleDirectoryReader(str(data_dir)).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)

  retriever = VectorIndexRetriever(index)

  # Create a knowledge base from the vector store
  knowledge_base = LlamaIndexKnowledgeBase(retriever=retriever)

  # Create an agent with the knowledge base
  agent = Agent(knowledge_base=knowledge_base, search_knowledge=True, debug_mode=True, show_tool_calls=True)

  # Use the agent to ask a question and print a response.
  agent.print_response("Explain what this text means: low end eats the high end", markdown=True)

  ```
</CodeGroup>

## LlamaIndexKnowledgeBase Params

| Parameter   | Type                 | Default | Description                                                        |
| ----------- | -------------------- | ------- | ------------------------------------------------------------------ |
| `retriever` | `BaseRetriever`      | -       | LlamaIndex retriever used for querying the knowledge base          |
| `loader`    | `Optional[Callable]` | `None`  | Optional loader function to load documents into the knowledge base |

## AgentKnowledge Params

`LlamaIndexKnowledgeBase` is a subclass of the `AgentKnowledge` class and has access to the same params

<Snippet file="kb-base-reference.mdx" />