LlamaIndex

Overview

LlamaIndex solves the fundamental challenge of RAG applications: connecting LLMs to custom data sources efficiently and reliably. The framework provides a complete data lifecycle: Load (ingest from 200+ sources via LlamaHub connectors), Transform (chunk documents with smart strategies, extract metadata, generate embeddings), Index (organize data in optimized structures), Store (persist to vector databases or local storage), and Query (retrieve relevant context with sophisticated strategies). Unlike generic LLM frameworks, LlamaIndex optimizes every step for retrieval accuracy. For example, the framework automatically handles PDF parsing (tables, images, layouts), applies semantic chunking (split by meaning, not arbitrary character counts), enriches with metadata (dates, authors, sections), and generates multiple index structures (vector, keyword, graph) for hybrid retrieval. Query engines implement advanced retrieval patterns: citation tracking (return sources with answers), multi-document synthesis (combine information from multiple docs), recursive retrieval (follow references across documents), and auto-merging (combine small chunks into larger context). This sophistication enables production systems to achieve 80-90% answer accuracy versus 50-60% with naive RAG implementations.

LlamaIndex's architecture separates concerns for independent optimization. Data Connectors (LlamaHub) provide 200+ integrations: databases (PostgreSQL, MongoDB, MySQL), file formats (PDF, Word, PowerPoint, CSV), APIs (Notion, Confluence, Google Drive, Slack), and custom sources. Node Parsers intelligently chunk documents: SentenceSplitter (semantic boundaries), TokenSplitter (LLM context limits), HierarchicalNodeParser (maintains document structure), and custom parsers. Index structures optimize different access patterns: VectorStoreIndex (semantic search), ListIndex (exhaustive search), TreeIndex (hierarchical summarization), KeywordTableIndex (exact keyword matching), and KnowledgeGraphIndex (entity relationships). Query Engines combine retrievers with LLMs: RetrieverQueryEngine (basic RAG), CitationQueryEngine (with source attribution), SubQuestionQueryEngine (decompose complex questions), RouterQueryEngine (route to specialized indexes). Observability via LlamaTrace tracks every component: data loading times, embedding generation, retrieval accuracy, LLM calls, and end-to-end latency. 21medien leverages LlamaIndex for clients requiring sophisticated RAG: we've built systems processing 50M+ documents, implementing custom retrievers for domain-specific ranking, and optimizing index structures reducing query costs 60% while improving accuracy 25%.

Key Features

• LlamaHub: 200+ data connectors for databases, file formats, APIs, and custom sources with standardized interfaces
• Smart chunking: Semantic splitting, hierarchical parsing, metadata extraction, and token-aware strategies versus naive character splits
• Multiple index types: Vector, keyword, tree, graph, and hybrid indexes optimized for different retrieval patterns
• Advanced query engines: Citation tracking, multi-document synthesis, recursive retrieval, sub-question decomposition for complex queries
• Reranking: Post-retrieval reranking using LLMs, cross-encoders, or custom models to improve top-k accuracy 20-40%
• Streaming: Token-by-token response streaming for real-time user experiences, progress indicators during long operations
• Metadata filtering: Query with constraints (date ranges, categories, authors) combined with semantic search
• Observability: Built-in tracing via LlamaTrace, monitoring every step from data loading to response generation
• Agents: Build autonomous systems that select tools, query indexes, and reason through multi-step tasks
• Production ready: Async support, batching, caching, error handling, and retry logic for reliable deployments

Index Types and When to Use Them

LlamaIndex provides multiple index structures, each optimized for specific use cases. VectorStoreIndex: Most common, stores document embeddings in vector databases (Pinecone, Weaviate, Qdrant) for semantic similarity search. Use for: general Q&A, semantic search, finding conceptually related information. Pros: fast (sub-50ms), scales to billions of documents, good for most RAG applications. Cons: may miss exact keyword matches, requires vector database. ListIndex: Stores documents in a simple list, queries by iterating through all documents. Use for: small datasets (< 100 docs), exhaustive search when you need to consider every document, debugging retrieval. Pros: simple, no dependencies, guaranteed to consider all data. Cons: slow for large datasets (O(n) complexity), expensive (reads all documents). TreeIndex: Organizes documents in a hierarchical tree, queries by summarizing branches. Use for: document summarization, hierarchical data (org charts, taxonomies), multi-level reasoning. Pros: efficient summarization, handles document structure. Cons: build time increases, complex queries. KeywordTableIndex: Extracts keywords, builds inverted index for exact matching. Use for: keyword search, technical documentation, finding specific terms. Pros: fast exact matching, complements vector search. Cons: misses semantic similarity, requires good keywords. KnowledgeGraphIndex: Extracts entities and relationships, stores as graph. Use for: relationship queries ('who works with whom'), multi-hop reasoning, structured knowledge. Pros: captures relationships, enables graph queries. Cons: extraction overhead, requires entity recognition. 21medien helps clients select optimal index combinations: typically VectorStoreIndex for semantic search + KeywordTableIndex for exact matches, achieving 30% better accuracy than single-index approaches.

Common Use Cases

• Enterprise knowledge bases: Q&A over company documents, policies, procedures with citation tracking and 75-85% answer accuracy
• Customer support: Auto-generate responses from documentation, ticket history, and product manuals with source attribution
• Legal research: Search case law, contracts, regulations with hierarchical summarization and relationship extraction
• Medical research: Literature review, clinical trial search, drug interaction analysis from research papers and databases
• Financial analysis: Query earnings reports, SEC filings, market data with multi-document synthesis for investment research
• Code documentation: Search codebases, API docs, Stack Overflow, GitHub issues with semantic understanding of programming concepts
• Content management: Semantic search across CMS content, blog posts, marketing materials with automatic categorization
• Academic research: Literature review, paper recommendations, citation analysis from arXiv, PubMed, academic databases
• Sales enablement: Search sales collateral, case studies, competitive intelligence with personalized content recommendations
• Compliance monitoring: Search policies, regulations, audit logs with keyword + semantic hybrid search for regulatory requirements

Integration with 21medien Services

21medien provides comprehensive LlamaIndex implementation services. Phase 1 (Data Strategy): We audit your data sources (structured databases, unstructured documents, real-time APIs), design ingestion pipelines, select appropriate connectors, and plan metadata schemas for optimal retrieval. Document analysis identifies chunking strategies, determines index types, and establishes quality metrics. Phase 2 (Pipeline Development): We build production data pipelines using LlamaHub connectors, implement custom parsers for proprietary formats, configure semantic chunking with overlap strategies, extract and enrich metadata (entities, dates, categories), and generate embeddings using optimal models (OpenAI, Cohere, domain-specific). Pipelines include validation, error handling, monitoring, and incremental updates. Phase 3 (Index Optimization): We configure vector stores (Pinecone, Weaviate, Qdrant), implement hybrid indexes (vector + keyword), tune retrieval parameters (top_k, similarity thresholds, filters), and establish reranking strategies using cross-encoders or LLMs for 20-40% accuracy improvements. Phase 4 (Query Engine Development): We implement appropriate query engines (citation, sub-question, router), build custom retrievers for domain-specific ranking, add metadata filters for scoped search, and integrate streaming for real-time responses. Observability includes LlamaTrace integration, custom metrics, and alerting. Phase 5 (Deployment & Operations): We deploy via REST APIs (FastAPI), containerize with Docker, orchestrate with Kubernetes, implement caching layers (Redis), configure auto-scaling, and establish monitoring dashboards (Grafana). Continuous improvement includes A/B testing retrieval strategies, retraining on user feedback, and optimizing costs. Example: For a legal research platform, we built a LlamaIndex system ingesting 500K legal documents, hybrid index (vector + keyword + graph), custom reranker for legal citation patterns, achieving 82% answer accuracy, sub-200ms p95 latency, serving 5K concurrent users, reducing research time 70% and saving clients $5M annually in manual research costs.

Code Examples

Basic RAG with LlamaIndex (Python): from llama_index import VectorStoreIndex, SimpleDirectoryReader, ServiceContext; from llama_index.llms import OpenAI; from llama_index.embeddings import OpenAIEmbedding; # Configure LLM and embeddings; llm = OpenAI(model='gpt-4', temperature=0); embed_model = OpenAIEmbedding(model='text-embedding-3-small'); service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model); # Load documents; documents = SimpleDirectoryReader('./data').load_data(); # Create index; index = VectorStoreIndex.from_documents(documents, service_context=service_context); # Query; query_engine = index.as_query_engine(similarity_top_k=5); response = query_engine.query('What is our refund policy?'); print(response) — Advanced: Citation query engine with reranking: from llama_index.query_engine import CitationQueryEngine; from llama_index.postprocessor import SentenceTransformerRerank; # Add reranker; reranker = SentenceTransformerRerank(top_n=3, model='cross-encoder/ms-marco-MiniLM-L-12-v2'); query_engine = CitationQueryEngine.from_args(index, citation_chunk_size=512, similarity_top_k=10, node_postprocessors=[reranker]); response = query_engine.query('Explain the refund process'); print(f'Answer: {response}'); print(f'Sources: {response.source_nodes}') — Custom data connector with metadata: from llama_index import Document; from llama_index.node_parser import SimpleNodeParser; # Load custom data; docs = [Document(text=content, metadata={'source': 'kb', 'category': 'policy', 'date': '2025-01-15'}) for content in data]; # Parse with metadata preservation; parser = SimpleNodeParser.from_defaults(chunk_size=512, chunk_overlap=50); nodes = parser.get_nodes_from_documents(docs); index = VectorStoreIndex(nodes); # Query with metadata filtering; query_engine = index.as_query_engine(filters={'category': 'policy'}); response = query_engine.query('customer refund rules') — 21medien provides LlamaIndex training, architecture consulting, and custom retriever development for production deployments.

Best Practices

• Start with VectorStoreIndex for prototyping—90% of use cases work well, optimize only when needed with specialized indexes
• Implement semantic chunking strategies—respect document structure (sections, paragraphs), maintain context with overlap (50-100 tokens)
• Enrich with metadata—dates, categories, authors, sources enable powerful filtering and improve retrieval accuracy 20-30%
• Use reranking for production—post-retrieval reranking with cross-encoders improves top-3 accuracy 25-40% at minimal cost
• Implement hybrid search—combine vector (semantic) + keyword (exact) using alpha parameter, start at 0.75 (75% semantic)
• Enable citation tracking—CitationQueryEngine provides source attribution, essential for enterprise trust and verification
• Monitor retrieval quality—track retrieval precision, answer accuracy, latency, and cost using LlamaTrace for continuous improvement
• Handle incremental updates—implement delta updates for changing data versus full reindexing, reduces costs and downtime
• Configure appropriate chunk sizes—balance context (larger chunks) vs retrieval precision (smaller chunks), 512 tokens typical sweet spot
• Test with domain-specific embeddings—fine-tuned or domain-specific embeddings (medical, legal, code) improve accuracy 15-25% over general models

LlamaIndex vs LangChain

LlamaIndex and LangChain serve complementary roles in the LLM ecosystem. LangChain: General-purpose LLM orchestration framework emphasizing chains, agents, and tool integration. Strengths: versatile (handles any LLM task), extensive integrations (100+ tools), mature agent systems, strong community. Use when: building conversational AI, autonomous agents, workflow automation, multi-step reasoning with tool use. LlamaIndex: Specialized data framework for connecting LLMs to custom data. Strengths: production-ready data pipelines, sophisticated indexing strategies, advanced retrieval (reranking, hybrid search), observability built-in. Use when: building RAG applications, knowledge bases, semantic search, document Q&A systems requiring high accuracy. Key differences: LlamaIndex provides deeper data ingestion capabilities (200+ LlamaHub connectors vs LangChain's document loaders), more index types (5+ vs 2-3), and better retrieval strategies (reranking, recursive retrieval, sub-questions). LangChain offers broader LLM functionality (chains, memory, agents) and easier prototyping for general tasks. Many production systems use both: LlamaIndex for data layer (ingestion, indexing, retrieval) + LangChain for application layer (chains, agents, tools). Example architecture: LlamaIndex loads and indexes documents → LangChain agent queries LlamaIndex retriever → LangChain chains process results with memory and tools. 21medien helps clients select the right tool: LlamaIndex for data-intensive RAG (legal research, medical knowledge bases), LangChain for agent systems (customer support bots, research assistants), or hybrid architectures combining both frameworks' strengths.