现在可以调用大模型的function call

requirements.txt

@@ -12,3 +12,4 @@ pytorch==1.11.0
 torchvision==0.12.0
 torchaudio==0.11.0
 cudatoolkit=11.3
+loguru

robowaiter/behavior_lib/_base/Behavior.py

@@ -24,10 +24,7 @@ class Bahavior(ptree.behaviour.Behaviour):
         return ins_name
 
     def __init__(self,*args):
-        name = self.__class__.__name__
+        self.name = Bahavior.get_ins_name(*args)
-        if len(args)>0:
-            name = f'{name}({",".join(list(args))})'
-        self.name = name
         #get valid args
         # self.valid_arg_list = []
         # lines = self.valid_params.strip().splitlines()

robowaiter/llm_client/openai_api_request.py

@@ -0,0 +1,67 @@
+# 使用curl命令测试返回
+# curl -X POST "http://127.0.0.1:8000/v1/chat/completions" \
+# -H "Content-Type: application/json" \
+# -d "{\"model\": \"chatglm3-6b\", \"messages\": [{\"role\": \"system\", \"content\": \"You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.\"}, {\"role\": \"user\", \"content\": \"你好，给我讲一个故事，大概100字\"}], \"stream\": false, \"max_tokens\": 100, \"temperature\": 0.8, \"top_p\": 0.8}"
+
+# 使用Python代码测返回
+import requests
+import json
+
+import urllib3
+########################################
+#   该文件实现了与大模型的简单通信
+########################################
+
+# 忽略https的安全性警告
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
+
+def create_chat_completion(model, messages, use_stream=False):
+    data = {
+        "model": model, # 模型名称
+        "messages": messages, # 会话历史
+        "stream": use_stream, # 是否流式响应
+        "max_tokens": 100, # 最多生成字数
+        "temperature": 0.8, # 温度
+        "top_p": 0.8, # 采样概率
+    }
+
+    response = requests.post(f"{base_url}/v1/chat/completions", json=data, stream=use_stream, verify=False)
+    if response.status_code == 200:
+        if use_stream:
+            # 处理流式响应
+            for line in response.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')[6:]
+                    try:
+                        response_json = json.loads(decoded_line)
+                        content = response_json.get("choices", [{}])[0].get("delta", {}).get("content", "")
+                        print(content)
+                    except:
+                        print("Special Token:", decoded_line)
+        else:
+            # 处理非流式响应
+            decoded_line = response.json()
+            print(decoded_line)
+            content = decoded_line.get("choices", [{}])[0].get("message", "").get("content", "")
+            print(content)
+    else:
+        print("Error:", response.status_code)
+        return None
+
+
+if __name__ == "__main__":
+    chat_messages = [
+        {
+            "role": "system",
+            "content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.",
+        },
+        {
+            "role": "user",
+            "content": "你好，给我讲一个故事，大概100字"
+        }
+    ]
+    create_chat_completion("chatglm3-6b", chat_messages, use_stream=False)
+
+

robowaiter/llm_client/tool_api.py

@@ -0,0 +1,86 @@
+import json
+
+import openai
+from colorama import init, Fore
+from loguru import logger
+
+from tool_register import get_tools, dispatch_tool
+
+init(autoreset=True)
+
+# 使用Python代码测返回
+import requests
+import json
+
+import urllib3
+########################################
+#   该文件实现了与大模型的简单通信
+########################################
+
+# 忽略https的安全性警告
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
+
+def get_response(**kwargs):
+    data = kwargs
+
+    response = requests.post(f"{base_url}/v1/chat/completions", json=data, stream=data["stream"], verify=False)
+    decoded_line = response.json()
+    return decoded_line
+
+functions = get_tools()
+
+def run_conversation(query: str, stream=False, functions=None, max_retry=5):
+    params = dict(model="chatglm3", messages=[{"role": "user", "content": query}], stream=stream)
+    if functions:
+        params["functions"] = functions
+    response = get_response(**params)
+
+    for _ in range(max_retry):
+        if response["choices"][0]["message"].get("function_call"):
+            function_call = response["choices"][0]["message"]["function_call"]
+            logger.info(f"Function Call Response: {function_call}")
+            function_args = json.loads(function_call["arguments"])
+            tool_response = dispatch_tool(function_call["name"], function_args)
+            logger.info(f"Tool Call Response: {tool_response}")
+
+            params["messages"].append(response["choices"][0]["message"])
+            params["messages"].append(
+                {
+                    "role": "function",
+                    "name": function_call["name"],
+                    "content": tool_response,  # 调用函数返回结果
+                }
+            )
+        else:
+            reply = response["choices"][0]["message"]["content"]
+            logger.info(f"Final Reply: \n{reply}")
+            return
+
+        response = get_response(**params)
+
+
+if __name__ == "__main__":
+
+    # chat_messages = [
+    #     {
+    #         "role": "system",
+    #         "content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.",
+    #     },
+    #     {
+    #         "role": "user",
+    #         "content": "你好，给我讲一个故事，大概100字"
+    #     }
+    # ]
+    # create_chat_completion("chatglm3-6b", chat_messages, use_stream=False)
+
+
+
+    query = "你是谁"
+    run_conversation(query, stream=False)
+
+    logger.info("\n=========== next conversation ===========")
+
+    query = "帮我查询北京的天气怎么样"
+    run_conversation(query, functions=functions, stream=False)

robowaiter/llm_client/tool_api_request.py

@@ -0,0 +1,67 @@
+# 使用curl命令测试返回
+# curl -X POST "http://127.0.0.1:8000/v1/chat/completions" \
+# -H "Content-Type: application/json" \
+# -d "{\"model\": \"chatglm3-6b\", \"messages\": [{\"role\": \"system\", \"content\": \"You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.\"}, {\"role\": \"user\", \"content\": \"你好，给我讲一个故事，大概100字\"}], \"stream\": false, \"max_tokens\": 100, \"temperature\": 0.8, \"top_p\": 0.8}"
+
+# 使用Python代码测返回
+import requests
+import json
+
+import urllib3
+########################################
+#   该文件实现了与大模型的简单通信
+########################################
+
+# 忽略https的安全性警告
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+base_url = "https://45.125.46.134:25344" # 本地部署的地址,或者使用你访问模型的API地址
+
+def create_chat_completion(model, messages, use_stream=False):
+    data = {
+        "model": model, # 模型名称
+        "messages": messages, # 会话历史
+        "stream": use_stream, # 是否流式响应
+        "max_tokens": 100, # 最多生成字数
+        "temperature": 0.8, # 温度
+        "top_p": 0.8, # 采样概率
+    }
+
+    response = requests.post(f"{base_url}/v1/chat/completions", json=data, stream=use_stream, verify=False)
+    if response.status_code == 200:
+        if use_stream:
+            # 处理流式响应
+            for line in response.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')[6:]
+                    try:
+                        response_json = json.loads(decoded_line)
+                        content = response_json.get("choices", [{}])[0].get("delta", {}).get("content", "")
+                        print(content)
+                    except:
+                        print("Special Token:", decoded_line)
+        else:
+            # 处理非流式响应
+            decoded_line = response.json()
+            print(decoded_line)
+            content = decoded_line.get("choices", [{}])[0].get("message", "").get("content", "")
+            print(content)
+    else:
+        print("Error:", response.status_code)
+        return None
+
+
+if __name__ == "__main__":
+    chat_messages = [
+        {
+            "role": "system",
+            "content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown.",
+        },
+        {
+            "role": "user",
+            "content": "你好，给我讲一个故事，大概100字"
+        }
+    ]
+    create_chat_completion("chatglm3-6b", chat_messages, use_stream=False)
+
+

robowaiter/llm_client/tool_register.py

@@ -0,0 +1,115 @@
+import inspect
+import traceback
+from copy import deepcopy
+from pprint import pformat
+from types import GenericAlias
+from typing import get_origin, Annotated
+
+_TOOL_HOOKS = {}
+_TOOL_DESCRIPTIONS = {}
+
+
+def register_tool(func: callable):
+    tool_name = func.__name__
+    tool_description = inspect.getdoc(func).strip()
+    python_params = inspect.signature(func).parameters
+    tool_params = []
+    for name, param in python_params.items():
+        annotation = param.annotation
+        if annotation is inspect.Parameter.empty:
+            raise TypeError(f"Parameter `{name}` missing type annotation")
+        if get_origin(annotation) != Annotated:
+            raise TypeError(f"Annotation type for `{name}` must be typing.Annotated")
+
+        typ, (description, required) = annotation.__origin__, annotation.__metadata__
+        typ: str = str(typ) if isinstance(typ, GenericAlias) else typ.__name__
+        if not isinstance(description, str):
+            raise TypeError(f"Description for `{name}` must be a string")
+        if not isinstance(required, bool):
+            raise TypeError(f"Required for `{name}` must be a bool")
+
+        tool_params.append({
+            "name": name,
+            "description": description,
+            "type": typ,
+            "required": required
+        })
+    tool_def = {
+        "name": tool_name,
+        "description": tool_description,
+        "params": tool_params
+    }
+
+    print("[registered tool] " + pformat(tool_def))
+    _TOOL_HOOKS[tool_name] = func
+    _TOOL_DESCRIPTIONS[tool_name] = tool_def
+
+    return func
+
+
+def dispatch_tool(tool_name: str, tool_params: dict) -> str:
+    if tool_name not in _TOOL_HOOKS:
+        return f"Tool `{tool_name}` not found. Please use a provided tool."
+    tool_call = _TOOL_HOOKS[tool_name]
+    try:
+        ret = tool_call(**tool_params)
+    except:
+        ret = traceback.format_exc()
+    return str(ret)
+
+
+def get_tools() -> dict:
+    return deepcopy(_TOOL_DESCRIPTIONS)
+
+
+# Tool Definitions
+
+@register_tool
+def random_number_generator(
+        seed: Annotated[int, 'The random seed used by the generator', True],
+        range: Annotated[tuple[int, int], 'The range of the generated numbers', True],
+) -> int:
+    """
+    Generates a random number x, s.t. range[0] <= x < range[1]
+    """
+    if not isinstance(seed, int):
+        raise TypeError("Seed must be an integer")
+    if not isinstance(range, tuple):
+        raise TypeError("Range must be a tuple")
+    if not isinstance(range[0], int) or not isinstance(range[1], int):
+        raise TypeError("Range must be a tuple of integers")
+
+    import random
+    return random.Random(seed).randint(*range)
+
+
+@register_tool
+def get_weather(
+        city_name: Annotated[str, 'The name of the city to be queried', True],
+) -> str:
+    """
+    Get the current weather for `city_name`
+    """
+
+    if not isinstance(city_name, str):
+        raise TypeError("City name must be a string")
+
+    key_selection = {
+        "current_condition": ["temp_C", "FeelsLikeC", "humidity", "weatherDesc", "observation_time"],
+    }
+    import requests
+    try:
+        resp = requests.get(f"https://wttr.in/{city_name}?format=j1")
+        resp.raise_for_status()
+        resp = resp.json()
+        ret = {k: {_v: resp[k][0][_v] for _v in v} for k, v in key_selection.items()}
+    except:
+        import traceback
+        ret = "Error encountered while fetching weather data!\n" + traceback.format_exc()
+
+    return str(ret)
+
+
+if __name__ == "__main__":
+    print(dispatch_tool("get_weather", {"city_name": "beijing"}))
+    print(get_tools())