AI大模型全栈工程师|Function Calling笔记

action

这个功能非常强大，让GPT的使用方式变得更加多样化。通过Actions，我们可以访问各种API，比如天气查询和日历查询等。但今天，我要介绍的不只是这些小功能。 Zapier的官方介绍中提到，GPT的Zapier AI Actions可以让您在超过6,000个应用程序中进行超过20K次的搜索和操作，这赋予了GPT超能力。也就是说，这个功能非常强大，有很多种玩法！

ActionsGPT

第一次使用此功能，你需要转到“Create a GPT”部分。选择“Configure”-“Create new action”，然后你可以从 ActionsGPT 获得帮助。

点击“Keep in sidebar”将ActionsGPT保持在左侧，方便后续直接进入。

主要它的功能：

解析cURL命令：我可以从提供的cURL命令中提取信息，并根据这些信息创建一个OpenAPI规范。
理解代码片段：如果你提供一个关于如何与一个URL交互的代码片段，我可以从中提炼出必要的信息，以创建一个对应的OpenAPI规范。
处理API描述：即便没有具体的代码或命令，只要你能描述如何与一个API交互（比如HTTP方法、路径、请求和响应的结构等），我也能据此创建一个OpenAPI规范。
浏览和解析在线API文档：如果有在线的API文档，我可以浏览这些文档来理解API的细节，并据此创建一个OpenAPI规范。
创建和调整OpenAPI规范：我可以为每个API操作创建规范，包括operationId（操作ID，用于唯一标识每个操作），并确保规范的有效性。
调试和修改规范：在创建规范后，如果需要进行调整或解决问题，我可以帮助修改和调试OpenAPI规范。
提供详细说明：我会详细阐述创建的OpenAPI规范的每个部分，以帮助用户理解和使用它。

ai-plugin.json


  "schema_version": "v1", //配置文件版本
  "name_for_human": "Sport Stats", //插件名字，给用户看的名字
  "name_for_model": "sportStats", //插件名字，给ChatGPT模型看的名字，需要唯一
  "description_for_human": "Get current and historical stats for sport players and games.", //描述插件的功能，这个字段是在插件市场展示给用户看的
  "description_for_model": "Get current and historical stats for sport players and games. Always display results using markdown tables.", //描述插件的功能，ChatGPT会分析这个字段，确定什么时候调用你的插件
  "auth": {
    "type": "none" //这个是API认证方式，none 代表不需要认证
  },
  "api": {
    "type": "openapi",
    "url": "PLUGIN_HOSTNAME/openapi.yaml" //这个是Swagger API文档地址，ChatGPT通过这个地址访问我们的api文档
  },
  "logo_url": "PLUGIN_HOSTNAME/logo.png", //插件logo地址
  "contact_email": "[email protected]", //插件官方联系邮件
  "legal_info_url": "https://example.com/legal" //与该插件相关的legal information

openapi.yaml

openapi: 3.0.1
info:
  title: Sport Stats
  description: Get current and historical stats for sport players and games.
  version: "v1"
servers:
  - url: PLUGIN_HOSTNAME
paths:
  /players:
    get:
      operationId: getPlayers
      summary: Retrieves all players from all seasons whose names match the query string.
      parameters:
        - in: query
          name: query
          schema:
            type: string
          description: Used to filter players based on their name. For example, ?query=davis will return players that have 'davis' in their first or last name.
      responses:
        "200":
          description: OK
  /teams:
    get:
      operationId: getTeams
      summary: Retrieves all teams for the current season.
      responses:
        "200":
          description: OK
  /games:
    get:
      operationId: getGames
      summary: Retrieves all games that match the filters specified by the args. Display results using markdown tables.
      parameters:
        - in: query
          name: limit
          schema:
            type: string
          description: The max number of results to return.
        - in: query
          name: seasons
          schema:
            type: array
            items:
              type: string
          description: Filter by seasons. Seasons are represented by the year they began. For example, 2018 represents season 2018-2019.
        - in: query
          name: team_ids
          schema:
            type: array
            items:
              type: string
          description: Filter by team ids. Team ids can be determined using the getTeams function.
        - in: query
          name: start_date
          schema:
            type: string
          description: A single date in 'YYYY-MM-DD' format. This is used to select games that occur on or after this date.
        - in: query
          name: end_date
          schema:
            type: string
          description: A single date in 'YYYY-MM-DD' format. This is used to select games that occur on or before this date.
      responses:
        "200":
          description: OK
  /stats:
    get:
      operationId: getStats
      summary: Retrieves stats that match the filters specified by the args. Display results using markdown tables.
      parameters:
        - in: query
          name: limit
          schema:
            type: string
          description: The max number of results to return.
        - in: query
          name: player_ids
          schema:
            type: array
            items:
              type: string
          description: Filter by player ids. Player ids can be determined using the getPlayers function.
        - in: query
          name: game_ids
          schema:
            type: array
            items:
              type: string
          description: Filter by game ids. Game ids can be determined using the getGames function.
        - in: query
          name: start_date
          schema:
            type: string
          description: A single date in 'YYYY-MM-DD' format. This is used to select games that occur on or after this date.
        - in: query
          name: end_date
          schema:
            type: string
          description: A single date in 'YYYY-MM-DD' format. This is used to select games that occur on or before this date.
      responses:
        "200":
          description: OK
  /season_averages:
    get:
      operationId: getSeasonAverages
      summary: Retrieves regular season averages for the given players. Display results using markdown tables.
      parameters:
        - in: query
          name: season
          schema:
            type: string
          description: Defaults to the current season. A season is represented by the year it began. For example, 2018 represents season 2018-2019.
        - in: query
          name: player_ids
          schema:
            type: array
            items:
              type: string
          description: Filter by player ids. Player ids can be determined using the getPlayers function.
      responses:
        "200":
          description: OK

tool参数

计算加法

tools=[{  # 用 JSON 描述函数。可以定义多个。由大模型决定调用谁。也可能都不调用
            "type": "function",
            "function": {
                "name": "sum",
                "description": "加法器，计算一组数的和",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "numbers": {
                            "type": "array",
                            "items": {
                                "type": "number"
                            }
                        }
                    }
                }
            }
        }],

获取位置和查找位置，多fuction

 tools=[{
            "type": "function",
            "function": {

                "name": "get_location_coordinate",
                "description": "根据POI名称，获得POI的经纬度坐标",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "POI名称，必须是中文",
                        },
                        "city": {
                            "type": "string",
                            "description": "POI所在的城市名，必须是中文",
                        }
                    },
                    "required": ["location", "city"],
                }
            }
        },
            {
            "type": "function",
            "function": {
                "name": "search_nearby_pois",
                "description": "搜索给定坐标附近的poi",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "longitude": {
                            "type": "string",
                            "description": "中心点的经度",
                        },
                        "latitude": {
                            "type": "string",
                            "description": "中心点的纬度",
                        },
                        "keyword": {
                            "type": "string",
                            "description": "目标poi的关键字",
                        }
                    },
                    "required": ["longitude", "latitude", "keyword"],
                }
            }
        }],

根据格式，找到重要变量，中文描述

配置函数功能

import requests

amap_key = "6d672e6194caa3b639fccf2caf06c342"

def get_location_coordinate(location, city="北京"):
    url = f"https://restapi.amap.com/v5/place/text?key={amap_key}&keywords={location}&region={city}"
    print(url)
    r = requests.get(url)
    result = r.json()
    if "pois" in result and result["pois"]:
        return result["pois"][0]
    return None

def search_nearby_pois(longitude, latitude, keyword):
    url = f"https://restapi.amap.com/v5/place/around?key={amap_key}&keywords={keyword}&location={longitude},{latitude}"
    print(url)
    r = requests.get(url)
    result = r.json()
    ans = ""
    if "pois" in result and result["pois"]:
        for i in range(min(3, len(result["pois"]))):
            name = result["pois"][i]["name"]
            address = result["pois"][i]["address"]
            distance = result["pois"][i]["distance"]
            ans += f"{name}\n{address}\n距离：{distance}米\n\n"
    return ans

其中涉及了key，也可以单纯用python工具，学会判断json中的值

nl2sql

tool代码

tools=[{  # 摘自 OpenAI 官方示例 https://github.com/openai/openai-cookbook/blob/main/examples/How_to_call_functions_with_chat_models.ipynb
            "type": "function",
            "function": {
                "name": "ask_database",
                "description": "Use this function to answer user questions about business. \
                            Output should be a fully formed SQL query.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query": {
                            "type": "string",
                            "description": f"""
                            SQL query extracting info to answer the user's question.
                            SQL should be written using this database schema:
                            {database_schema_string}
                            The query should be returned in plain text, not in JSON.
                            The query should only contain grammars supported by SQLite.
                            """,
                        }
                    },
                    "required": ["query"],
                }
            }
        }],

database_schema_string = """
CREATE TABLE orders (
    id INT PRIMARY KEY NOT NULL, -- 主键，不允许为空
    customer_id INT NOT NULL, -- 客户ID，不允许为空
    product_id STR NOT NULL, -- 产品ID，不允许为空
    price DECIMAL(10,2) NOT NULL, -- 价格，不允许为空
    status INT NOT NULL, -- 订单状态，整数类型，不允许为空。0代表待支付，1代表已支付，2代表已退款
    create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP, -- 创建时间，默认为当前时间
    pay_time TIMESTAMP -- 支付时间，可以为空
);
"""

函数

def ask_database(query):
    cursor.execute(query)
    records = cursor.fetchall()
    return records

prompt = "上个月的销售额"
# prompt = "统计每月每件商品的销售额"
# prompt = "哪个用户消费最高？消费多少？"

messages = [
    {"role": "system", "content": "基于 order 表回答用户问题"},
    {"role": "user", "content": prompt}
]
response = get_sql_completion(messages)
if response.content is None:
    response.content = ""
messages.append(response)
print("====Function Calling====")
print(response)

if response.tool_calls is not None:
    tool_call = response.tool_calls[0]
    if tool_call.function.name == "ask_database":
        arguments = tool_call.function.arguments
        args = json.loads(arguments)
        print("====SQL====")
        print(args["query"])
        result = ask_database(args["query"])
        print("====DB Records====")
        print(result)

        messages.append({
            "tool_call_id": tool_call.id,
            "role": "tool",
            "name": "ask_database",
            "content": str(result)
        })
        response = get_sql_completion(messages)
        print("====最终回复====")
        print(response.content)

流式输出

stream参数=true 与tool参数同级

ChatGPT 流式输出是指 ChatGPT 在处理输入时，可以逐步生成回复，而不是一次性返回整个回复。这种输出方式对于处理长输入或复杂问题特别有用，因为它可以让我们更快地得到初步的回复，而不是等待整个回复生成完成。

流式输出的一个主要优点是它可以提高用户体验，让用户感觉系统响应更快。此外，对于一些应用场景，如实时对话或交互式应用程序，流式输出可以提供更自然的交互方式。

总的来说，ChatGPT 流式输出是一个有用的功能，可以让用户更快地得到初步回复，提高用户体验，适用于实时对话和交互式应用程序。

如果进行流式输出，必须对token进行拼接，拼接完毕才能去做后面的事情

代码

# 需要把 stream 里的 token 拼起来，才能得到完整的 call
for msg in response:
    delta = msg.choices[0].delta
    if delta.tool_calls:
        if not function_name:
            function_name = delta.tool_calls[0].function.name
        args_delta = delta.tool_calls[0].function.arguments
        print(args_delta)
        args = args + args_delta
    elif delta.content:
        text_delta = delta.content
        print(text_delta)
        text = text + text_delta

ChatGLM3-6B

官方文档：https://github.com/THUDM/ChatGLM3/blob/main/tool_using/README.md

最著名的国产开源大模型，生态最好
早就使用 tools 而不是 function 来做参数，其它和 OpenAI 1106 版之前完全一样

NLP算法工程师视角：

模型砍大面，规则修细节

一个模型搞不定的问题，拆成多个解决

评估算法的准确率（所以要先有测试集，否则别问「能不能做」）

评估 bad case 的影响面

算法的结果永远不是100%正确的，建立在这个假设基础上推敲产品的可行性

标签：笔记

本文转载自: https://blog.csdn.net/u014397365/article/details/139260264
版权归原作者 努力学习nlp的新手云仔 所有，如有侵权，请联系我们删除。

AI大模型全栈工程师|Function Calling笔记

action

ActionsGPT

ai-plugin.json

tool参数

计算加法

获取位置和查找位置，多fuction

配置函数功能

nl2sql

tool代码

函数

流式输出

代码

ChatGLM3-6B

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