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Haystack

by deepset

Open-source LLM orchestration and RAG framework by deepset — composable pipeline architecture connecting document stores (pgvector, Weaviate, Elasticsearch), embedding models, retrievers, rankers, and LLMs for production-grade retrieval-augmented generation. APAC ML engineering teams choose Haystack for complex RAG pipelines requiring control over individual components.

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AIMenta verdict
Recommended
5/5

"Open-source RAG and LLM orchestration framework by deepset — modular pipeline architecture connecting document stores, embedding models, and LLMs. Preferred by APAC ML teams needing enterprise RAG with fine-grained control over retrieval, ranking, and generation steps."

Features
6
Use cases
3
Watch outs
3
What it does

Key features

  • Composable pipeline architecture — APAC ML teams mix and match retrieval, ranking, generation components
  • Multi-store integration — pgvector, Weaviate, Elasticsearch, Qdrant, Milvus for APAC vector storage
  • Hybrid retrieval — dense vector + BM25 sparse for APAC precision/recall balance
  • RAG evaluation framework — faithfulness, relevancy metrics on APAC domain test sets
  • Self-hosted LLM support — Ollama, vLLM, HuggingFace for APAC data sovereign pipelines
  • REST API serving — deploy APAC RAG pipeline as FastAPI endpoint
When to reach for it

Best for

  • APAC ML engineering teams building production RAG with complex retrieval requirements — Haystack's modular component model enables A/B testing retrieval strategies without pipeline rewrites
  • APAC organizations needing RAG evaluation — Haystack's built-in evaluation pipeline quantifies whether retrieval improvements actually help on APAC domain-specific content
  • APAC enterprises with existing search infrastructure (Elasticsearch, OpenSearch) — Haystack supports these stores as hybrid RAG backends alongside new vector stores
Don't get burned

Limitations to know

  • ! Steeper learning curve than LangChain or LlamaIndex for APAC teams starting RAG quickly — Haystack's component model requires more upfront APAC pipeline design
  • ! Smaller APAC community and fewer integrations than LangChain ecosystem — some APAC-specific tools may lack pre-built Haystack components
  • ! Haystack 2.x breaking changes from Haystack 1.x — APAC teams migrating existing Haystack 1.x pipelines must rewrite components to the Haystack 2.x component API
Context

About Haystack

Haystack is an open-source LLM orchestration and retrieval-augmented generation framework developed by deepset that enables APAC ML engineering teams to build production-grade RAG applications using composable pipeline components — document preprocessors, embedding models, vector store integrations, retrievers, rankers, reader models, and LLM generators — connected into declarative pipelines that can be configured, tested, and deployed independently of each other.

Haystack's component architecture — where APAC ML teams combine `DocumentStore` (pgvector, Weaviate, Elasticsearch, Qdrant, Milvus, OpenSearch), `Embedder` (OpenAI, Cohere, HuggingFace, Ollama), `Retriever` (dense vector, BM25 sparse, or hybrid), `Ranker` (cross-encoder, LLM-based), and `Generator` (Claude, GPT-4, Ollama, vLLM) components into an end-to-end pipeline — enables APAC ML engineers to swap individual components (changing from BM25 to hybrid retrieval, or from OpenAI embeddings to local sentence-transformers) without rewriting the entire APAC pipeline.

Haystack's evaluation framework — where APAC ML teams run RAG evaluation metrics (faithfulness, context precision, context recall, answer relevancy) against a test set of APAC question-answer pairs using the `RAGEvaluationPipeline` — enables APAC engineering teams to measure whether changes to retrieval strategy, embedding model, or generation prompt actually improve RAG quality on APAC-specific content (regulatory documents, product catalogs, internal knowledge bases) before deploying to production.

Haystack's REST API server (`haystack-experimental serve`) — where APAC platform teams deploy a Haystack pipeline as a FastAPI application with standard health check and query endpoints — enables APAC engineering teams to expose RAG pipelines as internal APAC APIs consumed by product applications without embedding the pipeline in application code.

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