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What is Haystack? An introduction to the NLP framework

Natural language processing (NLP) unlocks tremendous potential for businesses looking to understand and generate human language at scale. As an open source framework designed specifically for production NLP, Haystack is one tool worth exploring.

In this post, we’ll walk through what Haystack is, how it works under the hood, and why companies are using it to build enterprise-grade NLP applications today.

A quick primer on natural language processing

First, what exactly is NLP?

NLP refers to techniques that enable computers to process, analyze, and interpret human languages like English. Here are some common NLP applications:

  • Machine translation – Automatically translating text between languages
  • Text classification – Categorizing documents by topic, sentiment, etc.
  • Named entity recognition – Identifying people, places, organizations, etc. in text
  • Question answering – Answering natural language questions based on context
  • Summarization – Generating short summaries of longer documents
  • Speech recognition – Transcribing spoken audio to text

NLP powers many technologies we interact with daily. It’s the driving force behind innovations like chatbots, semantic search, autocorrect, and more recently, chat applications like ChatGPT.

So in short, NLP aims to make human language accessible and useful for machines!

Introducing Haystack

Haystack logo

Haystack is an end-to-end, open source framework for building NLP applications written in Python.

Haystack was created in 2018 by, a startup focused on making NLP accessible for enterprises. The goal was to simplify production NLP by providing an optimized framework tailored for transformer models.

Some key facts about Haystack:

  • Built for production NLP at scale. Used by companies like Accenture and McKinsey.
  • Modular architecture allows custom pipelines for different use cases.
  • Provides reusable components for tasks like classification, QA, summarization.
  • Designed to utilize powerful transformer models like BERT efficiently.
  • Integrates seamlessly with Hugging Face hub and other model APIs.
  • Infrastructure agnostic – can leverage Elasticsearch, FAISS, Milvus and more for storage.
  • Optimizations like prefiltering improve latency and costs.
  • Active open source project with 200+ contributors.

In a nutshell, Haystack makes it easier for developers to apply advanced deep learning models to real-world NLP applications.

How Haystack works

Under the hood, Haystack is structured around a few key concepts:

Modular components

Haystack provides pre-built, reusable components for common NLP tasks:

  • Readers apply models like BERT to generate embeddings or extract answers from text.
  • Retrievers fetch relevant documents from storage.
  • Classifiers categorize documents by attributes like sentiment.
  • Generators produce summaries, translations, or answers.

Developers can reuse these modular components to accelerate building custom pipelines. You also can create new components as needed.

Haystack component diagram

Diagram showing modular Haystack components


In Haystack, pipelines connect components with data stores to create end-to-end NLP systems.

A typical pattern is using a retriever to fetch candidate documents from storage, then feeding those to a reader model that generates an answer.

With pipelines, developers can build and customize workflows for their specific use case, while leveraging Haystack‘s optimizations under the hood.

Haystack pipeline

Example Haystack pipeline for question answering

Infrastructure integrations

Haystack integrates with standardized infrastructure like vector databases and search engines to provide production-grade capabilities:

  • Elasticsearch – A popular open source search and analytics engine. Haystack utilizes it for low-latency semantic document search.
  • FAISS – A library for efficient similarity search on vectors. Enables blazing fast nearest neighbor search.
  • Pinecone – Cloud native vector database optimized for NLP workflows.
  • Milvus – Open source vector similarity search engine.
  • Weaviate – Open source vector search engine with semantics.

These integrations allow Haystack to scale to enterprise workloads and get the best performance.


A few techniques Haystack employs to optimize pipelines:

  • Prefiltering documents – Using a fast retriever to reduce documents for slower neural models.
  • Reader-retriever workflows – Avoiding expensive reads unless retrievers find good candidates.
  • Batching – Grouping requests to amortize costs of queries.
  • Caching – Storing results to avoid duplicate expensive computations.

Together these optimizations help improve throughput and lower infrastructure costs – critical when operationalizing NLP models.

Key benefits of Haystack

Here are some of the main reasons teams choose Haystack to build production NLP systems:

  • Accelerate development – Reuse modular components instead of building everything from scratch.
  • Production ready – Designed to handle user traffic, security, scaling, monitoring needs.
  • Optimized performance – Prefiltering, caching, batching keep costs low and latency minimal.
  • Infrastructure agnostic – Run on your cloud provider and hardware of choice.
  • Portability – Package Haystack apps via REST APIs for easy integration and deployment.
  • Robust tooling – End-to-end support from annotation through deployment.
  • Future proof – Regularly updated to support latest transformer models from Hugging Face.

Haystack lowers barriers to putting state-of-the-art NLP models into production.

Companies using Haystack

Haystack sees wide adoption across enterprise companies like:

  • Accenture – Building an internal FAQ search assistant with Haystack.
  • Zalando – Implemented a fashion recommendation engine powered by Haystack.
  • McKinsey – Utilizes Haystack for NLP-driven research and analytics.
  • Credit Suisse – Created an anti-money laundering screening system with Haystack.

Other documented users include Daimler, UBS, Swiss Re, Swedbank, and German insurance providers.

Haystack also gets used in academia, for research and by NLP developers. The public showcases give examples of real-world applications.

Building an end-to-end pipeline

To see Haystack in action, let‘s walk through the steps to build a simple question answering pipeline:

  1. Install Haystack with pip install farm-haystack

  2. Create components like a Retriever and Reader configurable for your models:

from haystack.nodes import FARMReader, TransformersRetriever

retriever = TransformersRetriever(

reader = FARMReader(
  1. Load data into a DocumentStore like Elasticsearch:
from haystack.document_stores import ElasticsearchDocumentStore

doc_store = ElasticsearchDocumentStore()
  1. Initialize a pipeline tying components to the document store:
from haystack.pipelines import ExtractiveQAPipeline
pipe = ExtractiveQAPipeline(reader, retriever)
  1. Ask a question to trigger the end-to-end pipeline:
prediction =
    query="What is the capital of France?", 
    params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}}

And that‘s it! With just a few lines of code you can orchestrate a full pipeline for question answering.

Haystack provides end-to-end tutorials walking through more advanced examples like summarization, semantic search, and low-code solutions.

How Haystack simplifies production NLP

Building real-world NLP applications involves more challenges than just implementing models:

  • Data storage and pipelines at scale
  • Low latency for user queries
  • Monitoring, logging, testing
  • Deployment, CI/CD, scaling
  • Security, authentication, compliance

Haystack includes integrated solutions to these issues:

  • Multi-node support – Scale retrieval and inference across machines.
  • Async indexing – Quickly populate document stores.
  • Batching – Optimize batches for low latency.
  • REST API – Package apps as standardized APIs.
  • Logging – Integrates tracing solutions like Elastic APM.
  • Testing tools – Utilities to verify modules and systems.
  • Versioning – Git-based versioning for content and pipelines.

These features streamline going from an NLP prototype to production application.

How Haystack evolves with the NLP landscape

NLP is a rapidly evolving field driven by new techniques like transfer learning and progress on models like T5, BERT and GPT-3.

As a framework tailored for transformer models, Haystack easily incorporates the latest advancements:

  • Regular model updates – Continuous integration with Hugging Face Hub.
  • New component types – Adding readers, generators, etc.
  • Model binding layers – Abstract model implementation from business logic.
  • Modular design – Swap components with minimal code changes.
  • Infrastructure expansions – Adding support for new vector databases.

This means your Haystack apps improve as the NLP landscape advances without major rewrites.

Getting started with Haystack

If you want hands-on experience with Haystack, here are helpful resources:

The friendly community is happy to answer any questions you have getting up and running!

Within an hour or two, you can already be building and testing your own NLP apps with Haystack.

Wrapping up

In this post, we covered:

  • What natural language processing involves
  • Introducing Haystack – an open source production NLP framework
  • How Haystack utilizes components, pipelines, and infrastructure
  • Real-world examples of Haystack applications
  • Key benefits like modularity, optimizations, and scalability
  • How Haystack simplifies deploying NLP models in production

Companies like Accenture, McKinsey, Zalando, and Credit Suisse rely on Haystack for robust, scalable NLP applications.

If you‘re looking to level up your NLP skills and work on production systems, Haystack is definitely worth exploring further!

The resources shared provide plenty of avenues to get hands-on experience with Haystack. Within hours, you can already be building pipelines leveraging state-of-the-art NLP techniques.

Hopefully this gives you a solid starting point for evaluating if Haystack could be a good fit for your next NLP project or application!

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