document q&a template with rag pipeline
Pre-configured template implementing retrieval-augmented generation (RAG) for question-answering over document collections. Uses LlamaIndex's document ingestion pipeline to parse files (PDF, TXT, Markdown), chunk them with configurable strategies, embed chunks via vector stores, and retrieve relevant context before passing to an LLM for answer generation. Abstracts away index construction, retrieval configuration, and prompt engineering boilerplate.
Unique: Provides end-to-end template combining LlamaIndex's document loader abstraction (supporting 100+ file types), configurable chunking strategies, and multi-backend vector store integration in a single self-contained example, reducing boilerplate compared to building RAG from raw LLM APIs
vs alternatives: More flexible and framework-agnostic than LangChain's document loaders because LlamaIndex's index abstraction decouples storage backend from retrieval logic, enabling easier swaps between vector stores without code changes
multi-turn conversational chat with document context
Template implementing stateful conversation over documents using LlamaIndex's chat engine, which maintains conversation history while retrieving relevant document context for each turn. Handles context window management by summarizing or filtering conversation history, retrieves fresh context from the document index per query, and passes both history and context to the LLM to generate contextually-aware responses that reference previous turns.
Unique: LlamaIndex's chat engine abstracts context window management and retrieval scheduling, automatically deciding when to retrieve fresh context vs. rely on conversation history, whereas raw LLM APIs require manual orchestration of these decisions
vs alternatives: Simpler than building conversation state management with LangChain's memory abstractions because LlamaIndex's chat engine integrates retrieval and history in a single component, reducing glue code
evaluation and benchmarking of rag pipeline quality
Template providing utilities to evaluate RAG system quality across multiple dimensions: retrieval quality (precision, recall, NDCG), answer quality (relevance, factuality, citation accuracy), and end-to-end performance. Includes evaluation datasets, metrics computation, and comparison tools to measure impact of configuration changes. Supports both automated metrics (embedding-based similarity) and human evaluation workflows.
Unique: LlamaIndex's evaluation framework integrates retrieval and generation metrics in a single pipeline, enabling end-to-end quality assessment, whereas most RAG systems require separate evaluation tools for retrieval and generation
vs alternatives: More comprehensive than generic NLG evaluation because LlamaIndex's metrics include retrieval-specific measures (precision, recall) alongside generation metrics, providing holistic RAG quality assessment
cost and latency optimization for llm calls
Template providing utilities to monitor and optimize LLM API costs and latency in RAG pipelines. Tracks token usage per component (retrieval, synthesis, tool calls), identifies bottlenecks, and suggests optimizations (smaller models, caching, batching). Implements caching strategies (semantic caching, exact-match caching) to reduce redundant LLM calls, and provides cost estimation before execution.
Unique: LlamaIndex's cost tracking is integrated into the query engine, enabling automatic token counting and cost attribution per component, whereas most RAG systems require manual instrumentation
vs alternatives: More granular than LLM provider dashboards because LlamaIndex tracks costs at the component level (retrieval vs. synthesis), enabling targeted optimization
structured data extraction from unstructured documents
Template using LlamaIndex's structured output capabilities (via Pydantic schema definitions) to extract typed data from documents. Defines a Pydantic model representing desired output structure (e.g., invoice fields, entity lists), passes documents through LlamaIndex's extraction pipeline which uses the LLM to parse content and map it to the schema, and returns validated structured objects. Handles schema validation, type coercion, and optional field handling automatically.
Unique: Uses Pydantic schema as a declarative interface for extraction, enabling type-safe output and automatic validation, whereas most extraction templates rely on regex or rule-based parsing that lacks type guarantees
vs alternatives: More maintainable than prompt-based extraction because schema changes are code changes (caught by type checkers) rather than prompt tweaks, and Pydantic validation catches malformed extractions before they reach downstream systems
multi-document agent with tool-based reasoning
Template implementing an agentic loop where an LLM reasons over multiple documents and tools to answer complex queries. Uses LlamaIndex's agent framework to define tools (document search, calculation, external API calls), implements a ReAct-style loop where the agent plans actions, executes tools, observes results, and refines its approach. Manages context across multiple document indexes and tool invocations, handling tool selection, parameter binding, and result integration into the reasoning loop.
Unique: LlamaIndex's agent framework integrates document retrieval as a first-class tool alongside custom tools, enabling seamless reasoning over documents and external systems in a unified loop, whereas LangChain agents require explicit tool definitions for document access
vs alternatives: More document-aware than generic agent frameworks because LlamaIndex's agent tools are optimized for index queries and can leverage semantic search, whereas generic agent frameworks treat documents as opaque external tools
configurable document chunking and indexing strategy
Template exposing LlamaIndex's chunking and indexing configuration options (chunk size, overlap, separator strategy, node post-processors) as configurable parameters. Allows developers to experiment with different chunking strategies (fixed-size, semantic, hierarchical) and index types (vector, keyword, tree-based) without code changes. Includes utilities to evaluate chunking quality and measure retrieval performance across configurations.
Unique: Exposes LlamaIndex's low-level chunking and node post-processor APIs as configuration templates, enabling experimentation without modifying core indexing code, whereas most RAG templates hard-code chunking parameters
vs alternatives: More flexible than LangChain's text splitters because LlamaIndex's node abstraction allows post-processing (metadata enrichment, filtering) after chunking, enabling more sophisticated indexing strategies
multi-modal document indexing with image and text extraction
Template supporting indexing of multi-modal documents (PDFs with images, scanned documents, mixed text/image content) using LlamaIndex's image extraction and OCR capabilities. Automatically extracts images from documents, generates descriptions or embeddings for images, indexes both text and image content separately, and enables retrieval that matches queries against both text and visual content. Handles image-to-text mapping to preserve document structure.
Unique: Integrates image extraction, OCR, and multi-modal embedding in a single indexing pipeline, whereas most RAG templates treat images as opaque binary data or require manual extraction
vs alternatives: More comprehensive than LangChain's document loaders because LlamaIndex's image node abstraction preserves image-to-text relationships and enables cross-modal retrieval, whereas LangChain typically extracts images separately
+4 more capabilities