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+---
+layout: integration
+name: Ray
+description: Run and scale Haystack Pipelines with Ray in distributed manner
+authors:
+  - name: Sergey Bondarenco
+    socials:
+      github: prosto
+pypi: https://pypi.org/project/ray-haystack/
+repo: https://github.com/prosto/ray-haystack
+type: Distributed Computing
+report_issue: https://github.com/prosto/ray-haystack/issues
+logo: /logos/ray.png
+version: Haystack 2.0
+toc: true
+---
+
+### Table of Contents
+
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Start with an example](#start-with-an-example)
+  - [Read pipeline events](#read-pipeline-events)
+  - [Component Serialization](#component-serialization)
+  - [DocumentStore with Ray](#documentstore-with-ray)
+  - [RayPipeline Settings](#raypipeline-settings)
+  - [Middleware](#middleware)
+- [Resources](#resources)
+- [License](#license)
+
+## Overview
+
+`ray-haystack` is a python package which allows running [Haystack pipelines](https://docs.haystack.deepset.ai/docs/pipelines) on [Ray](https://docs.ray.io/en/latest/ray-overview/index.html)
+in a distributed manner. The package provides the same API to build and run Haystack pipelines, but under the hood, components are being distributed to remote nodes for execution using Ray primitives.
+Specifically, [Ray Actor](https://docs.ray.io/en/latest/ray-core/actors.html) is created for each component in a pipeline to `run` its logic.
+
+The purpose of this library is to showcase the ability to run Haystack in a distributed setup with Ray featuring its options to configure the payload, e.g:
+
+- Control with [resources](https://docs.ray.io/en/latest/ray-core/scheduling/resources.html) how much CPU/GPU is needed for a component to run (per each component if needed)
+- Manage [environment dependencies](https://docs.ray.io/en/latest/ray-core/handling-dependencies.html) for components to run on dedicated machines.
+- Run pipeline on Kubernetes using [KubeRay](https://docs.ray.io/en/latest/cluster/kubernetes/getting-started.html)
+
+Most of the time, you will run Haystack pipelines on your local environment; even in production, you will want to run the pipeline on a single node if the goal is to return a response quickly to the user without the overhead you would usually get with a distributed setup. However, in the case of long running and complex RAG pipelines distributed way might help:
+
+- Not every component needs GPU, most will use some external API calls. With Ray it should be possible to assign respective resource requirements (CPU, RAM) per component execution needs.
+- Some components might take longer to run, so ideally, if there is an option to parallelize component execution, it would decrease pipeline run time.
+- With asynchronous execution, it should be possible to interact with different component execution stages (e.g. fire an event before and after the component starts).
+
+`ray-haystack` provides a custom implementation for pipeline execution logic with the goal to stay **as compliant as possible with native Haystack implementation**.
+In most cases, you should expect the same results (outputs) from pipeline runs. On top of that, the package will parallelize component runs where possible.
+Components with no active dependencies can be scheduled without waiting for currently running components.
+
+![Ray Pipeline Parallel](https://raw.githubusercontent.com/deepset-ai/haystack-integrations/main/images/ray-pipeline-concurrent.gif)
+
+## Installation
+
+`ray-haystack` can be installed as any other Python library, using pip:
+
+```shell
+pip install ray-haystack
+```
+
+The package should work with python version 3.8 and onwards. If you plan to use `ray-haystack` with an existing Ray cluster, make sure you align python and `ray` versions with those running in the cluster.
+
+> **Note**
+> The `ray-haystack` package will install both `haystack-ai` and `ray` as transitive dependencies. The minimum supported version of haystack is `2.6.0`.
+
+If you would like to see [Ray dashboard](https://docs.ray.io/en/latest/ray-observability/getting-started.html) when starting Ray cluster locally, install Ray as follows:
+
+```shell
+pip install -U "ray[default]"
+pip install ray-haystack
+```
+
+While pipeline is running locally, access the dashboard in the browser at [http://localhost:8265](http://localhost:8265).
+
+## Usage
+
+### Start with an example
+
+Once `ray-haystack` is installed, let's demonstrate how it works by running a simple example.
+
+We will build a pipeline that fetches RSS news headlines from the list of given URLs and converts each headline to a `Document` with content equal to the headline title. We then ask LLM (`OpenAIGenerator`) to create a news summary from the list of converted Documents, given a prompt `template`.
+
+```python
+import io
+import os
+from typing import List, Optional
+from xml.etree.ElementTree import parse as parse_xml
+
+import ray # Import ray
+from haystack import Document, component
+from haystack.components.builders import PromptBuilder
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.generators import OpenAIGenerator
+from haystack.components.joiners import DocumentJoiner
+from haystack.dataclasses import ByteStream
+
+from ray_haystack import RayPipeline # Import RayPipeline (instead of `from haystack import Pipeline`)
+
+# Please introduce your OpenAI Key here
+os.environ["OPENAI_API_KEY"] = "You OpenAI Key"
+
+@component
+class XmlConverter:
+    """
+    Custom component which parses given RSS feed (from ByteStream) and extracts values by a
+    given XPath, e.g. ".//channel/item/title" will find "title" for each RSS feed item.
+    A Document is created for each extracted title. The `category` attribute can be used as
+    an additional metadata field.
+    """
+
+    def __init__(self, xpath: str = ".//channel/item/title", category: Optional[str] = None):
+        self.xpath = xpath
+        self.category = category
+
+    @component.output_types(documents=List[Document])
+    def run(self, sources: List[ByteStream]):
+        documents: List[Document] = []
+        for source in sources:
+            xml_content = io.StringIO(source.to_string())
+            documents.extend(
+                Document(content=elem.text, meta={"category": self.category})
+                for elem in parse_xml(xml_content).findall(self.xpath)  # noqa: S314
+                if elem.text
+            )
+        return {"documents": documents}
+
+template = """
+Given news headlines below provide a summary of what is happening in the world right now in a couple of sentences.
+You will be given headline titles in the following format: "<headline category>: <headline title>".
+When creating summary pay attention to common news headlines as those could be most insightful.
+
+HEADLINES:
+{% for document in documents %}
+    {{ document.meta["category"] }}: {{ document.content }}
+{% endfor %}
+
+SUMMARY:
+"""
+
+# Create instance of Ray pipeline
+pipeline = RayPipeline()
+
+pipeline.add_component("tech-news-fetcher", LinkContentFetcher())
+pipeline.add_component("business-news-fetcher", LinkContentFetcher())
+pipeline.add_component("politics-news-fetcher", LinkContentFetcher())
+pipeline.add_component("tech-xml-converter", XmlConverter(category="tech"))
+pipeline.add_component("business-xml-converter", XmlConverter(category="business"))
+pipeline.add_component("politics-xml-converter", XmlConverter(category="politics"))
+pipeline.add_component("document_joiner", DocumentJoiner(sort_by_score=False))
+pipeline.add_component("prompt_builder", PromptBuilder(template=template))
+pipeline.add_component("generator", OpenAIGenerator())  # "gpt-4o-mini" is the default model
+
+pipeline.connect("tech-news-fetcher", "tech-xml-converter.sources")
+pipeline.connect("business-news-fetcher", "business-xml-converter.sources")
+pipeline.connect("politics-news-fetcher", "politics-xml-converter.sources")
+pipeline.connect("tech-xml-converter", "document_joiner")
+pipeline.connect("business-xml-converter", "document_joiner")
+pipeline.connect("politics-xml-converter", "document_joiner")
+pipeline.connect("document_joiner", "prompt_builder")
+pipeline.connect("prompt_builder", "generator.prompt")
+
+# Draw pipeline and save it to `pipe.png`
+# pipeline.draw("pipe.png")
+
+# Start local Ray cluster
+ray.init()
+
+# Prepare pipeline inputs by specifying RSS urls for each fetcher
+pipeline_inputs = {
+    "tech-news-fetcher": {
+        "urls": [
+            "https://www.theverge.com/rss/frontpage/",
+            "https://techcrunch.com/feed",
+            "https://cnet.com/rss/news",
+            "https://wired.com/feed/rss",
+        ]
+    },
+    "business-news-fetcher": {
+        "urls": [
+            "https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10001147",
+            "https://www.business-standard.com/rss/home_page_top_stories.rss",
+            "https://feeds.a.dj.com/rss/WSJcomUSBusiness.xml",
+        ]
+    },
+    "politics-news-fetcher": {
+        "urls": [
+            "https://search.cnbc.com/rs/search/combinedcms/view.xml?partnerId=wrss01&id=10000113",
+            "https://rss.nytimes.com/services/xml/rss/nyt/Politics.xml",
+        ]
+    },
+}
+
+# Run pipeline with inputs
+result = pipeline.run(pipeline_inputs)
+
+# Print response from LLM
+print("RESULT: ", result["generator"]["replies"][0])
+```
+
+Key takeways from the example:
+
+- import `ray` module
+- import `RayPipeline` (from `ray_haystack`) instead of `Pipeline` class from `haystack`
+- before running the pipeline, start [local ray cluster](https://docs.ray.io/en/latest/ray-core/starting-ray.html#start-ray-init) with explicit `ray.init()` call
+
+Under the hood, `RyaPipeline` creates actors for each component in the pipeline and runs it in a distributed manner until no components are left to run. By default, `RyaPipeline` blocks until the pipeline finishes its execution.
+
+### Read pipeline events
+
+In some cases, you would want to react asynchronously to particular pipeline execution points:
+
+- when pipeline starts
+- before component runs
+- after component finishes
+- after pipeline finishes
+
+Internally, `RayPipeline` creates an instance of [Ray Queue](https://docs.ray.io/en/latest/ray-core/api/doc/ray.util.queue.Queue.html) where such events are stored and can be consumed.
+
+Except for the standard `run` method, `RayPipeline` provides a method called `run_nowait`, which returns pipeline execution results without blocking current logic. We can use `run_nowait` to iterate over pipeline events, e.g.
+
+```python
+result = pipeline.run_nowait(pipeline_inputs)
+
+for pipeline_event in result.pipeline_events_sync():
+    print(
+        f"\n>>> [{pipeline_event.time}] Source: {pipeline_event.source} | Type: {pipeline_event.type} | Data={pipeline_event.data}"
+    )
+```
+
+### Component Serialization
+
+If you run native Haystack pipeline locally, the components remain in the same python process, and there is no reason to care about the distributed setup. When RayPipeline runs in a distributed manner, it should be able to [serialize](https://docs.ray.io/en/latest/ray-core/objects/serialization.html) components before they end up in a remote task or actor.
+
+The `ray-haystack` package relies on Haystack's standard `to_dict` and `from_dict` methods to serialize and deserialize components, respectively.
+Please refer to the main [documentation](https://github.com/prosto/ray-haystack?tab=readme-ov-file#component-serialization) for a detailed explanation and features that handle edge cases.
+
+### DocumentStore with Ray
+
+When you use [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) or any DocumentStore which runs in memory with `RayPipeline`, you will stumble upon an apparent issue: in a distributed environment, these document stores running in memory will fail to operate as components that reference the store will not point to a single instance but rather a copy of it.
+
+`ray-haystack` package provides a wrapper around `InMemoryDocumentStore` by implementing a proxy pattern so that only a single instance of `InMemoryDocumentStore` across Ray cluster is present. With that, components can share a single store. Use `RayInMemoryDocumentStore`, `RayInMemoryEmbeddingRetriever` or `RayInMemoryBM25Retriever` in case you need in-memory document store in your Ray pipelines.
+
+### RayPipeline Settings
+
+When an actor is created in Ray, we can control its behavior by providing certain [settings](https://docs.ray.io/en/latest/ray-core/api/doc/ray.actor.ActorClass.options.html).
+
+`ray-haystack` provides means to configure pipeline Actors with the help of `RayPipelineSettings` dictionary:
+
+```python
+from typing import Any, Dict
+
+from ray_haystack import RayPipeline, RayPipelineSettings
+
+settings: RayPipelineSettings = {
+    "common": {
+        "actor_options": {
+            "namespace": "haystack",  # common namespace name for all actors
+        }
+    },
+    "components": {
+        "per_component": {
+            "generator": {
+                "actor_options": {
+                    "num_cpus": 2,  # component specific CPU resource requirement
+                }
+            }
+        }
+    },
+}
+
+# Option 1 - Pass settings through pipeline's metadata
+pipeline = RayPipeline(metadata={"ray": settings})
+
+pipeline_inputs: Dict[str, Any] = {}
+
+# Option 2 - Pass settings when in the `run` method
+pipeline.run(pipeline_inputs, ray_settings=settings)
+```
+
+### Middleware
+
+Sometimes it might be useful to let custom logic run before and after component actor runs the component.
+
+It is possible to build custom middleware:
+
+```python
+from typing import Any, Literal
+
+import ray
+from haystack.components.fetchers import LinkContentFetcher
+
+from ray_haystack import RayPipeline, RayPipelineSettings
+from ray_haystack.middleware import ComponentMiddleware, ComponentMiddlewareContext
+from ray_haystack.serialization import worker_asset
+
+ray.init()
+
+@worker_asset
+class TraceMiddleware(ComponentMiddleware):
+    def __init__(self, capture: Literal["input", "output", "input_and_output"] = "input_and_output"):
+        self.capture = capture
+
+    def __call__(self, component_input, ctx: ComponentMiddlewareContext) -> Any:
+        print(f"Tracer: Before running component '{ctx['component_name']}' with inputs: '{component_input}'")
+
+        outputs = self.next(component_input, ctx)
+
+        print(f"Tracer: After running component '{ctx['component_name']}' with outputs: '{outputs}'")
+
+        return outputs
+
+pipeline = RayPipeline()
+pipeline.add_component("cocktail_fetcher", LinkContentFetcher())
+
+settings: RayPipelineSettings = {
+    "components": {
+        "per_component": {
+            # Middleware applies only to "cocktail_fetcher" component
+            "cocktail_fetcher": {
+                "middleware": {
+                    "trace": {"type": "__main__.TraceMiddleware"},
+                },
+            },
+        }
+    },
+}
+
+response = pipeline.run(
+    {
+        "cocktail_fetcher": {"urls": ["https://www.thecocktaildb.com/api/json/v1/1/random.php"]},
+    },
+    ray_settings=settings,
+)
+```
+
+## Resources
+
+- The full documentation is available in the [repository](https://github.com/prosto/ray-haystack/tree/main)
+- Explore more advanced examples:
+  - [Trace Haystack Pipelines in Browser](https://github.com/prosto/ray-haystack/blob/main/examples/pipeline_watch/README.md)
+  - [Running Haystack Pipeline on Kubernetes](https://github.com/prosto/ray-haystack/blob/main/examples/pipeline_kubernetes/README.md)
+  - [Run pipeline with detached component actors](https://github.com/prosto/ray-haystack/tree/main/examples/pipeline_detached_actors)
+- [Learn more about Ray](https://docs.ray.io/en/latest/ray-overview/getting-started.html)
+
+## License
+
+`ray-haystack` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
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