{ "source_file": "0930", "source_platform": "Medium", "author": "John Adeojo", "title_listed": "How to Build an LLM Application", "keywords": "", "domain": "大模型算法", "content_type": "深度技术解析", "is_paid_flag": "否", "url": "https://towardsdatascience.com/how-to-build-an-llm-application-360848c957db/", "publish_date": "2023-07-21", "corpus_stage": "", "notes": "", "id": "90ec501fd86f", "collected_at": "2026-10-02T06:56:20", "title": "How to Build an LLM Application | 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. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. 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Specifically, two innovations have opened up a plethora of possibilities for building apps centred around large language models (LLMs): [Function calling](https://openai.com/blog/function-calling-and-other-api-updates) and [agents](https://python.langchain.com/docs/modules/agents/).\nIn this article, I demonstrate how you can utilise function calling and agents to perform searches on flight databases, enabling you to find cheap flights, short flights, long flights, or anything that suits your preference.\nPlease note - At the very least, you will require the following to make this work for yourself:\n * An [OpenAI API key](https://www.howtogeek.com/885918/how-to-get-an-openai-api-key/) - For accessing the large language models.\n * An [Amadeus API key](https://developers.amadeus.com/) - For accessing the flight data.\n\n\nNow, let's delve into the technical details.\n* * *\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer) and [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) roadmaps.\n## Autonomous Agent Chains\n[Langchain](https://python.langchain.com/docs/get_started/introduction.html) has been at the forefront of the LLM-powered agent. It's a simple, yet potent concept.\nEssentially, you can equip an Agent with the \"reasoning\" capability of an LLM, in our case, this will be GPT-4.\nYou can grant agents access to a variety of tools. These can include a search engine, pandas, SQL, Wolfram Alpha, etc. The list is expanding every month with developers adding more tools.\nThe agent, powered by a large language model, employs analytical reasoning to determine how to leverage the tool to complete a task you assign.\n* * *\n## Function Calling\nA development by OpenAI, function calling allows you to parse arguments for a function from a natural language input.\nThis has significant implications for how users can interact with our app using natural language or even speech.\nFunction calls will become clearer later with the coded examples provided.\n* * *\n## Building a Flight Search App\nWe can develop an app to query flights with natural language using just four components, excluding the front end.\n