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This commit is contained in:
wehub-resource-sync
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|
||||
---
|
||||
title: "Breaking Change Policy"
|
||||
id: breaking-change-policy
|
||||
slug: "/breaking-change-policy"
|
||||
description: "This document outlines the breaking change policy for Haystack, including the definition of breaking changes, versioning conventions, and the deprecation process for existing features."
|
||||
---
|
||||
|
||||
# Breaking Change Policy
|
||||
|
||||
This document outlines the breaking change policy for Haystack, including the definition of breaking changes, versioning conventions, and the deprecation process for existing features.
|
||||
|
||||
Haystack is under active development, which means that functionalities are being added, deprecated, or removed rather frequently. This policy aims to minimize the impact of these changes on current users and deployments. It provides a clear schedule and outlines the necessary steps before upgrading to a new Haystack version.
|
||||
|
||||
## Breaking Change Definition
|
||||
|
||||
A breaking change occurs when:
|
||||
|
||||
- A Component is removed, renamed, or the Python import path is changed.
|
||||
- A parameter is renamed, removed, or changed from optional to mandatory.
|
||||
- A new mandatory parameter is added.
|
||||
|
||||
Existing deployments might break, and the change is deemed a _breaking change_. The decision to declare a change as breaking has nothing to do with its potential impact: while the change might only impact a tiny subset of applications using a specific Haystack feature, it would still be treated as a breaking change.
|
||||
|
||||
The following cases are **not** considered a breaking change:
|
||||
|
||||
- A new functionality is added (for example, a new Component).
|
||||
- A component, class, or utility function gets a new optional parameter.
|
||||
- An existing parameter gets changed from mandatory to optional.
|
||||
|
||||
Existing deployments are not impacted, and the change is deemed non-breaking. Release notes will mention the change and possibly provide an upgrade path, but upgrading Haystack won’t break existing applications.
|
||||
|
||||
## Versioning
|
||||
|
||||
Haystack releases are labeled with a series of three numbers separated by dots, for example, `2.0.1`. Each number has a specific meaning:
|
||||
|
||||
- `2` is the Major version
|
||||
- `0` is the Minor version
|
||||
- `1` is the Patch version
|
||||
|
||||
:::note
|
||||
Albeit similar, Haystack DOES NOT follow the principles of [Semantic Versioning](https://semver.org). Read on to see the differences.
|
||||
|
||||
:::
|
||||
|
||||
Given a Haystack release with a version number of type `MAJOR.MINOR.PATCH`, you should expect:
|
||||
|
||||
1. **For Major version change:** fundamental, incompatible API changes. In this case, you would most likely need a migration process before being able to update Haystack. Major releases happen no more than once a year, changes are extensively documented, and a migration path is provided.
|
||||
2. **For Minor version change:** addition or removal of functionalities that might not be backward compatible. Most of the time, you will be able to upgrade your Haystack installation seamlessly, but always refer to the [release notes](https://github.com/deepset-ai/haystack/releases) for guidance. Deprecated components are the most common breaking change shipped in a Minor version release.
|
||||
3. **For Patch version change:** bugfixes. You can safely upgrade Haystack to the new version without concerns that your program will break.
|
||||
|
||||
## Deprecation of Existing Features
|
||||
|
||||
Haystack strives for robustness. To achieve this, we clean up our code by removing old features that are no longer used. This helps us maintain the codebase, improve security, and make it easier to keep everything running smoothly. Before we remove a feature, component, class, or utility function, we go through a process called deprecation.
|
||||
|
||||
A Major or Minor (but not Patch) version may deprecate certain features from previous releases, and this is what you should expect:
|
||||
|
||||
- If a feature is deprecated in Haystack version `X.Y`, it will continue to work but the Python code will raise warnings detailing the steps to take in order to upgrade.
|
||||
- Features deprecated in Haystack version `X.Y` will be removed in Haystack `X.Y+1`, giving affected users a timeframe of roughly a month to prepare the upgrade.
|
||||
|
||||
### Example
|
||||
|
||||
To clarify the process, here’s an example:
|
||||
|
||||
At some point, we decide to remove a `FooComponent` and declare it deprecated in Haystack version `2.99.0`. This is what will happen:
|
||||
|
||||
1. `FooComponent` keeps working as usual In Haystack `2.99.0`, but using the component raises a `FutureWarning` message in the code.
|
||||
2. In Haystack version `2.100.0`, we remove the `FooComponent` from the codebase. Trying to use it produces an error.
|
||||
|
||||
## Discontinuing an Integration
|
||||
|
||||
When existing features are changed or removed, integrations go through the same deprecation process as detailed on this page for Haystack. It’s important to note that integrations are independent and distributed with their own packages. In certain cases, a special form of deprecation may occur where the integration is discontinued and subsequently removed from the Core Integrations repository.
|
||||
|
||||
To give our community the opportunity to take over the integration and keep it maintained before being discontinued Core Integrations gradually go through different states, as detailed below:
|
||||
|
||||
- **Staged**
|
||||
- The source code of the integration is moved from `main` to a special `staging` branch of the Core Integrations repository.
|
||||
- The documentation pages are removed from the Haystack documentation website.
|
||||
- The main README of the Core Integrations repository shows a disclaimer explaining how the integration can be adopted from the community.
|
||||
- The integration tile is removed (it can be re-added later by the maintainer who adopted the integration).
|
||||
- The integration package on PyPI remains available.
|
||||
- A grace period of 3 months starts.
|
||||
- **Adopted**
|
||||
- An organization or an individual from the community accepts to take over the ownership of the Staged integration.
|
||||
- The adopter creates their own repository, and the source code of the discontinued integration is removed from the `staging` branch.
|
||||
- Ownership of the PyPI package is transferred to the new maintainer.
|
||||
- The adopter will create a new integration tile in [haystack-integrations](https://github.com/deepset-ai/haystack-integrations).
|
||||
- **Discontinued**
|
||||
- If the grace period expires and nobody adopts the Staged Integration, its source code is removed from the `staging` branch.
|
||||
- The PyPI package of the integration won’t be removed but won’t be further updated.
|
||||
@@ -0,0 +1,38 @@
|
||||
---
|
||||
title: "FAQ"
|
||||
id: faq
|
||||
slug: "/faq"
|
||||
description: "Here are the answers to the questions people frequently ask about Haystack."
|
||||
---
|
||||
|
||||
# FAQ
|
||||
|
||||
Here are the answers to the questions people frequently ask about Haystack.
|
||||
|
||||
### How can I make sure that my GPU is being engaged when I use Haystack?
|
||||
|
||||
You will want to ensure that a CUDA enabled GPU is being engaged when Haystack is running (you can check by running `nvidia-smi -l` on your command line). Components which can be sped up by GPU have a `device` argument in their constructor. For more details, check the [Device Management](../concepts/device-management.mdx) page.
|
||||
|
||||
### Are you tracking my Haystack usage?
|
||||
|
||||
We only collect _anonymous_ usage statistics of Haystack pipeline components. Read more about telemetry in Haystack or how you can opt out on the [Telemetry](telemetry.mdx) page.
|
||||
|
||||
### How can I ask my questions around Haystack?
|
||||
|
||||
For general questions, we recommend joining the [Haystack Discord ](https://discord.com/invite/xYvH6drSmA)or using [GitHub discussions](https://github.com/deepset-ai/haystack/discussions), where the community and maintainers can help. You can also explore [tutorials](https://haystack.deepset.ai/tutorials/40_building_chat_application_with_function_calling) and [examples](https://haystack.deepset.ai/cookbook/tools_support) on our website to find more info.
|
||||
|
||||
### How can I get expert support for Haystack?
|
||||
|
||||
If you’re a team running Haystack in production or want to move faster and scale with confidence, we recommend [Haystack Enterprise](https://haystack.deepset.ai/blog/announcing-haystack-enterprise). It gives you direct access to the Haystack team, proven best practices, and hands-on support to help you go from prototype to production smoothly.
|
||||
|
||||
👉 [Get in touch with our team to explore Haystack Enterprise](https://www.deepset.ai/products-and-services/haystack-enterprise)
|
||||
|
||||
### Where can I find tutorials and documentation for Haystack 1.x?
|
||||
|
||||
You can access old tutorials in the [GitHub history](https://github.com/deepset-ai/haystack-tutorials/tree/5917718cbfbb61410aab4121ee6fe754040a5dc7) and download the Haystack 1.x documentation as a [ZIP file](https://core-engineering.s3.eu-central-1.amazonaws.com/public/docs/haystack-v1-docs.zip).
|
||||
|
||||
The ZIP file contains documentation for all minor releases from version 1.0 to 1.26.
|
||||
|
||||
To download documentation for a specific release, replace the version number in the following URL: `https://core-engineering.s3.eu-central-1.amazonaws.com/public/docs/v1.26.zip`.
|
||||
|
||||
Learn how to migrate to Haystack version 2.x with our [migration guide](migration.mdx).
|
||||
@@ -0,0 +1,113 @@
|
||||
---
|
||||
title: "Get Started"
|
||||
id: get-started
|
||||
slug: "/get-started"
|
||||
description: "Have a look at this page to learn how to quickly get up and running with Haystack. It contains instructions for installing, running your first RAG pipeline, adding data and further resources."
|
||||
---
|
||||
|
||||
# Get Started
|
||||
|
||||
Have a look at this page to learn how to quickly get up and running with Haystack. It contains instructions for installing, running your first RAG pipeline, adding data and further resources.
|
||||
|
||||
## Build your first RAG application
|
||||
|
||||
Let's build your first Retrieval Augmented Generation (RAG) pipeline and see how Haystack answers questions.
|
||||
|
||||
First, install the minimal form of Haystack:
|
||||
|
||||
```shell
|
||||
pip install haystack-ai
|
||||
```
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Were you already using Haystack 1.x?</summary>
|
||||
|
||||
:::warning
|
||||
|
||||
Installing `farm-haystack` and `haystack-ai` in the same Python environment (virtualenv, Colab, or system) causes problems.
|
||||
|
||||
Installing both packages in the same environment can somehow work or fail in obscure ways. We suggest installing only one of these packages per Python environment. Make sure that you remove both packages if they are installed in the same environment, followed by installing only one of them:
|
||||
|
||||
```bash
|
||||
pip uninstall -y farm-haystack haystack-ai
|
||||
pip install haystack-ai
|
||||
```
|
||||
|
||||
If you have any questions, please reach out to us on the [GitHub Discussion](https://github.com/deepset-ai/haystack/discussions) or [Discord](https://discord.com/invite/VBpFzsgRVF).
|
||||
:::
|
||||
|
||||
</details>
|
||||
|
||||
In the example below, we show how to set an API key using a Haystack [Secret](../concepts/secret-management.mdx). However, for easier use, you can also set an OpenAI key as an `OPENAI_API_KEY` environment variable.
|
||||
|
||||
```python
|
||||
# import necessary dependencies
|
||||
from haystack import Pipeline, Document
|
||||
from haystack.components.generators.chat import OpenAIChatGenerator
|
||||
from haystack.components.retrievers import InMemoryBM25Retriever
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.builders import ChatPromptBuilder
|
||||
from haystack.utils import Secret
|
||||
from haystack.dataclasses import ChatMessage
|
||||
|
||||
# create a document store and write documents to it
|
||||
document_store = InMemoryDocumentStore()
|
||||
document_store.write_documents(
|
||||
[
|
||||
Document(content="My name is Jean and I live in Paris."),
|
||||
Document(content="My name is Mark and I live in Berlin."),
|
||||
Document(content="My name is Giorgio and I live in Rome."),
|
||||
],
|
||||
)
|
||||
|
||||
# A prompt corresponds to an NLP task and contains instructions for the model. Here, the pipeline will go through each Document to figure out the answer.
|
||||
prompt_template = [
|
||||
ChatMessage.from_system(
|
||||
"""
|
||||
Given these documents, answer the question.
|
||||
Documents:
|
||||
{% for doc in documents %}
|
||||
{{ doc.content }}
|
||||
{% endfor %}
|
||||
Question:
|
||||
""",
|
||||
),
|
||||
ChatMessage.from_user("{{question}}"),
|
||||
ChatMessage.from_system("Answer:"),
|
||||
]
|
||||
|
||||
# create the components adding the necessary parameters
|
||||
retriever = InMemoryBM25Retriever(document_store=document_store)
|
||||
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables="*")
|
||||
llm = OpenAIChatGenerator(
|
||||
api_key=Secret.from_env_var("OPENAI_API_KEY"),
|
||||
model="gpt-4o-mini",
|
||||
)
|
||||
|
||||
# Create the pipeline and add the components to it. The order doesn't matter.
|
||||
# At this stage, the Pipeline validates the components without running them yet.
|
||||
rag_pipeline = Pipeline()
|
||||
rag_pipeline.add_component("retriever", retriever)
|
||||
rag_pipeline.add_component("prompt_builder", prompt_builder)
|
||||
rag_pipeline.add_component("llm", llm)
|
||||
|
||||
# Arrange pipeline components in the order you need them. If a component has more than one inputs or outputs, indicate which input you want to connect to which output using the format ("component_name.output_name", "component_name, input_name").
|
||||
rag_pipeline.connect("retriever", "prompt_builder.documents")
|
||||
rag_pipeline.connect("prompt_builder", "llm")
|
||||
|
||||
# Run the pipeline by specifying the first component in the pipeline and passing its mandatory inputs. Optionally, you can pass inputs to other components.
|
||||
question = "Who lives in Paris?"
|
||||
results = rag_pipeline.run(
|
||||
{
|
||||
"retriever": {"query": question},
|
||||
"prompt_builder": {"question": question},
|
||||
},
|
||||
)
|
||||
|
||||
print(results["llm"]["replies"])
|
||||
```
|
||||
|
||||
### Adding Your Data
|
||||
|
||||
Instead of running the RAG pipeline on example data, learn how you can add your own custom data using [Document Stores](../concepts/document-store.mdx).
|
||||
@@ -0,0 +1,78 @@
|
||||
---
|
||||
title: "Installation"
|
||||
id: installation
|
||||
slug: "/installation"
|
||||
description: "See how to quickly install Haystack with pip or conda."
|
||||
---
|
||||
|
||||
# Installation
|
||||
|
||||
See how to quickly install Haystack with pip or conda.
|
||||
|
||||
## Package Installation
|
||||
|
||||
Use [pip](https://github.com/pypa/pip) to install only the Haystack code:
|
||||
|
||||
```shell
|
||||
pip install haystack-ai
|
||||
```
|
||||
|
||||
Alternatively, you can use [conda](https://docs.conda.io/projects/conda/en/stable/):
|
||||
|
||||
```shell
|
||||
conda config --add channels conda-forge/label/haystack-ai_rc
|
||||
conda install haystack-ai
|
||||
```
|
||||
|
||||
<br />
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Were you already using Haystack 1.x?</summary>
|
||||
|
||||
:::warning
|
||||
Warning
|
||||
|
||||
Installing `farm-haystack` and `haystack-ai` in the same Python environment (virtualenv, Colab, or system) causes problems.
|
||||
|
||||
Installing both packages in the same environment can somehow work or fail in obscure ways. We suggest installing only one of these packages per Python environment. Make sure that you remove both packages if they are installed in the same environment, followed by installing only one of them:
|
||||
|
||||
```bash
|
||||
pip uninstall -y farm-haystack haystack-ai
|
||||
pip install haystack-ai
|
||||
```
|
||||
|
||||
If you have any questions, please reach out to us on the [GitHub Discussion](https://github.com/deepset-ai/haystack/discussions) or [Discord](https://discord.com/invite/VBpFzsgRVF).
|
||||
:::
|
||||
|
||||
</details>
|
||||
|
||||
### Optional Dependencies
|
||||
|
||||
Some components in Haystack rely on additional optional dependencies.
|
||||
To keep the installation lightweight, these are not included by default – only the essentials are installed.
|
||||
If you use a feature that requires an optional dependency that hasn't been installed, Haystack will raise an error that instructs you to install missing dependencies, for example:
|
||||
|
||||
```shell
|
||||
ImportError: "Haystack failed to import the optional dependency 'pypdf'. Run 'pip install pypdf'.
|
||||
```
|
||||
|
||||
## Contributing to Haystack
|
||||
|
||||
If you would like to contribute to the Haystack, check our [Contributor Guidelines](https://github.com/deepset-ai/haystack/blob/main/CONTRIBUTING.md) first.
|
||||
|
||||
To be able to make changes to Haystack code, install with the following commands:
|
||||
|
||||
```shell
|
||||
## Clone the repo
|
||||
git clone https://github.com/deepset-ai/haystack.git
|
||||
|
||||
## Move into the cloned folder
|
||||
cd haystack
|
||||
|
||||
## Upgrade pip
|
||||
pip install --upgrade pip
|
||||
|
||||
## Install Haystack in editable mode
|
||||
pip install -e '.[dev]'
|
||||
```
|
||||
@@ -0,0 +1,512 @@
|
||||
---
|
||||
title: "Migration Guide"
|
||||
id: migration
|
||||
slug: "/migration"
|
||||
description: "Learn how to make the move to Haystack 2.x from Haystack 1.x."
|
||||
---
|
||||
|
||||
# Migration Guide
|
||||
|
||||
Learn how to make the move to Haystack 2.x from Haystack 1.x.
|
||||
|
||||
This guide is designed for those with previous experience with Haystack and who are interested in understanding the differences between Haystack 1.x and Haystack 2.x. If you're new to Haystack, skip this page and proceed directly to Haystack 2.x [documentation](get-started.mdx).
|
||||
|
||||
## Major Changes
|
||||
|
||||
Haystack 2.x represents a significant overhaul of Haystack 1.x, and it's important to note that certain key concepts outlined in this section don't have a direct correlation between the two versions.
|
||||
|
||||
### Package Name
|
||||
|
||||
Haystack 1.x was distributed with a package called `farm-haystack`. To migrate your application, you must uninstall `farm-haystack` and install the new `haystack-ai` package for Haystack 2.x.
|
||||
|
||||
:::warning
|
||||
Two versions of the project cannot coexist in the same Python environment.
|
||||
|
||||
One of the options is to remove both packages if they are installed in the same environment, followed by installing only one of them:
|
||||
|
||||
```bash
|
||||
pip uninstall -y farm-haystack haystack-ai
|
||||
pip install haystack-ai
|
||||
```
|
||||
:::
|
||||
|
||||
### Nodes
|
||||
|
||||
While Haystack 2.x continues to rely on the `Pipeline` abstraction, the elements linked in a pipeline graph are now referred to as just _components_, replacing the terms _nodes_ and _pipeline components_ used in the previous versions. The [_Migrating Components_](#migrating-components) paragraph below outlines which component in Haystack 2.x can be used as a replacement for a specific 1.x node.
|
||||
|
||||
### Pipelines
|
||||
|
||||
Pipelines continue to serve as the fundamental structure of all Haystack applications. While the concept of `Pipeline` abstraction remains consistent, Haystack 2.x introduces significant enhancements that address various limitations of its predecessor. For instance, the pipelines now support loops. Pipelines also offer greater flexibility in their input, which is no longer restricted to queries. The pipeline now allows to route the output of a component to multiple recipients. This increases flexibility, however, comes with notable differences in the pipeline definition process in Haystack 2.x compared to the previous version.
|
||||
|
||||
In Haystack 1.x, a pipeline was built by adding one node after the other. In the resulting pipeline graph, edges are automatically added to connect those nodes in the order they were added.
|
||||
|
||||
Building a pipeline in Haystack 2.x is a two-step process:
|
||||
|
||||
1. Initially, components are added to the pipeline without any specific order by calling the `add_component` method.
|
||||
2. Subsequently, the components must be explicitly connected by calling the `connect` method to define the final graph.
|
||||
|
||||
To migrate an existing pipeline, the first step is to go through the nodes and identify their counterparts in Haystack 2.x (see the following section, [_Migrating Components_](#migrating-components), for guidance). If all the nodes can be replaced by corresponding components, they have to be added to the pipeline with `add_component` and explicitly connected with the appropriate calls to `connect`. Here is an example:
|
||||
|
||||
**Haystack 1.x**
|
||||
|
||||
```python
|
||||
pipeline = Pipeline()
|
||||
|
||||
node_1 = SomeNode()
|
||||
node_2 = AnotherNode()
|
||||
|
||||
pipeline.add_node(node_1, name="Node_1", inputs=["Query"])
|
||||
pipeline.add_node(node_2, name="Node_2", inputs=["Node_1"])
|
||||
```
|
||||
|
||||
**Haystack 2.x**
|
||||
|
||||
```python
|
||||
pipeline = Pipeline()
|
||||
|
||||
component_1 = SomeComponent()
|
||||
component_2 = AnotherComponent()
|
||||
|
||||
pipeline.add_component("Comp_1", component_1)
|
||||
pipeline.add_component("Comp_2", component_2)
|
||||
|
||||
pipeline.connect("Comp_1", "Comp_2")
|
||||
```
|
||||
|
||||
In case a specific replacement component is not available for one of your nodes, migrating the pipeline might still be possible by:
|
||||
|
||||
- Either [creating a custom component](../concepts/components/custom-components.mdx), or
|
||||
- Changing the pipeline logic, as the last resort.
|
||||
|
||||
:::note
|
||||
Check out the [Pipelines](../concepts/pipelines.mdx) section of our 2.x documentation to understand how new pipelines work more granularly.
|
||||
|
||||
:::
|
||||
|
||||
### Document Stores
|
||||
|
||||
The fundamental concept of Document Stores as gateways to access text and metadata stored in a database didn’t change in Haystack 2.x, but there are significant differences against Haystack 1.x.
|
||||
|
||||
In Haystack 1.x, Document Stores were a special type of node that you can use in two ways:
|
||||
|
||||
- As the last node in an indexing pipeline (such as a pipeline whose ultimate goal is storing data in a database).
|
||||
- As a normal Python instance passed to a Retriever node.
|
||||
|
||||
In Haystack 2.x, the Document Store is not a component, so to migrate the two use cases above to version 2.x, you can respectively:
|
||||
|
||||
- Replace the Document Store at the end of the pipeline with a [`DocumentWriter`](../pipeline-components/writers/documentwriter.mdx) component.
|
||||
- Identify the right Retriever component and create it passing the Document Store instance, same as it is in Haystack 1.x.
|
||||
|
||||
### Retrievers
|
||||
|
||||
Haystack 1.x provided a set of nodes that filter relevant documents from different data sources according to a given query. Each of those nodes implements a certain retrieval algorithm and supports one or more types of Document Stores. For example, the `BM25Retriever` node in Haystack 1.x can work seamlessly with OpenSearch and Elasticsearch but not with Qdrant; the `EmbeddingRetriever`, on the contrary, can work with all the three databases.
|
||||
|
||||
In Haystack 2.x, the concept is flipped, and each Document Store provides one or more retriever components, depending on which retrieval methods the underlying vector database supports. For example, the `OpenSearchDocumentStore` comes with [two Retriever components](../document-stores/opensearch-document-store.mdx#supported-retrievers), one relying on BM25, and the other on vector similarity.
|
||||
|
||||
To migrate a 1.x retrieval pipeline to 2.x, the first step is to identify the Document Store being used and replace the Retriever node with the corresponding Retriever component from Haystack 2.x with the Document Store of choice. For example, a `BM25Retriever` node using Elasticsearch in a Haystack 1.x pipeline should be replaced with the [`ElasticsearchBM25Retriever`](../pipeline-components/retrievers/elasticsearchbm25retriever.mdx) component.
|
||||
|
||||
### PromptNode
|
||||
|
||||
The `PromptNode` in Haystack 1.x represented the gateway to any Large Language Model (LLM) inference provider, whether it is locally available or remote. Based on the name of the model, Haystack infers the right provider to call and forward the query.
|
||||
|
||||
In Haystack 2.x, the task of using LLMs is assigned to [Generators](../pipeline-components/generators.mdx). These are a set of components that are highly specialized and tailored for each inference provider.
|
||||
|
||||
The first step when migrating a pipeline with a `PromptNode` is to identify the model provider used and to replace the node with two components:
|
||||
|
||||
- A Generator component for the model provider of choice,
|
||||
- A `PromptBuilder` or `ChatPromptBuilder` component to build the prompt to be used.
|
||||
|
||||
The [_Migration examples_](#migration-examples) section below shows how to port a `PromptNode` using OpenAI with a prompt template to a corresponding Haystack 2.x pipeline using the `OpenAIGenerator` in conjunction with a `PromptBuilder` component.
|
||||
|
||||
### Agents
|
||||
|
||||
The agentic approach facilitates the answering of questions that are significantly more complex than those typically addressed by extractive or generative question answering techniques.
|
||||
|
||||
Haystack 1.x provided Agents, enabling the use of LLMs in a loop.
|
||||
|
||||
Currently in Haystack 2.x, you can build Agents using three main elements in a pipeline: Chat Generators, ToolInvoker component, and Tools. A standalone Agent abstraction in Haystack 2.x is in an experimental phase.
|
||||
|
||||
:::note
|
||||
Agents Documentation Page
|
||||
|
||||
Take a look at our 2.x [Agents](../concepts/agents.mdx) documentation page for more information and detailed examples.
|
||||
:::
|
||||
|
||||
### REST API
|
||||
|
||||
Haystack 1.x enabled the deployment of pipelines through a RESTful API over HTTP. This feature is facilitated by a separate application named `rest_api` which is exclusively accessible in the form of a [source code on GitHub](https://github.com/deepset-ai/haystack/tree/v1.x/rest_api).
|
||||
|
||||
Haystack 2.x takes the same RESTful approach, but in this case, the application to be used to deploy pipelines is called [Hayhooks](../development/hayhooks.mdx) and can be installed with `pip install hayhooks`.
|
||||
|
||||
At the moment, porting an existing Haystack 1.x deployment using the `rest_api` project to Hayhooks would require a complete rewrite of the application.
|
||||
|
||||
## Dependencies
|
||||
|
||||
In order to minimize runtime errors, Haystack 1.x was distributed in a package that’s quite large, as it tries to set up the Python environment with as many dependencies as possible.
|
||||
|
||||
In contrast, Haystack 2.x strives for a more streamlined approach, offering a minimal set of dependencies right out of the box. It features a system that issues a warning when an additional dependency is required, thereby providing the user with the necessary instructions.
|
||||
|
||||
To make sure all the dependencies are satisfied when migrating a Haystack 1.x application to version 2.x, a good strategy is to run end-to-end tests and cover all the execution paths to ensure all the required dependencies are available in the target Python environment.
|
||||
|
||||
## Migrating Components
|
||||
|
||||
This table outlines which component (or a group of components) can be used to replace a certain node when porting a Haystack 1.x pipeline to the latest 2.x version. It’s important to note that when a Haystack 2.x replacement is not available, this doesn’t necessarily mean we are planning this feature.
|
||||
|
||||
If you need help migrating a 1.x node without a 2.x counterpart, open an [issue](https://github.com/deepset-ai/haystack/issues) in Haystack GitHub repository.
|
||||
|
||||
### Data Handling
|
||||
|
||||
| Haystack 1.x | Description | Haystack 2.x |
|
||||
| -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------ |
|
||||
| Crawler | Scrapes text from websites. **Example usage:** To run searches on your website content. | Not Available |
|
||||
| DocumentClassifier | Classifies documents by attaching metadata to them. **Example usage:** Labeling documents by their characteristic (for example, sentiment). | [TransformersZeroShotDocumentClassifier](../pipeline-components/classifiers/transformerszeroshotdocumentclassifier.mdx) |
|
||||
| DocumentLanguageClassifier | Detects the language of the documents you pass to it and adds it to the document metadata. | [DocumentLanguageClassifier](../pipeline-components/classifiers/documentlanguageclassifier.mdx) |
|
||||
| EntityExtractor | Extracts predefined entities out of a piece of text. **Example usage:** Named entity extraction (NER). | [NamedEntityExtractor](../pipeline-components/extractors/namedentityextractor.mdx) |
|
||||
| FileClassifier | Distinguishes between text, PDF, Markdown, Docx, and HTML files. **Example usage:** Routing files to appropriate converters (for example, it routes PDF files to `PDFToTextConverter`). | [FileTypeRouter](../pipeline-components/routers/filetyperouter.mdx) |
|
||||
| FileConverter | Cleans and splits documents in different formats. **Example usage:** In indexing pipelines, extracting text from a file and casting it into the Document class format. | [Converters](../pipeline-components/converters.mdx) |
|
||||
| PreProcessor | Cleans and splits documents. **Example usage:** Normalizing white spaces, getting rid of headers and footers, splitting documents into smaller ones. | [PreProcessors](../pipeline-components/preprocessors.mdx) |
|
||||
|
||||
### Semantic Search
|
||||
|
||||
| Haystack 1.x | Description | Haystack 2.x |
|
||||
| ----------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- |
|
||||
| Ranker | Orders documents based on how relevant they are to the query. **Example usage:** In a query pipeline, after a keyword-based Retriever to rank the documents it returns. | [Rankers](../pipeline-components/rankers.mdx) |
|
||||
| Reader | Finds an answer by selecting a text span in documents. **Example usage:** In a query pipeline when you want to know the location of the answer. | [ExtractiveReader](../pipeline-components/readers/extractivereader.mdx) |
|
||||
| Retriever | Fetches relevant documents from the Document Store. **Example usage:** Coupling Retriever with a Reader in a query pipeline to speed up the search (the Reader only goes through the documents it gets from the Retriever). | [Retrievers](../pipeline-components/retrievers.mdx) |
|
||||
| QuestionGenerator | When given a document, it generates questions this document can answer. **Example usage:**
|
||||
Auto-suggested questions in your search app. | Prompt [Builders](../pipeline-components/builders.mdx) with dedicated prompt, [Generators](../pipeline-components/generators.mdx) |
|
||||
|
||||
### Prompts and LLMs
|
||||
|
||||
| Haystack 1.x | Description | Haystack 2.x |
|
||||
| ------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |
|
||||
| PromptNode | Uses large language models to perform various NLP tasks in a pipeline or on its own. **Example usage:** It's a very versatile component that can perform tasks like summarization, question answering, translation, and more. | Prompt [Builders](../pipeline-components/builders.mdx),[Generators](../pipeline-components/generators.mdx) |
|
||||
|
||||
### Routing
|
||||
|
||||
| Haystack 1.x | Description | Haystack 2.x |
|
||||
| --- | --- | --- |
|
||||
| QueryClassifier | Categorizes queries. **Example usage:** Distinguishing between keyword queries and natural language questions and routing them to the Retrievers that can handle them best. | [TransformersZeroShotTextRouter](../pipeline-components/routers/transformerszeroshottextrouter.mdx) <br />[TransformersTextRouter](../pipeline-components/routers/transformerstextrouter.mdx) |
|
||||
| RouteDocuments | Routes documents to different branches of your pipeline based on their content type or metadata field. **Example usage:** Routing table data to `TableReader` and text data to `TransfomersReader` for better handling. | [Routers](../pipeline-components/routers.mdx) |
|
||||
|
||||
### Utility Components
|
||||
|
||||
| Haystack 1.x | Description | Haystack 2.x |
|
||||
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------- |
|
||||
| DocumentMerger | Concatenates multiple documents into a single one. **Example usage: **Merge the documents to summarize in a summarization pipeline. | Prompt [Builders](../pipeline-components/builders.mdx) |
|
||||
| Docs2Answers | Converts Documents into Answers. **Example usage:** When using REST API for document retrieval. REST API expects Answer as output, you can use `Doc2Answer` as the last node to convert the retrieved documents to answers. | [AnswerBuilder](../pipeline-components/builders/answerbuilder.mdx) |
|
||||
| JoinAnswers | Takes answers returned by multiple components and joins them in a single list of answers. **Example usage:** For running queries on different document types (for example, tables and text), where the documents are routed to different readers, and each reader returns a separate list of answers. | [AnswerJoiner](../pipeline-components/joiners/answerjoiner.mdx) |
|
||||
| JoinDocuments | Takes documents returned by different components and joins them to form one list of documents. **Example usage:** In document retrieval pipelines, where there are different types of documents, each routed to a different Retriever. Each Retriever returns a separate list of documents, and you can join them into one list using `JoinDocuments`. | [DocumentJoiner](../pipeline-components/joiners/documentjoiner.mdx) |
|
||||
| Shaper | Currently functions mostly as `PromptNode` helper making sure the `PromptNode` input or output is correct. **Example usage:** In a question answering pipeline using `PromptNode`, where the `PromptTemplate` expects questions as input, while Haystack pipelines use query. You can use Shaper to rename queries to questions. | Prompt [Builders](../pipeline-components/builders.mdx) |
|
||||
| Summarizer | Creates an overview of a document. **Example usage:** To get a glimpse of the documents the Retriever is returning. | Prompt [Builders](../pipeline-components/builders.mdx) with dedicated prompt, [Generators](../pipeline-components/generators.mdx) |
|
||||
| TransformersImageToText | Generates captions for images. **Example usage:** Automatically generate captions for a list of images that you can later use in your knowledge base. | [VertexAIImageQA](../pipeline-components/generators/vertexaiimageqa.mdx) |
|
||||
| Translator | Translates text from one language into another. **Example usage:** Running searches on documents in other languages. | Prompt [Builders](../pipeline-components/builders.mdx) with dedicated prompt, [Generators](../pipeline-components/generators.mdx) |
|
||||
|
||||
### Extras
|
||||
|
||||
| Haystack 1.x | Description | Haystack 2.x |
|
||||
| ---------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ |
|
||||
| AnswerToSpeech | Converts text answers into speech answers. **Example usage:** Improving accessibility of your search system by providing a way to have the answer and its context read out loud. | [ElevenLabs](https://haystack.deepset.ai/integrations/elevenlabs) Integration |
|
||||
| DocumentToSpeech | Converts text documents to speech documents. **Example usage:** Improving accessibility of a document retrieval pipeline by providing the option to read documents out loud. | [ElevenLabs](https://haystack.deepset.ai/integrations/elevenlabs) Integration |
|
||||
|
||||
## Migration examples
|
||||
|
||||
:::note
|
||||
This section might grow as we assist users with their use cases.
|
||||
|
||||
:::
|
||||
|
||||
### Indexing Pipeline
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Haystack 1.x</summary>
|
||||
|
||||
```python
|
||||
from haystack.document_stores import InMemoryDocumentStore
|
||||
from haystack.nodes.file_classifier import FileTypeClassifier
|
||||
from haystack.nodes.file_converter import TextConverter
|
||||
from haystack.nodes.preprocessor import PreProcessor
|
||||
from haystack.pipelines import Pipeline
|
||||
|
||||
## Initialize a DocumentStore
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
## Indexing Pipeline
|
||||
indexing_pipeline = Pipeline()
|
||||
|
||||
## Makes sure the file is a TXT file (FileTypeClassifier node)
|
||||
classifier = FileTypeClassifier()
|
||||
indexing_pipeline.add_node(classifier, name="Classifier", inputs=["File"])
|
||||
|
||||
## Converts a file into text and performs basic cleaning (TextConverter node)
|
||||
text_converter = TextConverter(remove_numeric_tables=True)
|
||||
indexing_pipeline.add_node(
|
||||
text_converter,
|
||||
name="Text_converter",
|
||||
inputs=["Classifier.output_1"],
|
||||
)
|
||||
|
||||
## Pre-processes the text by performing splits and adding metadata to the text (Preprocessor node)
|
||||
preprocessor = PreProcessor(
|
||||
clean_whitespace=True,
|
||||
clean_empty_lines=True,
|
||||
split_length=100,
|
||||
split_overlap=50,
|
||||
split_respect_sentence_boundary=True,
|
||||
)
|
||||
indexing_pipeline.add_node(preprocessor, name="Preprocessor", inputs=["Text_converter"])
|
||||
|
||||
## - Writes the resulting documents into the document store
|
||||
indexing_pipeline.add_node(
|
||||
document_store,
|
||||
name="Document_Store",
|
||||
inputs=["Preprocessor"],
|
||||
)
|
||||
|
||||
## Then we run it with the documents and their metadata as input
|
||||
result = indexing_pipeline.run(file_paths=file_paths, meta=files_metadata)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Haystack 2.x</summary>
|
||||
|
||||
```python
|
||||
from haystack import Pipeline
|
||||
from haystack.components.routers import FileTypeRouter
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.converters import TextFileToDocument
|
||||
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
|
||||
from haystack.components.writers import DocumentWriter
|
||||
|
||||
## Initialize a DocumentStore
|
||||
document_store = InMemoryDocumentStore()
|
||||
|
||||
## Indexing Pipeline
|
||||
indexing_pipeline = Pipeline()
|
||||
|
||||
## Makes sure the file is a TXT file (FileTypeRouter component)
|
||||
classifier = FileTypeRouter(mime_types=["text/plain"])
|
||||
indexing_pipeline.add_component("file_type_router", classifier)
|
||||
|
||||
## Converts a file into a Document (TextFileToDocument component)
|
||||
text_converter = TextFileToDocument()
|
||||
indexing_pipeline.add_component("text_converter", text_converter)
|
||||
|
||||
## Performs basic cleaning (DocumentCleaner component)
|
||||
cleaner = DocumentCleaner(
|
||||
remove_empty_lines=True,
|
||||
remove_extra_whitespaces=True,
|
||||
)
|
||||
indexing_pipeline.add_component("cleaner", cleaner)
|
||||
|
||||
## Pre-processes the text by performing splits and adding metadata to the text (DocumentSplitter component)
|
||||
preprocessor = DocumentSplitter(split_by="passage", split_length=100, split_overlap=50)
|
||||
indexing_pipeline.add_component("preprocessor", preprocessor)
|
||||
|
||||
## - Writes the resulting documents into the document store
|
||||
indexing_pipeline.add_component("writer", DocumentWriter(document_store))
|
||||
|
||||
## Connect all the components
|
||||
indexing_pipeline.connect("file_type_router.text/plain", "text_converter")
|
||||
indexing_pipeline.connect("text_converter", "cleaner")
|
||||
indexing_pipeline.connect("cleaner", "preprocessor")
|
||||
indexing_pipeline.connect("preprocessor", "writer")
|
||||
|
||||
## Then we run it with the documents and their metadata as input
|
||||
result = indexing_pipeline.run({"file_type_router": {"sources": file_paths}})
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### Query Pipeline
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Haystack 1.x</summary>
|
||||
|
||||
```python
|
||||
from haystack.document_stores import InMemoryDocumentStore
|
||||
from haystack.pipelines import ExtractiveQAPipeline
|
||||
from haystack import Document
|
||||
from haystack.nodes import BM25Retriever
|
||||
from haystack.nodes import FARMReader
|
||||
|
||||
document_store = InMemoryDocumentStore(use_bm25=True)
|
||||
document_store.write_documents(
|
||||
[
|
||||
Document(content="Paris is the capital of France."),
|
||||
Document(content="Berlin is the capital of Germany."),
|
||||
Document(content="Rome is the capital of Italy."),
|
||||
Document(content="Madrid is the capital of Spain."),
|
||||
],
|
||||
)
|
||||
|
||||
retriever = BM25Retriever(document_store=document_store)
|
||||
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
|
||||
extractive_qa_pipeline = ExtractiveQAPipeline(reader, retriever)
|
||||
|
||||
query = "What is the capital of France?"
|
||||
result = extractive_qa_pipeline.run(
|
||||
query=query,
|
||||
params={"Retriever": {"top_k": 10}, "Reader": {"top_k": 5}},
|
||||
)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Haystack 2.x</summary>
|
||||
|
||||
```python
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack import Document, Pipeline
|
||||
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
|
||||
from haystack.components.readers import ExtractiveReader
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
document_store.write_documents(
|
||||
[
|
||||
Document(content="Paris is the capital of France."),
|
||||
Document(content="Berlin is the capital of Germany."),
|
||||
Document(content="Rome is the capital of Italy."),
|
||||
Document(content="Madrid is the capital of Spain."),
|
||||
],
|
||||
)
|
||||
|
||||
retriever = InMemoryBM25Retriever(document_store)
|
||||
reader = ExtractiveReader(model="deepset/roberta-base-squad2")
|
||||
extractive_qa_pipeline = Pipeline()
|
||||
extractive_qa_pipeline.add_component("retriever", retriever)
|
||||
extractive_qa_pipeline.add_component("reader", reader)
|
||||
extractive_qa_pipeline.connect("retriever", "reader")
|
||||
|
||||
query = "What is the capital of France?"
|
||||
result = extractive_qa_pipeline.run(
|
||||
data={
|
||||
"retriever": {"query": query, "top_k": 3},
|
||||
"reader": {"query": query, "top_k": 2},
|
||||
},
|
||||
)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
### RAG Pipeline
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Haystack 1.x</summary>
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
|
||||
from haystack.pipelines import Pipeline
|
||||
from haystack.document_stores import InMemoryDocumentStore
|
||||
from haystack.nodes import EmbeddingRetriever, PromptNode, PromptTemplate, AnswerParser
|
||||
|
||||
document_store = InMemoryDocumentStore(embedding_dim=384)
|
||||
dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
|
||||
document_store.write_documents(dataset)
|
||||
retriever = EmbeddingRetriever(
|
||||
embedding_model="sentence-transformers/all-MiniLM-L6-v2",
|
||||
document_store=document_store,
|
||||
top_k=2,
|
||||
)
|
||||
document_store.update_embeddings(retriever)
|
||||
|
||||
rag_prompt = PromptTemplate(
|
||||
prompt="""Synthesize a comprehensive answer from the following text for the given question.
|
||||
Provide a clear and concise response that summarizes the key points and information presented in the text.
|
||||
Your answer should be in your own words and be no longer than 50 words.
|
||||
\n\n Related text: {join(documents)} \n\n Question: {query} \n\n Answer:""",
|
||||
output_parser=AnswerParser(),
|
||||
)
|
||||
|
||||
prompt_node = PromptNode(
|
||||
model_name_or_path="gpt-3.5-turbo",
|
||||
api_key=OPENAI_API_KEY,
|
||||
default_prompt_template=rag_prompt,
|
||||
)
|
||||
|
||||
pipe = Pipeline()
|
||||
pipe.add_node(component=retriever, name="retriever", inputs=["Query"])
|
||||
pipe.add_node(component=prompt_node, name="prompt_node", inputs=["retriever"])
|
||||
|
||||
output = pipe.run(query="What does Rhodes Statue look like?")
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
|
||||
<summary>Haystack 2.x</summary>
|
||||
|
||||
```python
|
||||
from datasets import load_dataset
|
||||
|
||||
from haystack import Document, Pipeline
|
||||
from haystack.document_stores.in_memory import InMemoryDocumentStore
|
||||
from haystack.components.builders import PromptBuilder
|
||||
from haystack.components.generators import OpenAIGenerator
|
||||
from haystack.components.embedders import SentenceTransformersDocumentEmbedder
|
||||
from haystack.components.embedders import SentenceTransformersTextEmbedder
|
||||
from haystack.components.retrievers import InMemoryEmbeddingRetriever
|
||||
|
||||
document_store = InMemoryDocumentStore()
|
||||
dataset = load_dataset("bilgeyucel/seven-wonders", split="train")
|
||||
embedder = SentenceTransformersDocumentEmbedder(
|
||||
"sentence-transformers/all-MiniLM-L6-v2",
|
||||
)
|
||||
embedder.warm_up()
|
||||
output = embedder.run([Document(**ds) for ds in dataset])
|
||||
document_store.write_documents(output.get("documents"))
|
||||
|
||||
template = """
|
||||
Given the following information, answer the question.
|
||||
|
||||
Context:
|
||||
{% for document in documents %}
|
||||
{{ document.content }}
|
||||
{% endfor %}
|
||||
|
||||
Question: {{question}}
|
||||
Answer:
|
||||
"""
|
||||
prompt_builder = PromptBuilder(template=template)
|
||||
|
||||
retriever = InMemoryEmbeddingRetriever(document_store=document_store, top_k=2)
|
||||
generator = OpenAIGenerator(model="gpt-3.5-turbo")
|
||||
query_embedder = SentenceTransformersTextEmbedder(
|
||||
model="sentence-transformers/all-MiniLM-L6-v2",
|
||||
)
|
||||
|
||||
basic_rag_pipeline = Pipeline()
|
||||
basic_rag_pipeline.add_component("text_embedder", query_embedder)
|
||||
basic_rag_pipeline.add_component("retriever", retriever)
|
||||
basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
|
||||
basic_rag_pipeline.add_component("llm", generator)
|
||||
|
||||
basic_rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
|
||||
basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
|
||||
basic_rag_pipeline.connect("prompt_builder", "llm")
|
||||
|
||||
query = "What does Rhodes Statue look like?"
|
||||
output = basic_rag_pipeline.run(
|
||||
{"text_embedder": {"text": query}, "prompt_builder": {"question": query}},
|
||||
)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## Documentation and Tutorials for Haystack 1.x
|
||||
|
||||
You can access old tutorials in the [GitHub history](https://github.com/deepset-ai/haystack-tutorials/tree/5917718cbfbb61410aab4121ee6fe754040a5dc7) and download the Haystack 1.x documentation as a [ZIP file](https://core-engineering.s3.eu-central-1.amazonaws.com/public/docs/haystack-v1-docs.zip).
|
||||
|
||||
The ZIP file contains documentation for all minor releases from version 1.0 to 1.26.
|
||||
|
||||
To download documentation for a specific release, replace the version number in the following URL: `https://core-engineering.s3.eu-central-1.amazonaws.com/public/docs/v1.26.zip`.
|
||||
@@ -0,0 +1,76 @@
|
||||
---
|
||||
title: "Telemetry"
|
||||
id: telemetry
|
||||
slug: "/telemetry"
|
||||
description: "Haystack relies on anonymous usage statistics to continuously improve. That's why some basic information, like the type of Document Store used, is shared automatically."
|
||||
---
|
||||
|
||||
# Telemetry
|
||||
|
||||
Haystack relies on anonymous usage statistics to continuously improve. That's why some basic information, like the type of Document Store used, is shared automatically.
|
||||
|
||||
## What Information Is Shared?
|
||||
|
||||
Telemetry in Haystack comprises anonymous usage statistics of base components, such as `DocumentStore`, `Retriever`, `Reader`, or any other pipeline component. We receive an event every time these components are initialized. This way, we know which components are most relevant to our community. For the same reason, an event is also sent when one of the tutorials is executed.
|
||||
|
||||
Each event contains an anonymous, randomly generated user ID (`uuid`) and a collection of properties about your execution environment. They **never ** contain properties that can be used to identify you, such as:
|
||||
|
||||
- IP addresses
|
||||
- Hostnames
|
||||
- File paths
|
||||
- Queries
|
||||
- Document contents
|
||||
|
||||
By taking the above steps, we ensure that only anonymized data is transmitted to our telemetry server.
|
||||
|
||||
Here is an exemplary event that is sent when tutorial 1 is executed by running `Tutorial1_Basic_QA_Pipeline.py`:
|
||||
|
||||
```json
|
||||
{
|
||||
"event": "tutorial 1 executed",
|
||||
"distinct_id": "9baab867-3bc8-438c-9974-a192c9d53cd1",
|
||||
"properties": {
|
||||
"os_family": "Darwin",
|
||||
"os_machine": "arm64",
|
||||
"os_version": "21.3.0",
|
||||
"haystack_version": "1.0.0",
|
||||
"python_version": "3.9.6",
|
||||
"torch_version": "1.9.0",
|
||||
"transformers_version": "4.13.0",
|
||||
"execution_env": "script",
|
||||
"n_gpu": 0,
|
||||
},
|
||||
}
|
||||
```
|
||||
|
||||
Our telemetry code can be directly inspected on [GitHub](https://github.com/deepset-ai/haystack/blob/5d66d040cc303ab49225587cd61290f1987a5d1f/haystack/telemetry/_telemetry.py).
|
||||
|
||||
## How Does Telemetry Help?
|
||||
|
||||
Thanks to telemetry, we can understand the needs of the community: _"What pipeline nodes are most popular?", "Should we focus on supporting one specific Document Store?", "How many people use Haystack on Windows?"_ are some of the questions telemetry helps us answer. Metadata about the operating system and installed dependencies allows us to quickly identify and address issues caused by specific setups.
|
||||
|
||||
In short, by sharing this information, you enable us to continuously improve Haystack for everyone.
|
||||
|
||||
## How Can I Opt Out?
|
||||
|
||||
You can disable telemetry with one of the following methods:
|
||||
|
||||
### Through an Environment Variable
|
||||
|
||||
You can disable telemetry by setting the environment variable `HAYSTACK_TELEMETRY_ENABLED` to `"False"` .
|
||||
|
||||
### Using a Bash Shell
|
||||
|
||||
If you are using a bash shell, add the following line to the file `~/.bashrc` to disable telemetry: `export HAYSTACK_TELEMETRY_ENABLED=False`.
|
||||
|
||||
### Using zsh
|
||||
|
||||
If you are using zsh as your shell, for example, on macOS, add the following line to the file `~/.zshrc`: `export HAYSTACK_TELEMETRY_ENABLED=False`.
|
||||
|
||||
### On Windows
|
||||
|
||||
To disable telemetry on Windows, set a user-level environment variable by running this command in the standard command prompt: `setx HAYSTACK_TELEMETRY_ENABLED "False"`.
|
||||
|
||||
Alternatively, run the following command in Windows PowerShell: `[Environment]::SetEnvironmentVariable("HAYSTACK_TELEMETRY_ENABLED","False","User")`.
|
||||
|
||||
You might need to restart the operating system for the command to take effect.
|
||||
Reference in New Issue
Block a user