The Premier Data Framework for Building RAG Applications with LLMs
LlamaIndex (formerly GPT Index) is a specialized data framework designed specifically for connecting LLMs with your external data sources. It excels at indexing, structuring, and querying data to build powerful Retrieval-Augmented Generation (RAG) applications.
At Predictive Tech Labs, we use LlamaIndex when data ingestion, indexing strategy, and query optimization are critical to application performance. It's the perfect choice for document-heavy applications requiring sophisticated retrieval mechanisms.
PDFs, APIs, Databases, Web Pages, Documents
LlamaHub loaders for 100+ data sources
Smart text splitting with overlap & metadata
Convert chunks to vector representations
Build optimized index structures
Natural language question input
Semantic search & retrieval
Rank & combine relevant chunks
Generate answer with context
Formatted answer with citations
Choose the right index structure for your use case
Built specifically for retrieval-augmented generation from the ground up. Every feature optimized for connecting data to LLMs.
LlamaHub provides pre-built connectors for virtually every data source: PDFs, Notion, Slack, SQL databases, and more.
Advanced query engines with routing, fusion, and multi-step retrieval strategies for maximum accuracy.
Multiple index types let you choose the best structure for your data and query patterns.
Built-in token usage tracking, retrieval metrics, and debugging tools for production monitoring.
Combine multiple indices, create hierarchies, and build complex RAG architectures with ease.
LlamaIndex provides sophisticated query strategies
Semantic similarity search using embeddings. Returns the most relevant chunks based on vector distance.
Breaks complex questions into sub-queries, retrieves for each, then synthesizes a comprehensive answer.
Routes queries to the most appropriate index or retrieval strategy based on query type.
Applies transformations like query rewriting, HyDE (Hypothetical Document Embeddings), and decomposition.
Converts natural language to SQL queries for structured database interrogation.
Sequential retrieval and reasoning for complex, multi-hop questions requiring multiple lookups.
Index thousands of internal documents, wikis, and procedures. Employees get instant answers with source citations.
Search through contracts, case law, and regulations. Extract clauses, identify risks, and ensure compliance.
Query research papers, clinical guidelines, and patient records. HIPAA-compliant with source verification.
Analyze earnings reports, 10-Ks, and market research. Compare companies and extract key metrics.
Build a RAG application in minutes
from llama_index import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores import PineconeVectorStore
# Load your documents
documents = SimpleDirectoryReader('data').load_data()
# Create vector store
vector_store = PineconeVectorStore(api_key="your-key")
# Build the index
index = VectorStoreIndex.from_documents(
documents,
vector_store=vector_store
)
# Query your data
query_engine = index.as_query_engine()
response = query_engine.query("What are the main findings?")
print(response)
Let's create a powerful document Q&A system tailored to your data