Can we use multiple indices on Elasticsearch?


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Haystack is an end-to-end LLM framework that allows you to build applications powered by LLMs, Transformer models, vector search and more. Whether you want to perform retrieval-augmented generation (RAG), document search, question answering or answer generation, Haystack can orchestrate state-of-the-art embedding models and LLMs into pipelines to build end-to-end NLP applications and solve your use case.

Installation

The simplest way to get Haystack is via pip:

pip install haystack-ai

Haystack supports multiple installation methods including Docker images. For a comprehensive guide please refer to the documentation.

Documentation

If you're new to the project, check out "What is Haystack?" then go through the "Get Started Guide" and build your first LLM application in a matter of minutes. Keep learning with the tutorials. For more advanced use cases, or just to get some inspiration, you can browse our Haystack recipes in the Cookbook.

At any given point, hit the documentation to learn more about Haystack, what can it do for you and the technology behind.

Features

Important

You are currently looking at the readme of Haystack 2.0. We are still maintaining Haystack 1.x to give everyone enough time to migrate to 2.0. Switch to Haystack 1.x here.

Technology agnostic: Allow users the flexibility to decide what vendor or technology they want and make it easy to switch out any component for another. Haystack allows you to use and compare models available from OpenAI, Cohere and Hugging Face, as well as your own local models or models hosted on Azure, Bedrock and SageMaker.
Explicit: Make it transparent how different moving parts can “talk” to each other so it's easier to fit your tech stack and use case.
Flexible: Haystack provides all tooling in one place: database access, file conversion, cleaning, splitting, training, eval, inference, and more. And whenever custom behavior is desirable, it's easy to create custom components.
Extensible: Provide a uniform and easy way for the community and third parties to build their own components and foster an open ecosystem around Haystack.

Some examples of what you can do with Haystack:

Build retrieval augmented generation (RAG) by making use of one of the available vector databases and customizing your LLM interaction, the sky is the limit 🚀
Perform Question Answering in natural language to find granular answers in your documents.
Perform semantic search and retrieve documents according to meaning.
Build applications that can make complex decisions making to answer complex queries: such as systems that can resolve complex customer queries, do knowledge search on many disconnected resources and so on.
Scale to millions of docs using retrievers and production-scale components.
Use off-the-shelf models or fine-tune them to your data.
Use user feedback to evaluate, benchmark, and continuously improve your models.

Tip

Are you looking for a managed solution that benefits from Haystack? deepset Cloud is our fully managed, end-to-end platform to integrate LLMs with your data, which uses Haystack for the LLM pipelines architecture.

Telemetry

Haystack collects anonymous usage statistics of pipeline components. We receive an event every time these components are initialized. This way, we know which components are most relevant to our community.

Read more about telemetry in Haystack or how you can opt out in Haystack docs.

🖖 Community

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 Discord channel. We also check 𝕏 (Twitter) and Stack Overflow.

Contributing to Haystack

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.

There are several ways you can contribute to Haystack:

Contribute to the main Haystack project
Contribute an integration on haystack-core-integrations

Tip

👉 Check out the full list of issues that are open to contributions

Who Uses Haystack

Here's a list of projects and companies using Haystack. Want to add yours? Open a PR, add it to the list and let the world know that you use Haystack!

	def get_all_documents(self):
	search = Search(using=self.client, index=self.index)
	documents = []
	for hit in search:
	documents.append(
	{
	"id": hit.meta["id"],
	"name": hit["name"],
	"text": hit["text"],
	}
	)

deepset-ai / haystack Goto Github PK

haystack's Introduction

Installation

Documentation

Features

Telemetry

🖖 Community

Contributing to Haystack

Who Uses Haystack

haystack's People

Contributors

Stargazers

Watchers

Forkers

haystack's Issues

Requirements:

Challenges:

Sketched Approach:

DocumentStore.add_eval_data(data=Squad-like-file, index=”eval_document”, embedding_retriever=None)

Retriever.eval(label_index=”feedback”, doc_index=”eval_document”, label_origin=”gold_label” ... )

Reader.eval_on_file()

Reader.eval(index = “feedback”)

Finder.eval()

When loading a saved model the "data_dir" param is missing.

Error

Minimal code to reproduce

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