LlamaIndex

LlamaIndex is a comprehensive data framework designed for building Large Language Model (LLM) applications, serving as the bridge between LLMs and various data sources. It provides a unified interface for building Retrieval-Augmented Generation (RAG) systems, enabling developers to connect their LLMs to structured and unstructured data including documents, APIs, databases, and knowledge bases.

The framework offers modular architecture with over 300 integration packages for different LLM providers, embedding models, and vector stores, allowing users to build customized solutions with their preferred technology stack. LlamaIndex simplifies the process of indexing, querying, and retrieving relevant information for LLM applications, supporting various data ingestion methods, advanced retrieval strategies, and sophisticated query engines that can handle complex multi-step reasoning tasks across diverse data sources.

Package Information

• Package Name: llama-index
• Language: Python
• Installation: pip install llama-index

Core Imports

import llama_index

Common usage patterns for core functionality:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.core.llms import LLM
from llama_index.core.embeddings import BaseEmbedding

Integration imports follow the pattern:

from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

Basic Usage

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

# Configure global settings
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")

# Load documents
documents = SimpleDirectoryReader("data").load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Create query engine
query_engine = index.as_query_engine()

# Query your data
response = query_engine.query("What are the main topics in the documents?")
print(response)

Architecture

LlamaIndex's modular architecture enables flexible RAG application development:

• Indices: Data structures for efficient information retrieval (vector, keyword, tree, graph-based)
• Query Engines: Orchestrate retrieval and response synthesis with various strategies
• Retrievers: Handle document retrieval with similarity search, keyword matching, and hybrid approaches
• Response Synthesizers: Generate coherent responses from retrieved context using different summarization strategies
• Node Parsers: Process documents into chunks with various splitting strategies
• Agents & Workflows: Support complex multi-step reasoning and tool usage patterns
• Storage: Persistent storage for indices and metadata with various backends
• Integrations: 300+ integrations for LLMs, embeddings, vector stores, and data sources

This design allows every component to be customized and provides the foundation for LlamaIndex's role as a comprehensive RAG framework, supporting everything from simple document Q&A to sophisticated multi-agent workflows.

Capabilities

Data Indexing

Core data structures for organizing and retrieving information, including vector-based semantic search, keyword extraction, hierarchical trees, and knowledge graphs.

class VectorStoreIndex:
    @classmethod
    def from_documents(cls, documents, **kwargs): ...
    def as_query_engine(self, **kwargs): ...
    def as_retriever(self, **kwargs): ...

class SummaryIndex:
    @classmethod
    def from_documents(cls, documents, **kwargs): ...

class TreeIndex:
    @classmethod
    def from_documents(cls, documents, **kwargs): ...

class KnowledgeGraphIndex:
    @classmethod
    def from_documents(cls, documents, **kwargs): ...

Data Indexing

Query Processing

Query engines that orchestrate retrieval and response generation with support for various strategies including basic retrieval, sub-question decomposition, routing, and multi-step reasoning.

class RetrieverQueryEngine:
    def __init__(self, retriever, response_synthesizer=None): ...
    def query(self, query_bundle): ...

class RouterQueryEngine:
    def __init__(self, selector, query_engines): ...

class SubQuestionQueryEngine:
    def __init__(self, question_gen, query_engines): ...

Query Processing

Document Processing

Document loading, parsing, and chunking functionality for various file formats with intelligent text splitting strategies.

class SimpleDirectoryReader:
    def __init__(self, input_dir=None, input_files=None, **kwargs): ...
    def load_data(self): ...

class Document:
    def __init__(self, text, metadata=None, **kwargs): ...

class SentenceSplitter:
    def __init__(self, chunk_size=1024, chunk_overlap=200, **kwargs): ...
    def split_text(self, text): ...

Document Processing

LLM Integration

Unified interface for various language models with support for completion, chat, and function calling APIs.

class LLM:
    def complete(self, prompt, **kwargs): ...
    def chat(self, messages, **kwargs): ...

class OpenAI(LLM):
    def __init__(self, model="gpt-3.5-turbo", **kwargs): ...

LLM Integration

Embedding Models

Text embedding functionality supporting various providers for semantic similarity and vector search operations.

class BaseEmbedding:
    def get_text_embedding(self, text): ...
    def get_query_embedding(self, query): ...

class OpenAIEmbedding(BaseEmbedding):
    def __init__(self, model="text-embedding-ada-002", **kwargs): ...

LLM Integration

Retrievers

Advanced retrieval strategies including fusion, hierarchical, and routing approaches for sophisticated document retrieval patterns.

class AutoMergingRetriever:
    def __init__(self, vector_retriever, storage_context, **kwargs): ...

class QueryFusionRetriever:
    def __init__(self, retrievers, similarity_top_k=None, **kwargs): ...

class RouterRetriever:
    def __init__(self, selector, retriever_tools, **kwargs): ...

Retrievers

Response Synthesis

Response generation strategies for combining retrieved context into coherent answers with various summarization approaches.

class TreeSummarize:
    def __init__(self, **kwargs): ...

class Refine:
    def __init__(self, **kwargs): ...

def get_response_synthesizer(response_mode="compact", **kwargs): ...

Response Synthesis

Agents & Workflows

Multi-agent systems and workflow orchestration for complex reasoning tasks, tool usage, and multi-step problem solving.

class ReActAgent:
    def __init__(self, tools, llm, **kwargs): ...
    def chat(self, message): ...

class Workflow:
    def __init__(self, **kwargs): ...
    def add_step(self, step_fn): ...

@step
def custom_step(ctx: Context, ev: Event) -> Event: ...

Agents & Workflows

Storage & Settings

Storage backends and global configuration for persisting indices, managing contexts, and configuring system-wide settings.

class StorageContext:
    @classmethod
    def from_defaults(cls, persist_dir=None, **kwargs): ...
    def persist(self, persist_dir=None): ...

class Settings:
    llm: LLM = None
    embed_model: BaseEmbedding = None
    node_parser: NodeParser = None

Storage & Settings

Prompts

Template system for customizing LLM prompts with support for various formatting options and conditional logic.

class PromptTemplate:
    def __init__(self, template, **kwargs): ...
    def format(self, **kwargs): ...

class ChatPromptTemplate:
    def __init__(self, message_templates, **kwargs): ...

Prompts