{ "source_file": "0930", "source_platform": "Medium", "author": "Julian Yip", "title_listed": "Build Autonomous AI Agents with Function Calling", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/build-autonomous-ai-agents-with-function-calling-0bb483753975/", "publish_date": "2024-04-02", "corpus_stage": "", "notes": "", "id": "9d6b4ccfd105", "collected_at": "2026-10-02T06:45:49", "title": "Build Autonomous AI Agents with Function Calling | Towards Data Science", "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Build Autonomous AI Agents with Function Calling\nTransform your chatbot into an agent that can interact with external APIs\n[Julian Yip](https://towardsdatascience.com/author/jyipkl/)\nApril 2, 202411 min read\nFunction Calling is not something new. In July 2023, OpenAI introduced Function Calling for their GPT models, a feature now being adopted by competitors. Google's Gemini API recently supported it, and Anthropic is integrating it into Claude. Function Calling is becoming essential for large language models (LLMs), enhancing their capabilities. All the more useful to learn this technique!\nWith this in mind, I aim to write a comprehensive tutorial covering Function Calling beyond basic introductions (there are already plenty of tutorials for it). The focus will be on practical implementation, building a fully autonomous AI agent and integrating it with Streamlit for a ChatGPT-like interface. Although OpenAI is used for demonstration, this tutorial can be easily adapted for other LLMs supporting Function Calling, such as Gemini.\n## **What is Function Calling for?**\nFunction Calling enables developers to describe functions (aka tools, you can consider this as actions for the model to take, like performing calculation, or making an order), and have the model intelligently choose to output a JSON object containing arguments to call those functions. In simpler terms, it allows for:\n * **Autonomous decision making** : Models can intelligently choose tools to respond to questions.\n * **Reliable parsing** : Responses are in JSON format, instead of the more typical dialogue-like response. It might not seem much from the first look, but this is what allows LLM to connect to external systems, say via APIs with structured inputs.\n\n\nIt opens up numerous possibilities:\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n * **Autonomous AI assistants** : Bots can interact with internal systems for tasks like customer orders and returns, beyond providing answers to enquiries\n * **Personal research assistants** : Say if you are planning for your travel, assistants can search the web, crawl content, compare options, and summarize results in Excel.\n * **IoT voice commands** : Models can control devices or suggest actions based on detected intents, such as adjusting the AC temperature.\n\n\n* * *\n_(Sidetracking a bit: To realize such potentials, we must have a systematic way to design our prompts and to test them. I have written an article about this too!)_\n> [**Prompt Like a Data Scientist: Auto Prompt Optimization and Testing with DSPy**](https://towardsdatascience.com/prompt-like-a-data-scientist-auto-prompt-optimization-and-testing-with-dspy-ff699f030cb7)\nWithout further ado, let's explore what Function Calling is about!\n## The Structure of Function Calling\nBorrowing from [Gemini's Function Calling documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling), Function Calling has the below structure, which works the same in OpenAI\n![Image from Gemini's Function Calling documentation](../images/000.png)\nImage from [Gemini's Function Calling documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling)\n 1. User issues prompt to the application\n 2. Application passes the user-provided prompt, and the Function Declaration(s), which is a description of the tool(s) that the model could use\n 3. Based on the Function Declaration, the model suggests the tool to use, and the relevant request parameters. **Notice the model outputs the suggested tool and parameters only, WITHOUT actually calling the functions**\n 4. & 5. Based on the response, the application invokes the relevant API\n 5. & 7. The response from API is fed into the model again to output a human-readable response\n 6. Application returns the final response to the user, then repeat from 1.\n\n\nThis might seem convuluted, but the concept will be illustrated in detail with example\n## Architecture\nBefore diving into the code, a few words about the demo application's architecture\n### **Solution**\nHere we build an assistant for tourists visiting a hotel. The assistant has access to the following tools, which allows the assistant to access external applications.\n * `get_items`, `purchase_item`: Connect to product catalog stored in database via API, for retrieving item list and making a purchase respectively\n * `rag_pipeline_func`: Connect to document store with Retrieval Augmented Generation (RAG) to obtain information from unstructured texts e.g. hotel's brochures\n\n\n![](../images/body-c1cc917801e07b42c0b8.png)\n### **Tech stack**\n * **Embedding model** : [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)\n * **Vector Database** : [Haystack's InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)\n * **LLM** : [GPT-4 Turbo accessed via OpenRouter](https://openrouter.ai/models/openai/gpt-4-1106-preview). With OpenRouter you can access different LLM APIs from Hong Kong without VPN. The flow can be adapted into using other LLMs with slight code change, provided they support Function Calling, say Gemini\n * **LLM Framework** : [Haystack](https://haystack.deepset.ai/) for their ease of use, great documentation and transparency in pipeline construction. This tutorial is actually an extension to their [fantastic tutorial](https://haystack.deepset.ai/tutorials/40_building_chat_application_with_function_calling) for the same topic\n\n\nNow let's begin!\n## Sample Application\n### Preparation\nHead over to [Github](https://github.com/yip-kl/llm_function_calling_demo) to clone my code. The contents below can be found in the `function_calling_demo` Notebook.\nPlease also create and activate a virtual environment, then `pip install -r requirements.txt` to install the required packages\n### **Initialization**\nWe first connect to OpenRouter. Alternatively using the original `OpenAIChatGenerator` without overwritting the `api_base_url`would also work, provided you have an OpenAI API key\npython\n\n```\nimport os\nfrom dotenv import load_dotenv\nfrom haystack.components.generators.chat import OpenAIChatGenerator\nfrom haystack.utils import Secret\nfrom haystack.dataclasses import ChatMessage\nfrom haystack.components.generators.utils import print_streaming_chunk\n\n# Set your API key as environment variable before executing this\nload_dotenv()\nOPENROUTER_API_KEY = os.environ.get('OPENROUTER_API_KEY')\n\nchat_generator = OpenAIChatGenerator(api_key=Secret.from_env_var(\"OPENROUTER_API_KEY\"),\n api_base_url=\"https://openrouter.ai/api/v1\",\n model=\"openai/gpt-4-turbo-preview\",\n streaming_callback=print_streaming_chunk)\n```\n\nThen we test can the `chat_generator` be successfully invoked\npython\n\n```\nchat_generator.run(messages=[ChatMessage.from_user(\"Return this text: 'test'\")])\n```\n\ntext\n\n```\n---------- The response should look like this ----------\n{'replies': [ChatMessage(content=\"'test'\", role=, name=None, meta={'model': 'openai/gpt-4-turbo-preview', 'index': 0, 'finish_reason': 'stop', 'usage': {}})]}\n```\n\n### Step 1: Establish data store\nHere we establish connection between our application and the two data sources: **Document store** for unstructured texts, and **application database** via API\n**Index Documents with a Pipeline**\nWe provide sample texts in `documents` for the model to perform Retrival Augmented Generation (RAG). The texts are turned into embeddings and stored in an in-memory document store\npython\n\n```\nfrom haystack import Pipeline, Document\nfrom haystack.document_stores.in_memory import InMemoryDocumentStore\nfrom haystack.components.writers import DocumentWriter\nfrom haystack.components.embedders import SentenceTransformersDocumentEmbedder\n\n# Sample documents\ndocuments = [\n Document(content=\"Coffee shop opens at 9am and closes at 5pm.\"),\n Document(content=\"Gym room opens at 6am and closes at 10pm.\")\n]\n\n# Create the document store\ndocument_store = InMemoryDocumentStore()\n\n# Create a pipeline to turn the texts into embeddings and store them in the document store\nindexing_pipeline = Pipeline()\nindexing_pipeline.add_component(\n \"doc_embedder\", SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n)\nindexing_pipeline.add_component(\"doc_writer\", DocumentWriter(document_store=document_store))\n\nindexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n\nindexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})\n```\n\nIt should output this, corresponding to the `documents` we created as sample\njson\n\n```\n{'doc_writer': {'documents_written': 2}}\n```\n\n**Spin up API server**\nAn API server made with Flask is created under `db_api.py` to connect to SQLite. Please spin it up by running `python db_api.py` in your terminal\n![This would be shown in the terminal, if successfully executed](../images/001.png)\nThis would be shown in the terminal, if successfully executed\nAlso notice that some initial data has been added in `db_api.py`\n![Sample data in the database](../images/002.png)\nSample data in the database\n### Step 2: Define the functions\nHere we prepare the actual functions for the model to invoke **AFTER** Function Calling (Step 4–5 as described in **The Structure of Function Calling**)\n**RAG function**\nNamely the `rag_pipeline_func`. This is for the model to provide an answer by searching through the texts stored in the Document Store. We first define the RAG retrieval as a Haystack pipeline\npython\n\n```\nfrom haystack.components.embedders import SentenceTransformersTextEmbedder\nfrom haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\nfrom haystack.components.builders import PromptBuilder\nfrom haystack.components.generators import OpenAIGenerator\n\ntemplate = \"\"\"\nAnswer the questions based on the given context.\n\nContext:\n{% for document in documents %}\n {{ document.content }}\n{% endfor %}\nQuestion: {{ question }}\nAnswer:\n\"\"\"\nrag_pipe = Pipeline()\nrag_pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\nrag_pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\nrag_pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n# Note to llm: We are using OpenAIGenerator, not the OpenAIChatGenerator, because the latter only accepts List[str] as input and cannot accept prompt_builder's str output\nrag_pipe.add_component(\"llm\", OpenAIGenerator(api_key=Secret.from_env_var(\"OPENROUTER_API_KEY\"),\n api_base_url=\"https://openrouter.ai/api/v1\",\n model=\"openai/gpt-4-turbo-preview\"))\n\nrag_pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\nrag_pipe.connect(\"retriever\", \"prompt_builder.documents\")\nrag_pipe.connect(\"prompt_builder\", \"llm\")\n```\n\nTest if the function works\npython\n\n```\nquery = \"When does the coffee shop open?\"\nrag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})\n```\n\nThis should yield the following output. Notice the `replies` that the model gave is from the sample documents we provided before\njson\n\n```\n{'llm': {'replies': ['The coffee shop opens at 9am.'],\n 'meta': [{'model': 'openai/gpt-4-turbo-preview',\n 'index': 0,\n 'finish_reason': 'stop',\n 'usage': {'completion_tokens': 9,\n 'prompt_tokens': 60,\n 'total_tokens': 69,\n 'total_cost': 0.00087}}]}}\n```\n\nWe can then turn the `rag_pipe` into a function, which provides the `replies` only without adding in the other details\npython\n\n```\ndef 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]}\n```\n\n**API calls**\nWe define the `get_items` and `purchase_item`functions for interacting with the database\npython\n\n```\n# Flask's default local URL, change it if necessary\ndb_base_url = 'http://127.0.0.1:5000'\n\n# Use requests to get the data from the database\nimport requests\nimport json\n\n# get_categories is supplied as part of the prompt, it is not used as a tool\ndef get_categories():\n response = requests.get(f'{db_base_url}/category')\n data = response.json()\n return data\n\ndef get_items(ids=None,categories=None):\n params = {\n 'id': ids,\n 'category': categories,\n }\n response = requests.get(f'{db_base_url}/item', params=params)\n data = response.json()\n return data\n\ndef purchase_item(id,quantity):\n\n headers = {\n 'Content-type':'application/json', \n 'Accept':'application/json'\n }\n\n data = {\n 'id': id,\n 'quantity': quantity,\n }\n response = requests.post(f'{db_base_url}/item/purchase', json=data, headers=headers)\n return response.json()\n```\n\n**Define the tool list**\nNow that we have defined the fuctions, we need to let the model recognize those functions, and to instruct them how they are used, by providing descriptions for them.\nSince we are using OpenAI here, the `tools` is formatted as below following the [format required by Open AI](https://cookbook.openai.com/examples/function_calling_with_an_openapi_spec)\npython\n\n```\ntools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_items\",\n \"description\": \"Get a list of items from the database\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"ids\": {\n \"type\": \"string\",\n \"description\": \"Comma separated list of item ids to fetch\",\n },\n \"categories\": {\n \"type\": \"string\",\n \"description\": \"Comma separated list of item categories to fetch\",\n },\n },\n \"required\": [],\n },\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"purchase_item\",\n \"description\": \"Purchase a particular item\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\n \"type\": \"string\",\n \"description\": \"The given product ID, product name is not accepted here. Please obtain the product ID from the database first.\",\n },\n \"quantity\": {\n \"type\": \"integer\",\n \"description\": \"Number of items to purchase\",\n },\n },\n \"required\": [],\n },\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"rag_pipeline_func\",\n \"description\": \"Get information from hotel brochure\",\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```\n\n### **Step 3: Putting it all together**\nWe now have the necessary inputs to test Function Calling! Here we do a few things:\n 1. Provide the initial prompt to the model, to give it some context\n 2. Provide a sample user-generated message\n 3. Most importantly, we pass the tool list to the chat generator in `tools`\n\n\npython\n\n```\n# 1. Initial prompt\ncontext = f\"\"\"You are an assistant to tourists visiting a hotel.\nYou have access to a database of items (which includes {get_categories()}) that tourists can buy, you also have access to the hotel's brochure.\nIf the tourist's question cannot be answered from the database, you can refer to the brochure.\nIf the tourist's question cannot be answered from the brochure, you can ask the tourist to ask the hotel staff.\n\"\"\"\nmessages = [\n ChatMessage.from_system(context),\n # 2. Sample message from user\n ChatMessage.from_user(\"Can I buy a coffee?\"),\n ]\n\n# 3. Passing the tools list and invoke the chat generator\nresponse = chat_generator.run(messages=messages, generation_kwargs= {\"tools\": tools})\nresponse\n```\n\ntext\n\n```\n---------- Response ----------\n{'replies': [ChatMessage(content='[{\"index\": 0, \"id\": \"call_AkTWoiJzx5uJSgKW0WAI1yBB\", \"function\": {\"arguments\": \"{\"categories\":\"Food and beverages\"}\", \"name\": \"get_items\"}, \"type\": \"function\"}]', role=, name=None, meta={'model': 'openai/gpt-4-turbo-preview', 'index': 0, 'finish_reason': 'tool_calls', 'usage': {}})]}\n```\n\nNow let's inspect the response. Notice how the Function Calling returns both the function chosen by the model, and the arguments for invoking the chosen function.\npython\n\n```\nfunction_call = json.loads(response[\"replies\"][0].content)[0]\nfunction_name = function_call[\"function\"][\"name\"]\nfunction_args = json.loads(function_call[\"function\"][\"arguments\"])\nprint(\"Function Name:\", function_name)\nprint(\"Function Arguments:\", function_args)\n```\n\ntext\n\n```\n---------- Response ----------\nFunction Name: get_items\nFunction Arguments: {'categories': 'Food and beverages'}\n```\n\nWhen presented with another question, the model will use another tool that is more relevant\npython\n\n```\n# Another question\nmessages.append(ChatMessage.from_user(\"Where's the coffee shop?\"))\n\n# Invoke the chat generator, and passing the tools list\nresponse = chat_generator.run(messages=messages, generation_kwargs= {\"tools\": tools})\nfunction_call = json.loads(response[\"replies\"][0].content)[0]\nfunction_name = function_call[\"function\"][\"name\"]\nfunction_args = json.loads(function_call[\"function\"][\"arguments\"])\nprint(\"Function Name:\", function_name)\nprint(\"Function Arguments:\", function_args)\n```\n\ntext\n\n```\n---------- Response ----------\nFunction Name: rag_pipeline_func\nFunction Arguments: {'query': \"Where's the coffee shop?\"}\n```\n\nAgain, notice that no actual function is invoked here, this is what we will do next!\n**Calling the function**\nWe can then feed the arguments into the chosen function\npython\n\n```\n## Find the correspoding function and call it with the given arguments\navailable_functions = {\"get_items\": get_items, \"purchase_item\": purchase_item,\"rag_pipeline_func\": rag_pipeline_func}\nfunction_to_call = available_functions[function_name]\nfunction_response = function_to_call(**function_args)\nprint(\"Function Response:\", function_response)\n```\n\ntext\n\n```\n---------- Response ----------\nFunction Response: {'reply': 'The provided context does not specify a physical location for the coffee shop, only its operating hours. Therefore, I cannot determine where the coffee shop is located based on the given information.'}\n```\n\nThe response from `rag_pipeline_func` can then passed as a context to the chat by appending it under the `messages`, for the model to provide the final answer\npython\n\n```\nmessages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\nresponse = chat_generator.run(messages=messages)\nresponse_msg = response[\"replies\"][0]\n\nprint(response_msg.content)\n```\n\ntext\n\n```\n---------- Response ----------\nFor the location of the coffee shop within the hotel, I recommend asking the hotel staff directly. They will be able to guide you to it accurately.\n```\n\nWe now have completed the chat cycle!\n### Step 4: Turn into an interactive chat\nThe code above shows how Function Calling can be done, but we want to go a step further by turning it into an interactive chat\nHere I showcase two methods to do it, from the more primitive `input()` that prints the dialogue into the notebook itself, to rendering it through **Streamlit** to provide it with an ChatGPT-like UI\n`**input()**`**loop**\nThe code is copied from [Haystack's tutorial](https://haystack.deepset.ai/tutorials/40_building_chat_application_with_function_calling), which allows us to quickly test the model. _Note: This application is created to demonstrate the idea of Function Calling, and is NOT meant to be perfectly robust e.g. supporting the order of multiple items at the same time, no hallucination, etc._\npython\n\n```\nimport json\nfrom haystack.dataclasses import ChatMessage, ChatRole\n\nresponse = None\nmessages = [\n ChatMessage.from_system(context)\n]\n\nwhile 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].content)\n\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 # Regular Conversation\n else:\n # Append assistant messages to the messages list\n if not messages[-1].is_from(ChatRole.SYSTEM):\n messages.append(response[\"replies\"][0])\n\n user_input = input(\"ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stopn\")\n if user_input.lower() == \"exit\" or user_input.lower() == \"quit\":\n break\n else:\n messages.append(ChatMessage.from_user(user_input))\n\n response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})\n```\n\n![Running interactive chats in the IDE](../images/003.png)\nRunning interactive chats in the IDE\nWhile it works, we might want to have something that looks nicer.\n**Streamlit interface**\nStreamlit turns data scripts into shareable web apps, which provides a neat UI for our application. The code shown above are adapted into a Streamlit application under the `streamlit` folder of my repo\nYou can run it by:\n 1. If you have not done so already, spin up the API server with `python db_api.py`\n 2. Set the OPENROUTER_API_KEY as environment variable e.g. `export OPENROUTER_API_KEY = '@REPLACE WITH YOUR API KEY'` assuming you are on Linux / executing with git bash\n 3. Navigate to the `streamlit` folder in the terminal with `cd streamlit`\n 4. Run Streamlit with `streamlit run app.py`. A new tab should be automatically created in your browser running the application\n\n\nThat's basically it! I hope you enjoy this article.\n![Streamlit UI](../images/004.png)\nStreamlit UI\n _*Unless otherwise noted, all images are by the author_\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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In July 2023, OpenAI introduced Function Calling for their GPT models, a feature now being adopted by competitors. Google's Gemini API recently supported it, and Anthropic is integrating it into Claude. Function Calling is becoming essential for large language models (LLMs), enhancing their capabilities. All the more useful to learn this technique!\nWith this in mind, I aim to write a comprehensive tutorial covering Function Calling beyond basic introductions (there are already plenty of tutorials for it). The focus will be on practical implementation, building a fully autonomous AI agent and integrating it with Streamlit for a ChatGPT-like interface. Although OpenAI is used for demonstration, this tutorial can be easily adapted for other LLMs supporting Function Calling, such as Gemini.\n## **What is Function Calling for?**\nFunction Calling enables developers to describe functions (aka tools, you can consider this as actions for the model to take, like performing calculation, or making an order), and have the model intelligently choose to output a JSON object containing arguments to call those functions. In simpler terms, it allows for:\n * **Autonomous decision making** : Models can intelligently choose tools to respond to questions.\n * **Reliable parsing** : Responses are in JSON format, instead of the more typical dialogue-like response. It might not seem much from the first look, but this is what allows LLM to connect to external systems, say via APIs with structured inputs.\n\n\nIt opens up numerous possibilities:\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap.\n * **Autonomous AI assistants** : Bots can interact with internal systems for tasks like customer orders and returns, beyond providing answers to enquiries\n * **Personal research assistants** : Say if you are planning for your travel, assistants can search the web, crawl content, compare options, and summarize results in Excel.\n * **IoT voice commands** : Models can control devices or suggest actions based on detected intents, such as adjusting the AC temperature.\n\n\n* * *\n_(Sidetracking a bit: To realize such potentials, we must have a systematic way to design our prompts and to test them. I have written an article about this too!)_\n> [**Prompt Like a Data Scientist: Auto Prompt Optimization and Testing with DSPy**](https://towardsdatascience.com/prompt-like-a-data-scientist-auto-prompt-optimization-and-testing-with-dspy-ff699f030cb7)\nWithout further ado, let's explore what Function Calling is about!\n## The Structure of Function Calling\nBorrowing from [Gemini's Function Calling documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling), Function Calling has the below structure, which works the same in OpenAI\n![Image from Gemini's Function Calling documentation](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/0rrgnwWr7Hif3TfH_.png)\nImage from [Gemini's Function Calling documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling)\n 1. User issues prompt to the application\n 2. Application passes the user-provided prompt, and the Function Declaration(s), which is a description of the tool(s) that the model could use\n 3. Based on the Function Declaration, the model suggests the tool to use, and the relevant request parameters. **Notice the model outputs the suggested tool and parameters only, WITHOUT actually calling the functions**\n 4. & 5. Based on the response, the application invokes the relevant API\n 5. & 7. The response from API is fed into the model again to output a human-readable response\n 6. Application returns the final response to the user, then repeat from 1.\n\n\nThis might seem convuluted, but the concept will be illustrated in detail with example\n## Architecture\nBefore diving into the code, a few words about the demo application's architecture\n### **Solution**\nHere we build an assistant for tourists visiting a hotel. The assistant has access to the following tools, which allows the assistant to access external applications.\n * `get_items`, `purchase_item`: Connect to product catalog stored in database via API, for retrieving item list and making a purchase respectively\n * `rag_pipeline_func`: Connect to document store with Retrieval Augmented Generation (RAG) to obtain information from unstructured texts e.g. hotel's brochures\n\n\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/12aXhxSCJKyOVzkC0IJofiQ.png)\n### **Tech stack**\n * **Embedding model** : [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)\n * **Vector Database** : [Haystack's InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore)\n * **LLM** : [GPT-4 Turbo accessed via OpenRouter](https://openrouter.ai/models/openai/gpt-4-1106-preview). With OpenRouter you can access different LLM APIs from Hong Kong without VPN. The flow can be adapted into using other LLMs with slight code change, provided they support Function Calling, say Gemini\n * **LLM Framework** : [Haystack](https://haystack.deepset.ai/) for their ease of use, great documentation and transparency in pipeline construction. This tutorial is actually an extension to their [fantastic tutorial](https://haystack.deepset.ai/tutorials/40_building_chat_application_with_function_calling) for the same topic\n\n\nNow let's begin!\n## Sample Application\n### Preparation\nHead over to [Github](https://github.com/yip-kl/llm_function_calling_demo) to clone my code. The contents below can be found in the `function_calling_demo` Notebook.\nPlease also create and activate a virtual environment, then `pip install -r requirements.txt` to install the required packages\n### **Initialization**\nWe first connect to OpenRouter. Alternatively using the original `OpenAIChatGenerator` without overwritting the `api_base_url`would also work, provided you have an OpenAI API key\npython\n\n```\nimport os\nfrom dotenv import load_dotenv\nfrom haystack.components.generators.chat import OpenAIChatGenerator\nfrom haystack.utils import Secret\nfrom haystack.dataclasses import ChatMessage\nfrom haystack.components.generators.utils import print_streaming_chunk\n\n# Set your API key as environment variable before executing this\nload_dotenv()\nOPENROUTER_API_KEY = os.environ.get('OPENROUTER_API_KEY')\n\nchat_generator = OpenAIChatGenerator(api_key=Secret.from_env_var(\"OPENROUTER_API_KEY\"),\n api_base_url=\"https://openrouter.ai/api/v1\",\n model=\"openai/gpt-4-turbo-preview\",\n streaming_callback=print_streaming_chunk)\n```\n\nThen we test can the `chat_generator` be successfully invoked\npython\n\n```\nchat_generator.run(messages=[ChatMessage.from_user(\"Return this text: 'test'\")])\n```\n\ntext\n\n```\n---------- The response should look like this ----------\n{'replies': [ChatMessage(content=\"'test'\", role=, name=None, meta={'model': 'openai/gpt-4-turbo-preview', 'index': 0, 'finish_reason': 'stop', 'usage': {}})]}\n```\n\n### Step 1: Establish data store\nHere we establish connection between our application and the two data sources: **Document store** for unstructured texts, and **application database** via API\n**Index Documents with a Pipeline**\nWe provide sample texts in `documents` for the model to perform Retrival Augmented Generation (RAG). The texts are turned into embeddings and stored in an in-memory document store\npython\n\n```\nfrom haystack import Pipeline, Document\nfrom haystack.document_stores.in_memory import InMemoryDocumentStore\nfrom haystack.components.writers import DocumentWriter\nfrom haystack.components.embedders import SentenceTransformersDocumentEmbedder\n\n# Sample documents\ndocuments = [\n Document(content=\"Coffee shop opens at 9am and closes at 5pm.\"),\n Document(content=\"Gym room opens at 6am and closes at 10pm.\")\n]\n\n# Create the document store\ndocument_store = InMemoryDocumentStore()\n\n# Create a pipeline to turn the texts into embeddings and store them in the document store\nindexing_pipeline = Pipeline()\nindexing_pipeline.add_component(\n \"doc_embedder\", SentenceTransformersDocumentEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\")\n)\nindexing_pipeline.add_component(\"doc_writer\", DocumentWriter(document_store=document_store))\n\nindexing_pipeline.connect(\"doc_embedder.documents\", \"doc_writer.documents\")\n\nindexing_pipeline.run({\"doc_embedder\": {\"documents\": documents}})\n```\n\nIt should output this, corresponding to the `documents` we created as sample\njson\n\n```\n{'doc_writer': {'documents_written': 2}}\n```\n\n**Spin up API server**\nAn API server made with Flask is created under `db_api.py` to connect to SQLite. Please spin it up by running `python db_api.py` in your terminal\n![This would be shown in the terminal, if successfully executed](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1S4Ty6XitWFrOHY5wWENaXg.png)\nThis would be shown in the terminal, if successfully executed\nAlso notice that some initial data has been added in `db_api.py`\n![Sample data in the database](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1tASIXxNUJThtf0-HuPtISw.png)\nSample data in the database\n### Step 2: Define the functions\nHere we prepare the actual functions for the model to invoke **AFTER** Function Calling (Step 4–5 as described in **The Structure of Function Calling**)\n**RAG function**\nNamely the `rag_pipeline_func`. This is for the model to provide an answer by searching through the texts stored in the Document Store. We first define the RAG retrieval as a Haystack pipeline\npython\n\n```\nfrom haystack.components.embedders import SentenceTransformersTextEmbedder\nfrom haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever\nfrom haystack.components.builders import PromptBuilder\nfrom haystack.components.generators import OpenAIGenerator\n\ntemplate = \"\"\"\nAnswer the questions based on the given context.\n\nContext:\n{% for document in documents %}\n {{ document.content }}\n{% endfor %}\nQuestion: {{ question }}\nAnswer:\n\"\"\"\nrag_pipe = Pipeline()\nrag_pipe.add_component(\"embedder\", SentenceTransformersTextEmbedder(model=\"sentence-transformers/all-MiniLM-L6-v2\"))\nrag_pipe.add_component(\"retriever\", InMemoryEmbeddingRetriever(document_store=document_store))\nrag_pipe.add_component(\"prompt_builder\", PromptBuilder(template=template))\n# Note to llm: We are using OpenAIGenerator, not the OpenAIChatGenerator, because the latter only accepts List[str] as input and cannot accept prompt_builder's str output\nrag_pipe.add_component(\"llm\", OpenAIGenerator(api_key=Secret.from_env_var(\"OPENROUTER_API_KEY\"),\n api_base_url=\"https://openrouter.ai/api/v1\",\n model=\"openai/gpt-4-turbo-preview\"))\n\nrag_pipe.connect(\"embedder.embedding\", \"retriever.query_embedding\")\nrag_pipe.connect(\"retriever\", \"prompt_builder.documents\")\nrag_pipe.connect(\"prompt_builder\", \"llm\")\n```\n\nTest if the function works\npython\n\n```\nquery = \"When does the coffee shop open?\"\nrag_pipe.run({\"embedder\": {\"text\": query}, \"prompt_builder\": {\"question\": query}})\n```\n\nThis should yield the following output. Notice the `replies` that the model gave is from the sample documents we provided before\njson\n\n```\n{'llm': {'replies': ['The coffee shop opens at 9am.'],\n 'meta': [{'model': 'openai/gpt-4-turbo-preview',\n 'index': 0,\n 'finish_reason': 'stop',\n 'usage': {'completion_tokens': 9,\n 'prompt_tokens': 60,\n 'total_tokens': 69,\n 'total_cost': 0.00087}}]}}\n```\n\nWe can then turn the `rag_pipe` into a function, which provides the `replies` only without adding in the other details\npython\n\n```\ndef 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]}\n```\n\n**API calls**\nWe define the `get_items` and `purchase_item`functions for interacting with the database\npython\n\n```\n# Flask's default local URL, change it if necessary\ndb_base_url = 'http://127.0.0.1:5000'\n\n# Use requests to get the data from the database\nimport requests\nimport json\n\n# get_categories is supplied as part of the prompt, it is not used as a tool\ndef get_categories():\n response = requests.get(f'{db_base_url}/category')\n data = response.json()\n return data\n\ndef get_items(ids=None,categories=None):\n params = {\n 'id': ids,\n 'category': categories,\n }\n response = requests.get(f'{db_base_url}/item', params=params)\n data = response.json()\n return data\n\ndef purchase_item(id,quantity):\n\n headers = {\n 'Content-type':'application/json', \n 'Accept':'application/json'\n }\n\n data = {\n 'id': id,\n 'quantity': quantity,\n }\n response = requests.post(f'{db_base_url}/item/purchase', json=data, headers=headers)\n return response.json()\n```\n\n**Define the tool list**\nNow that we have defined the fuctions, we need to let the model recognize those functions, and to instruct them how they are used, by providing descriptions for them.\nSince we are using OpenAI here, the `tools` is formatted as below following the [format required by Open AI](https://cookbook.openai.com/examples/function_calling_with_an_openapi_spec)\npython\n\n```\ntools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_items\",\n \"description\": \"Get a list of items from the database\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"ids\": {\n \"type\": \"string\",\n \"description\": \"Comma separated list of item ids to fetch\",\n },\n \"categories\": {\n \"type\": \"string\",\n \"description\": \"Comma separated list of item categories to fetch\",\n },\n },\n \"required\": [],\n },\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"purchase_item\",\n \"description\": \"Purchase a particular item\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\n \"type\": \"string\",\n \"description\": \"The given product ID, product name is not accepted here. Please obtain the product ID from the database first.\",\n },\n \"quantity\": {\n \"type\": \"integer\",\n \"description\": \"Number of items to purchase\",\n },\n },\n \"required\": [],\n },\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"rag_pipeline_func\",\n \"description\": \"Get information from hotel brochure\",\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```\n\n### **Step 3: Putting it all together**\nWe now have the necessary inputs to test Function Calling! Here we do a few things:\n 1. Provide the initial prompt to the model, to give it some context\n 2. Provide a sample user-generated message\n 3. Most importantly, we pass the tool list to the chat generator in `tools`\n\n\npython\n\n```\n# 1. Initial prompt\ncontext = f\"\"\"You are an assistant to tourists visiting a hotel.\nYou have access to a database of items (which includes {get_categories()}) that tourists can buy, you also have access to the hotel's brochure.\nIf the tourist's question cannot be answered from the database, you can refer to the brochure.\nIf the tourist's question cannot be answered from the brochure, you can ask the tourist to ask the hotel staff.\n\"\"\"\nmessages = [\n ChatMessage.from_system(context),\n # 2. Sample message from user\n ChatMessage.from_user(\"Can I buy a coffee?\"),\n ]\n\n# 3. Passing the tools list and invoke the chat generator\nresponse = chat_generator.run(messages=messages, generation_kwargs= {\"tools\": tools})\nresponse\n```\n\ntext\n\n```\n---------- Response ----------\n{'replies': [ChatMessage(content='[{\"index\": 0, \"id\": \"call_AkTWoiJzx5uJSgKW0WAI1yBB\", \"function\": {\"arguments\": \"{\"categories\":\"Food and beverages\"}\", \"name\": \"get_items\"}, \"type\": \"function\"}]', role=, name=None, meta={'model': 'openai/gpt-4-turbo-preview', 'index': 0, 'finish_reason': 'tool_calls', 'usage': {}})]}\n```\n\nNow let's inspect the response. Notice how the Function Calling returns both the function chosen by the model, and the arguments for invoking the chosen function.\npython\n\n```\nfunction_call = json.loads(response[\"replies\"][0].content)[0]\nfunction_name = function_call[\"function\"][\"name\"]\nfunction_args = json.loads(function_call[\"function\"][\"arguments\"])\nprint(\"Function Name:\", function_name)\nprint(\"Function Arguments:\", function_args)\n```\n\ntext\n\n```\n---------- Response ----------\nFunction Name: get_items\nFunction Arguments: {'categories': 'Food and beverages'}\n```\n\nWhen presented with another question, the model will use another tool that is more relevant\npython\n\n```\n# Another question\nmessages.append(ChatMessage.from_user(\"Where's the coffee shop?\"))\n\n# Invoke the chat generator, and passing the tools list\nresponse = chat_generator.run(messages=messages, generation_kwargs= {\"tools\": tools})\nfunction_call = json.loads(response[\"replies\"][0].content)[0]\nfunction_name = function_call[\"function\"][\"name\"]\nfunction_args = json.loads(function_call[\"function\"][\"arguments\"])\nprint(\"Function Name:\", function_name)\nprint(\"Function Arguments:\", function_args)\n```\n\ntext\n\n```\n---------- Response ----------\nFunction Name: rag_pipeline_func\nFunction Arguments: {'query': \"Where's the coffee shop?\"}\n```\n\nAgain, notice that no actual function is invoked here, this is what we will do next!\n**Calling the function**\nWe can then feed the arguments into the chosen function\npython\n\n```\n## Find the correspoding function and call it with the given arguments\navailable_functions = {\"get_items\": get_items, \"purchase_item\": purchase_item,\"rag_pipeline_func\": rag_pipeline_func}\nfunction_to_call = available_functions[function_name]\nfunction_response = function_to_call(**function_args)\nprint(\"Function Response:\", function_response)\n```\n\ntext\n\n```\n---------- Response ----------\nFunction Response: {'reply': 'The provided context does not specify a physical location for the coffee shop, only its operating hours. Therefore, I cannot determine where the coffee shop is located based on the given information.'}\n```\n\nThe response from `rag_pipeline_func` can then passed as a context to the chat by appending it under the `messages`, for the model to provide the final answer\npython\n\n```\nmessages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name))\nresponse = chat_generator.run(messages=messages)\nresponse_msg = response[\"replies\"][0]\n\nprint(response_msg.content)\n```\n\ntext\n\n```\n---------- Response ----------\nFor the location of the coffee shop within the hotel, I recommend asking the hotel staff directly. They will be able to guide you to it accurately.\n```\n\nWe now have completed the chat cycle!\n### Step 4: Turn into an interactive chat\nThe code above shows how Function Calling can be done, but we want to go a step further by turning it into an interactive chat\nHere I showcase two methods to do it, from the more primitive `input()` that prints the dialogue into the notebook itself, to rendering it through **Streamlit** to provide it with an ChatGPT-like UI\n`**input()**`**loop**\nThe code is copied from [Haystack's tutorial](https://haystack.deepset.ai/tutorials/40_building_chat_application_with_function_calling), which allows us to quickly test the model. _Note: This application is created to demonstrate the idea of Function Calling, and is NOT meant to be perfectly robust e.g. supporting the order of multiple items at the same time, no hallucination, etc._\npython\n\n```\nimport json\nfrom haystack.dataclasses import ChatMessage, ChatRole\n\nresponse = None\nmessages = [\n ChatMessage.from_system(context)\n]\n\nwhile 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].content)\n\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 # Regular Conversation\n else:\n # Append assistant messages to the messages list\n if not messages[-1].is_from(ChatRole.SYSTEM):\n messages.append(response[\"replies\"][0])\n\n user_input = input(\"ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stopn\")\n if user_input.lower() == \"exit\" or user_input.lower() == \"quit\":\n break\n else:\n messages.append(ChatMessage.from_user(user_input))\n\n response = chat_generator.run(messages=messages, generation_kwargs={\"tools\": tools})\n```\n\n![Running interactive chats in the IDE](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1A5nwZCw3JO_553WiBEGaDg.png)\nRunning interactive chats in the IDE\nWhile it works, we might want to have something that looks nicer.\n**Streamlit interface**\nStreamlit turns data scripts into shareable web apps, which provides a neat UI for our application. The code shown above are adapted into a Streamlit application under the `streamlit` folder of my repo\nYou can run it by:\n 1. If you have not done so already, spin up the API server with `python db_api.py`\n 2. Set the OPENROUTER_API_KEY as environment variable e.g. `export OPENROUTER_API_KEY = '@REPLACE WITH YOUR API KEY'` assuming you are on Linux / executing with git bash\n 3. Navigate to the `streamlit` folder in the terminal with `cd streamlit`\n 4. Run Streamlit with `streamlit run app.py`. A new tab should be automatically created in your browser running the application\n\n\nThat's basically it! I hope you enjoy this article.\n![Streamlit UI](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/04/1EPp8ZNpthZi8ksU03vuCAg.png)\nStreamlit UI\n _*Unless otherwise noted, all images are by the author_\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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