LlamaIndex: Data framework for LLM applications
Connect LLMs to external data via RAG workflows.
Learn more about llama_index
LlamaIndex is a Python data framework designed to integrate large language models with custom data sources. It works by ingesting data, structuring it into indexes, and enabling retrieval-augmented generation (RAG) workflows where relevant data is fetched to augment LLM prompts. The framework supports multiple LLM providers, embedding models, and vector databases through a modular integration system. Common applications include building question-answering systems over documents, creating agents that reason over structured data, and implementing multi-agent systems that coordinate across different data sources.
Modular Integration System
Core functionality separates from provider integrations across 300+ packages in LlamaHub. Install only required components for specific LLM, embedding, and vector store choices instead of bundling all dependencies.
Flexible Dependency Options
Choose between a starter package with common integrations or core-only package for custom setups. Balances convenience for quick starts with granular control for production deployments.
Multi-Agent Orchestration
Coordinates multiple specialized agents that collaborate on complex tasks. Define agent workflows with delegation, tool use, and memory sharing for sophisticated reasoning pipelines beyond single-agent capabilities.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("./documents").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the key findings in these documents?")
print(response)Adds early stopping for agent workflows and distributed data ingestion support.
- –feat: add earlystoppingmethod parameter to agent workflows
- –feat: Add token-based code splitting support to CodeSplitter
- –Add RayIngestionPipeline integration for distributed data ingestion
- –Added the multi-modal version of the Condensed Conversation & Context
- –Replace ChatMemoryBuffer with Memory
Introduces async tool spec support with improved function calling and node parsing.
- –Feat/async tool spec support
- –Improve `MockFunctionCallingLLM`
- –fix(openai): sanitize generic Pydantic model schema names
- –Element node parser
- –improve llama dev logging
Adds mock function calling LLM and Airweave tool integration with advanced search.
- –feat: add mock function calling llm
- –test: fix typo 'reponse' to 'response' in variable names
- –feat: add Airweave tool integration with advanced search features
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