LlamaIndex

Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.

Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.

Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.

Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.

Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.

Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.

Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.

Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.

Automatically transforms user queries to improve retrieval quality through techniques like query expansion (generating multiple query variants), decomposition (breaking complex queries into sub-queries), and rewriting (rephrasing for better embedding alignment). Uses LLM-based transformations with configurable prompts and supports both single-stage and multi-stage query processing pipelines.

Generates LLM responses grounded in retrieved documents, with automatic source attribution and citation tracking. Supports multiple generation modes (simple context injection, chain-of-thought, multi-step reasoning) and enables custom response synthesis through a pluggable ResponseSynthesizer interface. Tracks which source documents contributed to each response for transparency and fact-checking.

Enables LLM agents to reason over multiple steps, decide which tools to use, and execute actions autonomously. Agents can call retrieval tools, external APIs, code execution, and other functions based on LLM reasoning. Supports multiple agent architectures (ReAct, function-calling, custom) with automatic tool binding, error handling, and execution tracing for debugging.

Manages conversation history and context across multiple turns, with support for different memory types (buffer, summary, hybrid) and automatic context window optimization. Stores conversation state in memory backends (in-memory, persistent storage) and enables selective context retrieval to fit LLM token limits while preserving important information.

Extracts structured data from unstructured text using LLM-based extraction with schema validation and type coercion. Supports Pydantic models, JSON schemas, and custom output formats with automatic parsing, error handling, and retry logic. Enables reliable structured output from LLMs without manual parsing or validation code.

Provides built-in evaluation metrics for RAG systems including retrieval quality (precision, recall, NDCG), generation quality (BLEU, ROUGE, semantic similarity), and end-to-end correctness. Supports both automated metrics and human evaluation workflows, with integration to evaluation datasets and benchmarks for systematic quality assessment.