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66 lines
3.5 KiB
Markdown
66 lines
3.5 KiB
Markdown
---
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type: intro
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date: 1.1.2023
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---
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```bash
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pip install farm-haystack
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```
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## What to build with Haystack
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- **Ask questions in natural language** and find granular answers in your own documents.
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- Perform **semantic search** and retrieve documents according to meaning not keywords
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- Use **off-the-shelf models** or **fine-tune** them to your own domain.
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- Use **user feedback** to evaluate, benchmark and continuously improve your live models.
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- Leverage existing **knowledge bases** and better handle the long tail of queries that **chatbots** receive.
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- **Automate processes** by automatically applying a list of questions to new documents and using the extracted answers.
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## Core Features
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- **Latest models**: Utilize all latest transformer based models (e.g. BERT, RoBERTa, MiniLM) for extractive QA, generative QA and document retrieval.
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- **Modular**: Multiple choices to fit your tech stack and use case. Pick your favorite database, file converter or modeling framework.
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- **Open**: 100% compatible with HuggingFace's model hub. Tight interfaces to other frameworks (e.g. Transformers, FARM, sentence-transformers)
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- **Scalable**: Scale to millions of docs via retrievers, production-ready backends like Elasticsearch / FAISS and a fastAPI REST API
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- **End-to-End**: All tooling in one place: file conversion, cleaning, splitting, training, eval, inference, labeling ...
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- **Developer friendly**: Easy to debug, extend and modify.
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- **Customizable**: Fine-tune models to your own domain or implement your custom DocumentStore.
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- **Continuous Learning**: Collect new training data via user feedback in production & improve your models continuously
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| :ledger: [Docs](https://haystack.deepset.ai/overview/intro) | Usage, Guides, API documentation ...|
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| :beginner: [Quick Demo](https://github.com/deepset-ai/haystack/#quick-demo) | Quickly see what Haystack offers |
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| :floppy_disk: [Installation](https://github.com/deepset-ai/haystack/#installation) | How to install Haystack |
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| :art: [Key Components](https://github.com/deepset-ai/haystack/#key-components) | Overview of core concepts |
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| :mortar_board: [Tutorials](https://github.com/deepset-ai/haystack/#tutorials) | Jupyter/Colab Notebooks & Scripts |
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| :eyes: [How to use Haystack](https://github.com/deepset-ai/haystack/#how-to-use-haystack) | Basic explanation of concepts, options and usage |
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| :heart: [Contributing](https://github.com/deepset-ai/haystack/#heart-contributing) | We welcome all contributions! |
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| :bar_chart: [Benchmarks](https://haystack.deepset.ai/benchmarks/v0.9.0) | Speed & Accuracy of Retriever, Readers and DocumentStores |
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| :telescope: [Roadmap](https://haystack.deepset.ai/overview/roadmap) | Public roadmap of Haystack |
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| :pray: [Slack](https://haystack.deepset.ai/community/join) | Join our community on Slack |
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| :bird: [Twitter](https://twitter.com/deepset_ai) | Follow us on Twitter for news and updates |
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| :newspaper: [Blog](https://medium.com/deepset-ai) | Read our articles on Medium |
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## Quick Demo
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The quickest way to see what Haystack offers is to start a [Docker Compose](https://docs.docker.com/compose/) demo application:
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**1. Update/install Docker and Docker Compose, then launch Docker**
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```
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# apt-get update && apt-get install docker && apt-get install docker-compose
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# service docker start
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```
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**2. Clone Haystack repository**
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```
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# git clone https://github.com/deepset-ai/haystack.git
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```
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### 2nd level headline for testing purposes
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#### 3rd level headline for testing purposes
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