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</div>

[Haystack](https://haystack.deepset.ai/) is an end-to-end NLP framework that enables you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform question answering, answer generation, semantic document search, or build tools that are capable of complex decision-making and query resolution, you can use state-of-the-art NLP models with Haystack to build end-to-end NLP applications to solve your use case.
[Haystack](https://haystack.deepset.ai/) is an end-to-end LLM framework that enables you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform retrieval-augmented generation (RAG), documentation search, question answering or answer generation, you can use state-of-the-art embedding models and LLMs with Haystack to build end-to-end NLP applications to solve your use case.

## Quickstart

Haystack is built around the concept of pipelines. A pipeline is a powerful structure that performs an NLP task. It's made up of components connected together. For example, you can connect a `Retriever` and a `PromptNode` to build a Generative Question Answering pipeline that uses your own data.

Try out how Haystack answers questions about Game of Thrones using the Retrieval Augmented Generation (RAG) approach 👇
Haystack is built around the concept of pipelines. A pipeline is a powerful structure that performs an NLP task. It's made up of components connected together. For example, you can connect a [retriever](https://docs.haystack.deepset.ai/v2.0/docs/retrievers) and a [generator](https://docs.haystack.deepset.ai/v2.0/docs/generators) to build a Generative Question Answering pipeline that uses your own data.

First, run the minimal Haystack installation:

```sh
pip install farm-haystack
pip install haystack-ai
```

Then, index your data to the DocumentStore, build a RAG pipeline, and ask a question on your data:

```python
from haystack.document_stores import InMemoryDocumentStore
from haystack.utils import build_pipeline, add_example_data, print_answers

# We are model agnostic :) Here, you can choose from: "anthropic", "cohere", "huggingface", and "openai".
provider = "openai"
API_KEY = "sk-..." # ADD YOUR KEY HERE

# We support many different databases. Here, we load a simple and lightweight in-memory database.
document_store = InMemoryDocumentStore(use_bm25=True)

# Download and add Game of Thrones TXT articles to Haystack DocumentStore.
# You can also provide a folder with your local documents.
add_example_data(document_store, "data/GoT_getting_started")

# Build a pipeline with a Retriever to get relevant documents to the query and a PromptNode interacting with LLMs using a custom prompt.
pipeline = build_pipeline(provider, API_KEY, document_store)

# Ask a question on the data you just added.
result = pipeline.run(query="Who is the father of Arya Stark?")

# For details, like which documents were used to generate the answer, look into the <result> object
print_answers(result, details="medium")
```

The output of the pipeline will reference the documents used to generate the answer:

```
'Query: Who is the father of Arya Stark?'
'Answers:'
[{'answer': 'The father of Arya Stark is Lord Eddard Stark of '
'Winterfell. [Document 1, Document 4, Document 5]'}]
```

Congratulations, you have just built your first Haystack app!
👉 To build a minimal RAG pipeline that uses GPT-4 on your own data, use the [RAG Pipeline Recipe](https://docs.haystack.deepset.ai/v2.0/recipes/20-rag-pipeline)

## Core Concepts

🏃‍♀️ **[Pipelines](https://docs.haystack.deepset.ai/docs/pipelines):** This is the standard Haystack structure that builds on top of your data to perform various NLP tasks such as retrieval augmented generation, question answering and more. The data in a Pipeline flows from one Node to the next. You define how Nodes interact with each other and how one Node pushes data to the next.

An example pipeline would consist of one `Retriever` Node and one `PromptNode`. When the pipeline runs with a query, the Retriever first retrieves the relevant context to the query from your data, and then the PromptNode uses an LLM to generate the final answer.

⚛️ **[Nodes](https://docs.haystack.deepset.ai/docs/nodes_overview):** Each Node achieves one thing. Such as preprocessing documents, retrieving documents, using language models to answer questions, and so on.
⚛️ **[Components](https://docs.haystack.deepset.ai/v2.0/docs/components):** Each Component achieves one thing. Such as preprocessing documents, retrieving documents, using specific language models to answer questions, and so on. Components can `.connect()` to each other to form a complete pipeline.

🕵️ **[Agent](https://docs.haystack.deepset.ai/docs/agent):** (since 1.15) An Agent is a component that is powered by an LLM, such as GPT-3. It can decide on the next best course of action so as to get to the result of a query. It uses the Tools available to it to achieve this. While a pipeline has a clear start and end, an Agent is able to decide whether the query has been resolved or not. It may also make use of a Pipeline as a Tool.

🛠️ **[Tools](https://docs.haystack.deepset.ai/docs/agent#tools):** You can think of a Tool as an expert, that is able to do something really well. Such as a calculator, good at mathematics. Or a [WebRetriever](https://docs.haystack.deepset.ai/docs/agent#web-tools), good at retrieving pages from the internet. A Node or pipeline in Haystack can also be used as a Tool. A Tool is a component that is used by an Agent, to resolve complex queries.
🏃‍♀️ **[Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/pipelines):** This is the standard Haystack structure that builds on top of your data to perform various NLP tasks such as retrieval augmented generation, question answering and more. Pipelines in Haystack are Directed Multigraphs composed of components. Components can receive inputs from other components and produce outputs that can be forwarded to other components.

🗂️ **[DocumentStores](https://docs.haystack.deepset.ai/docs/document_store):** A DocumentStore is database where you store your text data for Haystack to access. Haystack DocumentStores are available with ElasticSearch, Opensearch, Weaviate, Pinecone, FAISS and more. For a full list of available DocumentStores, check out our [documentation](https://docs.haystack.deepset.ai/docs/document_store).

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## Resources
| | |
| ---------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 📒 [Docs](https://docs.haystack.deepset.ai) | Components, Pipeline Nodes, Guides, API Reference |
| 💾 [Installation](https://github.com/deepset-ai/haystack#-installation) | How to install Haystack |
| 📒 [Docs](https://docs.haystack.deepset.ai/v2.0/docs) | Components, Pipeline Nodes, Guides, API Reference |
| 🎓 [Tutorials](https://haystack.deepset.ai/tutorials) | See what Haystack can do with our Notebooks & Scripts |
| 🎉 [Haystack Extras](https://github.com/deepset-ai/haystack-extras) | A repository that lists extra Haystack packages and components that can be installed separately. |
| 🎉 [Integrations](https://haystack.deepset.ai/integrations) | The index of additional Haystack packages and components that can be installed separately. |
| 🔰 [Demos](https://github.com/deepset-ai/haystack-demos) | A repository containing Haystack demo applications with Docker Compose and a REST API |
| 🖖 [Community](https://github.com/deepset-ai/haystack#-community) | [Discord](https://discord.gg/haystack), [𝕏 (Twitter)](https://twitter.com/haystack_ai), [Stack Overflow](https://stackoverflow.com/questions/tagged/haystack), [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) |
| 💙 [Contributing](https://github.com/deepset-ai/haystack#-contributing) | We welcome all contributions! |
| 📊 [Benchmarks](https://haystack.deepset.ai/benchmarks/) | Speed & Accuracy of Retriever, Readers and DocumentStores |
| 🔭 [Roadmap](https://haystack.deepset.ai/overview/roadmap) | Public roadmap of Haystack |
| 📰 [Blog](https://haystack.deepset.ai/blog) | Learn about the latest with Haystack and NLP |
| ☎️ [Jobs](https://www.deepset.ai/jobs) | We're hiring! Have a look at our open positions |


## 💾 Installation

For a detailed installation guide see [the official documentation](https://docs.haystack.deepset.ai/docs/installation). There you’ll find instructions for custom installations handling Windows and Apple Silicon.
For a detailed installation guide see [the official documentation](https://docs.haystack.deepset.ai/v2.0/docs/installation). There you’ll find instructions for custom installations handling Windows and Apple Silicon.

**Basic Installation**

Use [pip](https://github.com/pypa/pip) to install a basic version of Haystack's latest release:

```sh
pip install farm-haystack
pip install haystack-ai
```

This command installs everything needed for basic Pipelines that use an in-memory DocumentStore and external LLM provider (e.g. OpenAI).

**Full Installation**

To use more advanced features, like certain DocumentStores, inference with local transformer models, FileConverters, OCR, or Ray,
you need to install further dependencies. The following command installs the [latest release](https://github.com/deepset-ai/haystack/releases) of Haystack and all its dependencies:

```sh
pip install 'farm-haystack[all]' ## or 'all-gpu' for the GPU-enabled dependencies
```

If you want to install only the dependencies needed for model inference on your local hardware (not remote API endpoints), such as torch and sentence-transformers, you can use the following command:
```sh
pip install 'farm-haystack[inference]' ## installs torch, sentence-transformers, sentencepiece, and huggingface-hub
```

If you want to try out the newest features that are not in an official release yet, you can install the unstable version from the main branch with the following command:

```sh
pip install git+https://github.com/deepset-ai/haystack.git@main#egg=farm-haystack
pip install git+https://github.com/deepset-ai/haystack.git@main#egg=haystack-ai
```

To be able to make changes to Haystack code, first of all clone this repo:
Expand All @@ -158,18 +97,6 @@ cd haystack && pip install -e '.[dev]'

If you want to contribute to the Haystack repo, check our [Contributor Guidelines](https://github.com/deepset-ai/haystack/blob/main/CONTRIBUTING.md) first.

See the list of [dependencies](https://github.com/deepset-ai/haystack/blob/main/pyproject.toml) to check which ones you want to install (for example, `[all]`, `[dev]`, or other).

**Installing the REST API**

Haystack comes packaged with a REST API so that you can deploy it as a service. Run the following command from the root directory of the Haystack repo to install REST_API:

```
pip install rest_api/
```

You can find out more about our PyPi package on our [PyPi page](https://pypi.org/project/farm-haystack/).

## 🔰Demos

You can find some of our hosted demos with instructions to run them locally too on our [haystack-demos](https://github.com/deepset-ai/haystack-demos) repository
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