{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "rkwAPsgBdnFf" }, "source": [ "# πŸ—žοΈ Newsletter Sending Agent with Haystack Tools\n", "\n", "πŸ§‘β€πŸ³ **Demo by Stefano Fiorucci ([X](https://x.com/theanakin87), [LinkedIn](https://www.linkedin.com/in/stefano-fiorucci/)) and Tuana Celik([X](https://x.com/tuanacelik), [LinkedIn](https://www.linkedin.com/in/tuanacelik/))**\n", "\n", "\n", "In this recipe, we will build a newsletter sending agent with 3 tools:\n", "- A tool that fetches the top stories from Hacker News\n", "- A tool that creates newsletters for a particular audience\n", "- A tool that can send emails (with Gmail)\n", "\n", "> This notebook is updated after [Haystack 2.9.0](https://github.com/deepset-ai/haystack/releases/tag/v2.9.0). Experimental features in the old version of this notebook are merged into Haystack core package.\n", "\n", "## πŸ“Ί Watch Along\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "UVEseyMhnfu_" }, "source": [ "### Install dependencies\n", "\n", "Install the latest versions of `haystack-ai` and `trafilatura`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LBNbJhcf6NCB" }, "outputs": [], "source": [ "! pip install haystack-ai trafilatura" ] }, { "cell_type": "markdown", "metadata": { "id": "q7eXbW9S0lrt" }, "source": [ "#### **Importing Features**\n", "\n", "In this demo, we are using Haystack's latest features: [`Tool`](https://docs.haystack.deepset.ai/docs/tool), [`ToolInvoker`](https://docs.haystack.deepset.ai/docs/toolinvoker) with extended [`ChatMessage`](https://docs.haystack.deepset.ai/docs/chatmessage) and [`OpenAIChatGenerator`](https://docs.haystack.deepset.ai/docs/openaichatgenerator)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k0qnuVES2p4t" }, "outputs": [], "source": [ "from typing import List\n", "from trafilatura import fetch_url, extract\n", "import requests\n", "from getpass import getpass\n", "import os\n", "\n", "from haystack import Pipeline\n", "from haystack.components.builders import ChatPromptBuilder\n", "from haystack.components.generators.chat import OpenAIChatGenerator\n", "from haystack.dataclasses import ChatMessage\n", "from haystack.components.tools import ToolInvoker\n", "from haystack.tools import Tool" ] }, { "cell_type": "markdown", "metadata": { "id": "uHElH_u323kd" }, "source": [ "## Hacker News Fetcher Tool\n", "\n", "In a previous article and recipe, we had shown how you can create a custom component for Haystack called the `HackerNewsFetcher`.\n", "\n", "Here, we are doing something very similar, but instead we are creating a function and using that as a `Tool` instead.\n", "\n", "πŸ“š [Hacker News Summaries with Custom Components](https://haystack.deepset.ai/cookbook/hackernews-custom-component-rag?utm_campaign=developer-relations&utm_source=tools-livestream)\n", "\n", "This tool expects `top_k` as input, and returns that many of the _current_ top stories on Hacker News πŸš€" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "8SrNAWvA27D3" }, "outputs": [], "source": [ "def hacker_news_fetcher(top_k: int = 3):\n", " newest_list = requests.get(url='https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty')\n", " urls = []\n", " articles = []\n", " for id_ in newest_list.json()[0:top_k]:\n", " article = requests.get(url=f\"https://hacker-news.firebaseio.com/v0/item/{id_}.json?print=pretty\")\n", " if 'url' in article.json():\n", " urls.append(article.json()['url'])\n", " elif 'text' in article.json():\n", " articles.append(article.json()['text'])\n", "\n", " for url in urls:\n", " try:\n", " downloaded = fetch_url(url)\n", " text = extract(downloaded)\n", " if text is not None:\n", " articles.append(text[:500])\n", " except Exception as e:\n", " print(e)\n", " print(f\"Couldn't download {url}, skipped\")\n", "\n", " return articles" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "_mmDM7mE3GtG" }, "outputs": [], "source": [ "hacker_news_fetcher_tool = Tool(name=\"hacker_news_fetcher\",\n", " description=\"Fetch the top k articles from hacker news\",\n", " function=hacker_news_fetcher,\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"top_k\": {\n", " \"type\": \"integer\",\n", " \"description\": \"The number of articles to fetch\"\n", " }\n", " },\n", " })" ] }, { "cell_type": "markdown", "metadata": { "id": "-qcpJPsb3MFe" }, "source": [ "## Newsletter generation Pipeline and Tool\n", "\n", "For the Newsletter gnereation tool, we will be creating a Haystack pipeline, and making our pipeline itself a tool.\n", "\n", "Our tool will expect the following inputs:\n", "\n", "- `articles`: Content to base the newsletter off of\n", "- `target_people`: The audience we want to target, for example \"engineers\" may be our target audience\n", "- `n_words`: The number of words we want to limit our newsletter to" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SIHSxnol3a3o", "outputId": "b60b382e-42df-4f17-aea7-4f03a08a31a3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your OpenAI API key: Β·Β·Β·Β·Β·Β·Β·Β·Β·Β·\n" ] } ], "source": [ "if not \"OPENAI_API_KEY\" in os.environ:\n", " os.environ[\"OPENAI_API_KEY\"] = getpass(\"Enter your OpenAI API key: \")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NhfQOF4v3KJu", "outputId": "02855bf1-b02b-4d40-96d7-0ec736c717eb" }, "outputs": [], "source": [ "template = [ChatMessage.from_user(\"\"\"\n", "Create a entertaining newsletter for {{target_people}} based on the following articles.\n", "The newsletter should be well structured, with a unique angle and a maximum of {{n_words}} words.\n", "\n", "Articles:\n", "{% for article in articles %}\n", " {{ article }}\n", " ---\n", "{% endfor %}\n", "\"\"\")]\n", "\n", "newsletter_pipe = Pipeline()\n", "newsletter_pipe.add_component(\"prompt_builder\", ChatPromptBuilder(template=template))\n", "newsletter_pipe.add_component(\"llm\", OpenAIChatGenerator(model=\"gpt-4o-mini\"))\n", "newsletter_pipe.connect(\"prompt_builder\", \"llm\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8YsxYyP83SL9" }, "outputs": [], "source": [ "def newsletter_pipeline_func(articles: List[str], target_people: str = \"programmers\", n_words: int = 100):\n", " result = newsletter_pipe.run({\"prompt_builder\": {\"articles\": articles, \"target_people\": target_people, \"n_words\": n_words}})\n", "\n", " return {\"reply\": result[\"llm\"][\"replies\"][0].text}\n", "\n", "newsletter_tool = Tool(name=\"newsletter_generator\",\n", " description=\"Generate a newsletter based on some articles\",\n", " function=newsletter_pipeline_func,\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"articles\": {\n", " \"type\": \"array\",\n", " \"items\": {\n", " \"type\": \"string\",\n", " \"description\": \"The articles to base the newsletter on\",\n", " }\n", " },\n", " \"target_people\": {\n", " \"type\": \"string\",\n", " \"description\": \"The target audience for the newsletter\",\n", " },\n", " \"n_words\": {\n", " \"type\": \"integer\",\n", " \"description\": \"The number of words to summarize the newsletter to\",\n", " }\n", " },\n", " \"required\": [\"articles\"],\n", " })" ] }, { "cell_type": "markdown", "metadata": { "id": "qNJLc2te1BOi" }, "source": [ "## Send Email Tool\n", "\n", "Here, we are creating a Gmail tool. You login with your gmail account, allowing the final Agent to send emails from your email, to others.\n", "\n", "> ⚠️ Note: To be able to use the gmail too, you have to create an app password for your Gmail account, which will be the sender. You can delete this after.\n", "\n", "To configure our `email` Tool, you have to provide the following information about the sender email account πŸ‘‡" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6btTAlL31DmE" }, "outputs": [], "source": [ "if not \"NAME\" in os.environ:\n", " os.environ[\"NAME\"] = input(\"What's your name? \")\n", "if not \"SENDER_EMAIL\" in os.environ:\n", " os.environ[\"SENDER_EMAIL\"] = getpass(\"Enter your Gmail e-mail: \")\n", "if not \"GMAIL_APP_PASSWORD\" in os.environ:\n", " os.environ[\"GMAIL_APP_PASSWORD\"] = getpass(\"Enter your Gmail App Password: \")" ] }, { "cell_type": "markdown", "metadata": { "id": "PYZs8Wjbq6Gy" }, "source": [ "Next, we create a `Tool` that expects the following input:\n", "- `receiver`: The email address that we want to send an email to\n", "- `body`: The body of the email\n", "- `subject`: The subject line for the email." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DIYBOe5l1AL-" }, "outputs": [], "source": [ "import smtplib, ssl\n", "from email.mime.text import MIMEText\n", "\n", "def send_email(receiver: str, body: str, subject: str):\n", " msg = MIMEText(body)\n", " sender_email = os.environ['SENDER_EMAIL']\n", " sender_name = os.environ['NAME']\n", " sender = f\"{sender_name} <{sender_email}>\"\n", " msg['Subject'] = subject\n", " msg['From'] = sender\n", " port = 465 # For SSL\n", " smtp_server = \"smtp.gmail.com\"\n", " password = os.environ[\"GMAIL_APP_PASSWORD\"]\n", " context = ssl.create_default_context()\n", " with smtplib.SMTP_SSL(smtp_server, port, context=context) as server:\n", " server.login(sender_email, password)\n", " server.sendmail(sender_email, receiver, msg.as_string())\n", " return 'Email sent!'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MrB2O_Yq3Dw0" }, "outputs": [], "source": [ "email_tool = Tool(name=\"email\",\n", " description=\"Send emails with specific content\",\n", " function=send_email,\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"receiver\": {\n", " \"type\": \"string\",\n", " \"description\": \"The email of the receiver\"\n", " },\n", " \"body\": {\n", " \"type\": \"string\",\n", " \"description\": \"The content of the email\"\n", " },\n", " \"subject\": {\n", " \"type\": \"string\",\n", " \"description\": \"The subject of the email\"\n", " }\n", " },\n", " })" ] }, { "cell_type": "markdown", "metadata": { "id": "K4qpD_nO3wol" }, "source": [ "## Newsletter Sending Chat Agent\n", "\n", "Now, we build a Newsletter creating chat agent which we can use to ask for newsletters, as well as sending them to given email addresses." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Bg3K8r6f33fF", "outputId": "817b509b-3f00-431c-9d96-d325d7e5bb49" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "waiting for input (type 'exit' or 'quit' to stop)\n", "πŸ§‘: What is the top HN article now?\n", "βŒ› iterating...\n", "\n", " TOOL CALL:\n", "\tcall_aOluHPdSMAGosryayVwNxOvL\n", "\thacker_news_fetcher\n", "\ttop_k: 1\n", "βŒ› iterating...\n", "\n", " TOOL CALL:\n", "\tcall_G6Z10LdGwJgdqxspgIWwvsnl\n", "\thacker_news_fetcher\n", "\ttop_k: 1\n", "βŒ› iterating...\n", "πŸ€–: It appears that I'm facing an issue retrieving data from Hacker News, as the response does not contain the actual article information. Unfortunately, I'm unable to provide the top article at this moment. You might want to check the Hacker News website directly for the latest articles.\n", "\n", "\n", "waiting for input (type 'exit' or 'quit' to stop)\n", "πŸ§‘: What's the top 2 HN articles?\n", "βŒ› iterating...\n", "\n", " TOOL CALL:\n", "\tcall_SWI39GuYw579wRDhQVs02WUu\n", "\thacker_news_fetcher\n", "\ttop_k: 2\n", "βŒ› iterating...\n", "πŸ€–: It seems I'm encountering difficulties retrieving the top articles from Hacker News properly. However, based on the partial information I received, one of the articles is:\n", "\n", "1. **Adobe's new image rotation tool** - \"Project Turnable\" lets users fully rotate 2D vectors. This tool was showcased at Adobe's annual MAX conference as part of their \"Sneaks\" segment, where engineers present innovative ideas that may or may not be fully developed.\n", "\n", "Unfortunately, the first article did not provide relevant content. For the most accurate and complete information on the top Hacker News articles, I recommend checking the Hacker News website directly.\n", "\n", "\n", "waiting for input (type 'exit' or 'quit' to stop)\n", "πŸ§‘: Create a newsletter targeted at engineers based on this article. No more than 100 words.\n", "βŒ› iterating...\n", "\n", " TOOL CALL:\n", "\tcall_duVE2eBKkCe3wpJOuq6NEJte\n", "\tnewsletter_generator\n", "\tarticles: [\"Adobe's new image rotation tool is one of the mo...\n", "\ttarget_people: engineers\n", "\tn_words: 100\n", "βŒ› iterating...\n", "πŸ€–: **Engineering Whirlwind** \n", "*Issue #42: AI Innovations Turned Up to Eleven*\n", "\n", "Hello, Innovators!\n", "\n", "Dive into Adobe's latest gem, *Project Turntable*! This fascinating tool offers engineers a chance to fully rotate 2D vectors like never before. Unveiled at their MAX conference, it sits at the intersection of imagination and engineering mastery. As Adobe's engineers sneak out innovative ideas, warm up those creative enginesβ€”who knows what else might come spinning your way?\n", "\n", "Stay sharp and keep spinning those ideas! \n", "β€” The Engineering Brigade 🌟\n", "\n", "\n", "waiting for input (type 'exit' or 'quit' to stop)\n", "πŸ§‘: Email this to tuana.celik@deepset.ai You can decide on the subjectline\n", "βŒ› iterating...\n", "\n", " TOOL CALL:\n", "\tcall_e7thnZ8Bq1kBjU4dyrGLP0jK\n", "\temail\n", "\treceiver: tuana.celik@deepset.ai\n", "\tbody: **Engineering Whirlwind** \n", "*Issue #42: AI Innovat...\n", "\tsubject: Latest Innovations: Adobe's Project Turntable\n", "βŒ› iterating...\n", "πŸ€–: The newsletter has been successfully emailed to tuana.celik@deepset.ai with the subject \"Latest Innovations: Adobe's Project Turntable.\" If you need any further assistance, feel free to ask!\n", "\n", "\n", "waiting for input (type 'exit' or 'quit' to stop)\n", "πŸ§‘: exit\n" ] } ], "source": [ "chat_generator = OpenAIChatGenerator(tools=[hacker_news_fetcher_tool, newsletter_tool, email_tool])\n", "\n", "tool_invoker = ToolInvoker(tools=[hacker_news_fetcher_tool, newsletter_tool, email_tool])\n", "\n", "messages = [\n", " ChatMessage.from_system(\n", " \"\"\"Prepare a tool call if needed, otherwise use your knowledge to respond to the user.\n", " If the invocation of a tool requires the result of another tool, prepare only one call at a time.\n", "\n", " Each time you receive the result of a tool call, ask yourself: \"Am I done with the task?\".\n", " If not and you need to invoke another tool, prepare the next tool call.\n", " If you are done, respond with just the final result.\"\"\"\n", " )\n", " ]\n", "\n", "while True:\n", " user_input = input(\"\\n\\nwaiting for input (type 'exit' or 'quit' to stop)\\nπŸ§‘: \")\n", " if user_input.lower() == \"exit\" or user_input.lower() == \"quit\":\n", " break\n", " messages.append(ChatMessage.from_user(user_input))\n", "\n", " while True:\n", " print(\"βŒ› iterating...\")\n", "\n", " replies = chat_generator.run(messages=messages)[\"replies\"]\n", " messages.extend(replies)\n", "\n", " # Check for tool calls and handle them\n", " if not replies[0].tool_calls:\n", " break\n", " tool_calls = replies[0].tool_calls\n", "\n", " # Print tool calls for debugging\n", " for tc in tool_calls:\n", " print(\"\\n TOOL CALL:\")\n", " print(f\"\\t{tc.id}\")\n", " print(f\"\\t{tc.tool_name}\")\n", " for k,v in tc.arguments.items():\n", " v_truncated = str(v)[:50]\n", " print(f\"\\t{k}: {v_truncated}{'' if len(v_truncated) == len(str(v)) else '...'}\")\n", "\n", " tool_messages = tool_invoker.run(messages=replies)[\"tool_messages\"]\n", " messages.extend(tool_messages)\n", "\n", "\n", " # Print the final AI response after all tool calls are resolved\n", " print(f\"πŸ€–: {messages[-1].text}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "-IbTLr8ueNRh" }, "source": [ "## Extras: Converting Tools" ] }, { "cell_type": "markdown", "metadata": { "id": "65DYH0JvePeO" }, "source": [ "### Convert functions into Tools" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "RCQJJb1Oesaf" }, "outputs": [], "source": [ "from typing import Annotated\n", "from pprint import pp" ] }, { "cell_type": "markdown", "metadata": { "id": "xVX72INMhGCS" }, "source": [ "Writing the JSON schema is not fun... πŸ€”" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "01FSg09De5bA" }, "outputs": [], "source": [ "def newsletter_pipeline_func(articles: List[str], target_people: str = \"programmers\", n_words: int = 100):\n", " result = newsletter_pipe.run({\"prompt_builder\": {\"articles\": articles, \"target_people\": target_people, \"n_words\": n_words}})\n", "\n", " return {\"reply\": result[\"llm\"][\"replies\"][0].text}\n", "\n", "newsletter_tool = Tool(name=\"newsletter_generator\",\n", " description=\"Generate a newsletter based on some articles\",\n", " function=newsletter_pipeline_func,\n", " parameters={\n", " \"type\": \"object\",\n", " \"properties\": {\n", " \"articles\": {\n", " \"type\": \"array\",\n", " \"items\": {\n", " \"type\": \"string\",\n", " \"description\": \"The articles to include in the newsletter\",\n", " }\n", " },\n", " \"target_people\": {\n", " \"type\": \"string\",\n", " \"description\": \"The target audience for the newsletter\",\n", " },\n", " \"n_words\": {\n", " \"type\": \"integer\",\n", " \"description\": \"The number of words to summarize the newsletter to\",\n", " }\n", " },\n", " \"required\": [\"articles\"],\n", " })" ] }, { "cell_type": "markdown", "metadata": { "id": "9laNlRiThU45" }, "source": [ "We can do this instead πŸ‘‡" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Htmwg90cfZLO", "outputId": "d4dc2485-8ddf-4309-c7d5-0e3823068108" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tool(name='newsletter_pipeline_func',\n", " description='Generate a newsletter based on some articles',\n", " parameters={'properties': {'articles': {'items': {'type': 'string'}, 'type': 'array', 'description': 'The articles to include in the newsletter'},\n", " 'target_people': {'default': 'programmers', 'type': 'string', 'description': 'The target audience for the newsletter'},\n", " 'n_words': {'default': 100, 'type': 'integer', 'description': 'The number of words to summarize the newsletter to'}},\n", " 'required': ['articles'],\n", " 'type': 'object'},\n", " function=)\n" ] } ], "source": [ "from haystack.tools import create_tool_from_function\n", " \n", "def newsletter_pipeline_func(\n", " articles: Annotated[List[str], \"The articles to include in the newsletter\"],\n", " target_people: Annotated[str, \"The target audience for the newsletter\"] = \"programmers\",\n", " n_words: Annotated[int, \"The number of words to summarize the newsletter to\"] = 100\n", " ):\n", " \"\"\"Generate a newsletter based on some articles\"\"\"\n", "\n", " result = newsletter_pipe.run({\"prompt_builder\": {\"articles\": articles, \"target_people\": target_people, \"n_words\": n_words}})\n", "\n", " return {\"reply\": result[\"llm\"][\"replies\"][0].text}\n", "\n", "newsletter_tool = create_tool_from_function(newsletter_pipeline_func)\n", "\n", "pp(newsletter_tool, width=200)" ] }, { "cell_type": "markdown", "metadata": { "id": "lnmBWMqQhm6f" }, "source": [ "### Convert Pre-Existing Tools into Haystack Tools\n", "\n", "Haystack is quite flexible. This means if you have tools already defined elsewhere, you are able to convert them to Haystack tools. For example,\n", " [LangChain has several interesting tools](https://python.langchain.com/docs/integrations/tools/) that we can seamlessly convert into Haystack tools.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BCjAE8fyjhPg" }, "outputs": [], "source": [ "! pip install langchain-community" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "0vU0n12zjz2T" }, "outputs": [], "source": [ "from pydantic import create_model\n", "from haystack.tools.from_function import _remove_title_from_schema\n", "\n", "def convert_langchain_tool_to_haystack_tool(langchain_tool):\n", " tool_name = langchain_tool.name\n", " tool_description = langchain_tool.description\n", "\n", " def invocation_adapter(**kwargs):\n", " return langchain_tool.invoke(input=kwargs)\n", "\n", " tool_function = invocation_adapter\n", "\n", " model_fields = langchain_tool.args_schema.model_fields\n", "\n", " fields = {name: (field.annotation, field.default) for name, field in model_fields.items()}\n", " descriptions = {name: field.description for name, field in model_fields.items()}\n", "\n", " model = create_model(tool_name, **fields)\n", " schema = model.model_json_schema()\n", "\n", " # we don't want to include title keywords in the schema, as they contain redundant information\n", " # there is no programmatic way to prevent Pydantic from adding them, so we remove them later\n", " # see https://github.com/pydantic/pydantic/discussions/8504\n", " _remove_title_from_schema(schema)\n", "\n", " # add parameters descriptions to the schema\n", " for name, description in descriptions.items():\n", " if name in schema[\"properties\"]:\n", " schema[\"properties\"][name][\"description\"] = description\n", "\n", " return Tool(name=tool_name, description=tool_description, parameters=schema, function=tool_function)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XbFgPSyXj6aD", "outputId": "f78ac9f4-fbb7-438f-b461-2514d2a3715b" }, "outputs": [ { "data": { "text/plain": [ "[CopyFileTool(root_dir='/'),\n", " DeleteFileTool(root_dir='/'),\n", " FileSearchTool(root_dir='/'),\n", " MoveFileTool(root_dir='/'),\n", " ReadFileTool(root_dir='/'),\n", " WriteFileTool(root_dir='/'),\n", " ListDirectoryTool(root_dir='/')]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from langchain_community.agent_toolkits import FileManagementToolkit\n", "toolkit = FileManagementToolkit(\n", " root_dir=\"/\"\n", ") # If you don't provide a root_dir, operations will default to the current working directory\n", "toolkit.get_tools()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3fDROQavkoKo" }, "outputs": [], "source": [ "langchain_listdir_tool = toolkit.get_tools()[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oBI83cqYkuBY" }, "outputs": [], "source": [ "haystack_listdir_tool = convert_langchain_tool_to_haystack_tool(langchain_listdir_tool)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DzyphA4NkxXY", "outputId": "1d18566c-1a58-4eed-ce99-ed152a894d30" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The files in the `/content/sample_data` directory are:\n", "\n", "1. anscombe.json\n", "2. README.md\n", "3. mnist_train_small.csv\n", "4. california_housing_train.csv\n", "5. california_housing_test.csv\n", "6. mnist_test.csv\n" ] } ], "source": [ "from haystack.components.generators.chat import OpenAIChatGenerator\n", "from haystack.components.tools import ToolInvoker\n", "from haystack.dataclasses import ChatMessage\n", "\n", "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\", tools=[haystack_listdir_tool])\n", "tool_invoker = ToolInvoker(tools=[haystack_listdir_tool])\n", "\n", "user_message = ChatMessage.from_user(\"List the files in /content/sample_data\")\n", "\n", "replies = chat_generator.run(messages=[user_message])[\"replies\"]\n", "# print(f\"assistant messages: {replies}\")\n", "\n", "if replies[0].tool_calls:\n", "\n", " tool_messages = tool_invoker.run(messages=replies)[\"tool_messages\"]\n", " # print(f\"tool messages: {tool_messages}\")\n", "\n", " # we pass all the messages to the Chat Generator\n", " messages = [user_message] + replies + tool_messages\n", " final_replies = chat_generator.run(messages=messages)[\"replies\"]\n", " print(f\"{final_replies[0].text}\")" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }