diff --git a/.github/workflows/run_tutorials.yml b/.github/workflows/run_tutorials.yml index 44a06b31..c7e71e10 100644 --- a/.github/workflows/run_tutorials.yml +++ b/.github/workflows/run_tutorials.yml @@ -67,6 +67,7 @@ jobs: "tutorials/23_Answering_Multihop_Questions_with_Agents.ipynb" "tutorials/24_Building_Chat_App.ipynb" "tutorials/25_Customizing_Agent.ipynb" + "tutorials/27_First_RAG_Pipeline.ipynb" ) for changed_file in ${{ steps.files.outputs.all_changed_files }}; do if [[ $changed_file == *".ipynb" ]]; then diff --git a/README.md b/README.md index 7b991710..6b99c002 100644 --- a/README.md +++ b/README.md @@ -44,3 +44,4 @@ To contribute to the tutorials, please check out our [Contributing Guidelines](. | [Building a Conversational Chat App](./tutorials/24_Building_Chat_App.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/24_Building_Chat_App.ipynb) | | [Customizing Agent to Chat with Your Documents](./tutorials/25_Customizing_Agent.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/25_Customizing_Agent.ipynb) | | [Creating a Hybrid Retrieval Pipeline](./tutorials/26_Hybrid_Retrieval.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/26_Hybrid_Retrieval.ipynb) | +| [Your First QA Pipeline with Retrieval-Augmentation](./tutorials/27_First_RAG_Pipeline.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepset-ai/haystack-tutorials/blob/main/tutorials/27_First_RAG_Pipeline.ipynb) | diff --git a/index.toml b/index.toml index ba016a14..3749eb3e 100644 --- a/index.toml +++ b/index.toml @@ -263,4 +263,15 @@ notebook = "26_Hybrid_Retrieval.ipynb" aliases = ["hybrid-retrieval"] completion_time = "15 min" created_at = 2023-10-10 -featured = true \ No newline at end of file +featured = true + +[[tutorial]] +title = "Creating Your First QA Pipeline with Retrieval-Augmentation" +description = "Build your first generative QA pipeline with OpenAI GPT models" +level = "beginner" +weight = 5 +notebook = "27_First_RAG_Pipeline.ipynb" +aliases = [] +completion_time = "10 min" +created_at = 2023-12-05 +haystack_2 = true \ No newline at end of file diff --git a/scripts/generate_markdowns.py b/scripts/generate_markdowns.py index 3332b916..89f156c6 100644 --- a/scripts/generate_markdowns.py +++ b/scripts/generate_markdowns.py @@ -24,6 +24,7 @@ def generate_frontmatter(config, tutorial): layout: {config["layout"]} featured: {tutorial.get("featured", False)} haystack_version: "{tutorial.get("haystack_version", "latest")}" +haystack_2: {tutorial.get("haystack_2", False)} hidden: {tutorial.get("hidden", False)} sitemap_exclude: {tutorial.get("sitemap_exclude", False)} colab: {tutorial.get("colab", f'{config["colab"]}{tutorial["notebook"]}')} diff --git a/scripts/generate_matrix.py b/scripts/generate_matrix.py index fb8098f8..4f9ca07d 100644 --- a/scripts/generate_matrix.py +++ b/scripts/generate_matrix.py @@ -37,6 +37,10 @@ def read_index(path): # so there's nothing to test continue + if tutorial.get("haystack_2", False): + # Haystack 2.0 tutorials should be skipped for now + continue + version = tutorial.get("haystack_version", args.version) if version[0] != "v": version = f"v{version}" diff --git a/tutorials/27_First_RAG_Pipeline.ipynb b/tutorials/27_First_RAG_Pipeline.ipynb new file mode 100644 index 00000000..49734e55 --- /dev/null +++ b/tutorials/27_First_RAG_Pipeline.ipynb @@ -0,0 +1,2555 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "2OvkPji9O-qX" + }, + "source": [ + "# Tutorial: Creating Your First QA Pipeline with Retrieval-Augmentation\n", + "\n", + "- **Level**: Beginner\n", + "- **Time to complete**: 10 minutes\n", + "- **Nodes Used**: `InMemoryDocumentStore`, `InMemoryBM25Retriever`, `PromptBuilder`, `GPTGenerator`\n", + "- **Prerequisites**: You must have an API key from an active OpenAI account as this tutorial is using the gpt-3.5-turbo model by OpenAI.\n", + "- **Goal**: After completing this tutorial, you'll have learned the new prompt syntax and how to use PromptBuilder and GPTGenerator to build a generative question-answering pipeline with retrieval-augmentation.\n", + "\n", + "> This tutorial uses Haystack 2.0 Beta. To learn more, read the [ Haystack 2.0 Beta announcement](https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent) or see [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/v2.0/docs)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LFqHcXYPO-qZ" + }, + "source": [ + "## Overview\n", + "\n", + "This tutorial shows you how to create a generative question-answering pipeline using the retrieval-augmentation ([RAG](https://www.deepset.ai/blog/llms-retrieval-augmentation)) approach with Haystack 2.0. The process involves three main components: [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever] for fetching relevant documents, [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) for creating a template prompt, and [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) for generating responses.\n", + "\n", + "For this tutorial, you'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents, but you can replace them with any text you want.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QXjVlbPiO-qZ" + }, + "source": [ + "## Preparing the Colab Environment\n", + "\n", + "- [Enable GPU Runtime in Colab](https://docs.haystack.deepset.ai/docs/enabling-gpu-acceleration#enabling-the-gpu-in-colab)\n", + "- [Set logging level to INFO](https://docs.haystack.deepset.ai/docs/log-level)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Kww5B_vXO-qZ" + }, + "source": [ + "## Installing Haystack\n", + "\n", + "Install Haystack 2.0 Beta and [datasets](https://pypi.org/project/datasets/) with `pip`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UQbU8GUfO-qZ" + }, + "outputs": [], + "source": [ + "%%bash\n", + "\n", + "pip install haystack-ai\n", + "pip install datasets>=2.6.1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wl_jYERtO-qa" + }, + "source": [ + "### Enabling Telemetry\n", + "\n", + "Knowing you're using this tutorial helps us decide where to invest our efforts to build a better product but you can always opt out by commenting the following line. See [Telemetry](https://docs.haystack.deepset.ai/v2.0/docs/enabling-telemetry) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A76B4S49O-qa" + }, + "outputs": [], + "source": [ + "from haystack.telemetry import tutorial_running\n", + "\n", + "tutorial_running(27)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_lvfew16O-qa" + }, + "source": [ + "## Initializing the DocumentStore\n", + "\n", + "You'll start creating your question answering system by initializing a DocumentStore. A DocumentStore stores the Documents that the question answering system uses to find answers to your questions. In this tutorial, you'll be using the `InMemoryDocumentStore`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CbVN-s5LO-qa" + }, + "outputs": [], + "source": [ + "from haystack.document_stores import InMemoryDocumentStore\n", + "\n", + "document_store = InMemoryDocumentStore()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yL8nuJdWO-qa" + }, + "source": [ + "> `InMemoryDocumentStore` is the simplest DocumentStore to get started with. It requires no external dependencies and it's a good option for smaller projects and debugging. But it doesn't scale up so well to larger Document collections, so it's not a good choice for production systems. To learn more about the different types of external databases that Haystack supports, see [DocumentStore Integrations](https://haystack.deepset.ai/integrations?type=Document+Store)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XvLVaFHTO-qb" + }, + "source": [ + "The DocumentStore is now ready. Now it's time to fill it with some Documents." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HryYZP9ZO-qb" + }, + "source": [ + "## Fetching and Writing Documents\n", + "\n", + "You'll use the Wikipedia pages of [Seven Wonders of the Ancient World](https://en.wikipedia.org/wiki/Wonders_of_the_World) as Documents. We preprocessed the data and uploaded to a Hugging Face Space: [Seven Wonders](https://huggingface.co/datasets/bilgeyucel/seven-wonders). Thus, you don't need to perform any additional cleaning or splitting.\n", + "\n", + "Fetch the data and write it to the DocumentStore:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 194, + "referenced_widgets": [ + "2b80b4402f1c4245835b0a6ff9724f17", + "46333addbfc5402b909f8671494b324f", + "ea2aad5049ba46339c12eb1d8f6e5069", + "de37e5d8932d4c6fab471c8e6aa69d38", + "42eb5725b3454b41924e9cbbe1526bda", + "d20a813e1de54cce85837e3f89d9ac72", + "f723dc909e054ff0a2dec3ce425793b4", + "3f9c5812fc6d480185467e804082ae2b", + "147c9c74cba740608709d4f20577ef01", + "5ee3bc5478824ab097a5f4205018bd7f", + "3b84d92477174d319edceab41aade78a", + "35cdea243a1e4bd29ea077d5a453b86a", + "fbfab62f10e14d909b5e64a8c982fa71", + "674f8660be5d4ee6ba5fe657934065f2", + "1bcea8e4134449eea12d43ffbbfe0c7d", + "9fde7b64a0964e1e929ba9a10ba9dce3", + "368afc1b3aa143e085dfbc053e34b1aa", + "4c26080f262b4688be7d67eccd871a5c", + "44388b7afeaa4d059bd93debb114b931", + "cbaba46a13ac45afbb7e37482608fbe1", + "9733632c4d2843a09551ba51ab29dff0", + "a80945a5c46a496a9e5c8811b3503472", + "fce43ee8cb7a45849045f4cfee69c206", + "6d6f5dab1c984c3e92e95a1eaca1ba0e", + "6ddacfb258594e5cb3ed198d43531b77", + "b442ede7736b46f98a170bd0246ae002", + "c0fc0d52594d469e91b71555139cc541", + "f093be7cc403446f96939e52507411c9", + "26e17b07b56e470383feb5eec36e5499", + "7440a265999e4e87ad92c3eace177171", + "7df6980a5f8c4feb8bfdc86231de6c5c", + "b2adc841e1d945c6a4619c34c74856a0", + "d0c0b275608d49e08a882c9601a57806", + "1f811c7951d2449595b8af976f0ab148", + "b067e4c0fa2c48029977f51ccc8f75f1", + "66b10c54d93a43efa092c314cd029c3a", + "654902128296465f9d808def143460fe", + "4742bb3afe0f4857860a6822d394931b", + "b58ffbecddf84ef98f06048dbce2b8ea", + "667ce03f6e8c4d11a18e533e83aebb78", + "d252d977929649b98053f553ca26185d", + "ec40181cd49f4218982907465a3095c5", + "c4951cfa0a7641cc8447e554b2170447", + "b871e9d440054eb3ba5518f0b7e388a3", + "397d6f7fb81947fc8a1f112da5735e05", + "abd891ee1af445b5936eb1c4c5a46718", + "443227b9a8664a8eabd2e2d3ee642392", + "5971f00c34e64ac698322b4d67f94a16", + "360dcbf1ae9645259be25619a1565f71", + "8bc32eebc7914cd3a1cbe6247908f4c1", + "d72fb4ea029b41b4a8c290c93cae3b35", + "bb10a9f77a5848d4a7f4302f26b0287e", + "d1fd0925a6264ce993dfaf567452d88d", + "7f0cb20fbe6e434db8b06ad7bee4b655", + "7e93188c9d0f486284651fe43530b6db" + ] + }, + "id": "INdC3WvLO-qb", + "outputId": "35a74956-06ca-4f50-a3be-b1c0871b0949" + }, + "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]\n", + "document_store.write_documents(docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0_cj-5m-O-qb" + }, + "source": [ + "## Initializing the Retriever\n", + "\n", + "Initialize a [InMemoryBM25Retriever](https://docs.haystack.deepset.ai/v2.0/docs/inmemorybm25retriever) and make it use the InMemoryDocumentStore we initialized earlier in this tutorial. This Retriever will get the relevant documents to the query:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-uo-6fjiO-qb" + }, + "outputs": [], + "source": [ + "from haystack.components.retrievers import InMemoryBM25Retriever\n", + "\n", + "retriever = InMemoryBM25Retriever(document_store)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6CEuQpB7O-qb" + }, + "source": [ + "## Defining a Template Prompt\n", + "\n", + "Create a custom prompt for a generative question answering task using the RAG approach. The prompt should take in two parameters: `documents`, which are retrieved from a document store, and a `question` from the user. Use the Jinja2 looping syntax to combine the content of the retrieved documents in the prompt.\n", + "\n", + "Next, initialize a [PromptBuilder](https://docs.haystack.deepset.ai/v2.0/docs/promptbuilder) instance with your prompt template. The PromptBuilder, when given the necessary values, will automatically fill in the variable values and generate a complete prompt. This approach allows for a more tailored and effective question-answering experience." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ObahTh45FqOT" + }, + "outputs": [], + "source": [ + "from haystack.components.builders import PromptBuilder\n", + "\n", + "template = \"\"\"\n", + "Given the following information, answer the question.\n", + "\n", + "Context:\n", + "{% for document in documents %}\n", + " {{ document.content }}\n", + "{% endfor %}\n", + "\n", + "Question: {{question}}\n", + "Answer:\n", + "\"\"\"\n", + "\n", + "prompt_builder = PromptBuilder(template=template)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HR14lbfcFtXj" + }, + "source": [ + "## Initializing a Generator\n", + "\n", + "\n", + "Generators are the components that interacts with large language models (LLMs). Now, initialize a [GPTGenerator](https://docs.haystack.deepset.ai/v2.0/docs/gptgenerator) that can communicate with OpenAI GPT models with an OpenAI `api_key` and the model name you'd like to use. If you don't provide any `model_name`, the GPTGenerator defaults to `gpt-3.5-turbo`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SavE_FAqfApo", + "outputId": "4c1fa657-8ca0-42ca-8e49-161edf3be505" + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "from haystack.components.generators import GPTGenerator\n", + "\n", + "openai_api_key = os.getenv(\"OPENAI_API_KEY\", getpass(\"Enter OpenAI API key:\"))\n", + "generator = GPTGenerator(api_key=openai_api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nenbo2SvycHd" + }, + "source": [ + "> You can replace `GPTGenerator` in your pipeline with another `Generator`. Check out the full list of generators [here](https://docs.haystack.deepset.ai/v2.0/docs/generators)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1bfHwOQwycHe" + }, + "source": [ + "## Building the Pipeline\n", + "\n", + "To build a pipeline, add all components to your pipeline and connect them. Create connections from `retriever` to the `prompt_builder` and from `prompt_builder` to `llm`. Explicitly connect the output of `retriever` with \"documents\" input of the `prompt_builder` to make the connection obvious as `prompt_builder` has two inputs (\"documents\" and \"question\"). For more information on pipelines and creating connections, refer to [Creating Pipelines](https://docs.haystack.deepset.ai/v2.0/docs/creating-pipelines) documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "f6NFmpjEO-qb" + }, + "outputs": [], + "source": [ + "from haystack.pipeline import Pipeline\n", + "\n", + "basic_rag_pipeline = Pipeline()\n", + "# Add components to your pipeline\n", + "basic_rag_pipeline.add_component(\"retriever\", retriever)\n", + "basic_rag_pipeline.add_component(\"prompt_builder\", prompt_builder)\n", + "basic_rag_pipeline.add_component(\"llm\", generator)\n", + "\n", + "# Now, connect the components to each other\n", + "basic_rag_pipeline.connect(\"retriever\", \"prompt_builder.documents\")\n", + "basic_rag_pipeline.connect(\"prompt_builder\", \"llm\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mtcrMpjdzBYo" + }, + "source": [ + "### Visualize the Pipeline\n", + "\n", + "Draw the pipeline with the `draw()` method to confirm the connections are correct. You can find the diagram in the Files section of this Colab." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DAvgZnaczEVd" + }, + "outputs": [], + "source": [ + "basic_rag_pipeline.draw(\"basic-rag-pipeline.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6NqyLhx7O-qc" + }, + "source": [ + "That's it! The pipeline's ready to generate answers to questions!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DBAyF5tVO-qc" + }, + "source": [ + "## Asking a Question\n", + "\n", + "When asking a question, use the `run()` method of the pipeline. Make sure to provide the question to both the `retriever` and the `prompt_builder`. This ensures that the `{{question}}` variable in the template prompt gets replaced with your specific question." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86, + "referenced_widgets": [ + "3536f342b4cc4293898aee442b595c99", + "2e3798280bda4190af5c4adb9fbdbb25", + "92c29977e67944bf87cc62e10f62d959", + "77b7b1d1d77040bfbcbb282ef5936fbd", + "935932e80bfb47949e32abe15b8e74b2", + "53f66341cde247088a714e24a66ca171", + "ffdb93e36b7b4029addb195e40d577e2", + "11febd0a9595480abd88aeaaae069b17", + "01a2be51bb574f878d63eb3fd2a65d81", + "a23da41a890f4dbc9fb0f20439b007ae", + "32b1264047ba43b08217db2889a43eca" + ] + }, + "id": "Vnt283M5O-qc", + "outputId": "1833cb17-ac1c-496f-fd65-74a9af9584a0" + }, + "outputs": [], + "source": [ + "question = \"What does Rhodes Statue look like?\"\n", + "\n", + "response = basic_rag_pipeline.run({\"retriever\": {\"query\": question}, \"prompt_builder\": {\"question\": question}})\n", + "\n", + "print(response[\"llm\"][\"replies\"][0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IWQN-aoGO-qc" + }, + "source": [ + "Here are some other example questions to test:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_OHUQ5xxO-qc" + }, + "outputs": [], + "source": [ + "examples = [\n", + " \"Where is Gardens of Babylon?\",\n", + " \"Why did people build Great Pyramid of Giza?\",\n", + " \"What does Rhodes Statue look like?\",\n", + " \"Why did people visit the Temple of Artemis?\",\n", + " \"What is the importance of Colossus of Rhodes?\",\n", + " \"What happened to the Tomb of Mausolus?\",\n", + " \"How did Colossus of Rhodes collapse?\",\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XueCK3y4O-qc" + }, + "source": [ + "🎉 Congratulations! 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