Chapter 1: Understanding LlamaIndex

In this section, we will explore the detailed functionalities of LlamaIndex, utilizing sample code to illustrate how to tailor it to your needs. While the subject may seem daunting initially, I plan to provide articles discussing various use cases and customizations based on this groundwork. I hope these resources prove useful for your future reference.

Setting Up Your Environment

To begin, you should establish a virtual environment on your local machine. Open your terminal and create a new virtual environment:

python -m venv venv

Next, activate it:

venvScriptsactivate

You should now see (Venv) in your terminal. Following this, install the necessary dependencies:

pip install langchain==0.0.234 llama-index==0.7.9 openai==0.27.82

Data Preparation

For data preparation, I utilized two texts, each containing approximately 3000 characters, located in the ./data/ directory:

from llama_index import SimpleDirectoryReader

from llama_index import ListIndex

documents = SimpleDirectoryReader(input_dir="./data").load_data()

list_index = ListIndex.from_documents(documents)

query_engine = list_index.as_query_engine()

response = query_engine.query("Please summarize this article in 300 characters")

for i in response.response.split("。"):

print(i + "。")

The typical workflow involves the following steps: loading the documents, creating an index, and establishing a query engine using as_query_engine.

Readers in LlamaIndex

LlamaIndex comprises various readers tailored for different data sources. For instance, the SimpleDirectoryReader is employed to load local text files:

documents = SimpleDirectoryReader(input_dir="./data").load_data()

Index Mechanisms

Many articles have elaborated on indexing mechanisms. Here's a brief overview of the four primary index structures:

• List Index: Maintains a sequential list of nodes for querying.
• Vector Store Index: Keeps an unordered list paired with vectors for each node.
• Tree Index: Organizes nodes in a tree structure for efficient searching.
• Keyword Table Index: Extracts keywords from nodes, mapping them to nodes for querying.

While other index types exist, such as Knowledge Graph Index and SQL Index, we will not cover them in this section.

Retrieving Nodes

Depending on the index type, you can choose a retrieval method using the "RetrieverMode" option. Note that when creating a QueryEngine, you must use as_query_engine and can select the desired RetrieverMode.

Contexts in LlamaIndex

The Index and Retriever are interconnected, yet we provide a distinct class for processing contexts. There are two types of contexts: Storage Context and Service Context. The following demonstrates how to define a Storage Context explicitly:

from llama_index import StorageContext

from llama_index.storage.docstore import SimpleDocumentStore

from llama_index.storage.index_store import SimpleIndexStore

from llama_index.vector_stores import SimpleVectorStore

from llama_index import ServiceContext

from llama_index.node_parser import SimpleNodeParser

from llama_index.embeddings.openai import OpenAIEmbedding

from llama_index import LLMPredictor

from llama_index.indices.prompt_helper import PromptHelper

from llama_index.logger.base import LlamaLogger

from llama_index.callbacks.base import CallbackManager

# Storage Context

storage_context = StorageContext.from_defaults(

docstore=SimpleDocumentStore(),

vector_store=SimpleVectorStore(),

index_store=SimpleIndexStore()

)

# Service Context

llm_predictor = LLMPredictor()

service_context = ServiceContext.from_defaults(

node_parser=SimpleNodeParser(),

embed_model=OpenAIEmbedding(),

llm_predictor=llm_predictor,

prompt_helper=PromptHelper.from_llm_metadata(llm_metadata=llm_predictor.metadata),

llama_logger=LlamaLogger(),

callback_manager=CallbackManager([])

)

# Creating an Index with Context

list_index = ListIndex.from_documents(

documents,

storage_context=storage_context,

service_context=service_context

)

# Proceeding with the query engine

query_engine = list_index.as_query_engine()

response = query_engine.query("Please summarize this article in 300 characters")

for i in response.response.split("。"):

print(i + "。")

Storage Contexts Overview

The Storage Context consists of three primary components: Vector Store, Document Store, and Index Store. The entire Storage Context can be saved to a JSON file:

import json

with open("store_context.json", "wt") as f:

json.dump(list_index.storage_context.to_dict(), f, indent=4)

Vector Store Insights

The Vector Store is where vectors are stored. You can save this to a JSON file as follows:

with open("vector_store.json", "wt") as f:

json.dump(list_index.storage_context.vector_store.to_dict(), f, indent=4)

By default, ListIndex does not utilize vector_store, which is why it appears blank.

In the upcoming segments, we will delve into Document Stores, Service Contexts, and LLM Predictors.

Up Next: Part 2

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