🦙 LlamaIndex Overview
LlamaIndex is a data framework designed to connect custom data sources to large language models. It provides advanced indexing, retrieval, and query capabilities for building sophisticated RAG applications, knowledge graphs, and AI agents that can reason over your data.
Data Connectors
100+ integrations for diverse data sources
Advanced RAG
Sophisticated retrieval and generation strategies
Agent Framework
Multi-tool agents for complex reasoning
Core Components
Data Ingestion
Load and parse documents from various sources and formats
Indexing Strategies
Vector, graph, tree, and list-based indexing approaches
Retrieval Methods
Semantic search, keyword search, and hybrid retrieval
Query Engines
Compose and orchestrate retrieval and generation
Agents
Multi-tool agents for complex reasoning tasks
Evaluation
Built-in metrics for RAG system performance
Implementation Patterns
Simple RAG Pipeline
Basic retrieval-augmented generation with documents
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
# Configure LLM and embeddings
Settings.llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
# Load documents
documents = SimpleDirectoryReader("./data").load_data()
# Create index
index = VectorStoreIndex.from_documents(documents)
# Create query engine
query_engine = index.as_query_engine()
# Query the index
response = query_engine.query("What are the main points in the document?")
print(response)
# Get source information
print("\nSources:")
for node in response.source_nodes:
print(f"- {node.node.metadata['file_name']}: {node.score:.3f}")
print(f" {node.node.text[:200]}...")
# Chat interface
chat_engine = index.as_chat_engine()
response = chat_engine.chat("Can you summarize the key findings?")
print(response)
# Follow-up question with context
response = chat_engine.chat("What are the implications of these findings?")
print(response)Index Types & Use Cases
Vector Store Index
Semantic search over embeddings for similarity-based retrieval
- • Best for: General purpose RAG, semantic search
- • Strengths: High recall, semantic understanding
- • Use when: Documents have diverse content and topics
Knowledge Graph Index
Entity-relationship extraction for structured knowledge
- • Best for: Relationship queries, entity analysis
- • Strengths: Captures connections between concepts
- • Use when: Need to understand relationships and dependencies
Tree Index
Hierarchical organization for summarization tasks
- • Best for: Document summarization, hierarchical queries
- • Strengths: Efficient for large document collections
- • Use when: Need summaries at different levels of detail
Keyword Table Index
Keyword mapping for precise term-based retrieval
- • Best for: Exact term matching, structured data
- • Strengths: High precision for specific terms
- • Use when: Users search with specific terminology
Advanced Features
✨ Capabilities
- • Multi-modal document parsing (PDF, images, tables)
- • Hybrid retrieval combining multiple strategies
- • Reranking and postprocessing pipelines
- • Chat engines with conversation memory
- • Sub-question query decomposition
- • Response synthesis with multiple modes
- • Evaluation frameworks for RAG metrics
- • Integration with 15+ vector databases
🛠️ Integrations
- • Vector stores: Pinecone, Weaviate, Chroma, Qdrant
- • LLMs: OpenAI, Anthropic, Cohere, Hugging Face
- • Data loaders: 100+ connectors (APIs, databases)
- • Embedding models: OpenAI, Cohere, Sentence Transformers
- • Observability: LangSmith, Weights & Biases
- • Cloud platforms: Azure, AWS, GCP
- • Frameworks: LangChain compatibility
- • Orchestration: LlamaHub ecosystem
Best Practices
✅ Do's
- • Choose the right index type for your use case
- • Experiment with chunk sizes and overlap
- • Use evaluation metrics to measure performance
- • Implement proper error handling and retries
- • Cache indexes and embeddings when possible
- • Use postprocessors for relevance filtering
- • Monitor retrieval quality and costs
- • Version your data and index configurations
❌ Don'ts
- • Don't use one index type for all scenarios
- • Don't ignore data preprocessing and cleaning
- • Don't skip evaluation and testing
- • Don't forget to handle document metadata
- • Don't ignore retrieval relevance scores
- • Don't over-engineer without measuring performance
- • Don't neglect token usage optimization
- • Don't mix different document types without strategy