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Sign in[Submit an Article](https://write.insightmediagroup.io) [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/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Data Science](https://towardsdatascience.com/category/data-science/) # Build Autonomous AI Agents with Function Calling Transform your chatbot into an agent that can interact with external APIs [Julian Yip](https://towardsdatascience.com/author/jyipkl/) April 2, 202411 min read Function 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! With 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. ## **What is Function Calling for?** Function 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: * **Autonomous decision making** : Models can intelligently choose tools to respond to questions. * **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. It opens up numerous possibilities: Learn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmap. * **Autonomous AI assistants** : Bots can interact with internal systems for tasks like customer orders and returns, beyond providing answers to enquiries * **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. * **IoT voice commands** : Models can control devices or suggest actions based on detected intents, such as adjusting the AC temperature. * * * _(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!)_ > [**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) Without further ado, let's explore what Function Calling is about! ## The Structure of Function Calling Borrowing 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 ![Image from Gemini's Function Calling documentation](../images/000.png) Image from [Gemini's Function Calling documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/function-calling) 1. User issues prompt to the application 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 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** 4. & 5. Based on the response, the application invokes the relevant API 5. & 7. The response from API is fed into the model again to output a human-readable response 6. Application returns the final response to the user, then repeat from 1. This might seem convuluted, but the concept will be illustrated in detail with example ## Architecture Before diving into the code, a few words about the demo application's architecture ### **Solution** Here 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. * `get_items`, `purchase_item`: Connect to product catalog stored in database via API, for retrieving item list and making a purchase respectively * `rag_pipeline_func`: Connect to document store with Retrieval Augmented Generation (RAG) to obtain information from unstructured texts e.g. hotel's brochures ![](../images/body-c1cc917801e07b42c0b8.png) ### **Tech stack** * **Embedding model** : [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) * **Vector Database** : [Haystack's InMemoryDocumentStore](https://docs.haystack.deepset.ai/docs/inmemorydocumentstore) * **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 * **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 Now let's begin! ## Sample Application ### Preparation Head 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. Please also create and activate a virtual environment, then `pip install -r requirements.txt` to install the required packages ### **Initialization** We 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 python ``` import os from dotenv import load_dotenv from haystack.components.generators.chat import OpenAIChatGenerator from haystack.utils import Secret from haystack.dataclasses import ChatMessage from haystack.components.generators.utils import print_streaming_chunk # Set your API key as environment variable before executing this load_dotenv() OPENROUTER_API_KEY = os.environ.get('OPENROUTER_API_KEY') chat_generator = OpenAIChatGenerator(api_key=Secret.from_env_var("OPENROUTER_API_KEY"), api_base_url="https://openrouter.ai/api/v1", model="openai/gpt-4-turbo-preview", streaming_callback=print_streaming_chunk) ``` Then we test can the `chat_generator` be successfully invoked python ``` chat_generator.run(messages=[ChatMessage.from_user("Return this text: 'test'")]) ``` text ``` ---------- The response should look like this ---------- {'replies': [ChatMessage(content="'test'", role=, name=None, meta={'model': 'openai/gpt-4-turbo-preview', 'index': 0, 'finish_reason': 'stop', 'usage': {}})]} ``` ### Step 1: Establish data store Here we establish connection between our application and the two data sources: **Document store** for unstructured texts, and **application database** via API **Index Documents with a Pipeline** We 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 python ``` from haystack import Pipeline, Document from haystack.document_stores.in_memory import InMemoryDocumentStore from haystack.components.writers import DocumentWriter from haystack.components.embedders import SentenceTransformersDocumentEmbedder # Sample documents documents = [ Document(content="Coffee shop opens at 9am and closes at 5pm."), Document(content="Gym room opens at 6am and closes at 10pm.") ] # Create the document store document_store = InMemoryDocumentStore() # Create a pipeline to turn the texts into embeddings and store them in the document store indexing_pipeline = Pipeline() indexing_pipeline.add_component( "doc_embedder", SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2") ) indexing_pipeline.add_component("doc_writer", DocumentWriter(document_store=document_store)) indexing_pipeline.connect("doc_embedder.documents", "doc_writer.documents") indexing_pipeline.run({"doc_embedder": {"documents": documents}}) ``` It should output this, corresponding to the `documents` we created as sample json ``` {'doc_writer': {'documents_written': 2}} ``` **Spin up API server** An 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 ![This would be shown in the terminal, if successfully executed](../images/001.png) This would be shown in the terminal, if successfully executed Also notice that some initial data has been added in `db_api.py` ![Sample data in the database](../images/002.png) Sample data in the database ### Step 2: Define the functions Here we prepare the actual functions for the model to invoke **AFTER** Function Calling (Step 4–5 as described in **The Structure of Function Calling**) **RAG function** Namely 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 python ``` from haystack.components.embedders import SentenceTransformersTextEmbedder from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever from haystack.components.builders import PromptBuilder from haystack.components.generators import OpenAIGenerator template = """ Answer the questions based on the given context. Context: {% for document in documents %} {{ document.content }} {% endfor %} Question: {{ question }} Answer: """ rag_pipe = Pipeline() rag_pipe.add_component("embedder", SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2")) rag_pipe.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store)) rag_pipe.add_component("prompt_builder", PromptBuilder(template=template)) # 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 rag_pipe.add_component("llm", OpenAIGenerator(api_key=Secret.from_env_var("OPENROUTER_API_KEY"), api_base_url="https://openrouter.ai/api/v1", model="openai/gpt-4-turbo-preview")) rag_pipe.connect("embedder.embedding", "retriever.query_embedding") rag_pipe.connect("retriever", "prompt_builder.documents") rag_pipe.connect("prompt_builder", "llm") ``` Test if the function works python ``` query = "When does the coffee shop open?" rag_pipe.run({"embedder": {"text": query}, "prompt_builder": {"question": query}}) ``` This should yield the following output. Notice the `replies` that the model gave is from the sample documents we provided before json ``` {'llm': {'replies': ['The coffee shop opens at 9am.'], 'meta': [{'model': 'openai/gpt-4-turbo-preview', 'index': 0, 'finish_reason': 'stop', 'usage': {'completion_tokens': 9, 'prompt_tokens': 60, 'total_tokens': 69, 'total_cost': 0.00087}}]}} ``` We can then turn the `rag_pipe` into a function, which provides the `replies` only without adding in the other details python ``` def rag_pipeline_func(query: str): result = rag_pipe.run({"embedder": {"text": query}, "prompt_builder": {"question": query}}) return {"reply": result["llm"]["replies"][0]} ``` **API calls** We define the `get_items` and `purchase_item`functions for interacting with the database python ``` # Flask's default local URL, change it if necessary db_base_url = 'http://127.0.0.1:5000' # Use requests to get the data from the database import requests import json # get_categories is supplied as part of the prompt, it is not used as a tool def get_categories(): response = requests.get(f'{db_base_url}/category') data = response.json() return data def get_items(ids=None,categories=None): params = { 'id': ids, 'category': categories, } response = requests.get(f'{db_base_url}/item', params=params) data = response.json() return data def purchase_item(id,quantity): headers = { 'Content-type':'application/json', 'Accept':'application/json' } data = { 'id': id, 'quantity': quantity, } response = requests.post(f'{db_base_url}/item/purchase', json=data, headers=headers) return response.json() ``` **Define the tool list** Now 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. Since 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) python ``` tools = [ { "type": "function", "function": { "name": "get_items", "description": "Get a list of items from the database", "parameters": { "type": "object", "properties": { "ids": { "type": "string", "description": "Comma separated list of item ids to fetch", }, "categories": { "type": "string", "description": "Comma separated list of item categories to fetch", }, }, "required": [], }, } }, { "type": "function", "function": { "name": "purchase_item", "description": "Purchase a particular item", "parameters": { "type": "object", "properties": { "id": { "type": "string", "description": "The given product ID, product name is not accepted here. Please obtain the product ID from the database first.", }, "quantity": { "type": "integer", "description": "Number of items to purchase", }, }, "required": [], }, } }, { "type": "function", "function": { "name": "rag_pipeline_func", "description": "Get information from hotel brochure", "parameters": { "type": "object", "properties": { "query": { "type": "string", "description": "The query to use in the search. Infer this from the user's message. It should be a question or a statement", } }, "required": ["query"], }, }, } ] ``` ### **Step 3: Putting it all together** We now have the necessary inputs to test Function Calling! Here we do a few things: 1. Provide the initial prompt to the model, to give it some context 2. Provide a sample user-generated message 3. Most importantly, we pass the tool list to the chat generator in `tools` python ``` # 1. Initial prompt context = f"""You are an assistant to tourists visiting a hotel. You have access to a database of items (which includes {get_categories()}) that tourists can buy, you also have access to the hotel's brochure. If the tourist's question cannot be answered from the database, you can refer to the brochure. If the tourist's question cannot be answered from the brochure, you can ask the tourist to ask the hotel staff. """ messages = [ ChatMessage.from_system(context), # 2. Sample message from user ChatMessage.from_user("Can I buy a coffee?"), ] # 3. Passing the tools list and invoke the chat generator response = chat_generator.run(messages=messages, generation_kwargs= {"tools": tools}) response ``` text ``` ---------- Response ---------- {'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': {}})]} ``` Now 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. python ``` function_call = json.loads(response["replies"][0].content)[0] function_name = function_call["function"]["name"] function_args = json.loads(function_call["function"]["arguments"]) print("Function Name:", function_name) print("Function Arguments:", function_args) ``` text ``` ---------- Response ---------- Function Name: get_items Function Arguments: {'categories': 'Food and beverages'} ``` When presented with another question, the model will use another tool that is more relevant python ``` # Another question messages.append(ChatMessage.from_user("Where's the coffee shop?")) # Invoke the chat generator, and passing the tools list response = chat_generator.run(messages=messages, generation_kwargs= {"tools": tools}) function_call = json.loads(response["replies"][0].content)[0] function_name = function_call["function"]["name"] function_args = json.loads(function_call["function"]["arguments"]) print("Function Name:", function_name) print("Function Arguments:", function_args) ``` text ``` ---------- Response ---------- Function Name: rag_pipeline_func Function Arguments: {'query': "Where's the coffee shop?"} ``` Again, notice that no actual function is invoked here, this is what we will do next! **Calling the function** We can then feed the arguments into the chosen function python ``` ## Find the correspoding function and call it with the given arguments available_functions = {"get_items": get_items, "purchase_item": purchase_item,"rag_pipeline_func": rag_pipeline_func} function_to_call = available_functions[function_name] function_response = function_to_call(**function_args) print("Function Response:", function_response) ``` text ``` ---------- Response ---------- Function 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.'} ``` The 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 python ``` messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name)) response = chat_generator.run(messages=messages) response_msg = response["replies"][0] print(response_msg.content) ``` text ``` ---------- Response ---------- For 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. ``` We now have completed the chat cycle! ### Step 4: Turn into an interactive chat The code above shows how Function Calling can be done, but we want to go a step further by turning it into an interactive chat Here 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 `**input()**`**loop** The 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._ python ``` import json from haystack.dataclasses import ChatMessage, ChatRole response = None messages = [ ChatMessage.from_system(context) ] while True: # if OpenAI response is a tool call if response and response["replies"][0].meta["finish_reason"] == "tool_calls": function_calls = json.loads(response["replies"][0].content) for function_call in function_calls: ## Parse function calling information function_name = function_call["function"]["name"] function_args = json.loads(function_call["function"]["arguments"]) ## Find the correspoding function and call it with the given arguments function_to_call = available_functions[function_name] function_response = function_to_call(**function_args) ## Append function response to the messages list using `ChatMessage.from_function` messages.append(ChatMessage.from_function(content=json.dumps(function_response), name=function_name)) # Regular Conversation else: # Append assistant messages to the messages list if not messages[-1].is_from(ChatRole.SYSTEM): messages.append(response["replies"][0]) user_input = input("ENTER YOUR MESSAGE 👇 INFO: Type 'exit' or 'quit' to stopn") if user_input.lower() == "exit" or user_input.lower() == "quit": break else: messages.append(ChatMessage.from_user(user_input)) response = chat_generator.run(messages=messages, generation_kwargs={"tools": tools}) ``` ![Running interactive chats in the IDE](../images/003.png) Running interactive chats in the IDE While it works, we might want to have something that looks nicer. **Streamlit interface** Streamlit 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 You can run it by: 1. If you have not done so already, spin up the API server with `python db_api.py` 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 3. Navigate to the `streamlit` folder in the terminal with `cd streamlit` 4. Run Streamlit with `streamlit run app.py`. A new tab should be automatically created in your browser running the application That's basically it! I hope you enjoy this article. ![Streamlit UI](../images/004.png) Streamlit UI _*Unless otherwise noted, all images are by the author_ ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. 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