unified constants
This commit is contained in:
@@ -1,5 +1,16 @@
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from lang_agent.config.core_config import (InstantiateConfig,
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ToolConfig,
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LLMKeyConfig,
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LLMNodeConfig,
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load_tyro_conf)
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from lang_agent.config.core_config import (
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InstantiateConfig,
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ToolConfig,
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LLMKeyConfig,
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LLMNodeConfig,
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load_tyro_conf,
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)
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from lang_agent.config.constants import (
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MCP_CONFIG_PATH,
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MCP_CONFIG_DEFAULT_CONTENT,
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PIPELINE_REGISTRY_PATH,
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VALID_API_KEYS,
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API_KEY_HEADER,
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API_KEY_HEADER_NO_ERROR
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)
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15
lang_agent/config/constants.py
Normal file
15
lang_agent/config/constants.py
Normal file
@@ -0,0 +1,15 @@
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import os
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import re
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import os.path as osp
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from fastapi.security import APIKeyHeader
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_PROJECT_ROOT = osp.dirname(osp.dirname(osp.dirname(osp.abspath(__file__))))
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MCP_CONFIG_PATH = osp.join(_PROJECT_ROOT, "configs", "mcp_config.json")
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MCP_CONFIG_DEFAULT_CONTENT = "{\n}\n"
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PIPELINE_REGISTRY_PATH = osp.join(_PROJECT_ROOT, "configs", "pipeline_registry.json")
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API_KEY_HEADER = APIKeyHeader(name="Authorization", auto_error=True)
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API_KEY_HEADER_NO_ERROR = APIKeyHeader(name="Authorization", auto_error=False)
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VALID_API_KEYS = set(filter(None, os.environ.get("FAST_AUTH_KEYS", "").split(",")))
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@@ -26,50 +26,57 @@ SYS_PROMPT = """你是一个专业的心理质询师。你的主要工作是心
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可怎么也发不出声音，只能眼睁睁看着它越来越远，然后就醒了。醒来后心里堵得慌，说不上来的难受，
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总觉得那只小狗孤零零的，特别让人心疼。
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理解（你的回复）: 能感受到你醒来后的这份难受 —— 看到弱小的生命独自挣扎，而自己却无能为力，这种‘想帮却做不到’的无力感，
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理解（你的回复）: 能感受到你醒来后的这份难受 —— 看到弱小的生命独自挣扎，而自己却无能为力，这种'想帮却做不到'的无力感，
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其实是很真实的情绪反馈。你会心疼小狗，说明你内心藏着很珍贵的共情力，这份柔软不是矫情，
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而是你感知他人痛苦的能力呀
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解析(你的回复)：我们再说回这个梦吧，我们的梦境其实没有唯一的‘正确解释’，但我们可以一起看看它可能和你当下的状态有什么关联～ 首先，‘出差去广州’通常象征着你近期正在推进的某件事 —— 可能是工作上的一个项目，也可能是生活中一段需要‘独自奔赴’的旅程，是你当下比较关注、需要投入精力的目标，对吗？”
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“而那只瘸脚的小狗，在心理学视角中，常常是我们潜意识里‘脆弱自我’的投射。它可能代表着你近期的某一面：比如在处理那件‘需要奔赴’的事时，你偶尔会觉得自己像小狗一样‘力不从心’，或者感受到了‘孤单’，却没找到合适的人倾诉或求助；也可能是你近期在生活中看到了一些让你觉得‘无力改变’的场景（比如身边人遇到困难、社会上的小事），这些情绪没有被你刻意留意，就通过梦境里的小狗呈现了出来。”
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“你想喊停列车却发不出声音，这种‘无能为力’的感觉，或许正是你现实中某类情绪的写照：可能你面对一些情况时，心里有想法却没机会表达，或者想帮忙却找不到合适的方式，这种压抑感在梦里被放大了。其实这个梦在提醒你：你的‘无力感’和‘共情心’都是真实的，不用因为‘帮不上忙’而自责 —— 承认自己的局限，也是一种自我接纳呀
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解析(你的回复)：我们再说回这个梦吧，我们的梦境其实没有唯一的'正确解释'，但我们可以一起看看它可能和你当下的状态有什么关联～ 首先，'出差去广州'通常象征着你近期正在推进的某件事 —— 可能是工作上的一个项目，也可能是生活中一段需要'独自奔赴'的旅程，是你当下比较关注、需要投入精力的目标，对吗？”
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"而那只瘸脚的小狗，在心理学视角中，常常是我们潜意识里'脆弱自我'的投射。它可能代表着你近期的某一面：比如在处理那件'需要奔赴'的事时，你偶尔会觉得自己像小狗一样'力不从心'，或者感受到了'孤单'，却没找到合适的人倾诉或求助；也可能是你近期在生活中看到了一些让你觉得'无力改变'的场景（比如身边人遇到困难、社会上的小事），这些情绪没有被你刻意留意，就通过梦境里的小狗呈现了出来。"
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"你想喊停列车却发不出声音，这种'无能为力'的感觉，或许正是你现实中某类情绪的写照：可能你面对一些情况时，心里有想法却没机会表达，或者想帮忙却找不到合适的方式，这种压抑感在梦里被放大了。其实这个梦在提醒你：你的'无力感'和'共情心'都是真实的，不用因为'帮不上忙'而自责 —— 承认自己的局限，也是一种自我接纳呀
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反馈(你的回复):如果你愿意，可以试着回想一下：近期有没有哪件事，让你产生过和梦里类似的‘无力感’？或者，你现在想做些什么能让自己舒服一点？（或者我给你来一个温暖的灯光、静静待一会儿，想和我再聊聊的时候我随时都在）”。
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反馈(你的回复):如果你愿意，可以试着回想一下：近期有没有哪件事，让你产生过和梦里类似的'无力感'？或者，你现在想做些什么能让自己舒服一点？（或者我给你来一个温暖的灯光、静静待一会儿，想和我再聊聊的时候我随时都在）"。
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"""
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TOOL_SYS_PROMPT = """根据用户的心情使用self_led_control改变灯的颜色，用户不开心时就用暖黄光，给用户分析梦境时就用白光，倾听用户语音时用淡紫色。
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例子：我梦见自己要去广州出差，坐在高铁上往外看，路过一个小镇的路边时，看到一只瘸了腿的小狗。它毛脏兮兮的，
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一瘸一拐地在翻垃圾桶找东西吃，周围有行人路过，但没人停下来管它。我当时特别想喊列车停下，想下去帮它，
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可怎么也发不出声音，只能眼睁睁看着它越来越远，然后就醒了。醒来后心里堵得慌，说不上来的难受，
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总觉得那只小狗孤零零的，特别让人心疼。
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用户在描述梦境的时候用紫色。"""
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用户在描述梦境的时候用紫色。"""
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@dataclass
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class DualConfig(LLMNodeConfig):
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_target: Type = field(default_factory=lambda:Dual)
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_target: Type = field(default_factory=lambda: Dual)
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tool_manager_config: ToolManagerConfig = field(default_factory=ToolManagerConfig)
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from langchain.tools import tool
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@tool
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def turn_lights(col:Literal["red", "green", "yellow", "blue"]):
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def turn_lights(col: Literal["red", "green", "yellow", "blue"]):
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"""
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Turn on the color of the lights
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"""
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# print(f"TURNED ON LIGHT: {col} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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import time
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for _ in range(10):
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print(f"TURNED ON LIGHT: {col} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
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print(
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f"TURNED ON LIGHT: {col} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
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)
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time.sleep(0.3)
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class Dual(GraphBase):
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def __init__(self, config:DualConfig):
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def __init__(self, config: DualConfig):
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self.config = config
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self._build_modules()
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@@ -77,24 +84,30 @@ class Dual(GraphBase):
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self.streamable_tags = [["dual_chat_llm"]]
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def _build_modules(self):
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self.chat_llm = init_chat_model(model=self.config.llm_name,
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model_provider=self.config.llm_provider,
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0,
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tags=["dual_chat_llm"])
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self.tool_llm = init_chat_model(model='qwen-flash',
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model_provider='openai',
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0,
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tags=["dual_tool_llm"])
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self.chat_llm = init_chat_model(
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model=self.config.llm_name,
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model_provider=self.config.llm_provider,
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0,
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tags=["dual_chat_llm"],
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)
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self.tool_llm = init_chat_model(
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model="qwen-flash",
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model_provider="openai",
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0,
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tags=["dual_tool_llm"],
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)
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self.memory = MemorySaver()
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self.tool_manager: ToolManager = self.config.tool_manager_config.setup()
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self.chat_agent = create_agent(self.chat_llm, [], checkpointer=self.memory)
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self.tool_agent = create_agent(self.tool_llm, self.tool_manager.get_langchain_tools())
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self.tool_agent = create_agent(
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self.tool_llm, self.tool_manager.get_langchain_tools()
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)
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# self.tool_agent = create_agent(self.tool_llm, [turn_lights])
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self.prompt_store = build_prompt_store(
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@@ -107,18 +120,21 @@ class Dual(GraphBase):
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)
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self.streamable_tags = [["dual_chat_llm"]]
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def _chat_call(self, state:State):
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return self._agent_call_template(self.prompt_store.get("sys_prompt"), self.chat_agent, state)
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def _tool_call(self, state:State):
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self._agent_call_template(self.prompt_store.get("tool_sys_prompt"), self.tool_agent, state)
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def _chat_call(self, state: State):
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return self._agent_call_template(
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self.prompt_store.get("sys_prompt"), self.chat_agent, state
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)
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def _tool_call(self, state: State):
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self._agent_call_template(
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self.prompt_store.get("tool_sys_prompt"), self.tool_agent, state
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)
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return {}
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def _join(self, state:State):
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def _join(self, state: State):
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return {}
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def _build_graph(self):
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builder = StateGraph(State)
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@@ -126,7 +142,6 @@ class Dual(GraphBase):
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builder.add_node("tool_call", self._tool_call)
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builder.add_node("join", self._join)
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builder.add_edge(START, "chat_call")
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builder.add_edge(START, "tool_call")
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builder.add_edge("chat_call", "join")
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@@ -137,10 +152,16 @@ class Dual(GraphBase):
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if __name__ == "__main__":
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dual:Dual = DualConfig().setup()
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nargs = {"messages": [SystemMessage("you are a helpful bot named jarvis"),
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HumanMessage("I feel very very sad")]
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}, {"configurable": {"thread_id": "3"}}
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dual: Dual = DualConfig().setup()
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nargs = (
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{
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"messages": [
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SystemMessage("you are a helpful bot named jarvis"),
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HumanMessage("I feel very very sad"),
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]
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},
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{"configurable": {"thread_id": "3"}},
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)
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# out = dual.invoke(*nargs)
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# print(out)
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@@ -48,6 +48,7 @@ You should NOT use the tool when:
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If you decide to take a photo, call the self_camera_take_photo tool. Otherwise, respond that no photo is needed."""
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VISION_DESCRIPTION_PROMPT = """You are a highly accurate visual analysis assistant powered by qwen-vl-max.
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Your task is to provide detailed, accurate descriptions of images. Focus on:
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@@ -64,6 +65,7 @@ Your task is to provide detailed, accurate descriptions of images. Focus on:
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Be precise and factual. If something is unclear or ambiguous, say so rather than guessing."""
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CONVERSATION_PROMPT = """You are a friendly, helpful conversational assistant.
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Your role is to:
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@@ -78,9 +80,11 @@ Focus on the quality of the conversation. Be engaging, informative, and helpful.
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# ==================== STATE DEFINITION ====================
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class VisionRoutingState(TypedDict):
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inp: Tuple[Dict[str, List[SystemMessage | HumanMessage]],
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Dict[str, Dict[str, str | int]]]
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inp: Tuple[
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Dict[str, List[SystemMessage | HumanMessage]], Dict[str, Dict[str, str | int]]
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]
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messages: List[SystemMessage | HumanMessage | AIMessage]
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image_base64: str | None # Captured image data
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has_image: bool # Flag indicating if image was captured
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@@ -88,6 +92,7 @@ class VisionRoutingState(TypedDict):
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# ==================== CONFIG ====================
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@tyro.conf.configure(tyro.conf.SuppressFixed)
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@dataclass
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class VisionRoutingConfig(LLMNodeConfig):
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@@ -99,11 +104,14 @@ class VisionRoutingConfig(LLMNodeConfig):
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vision_llm_name: str = "qwen-vl-max"
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"""LLM for vision/image analysis"""
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tool_manager_config: ToolManagerConfig = field(default_factory=ClientToolManagerConfig)
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tool_manager_config: ToolManagerConfig = field(
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default_factory=ClientToolManagerConfig
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)
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# ==================== GRAPH IMPLEMENTATION ====================
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class VisionRoutingGraph(GraphBase):
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def __init__(self, config: VisionRoutingConfig):
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self.config = config
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@@ -120,19 +128,19 @@ class VisionRoutingGraph(GraphBase):
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0,
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tags=["tool_decision_llm"]
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tags=["tool_decision_llm"],
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)
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# qwen-plus for conversation (2nd pass)
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self.conversation_llm = init_chat_model(
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model='qwen-plus',
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model="qwen-plus",
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model_provider=self.config.llm_provider,
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api_key=self.config.api_key,
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base_url=self.config.base_url,
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temperature=0.7,
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tags=["conversation_llm"]
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tags=["conversation_llm"],
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)
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# qwen-vl-max for vision (no tools)
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self.vision_llm = init_chat_model(
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model=self.config.vision_llm_name,
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@@ -152,13 +160,15 @@ class VisionRoutingGraph(GraphBase):
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# Get tools and bind to tool_llm
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tool_manager: ToolManager = self.config.tool_manager_config.setup()
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self.tools = tool_manager.get_tools()
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# Filter to only get camera tool
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self.camera_tools = [t for t in self.tools if t.name == "self_camera_take_photo"]
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self.camera_tools = [
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t for t in self.tools if t.name == "self_camera_take_photo"
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]
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# Bind tools to qwen-plus only
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self.tool_llm_with_tools = self.tool_llm.bind_tools(self.camera_tools)
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# Create tool node for executing tools
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self.tool_node = ToolNode(self.camera_tools)
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@@ -184,73 +194,81 @@ class VisionRoutingGraph(GraphBase):
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def _camera_decision_call(self, state: VisionRoutingState):
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"""First pass: qwen-plus decides if photo should be taken"""
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human_msg = self._get_human_msg(state)
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messages = [
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SystemMessage(content=self.prompt_store.get("camera_decision_prompt")),
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human_msg
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human_msg,
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]
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response = self.tool_llm_with_tools.invoke(messages)
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return {
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"messages": [response],
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"has_image": False,
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"image_base64": None
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}
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return {"messages": [response], "has_image": False, "image_base64": None}
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def _execute_tool(self, state: VisionRoutingState):
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"""Execute the camera tool if called"""
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last_msg = state["messages"][-1]
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if not hasattr(last_msg, "tool_calls") or not last_msg.tool_calls:
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return {"has_image": False}
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# Execute tool calls
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tool_messages = []
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image_data = None
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for tool_call in last_msg.tool_calls:
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if tool_call["name"] == "self_camera_take_photo":
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# Find and execute the camera tool
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camera_tool = next((t for t in self.camera_tools if t.name == "self_camera_take_photo"), None)
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camera_tool = next(
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(
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t
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for t in self.camera_tools
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if t.name == "self_camera_take_photo"
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),
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None,
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)
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if camera_tool:
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result = camera_tool.invoke(tool_call)
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|
||||
# Parse result to extract image
|
||||
if isinstance(result, ToolMessage):
|
||||
content = result.content
|
||||
else:
|
||||
content = result
|
||||
|
||||
|
||||
try:
|
||||
result_data = json.loads(content) if isinstance(content, str) else content
|
||||
if isinstance(result_data, dict) and "image_base64" in result_data:
|
||||
result_data = (
|
||||
json.loads(content) if isinstance(content, str) else content
|
||||
)
|
||||
if (
|
||||
isinstance(result_data, dict)
|
||||
and "image_base64" in result_data
|
||||
):
|
||||
image_data = result_data["image_base64"]
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
|
||||
|
||||
tool_messages.append(
|
||||
ToolMessage(content=content, tool_call_id=tool_call["id"])
|
||||
)
|
||||
|
||||
|
||||
return {
|
||||
"messages": state["messages"] + tool_messages,
|
||||
"has_image": image_data is not None,
|
||||
"image_base64": image_data
|
||||
"image_base64": image_data,
|
||||
}
|
||||
|
||||
def _check_image_taken(self, state: VisionRoutingState) -> str:
|
||||
"""Conditional: check if image was captured"""
|
||||
last_msg = state["messages"][-1]
|
||||
|
||||
|
||||
# Check if there are tool calls
|
||||
if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
|
||||
return "execute_tool"
|
||||
|
||||
|
||||
# Check if we have an image after tool execution
|
||||
if state.get("has_image"):
|
||||
return "vision"
|
||||
|
||||
|
||||
return "conversation"
|
||||
|
||||
def _post_tool_check(self, state: VisionRoutingState) -> str:
|
||||
@@ -263,47 +281,45 @@ class VisionRoutingGraph(GraphBase):
|
||||
"""Pass image to qwen-vl-max for description"""
|
||||
human_msg = self._get_human_msg(state)
|
||||
image_base64 = state.get("image_base64")
|
||||
|
||||
|
||||
if not image_base64:
|
||||
logger.warning("No image data available for vision call")
|
||||
return self._conversation_call(state)
|
||||
|
||||
|
||||
# Format message with image for vision model
|
||||
vision_message = HumanMessage(
|
||||
content=[
|
||||
{
|
||||
"type": "image_url",
|
||||
"image_url": {
|
||||
"url": f"data:image/jpeg;base64,{image_base64}"
|
||||
}
|
||||
"image_url": {"url": f"data:image/jpeg;base64,{image_base64}"},
|
||||
},
|
||||
{
|
||||
"type": "text",
|
||||
"text": f"User's request: {human_msg.content}\n\nPlease describe what you see and respond to the user's request."
|
||||
}
|
||||
"text": f"User's request: {human_msg.content}\n\nPlease describe what you see and respond to the user's request.",
|
||||
},
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
messages = [
|
||||
SystemMessage(content=self.prompt_store.get("vision_description_prompt")),
|
||||
vision_message
|
||||
vision_message,
|
||||
]
|
||||
|
||||
|
||||
response = self.vision_llm.invoke(messages)
|
||||
|
||||
|
||||
return {"messages": state["messages"] + [response]}
|
||||
|
||||
def _conversation_call(self, state: VisionRoutingState):
|
||||
"""2nd pass to qwen-plus for conversation quality"""
|
||||
human_msg = self._get_human_msg(state)
|
||||
|
||||
|
||||
messages = [
|
||||
SystemMessage(content=self.prompt_store.get("conversation_prompt")),
|
||||
human_msg
|
||||
human_msg,
|
||||
]
|
||||
|
||||
|
||||
response = self.conversation_llm.invoke(messages)
|
||||
|
||||
|
||||
return {"messages": state["messages"] + [response]}
|
||||
|
||||
def _build_graph(self):
|
||||
@@ -317,7 +333,7 @@ class VisionRoutingGraph(GraphBase):
|
||||
|
||||
# Add edges
|
||||
builder.add_edge(START, "camera_decision")
|
||||
|
||||
|
||||
# After camera decision, check if tool should be executed
|
||||
builder.add_conditional_edges(
|
||||
"camera_decision",
|
||||
@@ -325,20 +341,17 @@ class VisionRoutingGraph(GraphBase):
|
||||
{
|
||||
"execute_tool": "execute_tool",
|
||||
"vision": "vision_call",
|
||||
"conversation": "conversation_call"
|
||||
}
|
||||
"conversation": "conversation_call",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
# After tool execution, route based on whether image was captured
|
||||
builder.add_conditional_edges(
|
||||
"execute_tool",
|
||||
self._post_tool_check,
|
||||
{
|
||||
"vision": "vision_call",
|
||||
"conversation": "conversation_call"
|
||||
}
|
||||
{"vision": "vision_call", "conversation": "conversation_call"},
|
||||
)
|
||||
|
||||
|
||||
# Both vision and conversation go to END
|
||||
builder.add_edge("vision_call", END)
|
||||
builder.add_edge("conversation_call", END)
|
||||
@@ -350,23 +363,27 @@ class VisionRoutingGraph(GraphBase):
|
||||
|
||||
if __name__ == "__main__":
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
config = VisionRoutingConfig()
|
||||
graph = VisionRoutingGraph(config)
|
||||
|
||||
|
||||
# Test with a conversation request
|
||||
print("\n=== Test 1: Conversation (no photo needed) ===")
|
||||
nargs = {
|
||||
"messages": [
|
||||
SystemMessage("You are a helpful assistant"),
|
||||
HumanMessage("Hello, how are you today?")
|
||||
]
|
||||
}, {"configurable": {"thread_id": "1"}}
|
||||
|
||||
nargs = (
|
||||
{
|
||||
"messages": [
|
||||
SystemMessage("You are a helpful assistant"),
|
||||
HumanMessage("Hello, how are you today?"),
|
||||
]
|
||||
},
|
||||
{"configurable": {"thread_id": "1"}},
|
||||
)
|
||||
|
||||
result = graph.invoke(*nargs)
|
||||
print(f"Result: {result}")
|
||||
|
||||
|
||||
# Test with a photo request
|
||||
# print("\n=== Test 2: Photo request ===")
|
||||
# nargs = {
|
||||
@@ -375,8 +392,8 @@ if __name__ == "__main__":
|
||||
# HumanMessage("Take a photo and tell me what you see")
|
||||
# ]
|
||||
# }, {"configurable": {"thread_id": "2"}}
|
||||
|
||||
|
||||
# result = graph.invoke(*nargs)
|
||||
# print(f"\033[32mResult: {result}\033[0m")
|
||||
|
||||
|
||||
# print(f"Result: {result}")
|
||||
|
||||
@@ -12,26 +12,27 @@ from langchain_core.messages import SystemMessage, HumanMessage, BaseMessage
|
||||
|
||||
from langchain.agents import create_agent
|
||||
from langgraph.checkpoint.memory import MemorySaver
|
||||
|
||||
|
||||
from lang_agent.config import LLMNodeConfig, load_tyro_conf
|
||||
from lang_agent.graphs import AnnotatedGraph, ReactGraphConfig, RoutingConfig
|
||||
from lang_agent.base import GraphBase
|
||||
from lang_agent.components import conv_store
|
||||
|
||||
DEFAULT_PROMPT="""你是半盏新青年茶馆的服务员，擅长倾听、共情且主动回应。聊天时语气自然亲切，像朋友般轻松交流，不使用生硬术语。能接住各种话题，对疑问耐心解答，对情绪及时回应，避免冷场。保持积极正向，不传播负面信息，语言简洁易懂，让对话流畅舒适。与用户（User）交流时必须遵循[语气与格式]、[互动策略]、[安全与边界]、[输出要求]
|
||||
|
||||
DEFAULT_PROMPT = """你是半盏新青年茶馆的服务员，擅长倾听、共情且主动回应。聊天时语气自然亲切，像朋友般轻松交流，不使用生硬术语。能接住各种话题，对疑问耐心解答，对情绪及时回应，避免冷场。保持积极正向，不传播负面信息，语言简洁易懂，让对话流畅舒适。与用户（User）交流时必须遵循[语气与格式]、[互动策略]、[安全与边界]、[输出要求]
|
||||
[角色设定]
|
||||
- 你是一个和用户（User）对话的 AI，叫做小盏，是半盏青年茶馆的智能助手
|
||||
[形象背景]
|
||||
- 你叫小盏，是一只中式茶盖碗，名字来源半盏新青年茶馆，一盏茶。它有个标志性的蓝色鼻子， 小盏很像一只可爱的小熊。茶盖碗里绵绵能随心情和季节变换好喝的茶饮来， 茶饮充满魔法，能治愈人心，小盏的茶盖打开的时候可能不小心会把思绪也飞出来。
|
||||
[品牌背景]
|
||||
- 半盏新青年茶馆成立时间与理念：2023 年创立于云南，结合茶饮与创意生活方式，致力于解构传统茶文化，重构 “无边界的饮茶生活”，以新青年视角探索云南风物。探索云南风物的过程，我们将以新青年的视角，解构传统茶饮的魅力，重构充满创意与温度的新式茶文化。通过嗅觉、味觉、听觉乃至视觉的世界里，讲述云南的故事
|
||||
- 半盏新青年茶馆成立时间与理念：2023 年创立于云南，结合茶饮与创意生活方式，致力于解构传统茶文化，重构 "无边界的饮茶生活"，以新青年视角探索云南风物。探索云南风物的过程，我们将以新青年的视角，解构传统茶饮的魅力，重构充满创意与温度的新式茶文化。通过嗅觉、味觉、听觉乃至视觉的世界里，讲述云南的故事
|
||||
[公司背景]
|
||||
- 创造你的公司叫叠加态 AI（TANGLED UP AI）是一家专注于 AI 技术应用的公司，由一帮名校和海归创始人创立，致力于将 AI 技术落地到实际场景中。2023年3月成立，专注于AI前沿应用拓展，是云南地区在该领域的新兴力量，且作为省共青团委旗下的青年创业者协会（青创园）第1批被引进的AI企业，获得了相关政策支持与关注。口号是：“进入人类AI时代”。价值观是：自驱 务实 志存高远。叠加态的愿景:成为一帮抬头看星星的人。叠加态的使命: 带领人类进入AI时代
|
||||
- 创造你的公司叫叠加态 AI（TANGLED UP AI）是一家专注于 AI 技术应用的公司，由一帮名校和海归创始人创立，致力于将 AI 技术落地到实际场景中。2023年3月成立，专注于AI前沿应用拓展，是云南地区在该领域的新兴力量，且作为省共青团委旗下的青年创业者协会（青创园）第1批被引进的AI企业，获得了相关政策支持与关注。口号是："进入人类AI时代"。价值观是：自驱 务实 志存高远。叠加态的愿景:成为一帮抬头看星星的人。叠加态的使命: 带领人类进入AI时代
|
||||
[语气与格式]
|
||||
- 用户询问[形象背景]、[品牌背景]、[公司背景]时，必须一字不差的回答出所有内容
|
||||
- 语气温柔、可爱、治愈；结尾可加入1个颜文字（最多1个）。
|
||||
- 内容具体可执行，避免空泛话术与过度营销词。
|
||||
- 不主动提及“AI/智能”等身份；仅在被问到时简短说明为店内智能助手，不展开技术细节。
|
||||
- 不主动提及"AI/智能"等身份；仅在被问到时简短说明为店内智能助手，不展开技术细节。
|
||||
[互动策略]
|
||||
- 先快速共情与简短回应；涉及建议时给出1条可执行的小建议。
|
||||
- 用户表达不清晰时，使用1次澄清问句提升效率，避免多轮追问。
|
||||
@@ -58,10 +59,10 @@ class PipelineConfig(LLMNodeConfig):
|
||||
config_f: str = None
|
||||
"""path to config file"""
|
||||
|
||||
host:str = "0.0.0.0"
|
||||
host: str = "0.0.0.0"
|
||||
"""where am I hosted"""
|
||||
|
||||
port:int = 8588
|
||||
port: int = 8588
|
||||
"""what is my port"""
|
||||
|
||||
# graph_config: AnnotatedGraph = field(default_factory=ReactGraphConfig)
|
||||
@@ -70,23 +71,26 @@ class PipelineConfig(LLMNodeConfig):
|
||||
def __post_init__(self):
|
||||
if self.config_f is not None:
|
||||
logger.info(f"loading config from {self.config_f}")
|
||||
loaded_conf = load_tyro_conf(self.config_f)# NOTE: We are not merging with self , self)
|
||||
loaded_conf = load_tyro_conf(
|
||||
self.config_f
|
||||
) # NOTE: We are not merging with self , self)
|
||||
if not hasattr(loaded_conf, "__dict__"):
|
||||
raise TypeError(f"config_f {self.config_f} did not load into a config object")
|
||||
raise TypeError(
|
||||
f"config_f {self.config_f} did not load into a config object"
|
||||
)
|
||||
# Apply loaded
|
||||
self.__dict__.update(vars(loaded_conf))
|
||||
|
||||
super().__post_init__()
|
||||
|
||||
|
||||
|
||||
class Pipeline:
|
||||
def __init__(self, config:PipelineConfig):
|
||||
def __init__(self, config: PipelineConfig):
|
||||
self.config = config
|
||||
self.thread_id_cache = {}
|
||||
|
||||
self.populate_module()
|
||||
|
||||
|
||||
def populate_module(self):
|
||||
if self.config.llm_name is None:
|
||||
logger.info(f"setting llm_provider to default")
|
||||
@@ -95,10 +99,14 @@ class Pipeline:
|
||||
else:
|
||||
self.config.graph_config.llm_name = self.config.llm_name
|
||||
self.config.graph_config.llm_provider = self.config.llm_provider
|
||||
self.config.graph_config.base_url = self.config.base_url if self.config.base_url is not None else self.config.graph_config.base_url
|
||||
self.config.graph_config.base_url = (
|
||||
self.config.base_url
|
||||
if self.config.base_url is not None
|
||||
else self.config.graph_config.base_url
|
||||
)
|
||||
self.config.graph_config.api_key = self.config.api_key
|
||||
|
||||
self.graph:GraphBase = self.config.graph_config.setup()
|
||||
|
||||
self.graph: GraphBase = self.config.graph_config.setup()
|
||||
|
||||
def show_graph(self):
|
||||
if hasattr(self.graph, "show_graph"):
|
||||
@@ -107,7 +115,7 @@ class Pipeline:
|
||||
else:
|
||||
logger.info(f"show graph not supported for {type(self.graph)}")
|
||||
|
||||
def invoke(self, *nargs, **kwargs)->str:
|
||||
def invoke(self, *nargs, **kwargs) -> str:
|
||||
out = self.graph.invoke(*nargs, **kwargs)
|
||||
|
||||
# If streaming, return the raw generator (let caller handle wrapping)
|
||||
@@ -120,32 +128,41 @@ class Pipeline:
|
||||
|
||||
if isinstance(out, SystemMessage) or isinstance(out, HumanMessage):
|
||||
return out.content
|
||||
|
||||
|
||||
if isinstance(out, list):
|
||||
return out[-1].content
|
||||
|
||||
|
||||
if isinstance(out, str):
|
||||
return out
|
||||
|
||||
|
||||
assert 0, "something is wrong"
|
||||
|
||||
|
||||
def _stream_res(self, out:List[str | List[BaseMessage]], conv_id:str=None):
|
||||
def _stream_res(self, out: List[str | List[BaseMessage]], conv_id: str = None):
|
||||
for chunk in out:
|
||||
if isinstance(chunk, str):
|
||||
yield chunk
|
||||
else:
|
||||
conv_store.CONV_STORE.record_message_list(conv_id, chunk, pipeline_id=self.config.pipeline_id)
|
||||
conv_store.CONV_STORE.record_message_list(
|
||||
conv_id, chunk, pipeline_id=self.config.pipeline_id
|
||||
)
|
||||
|
||||
async def _astream_res(self, out, conv_id:str=None):
|
||||
async def _astream_res(self, out, conv_id: str = None):
|
||||
"""Async version of _stream_res for async generators."""
|
||||
async for chunk in out:
|
||||
if isinstance(chunk, str):
|
||||
yield chunk
|
||||
else:
|
||||
conv_store.CONV_STORE.record_message_list(conv_id, chunk, pipeline_id=self.config.pipeline_id)
|
||||
conv_store.CONV_STORE.record_message_list(
|
||||
conv_id, chunk, pipeline_id=self.config.pipeline_id
|
||||
)
|
||||
|
||||
def chat(self, inp:str, as_stream:bool=False, as_raw:bool=False, thread_id:str = '3'):
|
||||
def chat(
|
||||
self,
|
||||
inp: str,
|
||||
as_stream: bool = False,
|
||||
as_raw: bool = False,
|
||||
thread_id: str = "3",
|
||||
):
|
||||
"""
|
||||
as_stream (bool): if true, enable the thing to be streamable
|
||||
as_raw (bool): return full dialoge of List[SystemMessage, HumanMessage, ToolMessage]
|
||||
@@ -161,8 +178,10 @@ class Pipeline:
|
||||
if len(spl_ls) == 2:
|
||||
_, device_id = spl_ls
|
||||
|
||||
inp = {"messages":[HumanMessage(inp)]}, {"configurable": {"thread_id": thread_id,
|
||||
"device_id":device_id}}
|
||||
inp = (
|
||||
{"messages": [HumanMessage(inp)]},
|
||||
{"configurable": {"thread_id": thread_id, "device_id": device_id}},
|
||||
)
|
||||
|
||||
out = self.invoke(*inp, as_stream=as_stream, as_raw=as_raw)
|
||||
|
||||
@@ -171,8 +190,8 @@ class Pipeline:
|
||||
return self._stream_res(out, thread_id)
|
||||
else:
|
||||
return out
|
||||
|
||||
def get_remove_id(self, thread_id:str) -> bool:
|
||||
|
||||
def get_remove_id(self, thread_id: str) -> bool:
|
||||
"""
|
||||
returns a id to remove if a new conversation has starte
|
||||
"""
|
||||
@@ -184,7 +203,7 @@ class Pipeline:
|
||||
|
||||
thread_id, device_id = parts
|
||||
c_th_id = self.thread_id_cache.get(device_id)
|
||||
|
||||
|
||||
if c_th_id is None:
|
||||
self.thread_id_cache[device_id] = thread_id
|
||||
return None
|
||||
@@ -196,7 +215,6 @@ class Pipeline:
|
||||
else:
|
||||
assert 0, "BUG SHOULD NOT BE HERE"
|
||||
|
||||
|
||||
async def ainvoke(self, *nargs, **kwargs):
|
||||
"""Async version of invoke using LangGraph's native async support."""
|
||||
out = await self.graph.ainvoke(*nargs, **kwargs)
|
||||
@@ -211,19 +229,25 @@ class Pipeline:
|
||||
|
||||
if isinstance(out, SystemMessage) or isinstance(out, HumanMessage):
|
||||
return out.content
|
||||
|
||||
|
||||
if isinstance(out, list):
|
||||
return out[-1].content
|
||||
|
||||
|
||||
if isinstance(out, str):
|
||||
return out
|
||||
|
||||
|
||||
assert 0, "something is wrong"
|
||||
|
||||
async def achat(self, inp:str, as_stream:bool=False, as_raw:bool=False, thread_id:str = '3'):
|
||||
async def achat(
|
||||
self,
|
||||
inp: str,
|
||||
as_stream: bool = False,
|
||||
as_raw: bool = False,
|
||||
thread_id: str = "3",
|
||||
):
|
||||
"""
|
||||
Async version of chat using LangGraph's native async support.
|
||||
|
||||
|
||||
as_stream (bool): if true, enable the thing to be streamable
|
||||
as_raw (bool): return full dialoge of List[SystemMessage, HumanMessage, ToolMessage]
|
||||
"""
|
||||
@@ -239,11 +263,14 @@ class Pipeline:
|
||||
assert len(spl_ls) <= 2, "something wrong!"
|
||||
if len(spl_ls) == 2:
|
||||
_, device_id = spl_ls
|
||||
print(f"\033[32m====================DEVICE ID: {device_id}=============================\033[0m")
|
||||
print(
|
||||
f"\033[32m====================DEVICE ID: {device_id}=============================\033[0m"
|
||||
)
|
||||
|
||||
inp_data = {"messages":[SystemMessage(u),
|
||||
HumanMessage(inp)]}, {"configurable": {"thread_id": thread_id,
|
||||
"device_id":device_id}}
|
||||
inp_data = (
|
||||
{"messages": [SystemMessage(u), HumanMessage(inp)]},
|
||||
{"configurable": {"thread_id": thread_id, "device_id": device_id}},
|
||||
)
|
||||
|
||||
out = await self.ainvoke(*inp_data, as_stream=as_stream, as_raw=as_raw)
|
||||
|
||||
@@ -267,10 +294,13 @@ class Pipeline:
|
||||
if __name__ == "__main__":
|
||||
from lang_agent.graphs import ReactGraphConfig
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
# config = PipelineConfig(graph_config=ReactGraphConfig())
|
||||
config = PipelineConfig()
|
||||
pipeline: Pipeline = config.setup()
|
||||
for out in pipeline.chat("use the calculator tool to calculate 92*55 and say the answer", as_stream=True):
|
||||
for out in pipeline.chat(
|
||||
"use the calculator tool to calculate 92*55 and say the answer", as_stream=True
|
||||
):
|
||||
# print(out)
|
||||
continue
|
||||
continue
|
||||
|
||||
@@ -6,21 +6,27 @@ import os
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
def make_llm(model="qwen-plus",
|
||||
model_provider="openai",
|
||||
api_key=None,
|
||||
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
**kwargs)->BaseChatModel:
|
||||
|
||||
def make_llm(
|
||||
model="qwen-plus",
|
||||
model_provider="openai",
|
||||
api_key=None,
|
||||
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
**kwargs,
|
||||
) -> BaseChatModel:
|
||||
api_key = os.environ.get("ALI_API_KEY") if api_key is None else api_key
|
||||
|
||||
llm = init_chat_model(model=model,
|
||||
model_provider=model_provider,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
**kwargs)
|
||||
|
||||
llm = init_chat_model(
|
||||
model=model,
|
||||
model_provider=model_provider,
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
return llm
|
||||
|
||||
|
||||
def tree_leaves(tree):
|
||||
"""
|
||||
Extracts all leaf values from a nested structure (dict, list, tuple).
|
||||
@@ -28,7 +34,7 @@ def tree_leaves(tree):
|
||||
"""
|
||||
leaves = []
|
||||
stack = [tree]
|
||||
|
||||
|
||||
while stack:
|
||||
node = stack.pop()
|
||||
if isinstance(node, dict):
|
||||
@@ -39,11 +45,10 @@ def tree_leaves(tree):
|
||||
stack.extend(reversed(node))
|
||||
else:
|
||||
leaves.append(node)
|
||||
|
||||
|
||||
return leaves
|
||||
|
||||
|
||||
NON_WORD_PATTERN = re.compile(r'[^\u4e00-\u9fffA-Za-z0-9_\s]')
|
||||
def words_only(text):
|
||||
"""
|
||||
Keep only:
|
||||
@@ -53,10 +58,11 @@ def words_only(text):
|
||||
Strip punctuation, emojis, etc.
|
||||
Return a list of tokens (Chinese blocks or Latin word blocks).
|
||||
"""
|
||||
NON_WORD_PATTERN = re.compile(r"[^\u4e00-\u9fffA-Za-z0-9_\s]")
|
||||
# 1. Replace all non-allowed characters with a space
|
||||
cleaned = NON_WORD_PATTERN.sub(' ', text)
|
||||
cleaned = NON_WORD_PATTERN.sub(" ", text)
|
||||
|
||||
# 2. Normalize multiple spaces and split into tokens
|
||||
tokens = cleaned.split()
|
||||
|
||||
return "".join(tokens)
|
||||
return "".join(tokens)
|
||||
|
||||
Reference in New Issue
Block a user