{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "xvi_H9ugrYr2" }, "source": [ "# Running Haystack Pipelines in Asynchronous Environments\n", "\n", "_Notebook by [Madeeswaran Kannan](https://www.linkedin.com/in/m-kannan) & [Mathis Lucka](https://www.linkedin.com/in/mathis-lucka-685037201/)_\n", "\n", "In this notebook, you'll learn how to use the `AsyncPipeline` and async-enabled components to build and execute a Haystack pipeline in an asynchronous environment. It's based on [this short Haystack tutorial](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline), so it would be a good idea to familiarize yourself with it before we begin. A further prerequisite is working knowledge of cooperative scheduling and [async programming in Python](https://docs.python.org/3/library/asyncio.html).\n", "\n", "## Motivation\n", "\n", "By default, the `Pipeline` class in `haystack` is a regular Python object class that exposes non-`async` methods to add/connect components and execute the pipeline logic. Currently, it *can* be used in async environments, but it's not optimal to do so since it executes its logic in a '[blocking](https://en.wikipedia.org/wiki/Blocking_(computing))' fashion, i.e., once the `Pipeline.run` method is invoked, it must run to completion and return the outputs before the next statement of code can be executed1. In a typical async environment, this prevents active async event loop from scheduling other `async` coroutines, thereby reducing throughput. To mitigate this bottleneck, we introduce the concept of async-enabled Haystack components and an `AsyncPipeline` class that cooperatively schedules the execution of both async and non-async components.\n", "\n", "\n", "1 - This is a simplification as the Python runtime can potentially schedule another thread, but it's a detail that we can ignore in this case.\n", "\n", "### AsyncPipeline Benefits\n", "- Execute components concurrently to speed-up pipeline execution.\n", "- Execute components step by step to debug your pipelines.\n", "- Increase throughput in async environments, e.g. when executing pipelines behind a FastAPI-endpoint.\n", "- Allow individual components to opt into `async` support.\n", " - Not all components benefit from being async-enabled - I/O-bound components are the most suitable candidates.\n", "- Provide a backward-compatible way to execute Haystack pipelines containing both async and non-async components.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "ofGFRy0hrYr3" }, "source": [ "Let's now go ahead and see what it takes to add async support to the original tutorial, starting with installing Haystack, and the requisite dependencies.\n", "\n", "## Development Environment\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:12:29.606493Z", "start_time": "2025-03-03T15:12:25.813915Z" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "s-s8odIUrYr4", "outputId": "acc26c93-6328-4886-b23f-93e8e4330016" }, "outputs": [], "source": [ "%%bash\n", "\n", "pip install -U haystack-ai -q\n", "pip install datasets -q\n", "pip install sentence-transformers-haystack -q\n", "pip install nest_asyncio -q" ] }, { "cell_type": "markdown", "metadata": { "id": "S7ElROeVrYr4" }, "source": [ "Provide an [OpenAI API key](https://platform.openai.com/api-keys) to ensure that LLM generator can query the OpenAI API." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:12:34.615394Z", "start_time": "2025-03-03T15:12:34.613062Z" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "DwEK6P0nrYr4", "outputId": "c80267a3-71f3-472f-d499-5af5684dcced" }, "outputs": [], "source": [ "import os\n", "from getpass import getpass\n", "\n", "if \"OPENAI_API_KEY\" not in os.environ:\n", " os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter OpenAI API key:\")\n", "\n", "# If you're running this notebook on Google Colab, you can do the following instead:\n", "#\n", "# from google.colab import userdata\n", "# if \"OPENAI_API_KEY\" not in os.environ:\n", "# os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:23:16.397131Z", "start_time": "2025-03-03T15:23:16.389062Z" } }, "outputs": [], "source": [ "# The IPython environment is already running an event-loop.\n", "# We will later use a method that tries to create another event-loop which would fail without this snippet.\n", "import nest_asyncio\n", "nest_asyncio.apply()" ] }, { "cell_type": "markdown", "metadata": { "id": "91DT3OVArYr4" }, "source": [ "## Creating an AsyncPipeline\n", "\n", "### Fetching and Indexing Documents\n", "Initialize a `DocumentStore` to index your documents." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:23:21.608201Z", "start_time": "2025-03-03T15:23:21.602501Z" }, "id": "1pwhqTvYrYr5" }, "outputs": [], "source": [ "from haystack.document_stores.in_memory import InMemoryDocumentStore\n", "\n", "document_store = InMemoryDocumentStore()" ] }, { "cell_type": "markdown", "metadata": { "id": "14gSbwJDrYr5" }, "source": [ "Fetch the data and convert it into Haystack `Document`s." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:23:26.533030Z", "start_time": "2025-03-03T15:23:23.178688Z" }, "colab": { "base_uri": "https://localhost:8080/", "height": 217, "referenced_widgets": [ "3a0fbeefc2fc485fb472c5b947bad670", "804d0c71f14e45ff9b0f0616d1722cf9", "0464c4fd609344e5b8e73e0456094eef", "10a464b5650148e2a2983339d12ba0b0", "d807b817f4604655bfc2e671e88ac312", "f3a424d0e7ab47f8ae8f27fa82d1df62", "2fe32d4da11442beaacc36aa2413daa4", "9cc29a4e75a44df99d39a85e619440cc", "daea1219b6e3450ab6bbc2716c5504ef", "80fa048faa6242c08e469f7fc8688a9c", "bf63b15dd5044a08b34a65786bc619de", "ae167d913e414a6693273bcb8149e71d", "826c8d875def49e3a01f010311d00675", "3f004271fa8542218cac085f45280823", "d8edbb93166048f6897e81508add3497", "0467493fe542422f809f708b720a3847", "1e023a0d9cd9485aaf7452cd48015838", "88eb5478e1ac4ddf89a7bbc1f4f4909f", "5266118b0b5a4b07a73d62c9601e4ff5", "95b2ad3502b240d084154815f7be6075", "d0f0c988eb33410eb5e1b2828be76a50", "b3185c43f5ed4ca087bf5951176697cf", "fbea8f0ed3544ece9022b3f0a7f3f132", "cd5861fdcc3a4239a2273bbbffb40f5b", "09a6436a89d64945abe327de420ba78a", "5d9e0516562b42fa935c8e53436da356", "160efbe0a0c44c3884a2ed98fc9f306e", "5137be13717344ea8d7900d9102a9fd9", "baf8a05125334f478dbf733ba2041527", "95be52a6563a4b6cb24befee92d2b0a4", "5ee51314ab944bbf96e3ab2942d7367d", "2305f766ec9d453dbc4d3360b9a314f7", "58c7444931fa415da28c6a02c84a4811" ] }, "id": "kNQCLtA8rYr5", "outputId": "687146f2-cb10-4735-ac61-0e14fc5a18cf" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "from haystack import Document\n", "\n", "dataset = load_dataset(\"bilgeyucel/seven-wonders\", split=\"train\")\n", "docs = [Document(content=doc[\"content\"], meta=doc[\"meta\"]) for doc in dataset]" ] }, { "cell_type": "markdown", "metadata": { "id": "jMHigLOjrYr5" }, "source": [ "To store your data in the `DocumentStore` with embeddings, initialize a `SentenceTransformersDocumentEmbedder`.\n", "Then, run the embedder to calculate the embeddings of the docs and write the documents to the document store." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:23:29.093689Z", "start_time": "2025-03-03T15:23:28.138876Z" }, "colab": { "base_uri": "https://localhost:8080/", "height": 418, "referenced_widgets": [ "d990783fa5ab4f9b86867c499a744ece", "1224d075f6034acea2d98fbe15c941d1", "57bc23e84c4248baa05a4eaeab36c47e", "f061d77bd7064babbb22a2561e63b933", "7fec8cfba21c40b39fc462497cd2e4b9", "a193c506e9da457b9eb5f967e0957483", "178bd7188f2f4626af8034d5930fdf9f", "cb7fea83d4bd4d17a76307cc0ebb3031", "b7e9a93948be4fff8a1e61d477b39413", "cc96828a01744575bf0a37b3e7238b11", "13dd007895c2444abb1e7c504ad4d7ee", "26d4d5ead3eb4c4db5a63597abd1ea6b", "35674ad2a25e4d299d0dcf03a4787eb6", "40423d3de48d4a5d9371268abc471497", "06c2093346f441abb395a0887065f138", "c7efcff6a1834d42be08b4c35cc6d6b6", "4ceb6634326d4b0694c2b57b2b9c64a0", "41123ed05efd4c3ab7eca81a069fb6e0", "ac0bc599463d4aec829b6225ee570165", "e29878707745405f9ed90d503144dc6d", "777b8d33801247b08aa429a36f6c2799", "dd6b184672d1445ca130f2c6c5a32a2b", "46cc3f3ff5cd4569a2ad188e956d1e67", "70334c5970164b70b0f1daa2d481795d", "79332711fdc64c45a264a6b9187bc54a", "649b4339d07d4f4ab931e332706ce806", "bd710a2e41c64a5494d5ca5550b118d2", "92c73fa1636f499fbb42e82dbe5dcd52", "668f4b9197c94fba805d5008bedaa6ee", "f3bb9cccf38e48d9ae5aae1a69525faa", "706fa5250f70461889f2244f5f0a7fbd", "69fa80129fe04199ac5df91918b7b208", "774d9d5936bb423abd88e29472862743", "d223ea7c08314f98b4c725b2b53e37ce", "4f136be4a11f4b9db53add2371014aca", "d54be5829b3e4be4ba9475076dc20c35", "8552680f8c1d41d78ff03ad81f532b1a", "2fc7e1d6fdb94268ac1a136e29fd9c85", "ba59119eef7f4e749d76bd6e57dbca05", "37b3a779836f47f9b0b697343db17c43", "1e3dd37d8f074fd991909d25439b9a93", "a51256a4e1194855b5d64aaac4755b69", "0ff66d54987c4edd8829341f1330c920", "86bfefd42f1440ab8f6dce949d5febd1", "dbd49235ff8f48f7bedc0a8b6e83e71e", "d382c0ffd9d448ae992ef2e1bef1d810", "e0dd49a7fafd4ffeb8c817dfdc597fbc", "aafef5dd4c81449494453cda83187a1d", "e9b3181c070f40bd815ca8912222d00d", "6cb3f9f89ad24d74ab49754101b4dfa0", "d43ec271842f491a97de3de8ccf0c21d", "2982207ef080499a9b7b069199bc93b9", "bdbfa6e1408b4027a369ca566d17158c", "e1f6597e56594edabd02b9ddff50f45a", "104f3aae35e44ed3985ecf02317931ce", "d6b95d7dac6d4de8a631c03073141cab", "583eef47d0aa4a6485ab070e06f627f8", "aac7d09e010741fc91925cdb4f39abf2", "966c2d37ba4b4c4789f248338fd73cc3", "7234cfdf3a9040fc986191f3ab384c0e", "201216e28af94361bf09a5a692e014c1", "b4791d9da44e4e2a9e9757f0d472579b", "8b09bd4eba3546bca2b996cac32e11c3", "b3c766d3a3db48839c24496fc5ce47a0", "0c9e12074bf841269a208c570c209e90", "a5afbce4634246069ea1a8c8f49de064", "98d5f0234090456cbd6d7c8e84a40f36", "432fa1682df240e0856c55b94e3e41de", "652fe2d899ab488ba9960563ad1ae11d", "1fb7a0cb16c742d59d02de07bd7cb8ca", "0d8a956628f34b63981ecf6502ce40cd", "0b012753db8741b09bbf65a0f70b7f3b", "6807eb9d418f4075982dd7c92e8f430a", "b5f7c85464174d6480a559c82d5d5124", "9d0c9a9af3ff4b5388aa67dab2b8f90b", "a0b72a58ec7d4dceaeda2489fa2c68a8", "74f59510b0fd4b5b8327e7959689aa83", "332bf0fb3f6742d984efb641b2a7bf1d", "2d4436c5982942d38fc14083f5c623a1", "d2d8a79415d3445bb4c7ad092f7d416a", "c7f04797fdf046ea8cbd32ae0b047b5a", "0af3c5bf546d4eff93892cc9588dd7a4", "cd76d5ff91724fbeb15b37442aef38b7", "4e7dc46654074855bedda97739d4f483", "27ade8d91ca3423ca7a9b839cbfa22d3", "0d5f27d7f09a4600a810ee4ab60f8468", "9ac9924a8f30417e9e1b09fec4019a1a", "8e9a4da917b54913be307450ec652c58", "a9849273f0c74ec782769d3c0f15a50b", "efff99d499784ef49cc5463706835614", "94f6da987fa04e109e851f1280d4ba7a", "0cbfa5faece6454d9822cf3a2c18f816", "ea7e4ea22bc042beb70aecc41594e4be", "10c8c2ccc32d441f921f1febb4ef9eb0", "1994a0bf0f284f91bbec5055c4880070", "2adbb53caea34d49803328fcc9434d64", "862e5c18782540c4ba2cbb3034026736", "c1a9ef00c4aa429e95d23676c9f98c9f", "c791f456b35449e0b65ab29d58a378d3", "e13fd935fd4b464e99281cd62814bcdd", "6d15365ce73d4d5a9150a2d999d7afaa", "82d66eb59d9747fb8c6f9876e5360eee", "9a2a0f029f044fe99debca5d9ed6b7ae", "35e25d3e2dfa41b2bd1c683e5c64e4ce", "919e7f83e8c847a09aa8b8dd32be3ac7", "908798602558404c81c75e93e799d18d", "115175b13b6040fbac5d93e545e2e41a", "0841d279641649699461eaa3946dd484", "e0d56d5c5b8b4e63a6bfad1333ab56e4", "fe7c58bc3d04419a81011589f055f03d", "8e0a34d71b824e3ea26d99b8d9e5b599", "a3e48700110a4a7bb140cbbb3e8d3747", "c83d23f5cd80425bbc7ddea4735f537b", "35d27dd0eec34f1e8a80c5e2d0aa18e3", "bc29c5e671fa4e658f21d60f175bcc8f", "e6372bee64784f7a925b8b5f5dcf2a8c", "c84f8e9cb6344af9a713519c95d157ed", "55ac29b23150410d8f7ce917a7e330cf", "8608b400a9f84d359376a3895ac3e921", "db0e123c9daa49d0ba4f34f0f351cc58", "b6dedac5aa0949aab76b44435611861f", "56dbeabe1d28434fba0fdbecc3140112", "6981cc3c217f4a468b180b96e50b5c4e", "86ecd124d7db47898f6fde85abcd122a", "a6d486fccb91418986c6a1b38499b8d2", "483c8f1d14fd40b0b488ceff5e7e1e91", "d9270e6179ab4123882abf0f3446f415", "217c522aeeb04d7dbdd0bcd8ff78c06d", "e9436efaa04f435d97b31443cec8bf57", "aa3d0ffc75bd479a81e4030813404483", "99e02dabf0df4882b449ccd0e5f86f61", "a5aea33097e2488d8d9b001e05ae4f61" ] }, "id": "l-f9RC-JrYr5", "outputId": "1342b08b-5c17-4a1a-e2b9-ec6a9aa2c477" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": "\rLoading weights: 0%| | 0/103 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from haystack import AsyncPipeline\n", "\n", "async_rag_pipeline = AsyncPipeline()\n", "# Add components to your pipeline\n", "async_rag_pipeline.add_component(\"text_embedder\", text_embedder)\n", "async_rag_pipeline.add_component(\"embedding_retriever\", embedding_retriever)\n", "async_rag_pipeline.add_component(\"bm25_retriever\", bm25_retriever)\n", "async_rag_pipeline.add_component(\"joiner\", joiner)\n", "async_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n", "async_rag_pipeline.add_component(\"llm\", generator)\n", "\n", "# Now, connect the components to each other\n", "async_rag_pipeline.connect(\"text_embedder.embedding\", \"embedding_retriever.query_embedding\")\n", "async_rag_pipeline.connect(\"bm25_retriever.documents\", \"joiner.documents\")\n", "async_rag_pipeline.connect(\"embedding_retriever.documents\", \"joiner.documents\")\n", "async_rag_pipeline.connect(\"joiner.documents\", \"prompt_builder.documents\")\n", "async_rag_pipeline.connect(\"prompt_builder.prompt\", \"llm.messages\")\n", "\n", "async_rag_pipeline.show()\n", "# You can see from the visual pipeline representation that embedding retriever and bm25 retriever do not depend on each other; they could run concurrently" ] }, { "cell_type": "markdown", "metadata": { "id": "msBcivLZrYr6" }, "source": [ "Now, we create a coroutine that queries the pipeline with a question.\n", "\n", "We use the `run_async_generator` method to execute the `AsyncPipeline`. `run_async_generator` returns an `AsyncIterator` that we need to iterate over to make progress in the pipeline's execution.\n", "\n", "Essentially, this allows us to step through the pipeline execution component by component, which is useful for debugging a pipeline or when you want to run custom logic upon any component's completion.\n", "\n", "The `AsyncPipeline` also exposes:\n", " \n", "- a `run_async` method that executes the full pipeline before returning the final outputs\n", "- a `run` method that can be called from non-async environments but still executes components concurrently; the `run` method is a drop-in replacement for `Pipeline.run`\n", "\n", "\n", "We iterate over the `AsyncIterator` and print intermediate outputs from the retrievers and the joiner.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T14:48:42.342617Z", "start_time": "2025-03-03T14:48:39.908830Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Outputs from `bm25_retriever`.\n", "Score: 13.579097993994793\n", "Content: [21] However, the gardens were said to still exist at the time that later writers described them, and some of these accounts are regarded as deriving from people who had visited Babylon.[2] Herodotus, who describes Babylon in his Histories, does not mention the Hanging Gardens,[22] although it could be that the gardens were not yet well known to the Greeks at the time of his visit.[2]\n", "To date, no archaeological evidence has been found at Babylon for the Hanging Gardens.[6] It is possible that ev...\n", "------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": "\rBatches: 0%| | 0/1 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from haystack import component\n", "\n", "@component\n", "class WaitingComponent:\n", " \"\"\"\n", " A test component that simulates async operations by waiting for a specified time\n", " before returning a message.\n", "\n", " ### Usage example\n", " ```python\n", " test_comp = AsyncTestComponent(name=\"TestComponent\", wait_time=2)\n", "\n", " # Sync usage\n", " result = test_comp.run(user_msg=\"Hello\")\n", " print(result[\"message\"]) # prints after 2 seconds\n", "\n", " # Async usage\n", " result = await test_comp.run_async(user_msg=\"Hello\")\n", " print(result[\"message\"]) # prints after 2 seconds\n", " ```\n", " \"\"\"\n", "\n", " def __init__(self, name: str, wait_time: int = 1):\n", " self.name = name\n", " self.wait_time = wait_time\n", "\n", " @component.output_types(message=str)\n", " def run(self, user_msg: str) -> dict:\n", " \"\"\"\n", " Synchronous method that waits for the specified time and returns a message.\n", "\n", " :param user_msg: Input message from the user (unused in output but required for example)\n", " :return: Dictionary containing the output message\n", " \"\"\"\n", " print(f\"Component {self.name} starts running...\")\n", " time.sleep(self.wait_time)\n", " print(f\"Component {self.name} is done!\")\n", " return {\"message\": f\"Message from {self.name}\"}\n", "\n", "\n", "wait_1 = WaitingComponent(name=\"wait_1\", wait_time=1)\n", "wait_2 = WaitingComponent(name=\"wait_2\", wait_time=2)\n", "wait_3 = WaitingComponent(name=\"wait_3\", wait_time=3)\n", "wait_4 = WaitingComponent(name=\"wait_4\", wait_time=4)\n", "wait_5 = WaitingComponent(name=\"wait_5\", wait_time=5)\n", "\n", "pp = AsyncPipeline()\n", "\n", "\n", "pp.add_component(\"wait_1\", wait_1)\n", "pp.add_component(\"wait_2\", wait_2)\n", "pp.add_component(\"wait_3\", wait_3)\n", "pp.add_component(\"wait_4\", wait_4)\n", "pp.add_component(\"wait_5\", wait_5)\n", "\n", "pp.connect(\"wait_1\", \"wait_2\")\n", "pp.connect(\"wait_3\", \"wait_4\")\n", "\n", "pp.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that this pipeline has 3 parallel branches. Let's run this pipeline to see how it executes components concurrently." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2025-03-03T15:49:28.647997Z", "start_time": "2025-03-03T15:49:21.629549Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Component wait_1 starts running...Component wait_3 starts running...\n", "\n", "Component wait_5 starts running...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Component wait_1 is done!\n", "{'wait_1': {'message': 'Message from wait_1'}}\n", "Component wait_2 starts running...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Component wait_3 is done!\n", "{'wait_3': {'message': 'Message from wait_3'}}\n", "Component wait_4 starts running...\n", "Component wait_2 is done!\n", "{'wait_2': {'message': 'Message from wait_2'}}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Component wait_5 is done!\n", "{'wait_5': {'message': 'Message from wait_5'}}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Component wait_4 is done!\n", "{'wait_4': {'message': 'Message from wait_4'}}\n" ] } ], "source": [ "async for output in pp.run_async_generator({\"user_msg\": \"Hello\"}, include_outputs_from=[\"wait_1\", \"wait_2\", \"wait_3\", \"wait_4\", \"wait_10\"]):\n", " if len(output.keys()) == 1:\n", " print(output)" ] }, { "cell_type": "markdown", "metadata": { "id": "BL62x-KKrYr6" }, "source": [ "## Custom Asynchronous Components\n", "\n", "Individual components can opt into async by implementing a `run_async` coroutine that has the same signature, i.e., input parameters and outputs as the `run` method. This constraint is placed to ensure that pipeline connections are the same irrespective of whether a component supports async execution, allowing for plug-n-play backward compatibility with existing pipelines.\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "OnNtMXI6rYr6" }, "outputs": [], "source": [ "from typing import Dict, Any\n", "from haystack import component\n", "\n", "@component\n", "class MyCustomComponent:\n", " def __init__(self, my_custom_param: str):\n", " self.my_custom_param = my_custom_param\n", "\n", " @component.output_types(original=str, concatenated=str)\n", " def run(self, input: str) -> Dict[str, Any]:\n", " return {\n", " \"original\": input,\n", " \"concatenated\": input + self.my_custom_param\n", " }\n", "\n", " async def do_io_bound_op(self, input: str) -> str:\n", " # Do some IO-bound operation here\n", " return input + self.my_custom_param\n", "\n", " @component.output_types(original=str, concatenated=str)\n", " async def run_async(self, input: str) -> Dict[str, Any]:\n", " return {\n", " \"original\": input,\n", " \"concatenated\": await self.do_io_bound_op(input)\n", " }" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0464c4fd609344e5b8e73e0456094eef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": 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