Exploring Vectorstoreindex.from_documents in LlamaIndex: A How-To Guide

Understanding the Basics

What is a VectorStoreIndex?

Setting Up Your Environment

1. Install LlamaIndex: Ensure you have the library installed in your Python environment. You can simply run:
   1 2 bash pip install llama-index
2. Prepare your documents: Make sure you have a directory of documents ready to be indexed. In our example, we'll use a folder containing some text files related to the renowned essayist, Paul Graham.

Basic Usage of VectorStoreIndex.from_documents

Step-by-Step Implementation

1. Import Necessary Modules: We need to import both

   1 VectorStoreIndex

   &

   1 SimpleDirectoryReader

   which helps in reading documents from a specified directory.

   1 2 python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
2. Load Documents & Build Index: Using the

   1 SimpleDirectoryReader

   , we can load documents simply & create the index using

   1 from_documents

   . Here’s an example:

   1 2 3 python documents = SimpleDirectoryReader("../../examples/data/paul_graham").load_data() index = VectorStoreIndex.from_documents(documents)

   With just two lines of code, we’ve turned our set of documents into a searchable vector index!
3. Display Progress Bar: If you want to keep track of the indexing process on your console (because who doesn’t like visuals?), you can include

   1 show_progress=True

   when loading documents.

   1 2 python index = VectorStoreIndex.from_documents(documents, show_progress=True)

Understanding

1 Node

Objects

Advanced Usage: Creating & Managing Nodes Directly

Create Nodes Manually

1. Import Node Class: We start by importing the

   1 TextNode

   class that’s used to create our Node objects:

   1 2 python from llama_index.core.schema import TextNode
2. Define Some Nodes: Next, let’s create a couple of nodes with sample text:

   1 2 3 4 python node1 = TextNode(text="<text_chunk>", id_="<node_id>") node2 = TextNode(text="<text_chunk>", id_="<node_id>") nodes = [node1, node2]
3. Create an Index from Nodes: Finally, we can create the vector store index from our customized nodes.

   1 2 python index = VectorStoreIndex(nodes)

Handling Document Updates

• Insertion
• Deletion
• Update
• Refresh

Storing Your Vector Index

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import pinecone
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore

# Initialize Pinecone
pinecone.init(api_key="<api_key>", environment="<environment>")
pinecone.create_index("quickstart", dimension=1536, metric="euclidean", pod_type="p1")

# Customize the Storage Context
storage_context = StorageContext.from_defaults(vector_store=PineconeVectorStore(pinecone.Index("quickstart")))

# Load documents & build your index
documents = SimpleDirectoryReader("../../examples/data/paul_graham").load_data()
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

Composable Retrieval with VectorStoreIndex

• What is Composable Retrieval?
  The
  1 VectorStoreIndex
  allows for flexible retrieval of generic objects including references, query engines, retrievers, and query pipelines! This means you can structure deeply integrated systems where each part plays a vital role.

Example of Composable Retrieval

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Conclusion