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Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface's Transformers, Elasticsearch, or Milvus.

• Ask questions in natural language and find granular answers in your documents.
• Perform semantic search and retrieve documents according to meaning, not keywords
• Use off-the-shelf models or fine-tune them to your domain.
• Use user feedback to evaluate, benchmark, and continuously improve your live models.
• Leverage existing knowledge bases and better handle the long tail of queries that chatbots receive.
• Automate processes by automatically applying a list of questions to new documents and using the extracted answers.

• Latest models: Utilize all latest transformer-based models (e.g., BERT, RoBERTa, MiniLM) for extractive QA, generative QA, and document retrieval.
• Modular: Multiple choices to fit your tech stack and use case. Pick your favorite database, file converter, or modeling framework.
• Pipelines: The Node and Pipeline design of Haystack allows for custom routing of queries to only the relevant components.
• Open: 100% compatible with HuggingFace's model hub. Tight interfaces to other frameworks (e.g., Transformers, FARM, sentence-transformers)
• Scalable: Scale to millions of docs via retrievers, production-ready backends like Elasticsearch / FAISS, and a fastAPI REST API
• End-to-End: All tooling in one place: file conversion, cleaning, splitting, training, eval, inference, labeling, etc.
• Developer friendly: Easy to debug, extend and modify.
• Customizable: Fine-tune models to your domain or implement your custom DocumentStore.
• Continuous Learning: Collect new training data via user feedback in production & improve your models continuously

If you're interested in learning more about Haystack and using it as part of your application, we offer several options.

1. Installing from a package

You can install Haystack by using pip.

    pip3 install farm-haystack

Please check our page on PyPi for more information.

2. Installing from GitHub

You can also clone it from GitHub — in case you'd like to work with the master branch and check the latest features:

    git clone https://github.com/deepset-ai/haystack.git
    cd haystack
    pip install --editable .

To update your installation, do a git pull. The --editable flag will update changes immediately.

3. Installing on Windows

On Windows, you might need:

    pip install farm-haystack -f https://download.pytorch.org/whl/torch_stable.html

Follow our introductory tutorial to setup a question answering system using Python and start performing queries! Explore the rest of our tutorials to learn how to tweak pipelines, train models and perform evaluation.

• Tutorial 1 - Basic QA Pipeline: Jupyter notebook | Colab | Python
• Tutorial 2 - Fine-tuning a model on own data: Jupyter notebook | Colab | Python
• Tutorial 3 - Basic QA Pipeline without Elasticsearch: Jupyter notebook | Colab | Python
• Tutorial 4 - FAQ-style QA: Jupyter notebook | Colab | Python
• Tutorial 5 - Evaluation of the whole QA-Pipeline: Jupyter noteboook | Colab | Python
• Tutorial 6 - Better Retrievers via "Dense Passage Retrieval": Jupyter noteboook | Colab | Python
• Tutorial 7 - Generative QA via "Retrieval-Augmented Generation": Jupyter noteboook | Colab | Python
• Tutorial 8 - Preprocessing: Jupyter noteboook | Colab | Python
• Tutorial 9 - DPR Training: Jupyter noteboook | Colab | Python
• Tutorial 10 - Knowledge Graph: Jupyter noteboook | Colab | Python
• Tutorial 11 - Pipelines: Jupyter noteboook | Colab | Python
• Tutorial 12 - Long-Form Question Answering: Jupyter noteboook | Colab | Python
• Tutorial 13 - Question Generation: Jupyter noteboook | Colab | Python
• Tutorial 14 - Query Classifier: Jupyter noteboook | Colab | Python
• Tutorial 15 - TableQA: Jupyter noteboook | Colab | Python

Hosted

Try out our hosted Explore The World live demo here! Ask any question on countries or capital cities and let Haystack return the answers to you.

Local

Start up a Haystack service via Docker Compose. With this you can begin calling it directly via the REST API or even interact with it using the included Streamlit UI.

    apt-get update && apt-get install docker && apt-get install docker-compose
    service docker start

    git clone https://github.com/deepset-ai/haystack.git

    cd haystack
    docker-compose pull
    docker-compose up
    
    # Or on a GPU machine: docker-compose -f docker-compose-gpu.yml up

..
ui_1             |   You can now view your Streamlit app in your browser.
..
ui_1             |   External URL: http://192.168.108.218:8501
..
haystack-api_1   | [2021-01-01 10:21:58 +0000] [17] [INFO] Application startup complete.

There is a very vibrant and active community around Haystack which we are regularly interacting with! If you have a feature request or a bug report, feel free to open an issue in Github. We regularly check these and you can expect a quick response. If you'd like to discuss a topic, or get more general advice on how to make Haystack work for your project, you can start a thread in Github Discussions or our Slack channel. We also check Twitter and Stack Overflow.

We are very open to the community's contributions - be it a quick fix of a typo, or a completely new feature! You don't need to be a Haystack expert to provide meaningful improvements. To learn how to get started, check out our Contributor Guidelines first. You can also find instructions to run the tests locally there.

Thanks so much to all those who have contributed to our project!