tutorials/40_Building_Chat_Application_with_Function_Calling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HJsHqaTksDHt"
   },
   "source": [
    "# Tutorial: Building a Chat Application with Function Calling\n",
    "\n",
    "- **Level**: Advanced\n",
    "- **Time to complete**: 20 minutes\n",
    "- **Components Used**: [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore), [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder), [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [ChatPromptBuilder](https://docs.haystack.deepset.ai/docs/chatpromptbuilder), [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
    "- **Prerequisites**: You must have an [OpenAI API Key](https://platform.openai.com/api-keys) and be familiar with [creating pipelines](https://docs.haystack.deepset.ai/docs/creating-pipelines)\n",
    "- **Goal**: After completing this tutorial, you will have learned how to build chat applications that demonstrate agent-like behavior using OpenAI's function calling feature.\n",
    "\n",
    "> This tutorial uses Haystack 2.0. To learn more, read the [Haystack 2.0 announcement](https://haystack.deepset.ai/blog/haystack-2-release) or visit the [Haystack 2.0 Documentation](https://docs.haystack.deepset.ai/docs/intro).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PWXXqq_MPn7y"
   },
   "source": [
    "## Overview\n",
    "\n",
    "\n",
    "\n",
    "📚 Useful Sources:\n",
    "* [OpenAIChatGenerator Docs](https://docs.haystack.deepset.ai/docs/openaichatgenerator)\n",
    "* [OpenAIChatGenerator API Reference](https://docs.haystack.deepset.ai/reference/generator-api#openaichatgenerator)\n",
    "* [🧑‍🍳 Cookbook: Function Calling with OpenAIChatGenerator](https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/function_calling_with_OpenAIChatGenerator.ipynb)\n",
    "\n",
    "[OpenAI's function calling](https://platform.openai.com/docs/guides/function-calling) connects large language models to external tools. By providing a `tools` list with functions and their specifications to the OpenAI API calls, you can easily build chat assistants that can answer questions by calling external APIs or extract structured information from text.\n",
    "\n",
    "In this tutorial, you'll learn how to convert your Haystack pipeline into a function-calling tool and how to implement applications using OpenAI's Chat Completion API through `OpenAIChatGenerator` for agent-like behavior."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K04cnh_IleMV"
   },
   "source": [
    "## Setting up the Development Environment\n",
    "\n",
    "Install Haystack 2.0 and [sentence-transformers](https://pypi.org/project/sentence-transformers/) using pip:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zNyqNVFaPN1A"
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "pip install haystack-ai\n",
    "pip install \"sentence-transformers>=3.0.0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XwlBLN-GwuuU"
   },
   "source": [
    "### Enable 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/docs/telemetry) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "9V0-VuEgwt2u"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amna.mubashar/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:35: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
      "  warnings.warn(\n",
      "/Users/amna.mubashar/Library/Python/3.9/lib/python/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from haystack.telemetry import tutorial_running\n",
    "\n",
    "tutorial_running(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2owelT4NpXtw"
   },
   "source": [
    "Save your OpenAI API key as an environment variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "WM-sVkYonutA"
   },
   "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:\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0_mGdadLrcNr"
   },
   "source": [
    "## Learning about the OpenAIChatGenerator\n",
    "\n",
    "[OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator) is a component that supports the function calling feature of OpenAI through Chat Completion API. In contrary to `OpenAIGenerator`, the way to communicate with `OpenAIChatGenerator` is through [`ChatMessage`](https://docs.haystack.deepset.ai/docs/data-classes#chatmessage) list. Read more about the difference between them in [Generators vs Chat Generators](https://docs.haystack.deepset.ai/docs/generators-vs-chat-generators).\n",
    "\n",
    "\n",
    "To start working with the `OpenAIChatGenerator`, create a `ChatMessage` object with \"SYSTEM\" role using `ChatMessage.from_system()` and another `ChatMessage` with \"USER\" role using `ChatMessage.from_user()`. Then, pass this messages list to `OpenAIChatGenerator` and run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yzuTzWpJFVbH",
    "outputId": "dd08bf6b-3ca5-4fc8-8f53-f40ada2f7f84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'replies': [ChatMessage(content='Natural Language Processing (NLP) ist ein Teilbereich der Künstlichen Intelligenz, der sich mit der Interaktion zwischen Computern und menschlicher Sprache beschäftigt. Ziel ist es, Maschinen zu ermöglichen, Text und Sprache in einer für Menschen verständlichen Weise zu verstehen, zu interpretieren und zu erzeugen.', role=<ChatRole.ASSISTANT: 'assistant'>, name=None, meta={'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 63, 'prompt_tokens': 33, 'total_tokens': 96, 'prompt_tokens_details': {'cached_tokens': 0, 'audio_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0, 'audio_tokens': 0, 'accepted_prediction_tokens': 0, 'rejected_prediction_tokens': 0}}})]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from haystack.dataclasses import ChatMessage\n",
    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
    "\n",
    "messages = [\n",
    "    ChatMessage.from_system(\"Always respond in German even if some input data is in other languages.\"),\n",
    "    ChatMessage.from_user(\"What's Natural Language Processing? Be brief.\"),\n",
    "]\n",
    "\n",
    "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\")\n",
    "chat_generator.run(messages=messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cbl0xs0MJ76Z"
   },
   "source": [
    "### Basic Streaming\n",
    "\n",
    "`OpenAIChatGenerator` supports streaming, provide a `streaming_callback` function and run the `chat_generator` again to see the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "D0nEEd5PJ1X2",
    "outputId": "dbc6a460-991c-43bd-9fba-be1d6ca67159"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Natural Language Processing (NLP) ist ein Bereich der künstlichen Intelligenz, der sich mit der Interaktion zwischen Computern und Menschen in natürlicher Sprache beschäftigt. Ziel ist es, Maschinen zu befähigen, Texte und gesprochene Sprache zu verstehen, zu interpretieren und darauf zu reagieren."
     ]
    }
   ],
   "source": [
    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
    "from haystack.components.generators.utils import print_streaming_chunk\n",
    "\n",
    "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\", streaming_callback=print_streaming_chunk)\n",
    "response = chat_generator.run(messages=messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FW91cHchCw_W"
   },
   "source": [
    "## Creating a Function Calling Tool from a Haystack Pipeline\n",
    "\n",
    "To use the function calling of OpenAI, you need to introduce `tools` to your `OpenAIChatGenerator` using its `generation_kwargs` param.\n",
    "\n",
    "For this example, you'll use a Haystack RAG pipeline as one of your tools. Therefore, you need to index documents to a document store and then build a RAG pipeline on top of it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FWkDXKbeoNqZ"
   },
   "source": [
    "### Index Documents with a Pipeline\n",
    "\n",
    "Create a pipeline to store the small example dataset in the [InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) with their embeddings. You will use [SentenceTransformersDocumentEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformersdocumentembedder) to generate embeddings for your Documents and write them to the document store with the [DocumentWriter](https://docs.haystack.deepset.ai/docs/documentwriter).\n",
    "\n",
    "After adding these components to your pipeline, connect them and run the pipeline.\n",
    "\n",
    "> If you'd like to learn about preprocessing files before you index them to your document store, follow the [Preprocessing Different File Types](https://haystack.deepset.ai/tutorials/30_file_type_preprocessing_index_pipeline) tutorial."
   ]
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    },
    "id": "ZE0SEGY92GHJ",
    "outputId": "6a2bbfeb-5a61-41d1-c6f6-307550292a85"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amna.mubashar/Library/Python/3.9/lib/python/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "Batches: 100%|██████████| 1/1 [00:01<00:00,  1.11s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'doc_writer': {'documents_written': 5}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from haystack import Pipeline, Document\n",
    "from haystack.document_stores.in_memory import InMemoryDocumentStore\n",
    "from haystack.components.writers import DocumentWriter\n",
    "from haystack.components.embedders import SentenceTransformersDocumentEmbedder\n",
    "\n",
    "documents = [\n",
    "    Document(content=\"My name is Jean and I live in Paris.\"),\n",
    "    Document(content=\"My name is Mark and I live in Berlin.\"),\n",
    "    Document(content=\"My name is Giorgio and I live in Rome.\"),\n",
    "    Document(content=\"My name is Marta and I live in Madrid.\"),\n",
    "    Document(content=\"My name is Harry and I live in London.\"),\n",
    "]\n",
    "\n",
    "document_store = InMemoryDocumentStore()\n",
    "\n",
    "indexing_pipeline = Pipeline()\n",
    "indexing_pipeline.add_component(\n",
    "    instance=SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"), name=\"doc_embedder\"\n",
    ")\n",
    "indexing_pipeline.add_component(instance=DocumentWriter(document_store=document_store), name=\"doc_writer\")\n",
    "\n",
    "indexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n",
    "\n",
    "indexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dwC6fWQy-7pI"
   },
   "source": [
    "### Build a RAG Pipeline\n",
    "\n",
    "Build a basic retrieval augmented generative pipeline with [SentenceTransformersTextEmbedder](https://docs.haystack.deepset.ai/docs/sentencetransformerstextembedder), [InMemoryEmbeddingRetriever](https://docs.haystack.deepset.ai/docs/inmemoryembeddingretriever), [ChatPromptBuilder](https://docs.haystack.deepset.ai/docs/chatpromptbuilder) and [OpenAIChatGenerator](https://docs.haystack.deepset.ai/docs/openaichatgenerator).\n",
    "\n",
    "> For a step-by-step guide to create a RAG pipeline with Haystack, follow the [Creating Your First QA Pipeline with Retrieval-Augmentation](https://haystack.deepset.ai/tutorials/27_first_rag_pipeline) tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "23JuUuql7PZ8",
    "outputId": "b33cbdf5-93d3-46c2-d618-ed9bb76c2d4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<haystack.core.pipeline.pipeline.Pipeline object at 0x3157312e0>\n",
       "🚅 Components\n",
       "  - embedder: SentenceTransformersTextEmbedder\n",
       "  - retriever: InMemoryEmbeddingRetriever\n",
       "  - prompt_builder: ChatPromptBuilder\n",
       "  - llm: OpenAIChatGenerator\n",
       "🛤️ Connections\n",
       "  - embedder.embedding -> retriever.query_embedding (List[float])\n",
       "  - retriever.documents -> prompt_builder.documents (List[Document])\n",
       "  - prompt_builder.prompt -> llm.messages (List[ChatMessage])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from haystack.components.embedders import SentenceTransformersTextEmbedder\n",
    "from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\n",
    "from haystack.components.builders import ChatPromptBuilder\n",
    "from haystack.dataclasses import ChatMessage\n",
    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
    "\n",
    "template = [ChatMessage.from_system(\"\"\"\n",
    "Answer the questions based on the given context.\n",
    "\n",
    "Context:\n",
    "{% for document in documents %}\n",
    "    {{ document.content }}\n",
    "{% endfor %}\n",
    "Question: {{ question }}\n",
    "Answer:\n",
    "\"\"\")]\n",
    "rag_pipe = Pipeline()\n",
    "rag_pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\n",
    "rag_pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\n",
    "rag_pipe.add_component(\"prompt_builder\", ChatPromptBuilder(template=template))\n",
    "rag_pipe.add_component(\"llm\", OpenAIChatGenerator(model=\"gpt-4o-mini\"))\n",
    "\n",
    "rag_pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\n",
    "rag_pipe.connect(\"retriever\", \"prompt_builder.documents\")\n",
    "rag_pipe.connect(\"prompt_builder.prompt\", \"llm.messages\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3vjtLi5A-XAi"
   },
   "source": [
    "### Run the Pipeline\n",
    "Test this pipeline with a query and see if it works as expected before you start using it as a function calling tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 171,
     "referenced_widgets": [
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    },
    "id": "h4bXXC2Z7eaj",
    "outputId": "c069ff2a-7399-4605-f3cc-998c1ebe1504"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Batches: 100%|██████████| 1/1 [00:00<00:00,  1.96it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'llm': {'replies': [ChatMessage(content='Mark lives in Berlin.', role=<ChatRole.ASSISTANT: 'assistant'>, name=None, meta={'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 5, 'prompt_tokens': 83, 'total_tokens': 88, 'prompt_tokens_details': {'cached_tokens': 0, 'audio_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0, 'audio_tokens': 0, 'accepted_prediction_tokens': 0, 'rejected_prediction_tokens': 0}}})]}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"Where does Mark live?\"\n",
    "rag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xeQf1QT0_OA0"
   },
   "source": [
    "### Convert the Haystack Pipeline into a Tool\n",
    "\n",
    "Wrap the `rag_pipe.run` call with a function called `rag_pipeline_func`. This `rag_pipeline_func` function will accept a `query` and return the response coming from the LLM of the RAG pipeline you built before. You will then introduce this function as a tool to your `OpenAIChatGenerator`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "hCUFdKV5_Z7k"
   },
   "outputs": [],
   "source": [
    "def rag_pipeline_func(query: str):\n",
    "    result = rag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})\n",
    "\n",
    "    return {\"reply\": result[\"llm\"][\"replies\"][0].content}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zJt-mzb4oHxj"
   },
   "source": [
    "## Creating Your `tools` List\n",
    "\n",
    "In addition to the `rag_pipeline_func` tool, create a new tool called `get_current_weather` to be used to get weather information of cities. For demonstration purposes, you can use hardcoded data in this function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "XaQgnmPoJD13"
   },
   "outputs": [],
   "source": [
    "WEATHER_INFO = {\n",
    "    \"Berlin\": {\"weather\": \"mostly sunny\", \"temperature\": 7, \"unit\": \"celsius\"},\n",
    "    \"Paris\": {\"weather\": \"mostly cloudy\", \"temperature\": 8, \"unit\": \"celsius\"},\n",
    "    \"Rome\": {\"weather\": \"sunny\", \"temperature\": 14, \"unit\": \"celsius\"},\n",
    "    \"Madrid\": {\"weather\": \"sunny\", \"temperature\": 10, \"unit\": \"celsius\"},\n",
    "    \"London\": {\"weather\": \"cloudy\", \"temperature\": 9, \"unit\": \"celsius\"},\n",
    "}\n",
    "\n",
    "\n",
    "def get_current_weather(location: str):\n",
    "    if location in WEATHER_INFO:\n",
    "        return WEATHER_INFO[location]\n",
    "\n",
    "    # fallback data\n",
    "    else:\n",
    "        return {\"weather\": \"sunny\", \"temperature\": 21.8, \"unit\": \"fahrenheit\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M6Pfmf7YSwsL"
   },
   "source": [
    "Now, add function specifications for `rag_pipeline_func` and `get_current_weather` to your `tools` list by following [OpenAI's tool schema](https://platform.openai.com/docs/api-reference/chat/create#chat-create-tools). Provide detailed descriptions about `rag_pipeline_func` and `query` so that OpenAI can generate the adaquate arguments for this tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "rfYYSytSVtPm"
   },
   "outputs": [],
   "source": [
    "tools = [\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"rag_pipeline_func\",\n",
    "            \"description\": \"Get information about where people live\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"query\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"The query to use in the search. Infer this from the user's message. It should be a question or a statement\",\n",
    "                    }\n",
    "                },\n",
    "                \"required\": [\"query\"],\n",
    "            },\n",
    "        },\n",
    "    },\n",
    "    {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"get_current_weather\",\n",
    "            \"description\": \"Get the current weather\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"location\": {\"type\": \"string\", \"description\": \"The city and state, e.g. San Francisco, CA\"}\n",
    "                },\n",
    "                \"required\": [\"location\"],\n",
    "            },\n",
    "        },\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bkRPp3JKpZgf"
   },
   "source": [
    "## Running OpenAIChatGenerator with Tools\n",
    "\n",
    "To use the function calling feature, you need to pass the list of tools in the `run()` method of OpenAIChatGenerator as `generation_kwargs`.\n",
    "\n",
    "Instruct the model to use provided tools with a system message and then provide a query that requires a function call as a user message:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "OEScMyqctzFN"
   },
   "outputs": [],
   "source": [
    "from haystack.dataclasses import ChatMessage\n",
    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
    "from haystack.components.generators.utils import print_streaming_chunk\n",
    "\n",
    "messages = [\n",
    "    ChatMessage.from_system(\n",
    "        \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"\n",
    "    ),\n",
    "    ChatMessage.from_user(\"Can you tell me where Mark lives?\"),\n",
    "]\n",
    "\n",
    "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\", streaming_callback=print_streaming_chunk)\n",
    "response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zYwb-SqaGL3O"
   },
   "source": [
    "As a response, you'll get a `ChatMessage` with information about the tool name and arguments in JSON format:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "{'replies': [\n",
    "    ChatMessage(\n",
    "        content='[{\"index\": 0, \"id\": \"call_3VnT0XQH0ye41g3Ip5CRz4ri\", \"function\": {\"arguments\": \"{\\\\\"query\\\\\":\\\\\"Where does Mark live?\\\\\"}\", \"name\": \"rag_pipeline_func\"}, \"type\": \"function\"}]', role=<ChatRole.ASSISTANT: 'assistant'>, \n",
    "        name=None, \n",
    "        meta={'model': 'gpt-4o-mini-2024-07-18', 'index': 0, 'finish_reason': 'tool_calls', 'usage': {}}\n",
    "        )\n",
    "    ]\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_ulFfHcnGfsw"
   },
   "source": [
    "You can then parse the message content string into JSON and call the corresponding function with the provided arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101,
     "referenced_widgets": [
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    "id": "JkU5S2t_Id_f",
    "outputId": "1b2776ec-a53f-4cd6-ebd8-cca1eb3f7a46"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function Name: rag_pipeline_func\n",
      "Function Arguments: {'query': 'Where does Mark live?'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Batches: 100%|██████████| 1/1 [00:00<00:00, 16.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Function Response: {'reply': 'Mark lives in Berlin.'}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "## Parse function calling information\n",
    "function_call = json.loads(response[\"replies\"][0].text)[0]\n",
    "function_name = function_call[\"function\"][\"name\"]\n",
    "function_args = json.loads(function_call[\"function\"][\"arguments\"])\n",
    "print(\"Function Name:\", function_name)\n",
    "print(\"Function Arguments:\", function_args)\n",
    "\n",
    "## Find the correspoding function and call it with the given arguments\n",
    "available_functions = {\"rag_pipeline_func\": rag_pipeline_func, \"get_current_weather\": get_current_weather}\n",
    "function_to_call = available_functions[function_name]\n",
    "function_response = function_to_call(**function_args)\n",
    "print(\"Function Response:\", function_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zKwSQm9EMP4e"
   },
   "source": [
    "As the last step, run the OpenAIChatGenerator again by appending the function response to the `messages` list as a new message with `ChatMessage.from_function()` and let the model summarize the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7szLnSZVMXVD",
    "outputId": "18398a55-e83b-48ee-c4e9-fca2bc1dd4be"
   },
   "outputs": [],
   "source": [
    "from haystack.dataclasses import ChatMessage\n",
    "\n",
    "function_message = ChatMessage.from_function(content=json.dumps(function_response), name=function_name)\n",
    "messages.append(function_message)\n",
    "\n",
    "response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5UkjGYEfwPzS"
   },
   "source": [
    "## Building the Chat Application\n",
    "\n",
    "As you notice above, OpenAI Chat Completions API does not call the function; instead, the model generates JSON that you can use to call the function in your code. That's why, to build an end-to-end chat application, you need to check if the OpenAI response is a `tool_calls` for every message. If so, you need to call the corresponding function with the provided arguments and send the function response back to OpenAI. Otherwise, append both user and messages to the `messages` list to have a regular conversation with the model. \n",
    "\n",
    "To build a nice UI for your application, you can use [Gradio](https://www.gradio.app/) that comes with a chat interface. Install `gradio`, run the code cell below and use the input box to interact with the chat application that has access to two tools you've created above.  \n",
    "\n",
    "Example queries you can try:\n",
    "* \"***What is the capital of Sweden?***\": A basic query without any function calls\n",
    "* \"***Can you tell me where Giorgio lives?***\": A basic query with one function call\n",
    "* \"***What's the weather like in Berlin?***\", \"***Is it sunny there?***\": To see the messages are being recorded and sent\n",
    "* \"***What's the weather like where Jean lives?***\": To force two function calls\n",
    "* \"***What's the weather like today?***\": To force OpenAI to ask more clarification\n",
    "\n",
    "> Keep in mind that OpenAI models can sometimes hallucinate answers or tools and might not work as expected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "pip install gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153,
     "referenced_widgets": [
      "235721f8c1c14ce3885fba24638bc27f",
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      "3d1550f31d82429b9f502ff8a029feb3",
      "8013ed5cc4b44f3a811a9b84f4f31117",
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      "384f9461abe640f0bb963dfc377d8a0e",
      "ba23159c36d442098ea77b188ac6f23f",
      "6ce7c086bd9348708f3ec399011af452",
      "c96c3d7d4aba49d29d30f69e6114a78c"
     ]
    },
    "id": "sK_JeKZLhXcy",
    "outputId": "b0c8c776-4151-44a0-9acd-b12f53119af8"
   },
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "import json\n",
    "\n",
    "from haystack.dataclasses import ChatMessage\n",
    "from haystack.components.generators.chat import OpenAIChatGenerator\n",
    "\n",
    "chat_generator = OpenAIChatGenerator(model=\"gpt-4o-mini\")\n",
    "response = None\n",
    "messages = [\n",
    "    ChatMessage.from_system(\n",
    "        \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"\n",
    "    )\n",
    "]\n",
    "\n",
    "\n",
    "def chatbot_with_fc(message, history):\n",
    "    messages.append(ChatMessage.from_user(message))\n",
    "    response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})\n",
    "\n",
    "    while True:\n",
    "        # if OpenAI response is a tool call\n",
    "        if response and response[\"replies\"][0].meta[\"finish_reason\"] == \"tool_calls\":\n",
    "            function_calls = json.loads(response[\"replies\"][0].text)\n",
    "            print(response[\"replies\"][0])\n",
    "            for function_call in function_calls:\n",
    "                ## Parse function calling information\n",
    "                function_name = function_call[\"function\"][\"name\"]\n",
    "                function_args = json.loads(function_call[\"function\"][\"arguments\"])\n",
    "\n",
    "                ## Find the correspoding function and call it with the given arguments\n",
    "                function_to_call = available_functions[function_name]\n",
    "                function_response = function_to_call(**function_args)\n",
    "\n",
    "                ## Append function response to the messages list using `ChatMessage.from_function`\n",
    "                messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\n",
    "\n",
    "            # After processing all function calls, call `chat_generator.run` once\n",
    "            response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})\n",
    "\n",
    "        # Regular Conversation\n",
    "        else:\n",
    "            messages.append(response[\"replies\"][0])\n",
    "            break\n",
    "    return response[\"replies\"][0].text\n",
    "\n",
    "\n",
    "demo = gr.ChatInterface(\n",
    "    fn=chatbot_with_fc,\n",
    "    examples=[\n",
    "        \"Can you tell me where Giorgio lives?\",\n",
    "        \"What's the weather like in Madrid?\",\n",
    "        \"Who lives in London?\",\n",
    "        \"What's the weather like where Mark lives?\",\n",
    "    ],\n",
    "    title=\"Ask me about weather or where people live!\",\n",
    ")\n",
    "\n",
    "## Uncomment the line below to launch the chat app with UI\n",
    "# demo.launch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rgGKjm7cpv1_"
   },
   "source": [
    "## What's next\n",
    "\n",
    "🎉 Congratulations! You've learned how to build chat applications that demonstrate agent-like behavior using OpenAI function calling and Haystack Pipelines.\n",
    "\n",
    "If you liked this tutorial, there's more to learn about Haystack 2.0:\n",
    "- [Serializing LLM Pipelines](https://haystack.deepset.ai/tutorials/29_serializing_pipelines)\n",
    "- [Model-Based Evaluation of RAG Pipelines](https://haystack.deepset.ai/tutorials/35_model_based_evaluation_of_rag_pipelines)\n",
    "\n",
    "To stay up to date on the latest Haystack developments, you can [sign up for our newsletter](https://landing.deepset.ai/haystack-community-updates) or [join Haystack discord community](https://discord.gg/haystack).\n",
    "\n",
    "Thanks for reading!"
   ]
  }
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