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RAG Platforms

Frameworks for building production retrieval-augmented generation (RAG) systems — document ingestion, indexing, retrieval, and augmented generation.

What RAG Platforms Solve​

RAG connects LLMs to external knowledge, solving hallucination and knowledge cutoff problems. Production RAG requires:

  • Document processing — parsing, chunking, and cleaning diverse document formats
  • Indexing — embedding generation and vector store management
  • Retrieval — similarity search, hybrid search, re-ranking
  • Augmentation — context formatting and prompt construction
  • Generation — LLM-based answer synthesis with citations

Tool Comparison​

FeatureHaystackLlamaIndex
Primary FocusProduction RAG & search pipelinesData framework for LLM apps
ArchitectureDirected pipeline graphs with typed componentsIndex-based with query engines
Document ProcessingExcellent — built-in converters, cleaners, splittersGood — LlamaParse for advanced parsing
RetrievalElasticsearch, OpenSearch, Weaviate, Pinecone40+ vector store integrations
Hybrid Search✅ Built-in BM25 + semantic search✅ Via query engines and retrievers
Re-ranking✅ Built-in cross-encoder support✅ Via node postprocessors
Multi-modal✅ Images, tables, structured data✅ Multi-modal RAG support
Enterprisedeepset Cloud managed platformLlamaCloud managed service
API Stability✅ Pipeline API is stable across versions⚠️ API changes more frequently
Best ForDocument search, enterprise RAG, production pipelinesComplex data connectors, multi-source RAG

Haystack​

Production-ready framework for RAG and NLP pipelines by deepset.

Haystack provides a pipeline-based architecture with typed components for document processing, retrieval, and generation. Optimized for production stability and enterprise deployments.

Architecture​

┌──────────────────────────────────────────────────┐
│              Haystack Pipeline                    │
│                                                   │
│  ┌──────────┐    ┌──────────┐    ┌────────────┐  │
│  │ Document │    │ Retriever│    │ Generator  │  │
│  │ Store    │◄───│ (BM25 +  │◄───│ (LLM)     │  │
│  │          │    │ Semantic)│    │            │  │
│  └──────────┘    └──────────┘    └────────────┘  │
│       ▲                                          │
│  ┌────┴─────┐                                    │
│  │Converters│ ← PDF, DOCX, HTML, Markdown        │
│  │Cleaners  │ ← Text normalization               │
│  │Splitters │ ← Chunking strategies              │
│  └──────────┘                                    │
└──────────────────────────────────────────────────┘

Use Cases​

  • Enterprise document search and question answering
  • Semantic search pipelines with hybrid retrieval
  • Multi-modal RAG (text + tables + images)
  • Production systems requiring API stability

When to Choose Haystack​

Choose Haystack when you need a stable, production-grade RAG framework with excellent document processing. Best for teams building enterprise search and document Q&A systems.

→ LangChain vs Haystack

LlamaIndex​

Data framework connecting LLMs to enterprise data sources.

LlamaIndex excels at data ingestion, indexing, and query planning over complex data sources. Provides specialized data connectors for enterprise systems and advanced query engines.

Architecture​

┌──────────────────────────────────────────────────┐
│              LlamaIndex                           │
│                                                   │
│  ┌──────────────┐  ┌─────────────────────────┐   │
│  │ Data Loaders │  │ Index Types             │   │
│  │ (150+ src)   │  │ • Vector Store Index    │   │
│  │ • APIs       │  │ • Summary Index         │   │
│  │ • Databases  │  │ • Knowledge Graph Index │   │
│  │ • File sys   │  │ • SQL Index             │   │
│  └──────┬───────┘  └──────────┬──────────────┘   │
│         │                     │                   │
│  ┌──────▼─────────────────────▼──────────────┐   │
│  │         Query Engine                       │   │
│  │  • Sub-question decomposition              │   │
│  │  • Multi-index routing                     │   │
│  │  • Response synthesis                      │   │
│  └────────────────────────────────────────────┘   │
└──────────────────────────────────────────────────┘

Use Cases​

  • Multi-source enterprise knowledge bases (databases + docs + APIs)
  • Complex query planning over heterogeneous data
  • Structured + unstructured RAG with SQL and graph integrations
  • Advanced data connectors for enterprise systems (Confluence, Notion, Slack, etc.)

When to Choose LlamaIndex​

Choose LlamaIndex when you need to connect LLMs to diverse enterprise data sources with advanced query planning. Best for teams with complex data landscapes.

RAG Architecture Patterns​

For production RAG system design patterns, deployment strategies, and optimization techniques:

→ Production RAG Systems Architecture Guide →