升级新库

bbit_ai/Dockerfile_ai_lab

@@ -1,10 +1,10 @@
-# 使用官方 Python 镜像
+FROM ubuntu:22.04
-FROM python:3.10-slim
 
 WORKDIR /app
 
 RUN apt-get update && \
     apt-get install -y --no-install-recommends \
+        ca-certificates \
         libpq5 \
         unixodbc \
         curl \
@@ -20,31 +20,4 @@ RUN apt-get update && \
     ACCEPT_EULA=Y apt-get install -y msodbcsql18 && \
     rm -rf /var/lib/apt/lists/*
 
-COPY app/requirements.txt .
+COPY app/ /app
-# 安装 Python 依赖
-RUN pip install --no-cache-dir -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
-RUN python -m pip uninstall -y opencv-python
-RUN python -m pip install opencv-python-headless
-
-
-
-# 复制并解压 JRE
-COPY docker/OpenJDK17U-jre_x64_linux_hotspot_17.0.16_8.tar.gz /opt/
-RUN tar -xzf /opt/OpenJDK17U-jre_x64_linux_hotspot_17.0.16_8.tar.gz -C /opt/ && \
-    rm /opt/OpenJDK17U-jre_x64_linux_hotspot_17.0.16_8.tar.gz
-
-# 配置 Java 环境
-ENV JAVA_HOME=/opt/jdk-17.0.16+8-jre
-ENV PATH="$JAVA_HOME/bin:$PATH"
-
-# 复制项目代码
-COPY app/ .
-# 复制 pyzxing 的 jar 文件到默认路径
-COPY docker/javase-3.4.1-SNAPSHOT-jar-with-dependencies.jar /root/.local/pyzxing/javase-3.4.1-SNAPSHOT-jar-with-dependencies.jar
-
-EXPOSE 13011
-
-# 启动命令（使用 uvicorn 启动 FastAPI）
-CMD ["python", "app.py"]
-
-

bbit_ai/app/agent/dataAgent.py

@@ -1,23 +1,15 @@
-
-from langchain.prompts import PromptTemplate
-from config.llm import llm
-from config.ssDb import ssDBLC 
-from typing import Annotated
-from typing_extensions import TypedDict
-from langgraph.graph import StateGraph, START, END
-from langgraph.graph.message import add_messages
-from langchain_community.agent_toolkits import create_sql_agent
-from langchain.prompts import PromptTemplate
-from config.llm import llm
-from config.ssDb import ssDBLC 
-from typing import Annotated
-from langgraph.graph.message import add_messages
 import os
-from langchain_tavily import TavilySearch
-from langgraph.prebuilt import ToolNode, tools_condition
-from llm.chatLLM import get_chat_response
 from typing import TypedDict
+
+from langchain_community.agent_toolkits import create_sql_agent
+from langchain_core.prompts import PromptTemplate
+from langchain_tavily import TavilySearch
+from langgraph.graph import START
 from langgraph.graph import StateGraph, END
+
+from config.llm import llm
+from config.ssDb import ssDBLC
+from llm.chatLLM import get_chat_response
 from llm.summarizeLLM import getSummary
 
 
@@ -30,6 +22,7 @@ class State(TypedDict):
     history: str  # 聊天历史
     reply: str  # 最终回复
 
+
 # -------- 定义节点 --------
 # ------------------------------------------------------------------------ 路径选择 --------
 
@@ -45,19 +38,26 @@ pathSelectPrompt = PromptTemplate(
         用户最新输入：
         {userStr}
         请做出你的选择：
-    """
+    """,
 )
 pathSelectChain = pathSelectPrompt | llm
 
+
 def decide_source(state: State, max_retry=3):
     print("根据用户输入选择数据来源，用户输入：", state["userInput"])
     """根据用户输入选择数据来源"""
     for _ in range(max_retry):
-        choice = pathSelectChain.invoke({
+        choice = (
+            pathSelectChain.invoke(
+                {
                     "aiRole": state["aiRole"],
                     "history": state["history"],
                     "userStr": state["userInput"],
-        }).content.strip().lower()
+                }
+            )
+            .content.strip()
+            .lower()
+        )
         if choice in ["web", "db", "chat"]:
             state["source"] = choice
             break
@@ -72,36 +72,45 @@ def decide_source(state: State, max_retry=3):
 os.environ["TAVILY_API_KEY"] = "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"
 tool = TavilySearch(max_results=2)
 
+
 def fetch_web(state: State):
     result = tool.invoke(state["userInput"])
     state["infomation"] = result.get("content") or result
     print("调用了联网工具，结果是：", state["infomation"])
     return state
 
+
 # ------------------------------------------------------------------------ 数据库查询 --------
-agent = create_sql_agent(
+agent = create_sql_agent(llm=llm, db=ssDBLC, agent_type="tool-calling", verbose=True)
-    llm=llm,
+
-    db=ssDBLC,
+
-    agent_type="tool-calling",  
-    verbose=True
-)
 def fetch_db(state: State):
     state["infomation"] = agent.invoke({"input": state["userInput"]})["output"]
     print("调用了数据库工具，结果是：", state["infomation"])
     return state
 
+
 # ------------------------------------------------------------------------ 整理结果 --------
 def summarize_ai(state: State):
     """AI 总结输出"""
-    state["reply"] = getSummary(aiRole=state["aiRole"], history=state["history"], userInput= state["userInput"], infomation= state["infomation"])
+    state["reply"] = getSummary(
+        aiRole=state["aiRole"],
+        history=state["history"],
+        userInput=state["userInput"],
+        infomation=state["infomation"],
+    )
     return state
 
+
 # ------------------------------------------------------------------------ 普通聊天 --------
 def chat(state: State):
-    state["reply"] = get_chat_response(aiRole=state["aiRole"],history=state["history"], userInput= state["userInput"]).content
+    state["reply"] = get_chat_response(
+        aiRole=state["aiRole"], history=state["history"], userInput=state["userInput"]
+    ).content
     print("直接回复")
     return state
 
+
 # ------------------------------------------------------------------------ 构建有向图 --------
 workflow = StateGraph(State)
 workflow.add_node("decide", decide_source)
@@ -119,21 +128,20 @@ workflow.add_edge("fetch_db", "summarize")
 workflow.add_conditional_edges(
     "decide",
     lambda state: state["source"],  # 返回 state["source"] 的值
-    {
+    {"web": "fetch_web", "chat": "chat", "db": "fetch_db"},
-        "web": "fetch_web",
-        "chat": "chat",
-        "db": "fetch_db"
-    }
 )
 workflow.add_edge("summarize", END)
 workflow.add_edge("chat", END)
 graph = workflow.compile()
 
+
 # 执行函数
 def get_graph_output(aiRole: str, history: str, userInput: str) -> str:
-    final_state = graph.invoke({
+    final_state = graph.invoke(
+        {
             "aiRole": aiRole,
             "history": history,
             "userInput": userInput,
-    })
+        }
+    )
     return final_state["reply"]

bbit_ai/app/agent/dbAgent.py

@@ -1,31 +1,12 @@
-from typing import Literal
-from langchain_core.messages import AIMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.graph import END, START, MessagesState, StateGraph
-from langgraph.prebuilt import ToolNode
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from config.llm import llm, llmThink
-from langgraph.graph import StateGraph, END
-from langchain.prompts import PromptTemplate
-from config.llm import llm
-from typing import Annotated
-from typing_extensions import TypedDict
-from langgraph.graph import StateGraph, START, END
-from langgraph.graph.message import add_messages
-from langchain_community.agent_toolkits import create_sql_agent
-from langchain.prompts import PromptTemplate
-from config.llm import llm
-from typing import Annotated
-from langgraph.graph.message import add_messages
-import os
-from langchain_tavily import TavilySearch
-from langgraph.prebuilt import ToolNode, tools_condition
-from llm.chatLLM import get_chat_response
 from typing import TypedDict
+
+from langchain_core.prompts import PromptTemplate
 from langgraph.graph import StateGraph, END
-from llm.summarizeLLM import getSummary
+
 import db.postgres as pgdb
 import db.sqlserver as sqlserver
+from config.llm import llm
+from config.llm import llmThink
 
 
 # -------- 定义状态 --------
@@ -69,7 +50,7 @@ gen_sql_prompt = PromptTemplate(
 
 
 请直接输出完整可执行的 SQL 语句，不要任何其他文字或格式化，例如反引号或 ```sql。
-"""
+""",
 )
 sqlChain = gen_sql_prompt | llm
 
@@ -89,32 +70,33 @@ fix_prompt = PromptTemplate(
 
 # 输出要求
 只返回修正后的 SQL 语句，不包含任何额外的解释或说明。
-"""
+""",
 )
 fixSQLChain = fix_prompt | llm
 
 
 def sql(state: State):
     if state["isFirstGenSQL"]:
-        state['sql'] = sql_1(state)
+        state["sql"] = sql_1(state)
     else:
-        state['sql'] = sql_2(state)
+        state["sql"] = sql_2(state)
     for attempt in range(2):
         try:
             # 执行 SQL
-            result = sqlserver.executeSQL(state['sql'])
+            result = sqlserver.executeSQL(state["sql"])
-            state['sql_result'] = result
+            state["sql_result"] = result
             # print("SQL 执行成功，结果:", result)
             break
         except Exception as e:
             error_msg = str(e)
             print(f"SQL 执行出错: {error_msg}")
             # 调用 LLM 修正 SQL
-            state['sql'] = fixSQLChain.invoke({
+            state["sql"] = fixSQLChain.invoke(
-                "sql": state['sql'],
+                {
+                    "sql": state["sql"],
                     "error_msg": error_msg,
-                "table_info": state['table_info'],
+                    "table_info": state["table_info"],
-                "tenant_id": state['tenant_id']
+                    "tenant_id": state["tenant_id"],
                 }
             ).content
             # print(f"LLM 生成修正 SQL: {state['sql']}")
@@ -124,11 +106,13 @@ def sql(state: State):
 
 
 def sql_1(state: State):
-    return sqlChain.invoke({
+    return sqlChain.invoke(
-        "table_info": state['table_info'],
+        {
+            "table_info": state["table_info"],
             "userInput": state["userInput"],
-        "tenant_id": state['tenant_id']
+            "tenant_id": state["tenant_id"],
-    }).content
+        }
+    ).content
 
 
 improve_sql_prompt = PromptTemplate(
@@ -151,18 +135,20 @@ improve_sql_prompt = PromptTemplate(
 7. 通常来说，不查询对用户来说意义不大的字段，比如主键、外键、id等。
 8. 查询的SQL字段要用别名，取名参考描述。
 9. 一般情况下，如果能限制租户Id（通常为tenantid 字段），则尽量加上WHERE tenantid = {tenant_id}。
-"""
+""",
 )
 improveSqlChain = improve_sql_prompt | llm
 
 
 def sql_2(state: State):
-    return improveSqlChain.invoke({
+    return improveSqlChain.invoke(
-        "sql": state['sql'],
+        {
-        "table_info": state['table_info'],
+            "sql": state["sql"],
+            "table_info": state["table_info"],
             "userInput": state["userInput"],
-        "tenant_id": state['tenant_id']
+            "tenant_id": state["tenant_id"],
-    }).content
+        }
+    ).content
 
 
 # ------------------------------------------------------------------------ 路径选择 --------
@@ -199,7 +185,7 @@ chat → 无法直接生成 SQL，需要进一步解释或澄清。
 
 回答内容仅限于db或者chat，请勿输出其他内容。
 你的回复：
-    """
+    """,
 )
 pathSelectChain = pathSelectPrompt | llmThink
 
@@ -207,12 +193,18 @@ pathSelectChain = pathSelectPrompt | llmThink
 def decide_source(state: State, max_retry=3):
     """根据用户输入选择数据来源"""
     for _ in range(max_retry):
-        choice = pathSelectChain.invoke({
+        choice = (
+            pathSelectChain.invoke(
+                {
                     "userInput": state["userInput"],
                     "table_info": state["table_info"],
                     "ai_service": state["ai_service"],
-            "sql": state["sql"]
+                    "sql": state["sql"],
-        }).content.strip().lower()
+                }
+            )
+            .content.strip()
+            .lower()
+        )
         print("根据用户输入选择数据来源，路径是：", choice)
         if choice in ["db", "chat"]:
             state["path"] = choice
@@ -242,17 +234,16 @@ noChatPrompt = PromptTemplate(
 3. 引导用户提出与你业务相关的问题。
 4. 使用礼貌和友好的语气。
 你的回答：
-    """
+    """,
 )
 
 noChatChain = noChatPrompt | llm
 
 
 def chat(state: State):
-    state["reply"] = noChatChain.invoke({
+    state["reply"] = noChatChain.invoke(
-        "userInput": state["userInput"],
+        {"userInput": state["userInput"], "ai_service": state["ai_service"]}
-        "ai_service": state["ai_service"]
+    ).content
-    }).content
     print("直接回复")
     return state
 
@@ -291,19 +282,21 @@ summarizePrompt = PromptTemplate(
 2. 提供进一步可选的查询示例，基于当前的数据库表结构，引导用户提出更具体需求。 
 
 你的回复：
-    """
+    """,
 )
 summarizeChain = summarizePrompt | llm
 
 
 def summarize_ai(state: State):
     """AI 总结输出"""
-    state["reply"] = summarizeChain.invoke({
+    state["reply"] = summarizeChain.invoke(
+        {
             "ai_role": state["ai_role"],
-        "sql": state['sql'],
+            "sql": state["sql"],
-        "userInput": state['userInput'],
+            "userInput": state["userInput"],
-        "table_info": state['table_info'],
+            "table_info": state["table_info"],
-    }).content
+        }
+    ).content
     return state
 
 
@@ -322,7 +315,7 @@ workflow.add_conditional_edges(
     {
         "db": "sql_1",
         "chat": "chat",
-    }
+    },
 )
 workflow.add_edge("summarize", END)
 workflow.add_edge("chat", END)
@@ -334,7 +327,8 @@ def get_db_agent_reply(aiId: str, userInput: str, tenant_id: str, sql: str = "")
     json = pgdb.get_ai_personality(aiId)
     ai_service = json["业务"]
     ai_role = json["性格"]
-    final_state = graph.invoke({
+    final_state = graph.invoke(
+        {
             "ai_service": ai_service,
             "ai_role": ai_role,
             "table_info": pgdb.get_available_tables_str(aiId),
@@ -342,5 +336,6 @@ def get_db_agent_reply(aiId: str, userInput: str, tenant_id: str, sql: str = "")
             "userInput": userInput,
             "sql": sql,
             "isFirstGenSQL": sql == "",
-    })
+        }
+    )
     return final_state

bbit_ai/app/agent/serviceAgent.py

@@ -1,34 +1,13 @@
-
+from typing import List
-from typing import Literal
-from langchain_core.messages import AIMessage
-from langchain_core.runnables import RunnableConfig
-from langgraph.graph import END, START, MessagesState, StateGraph
-from langgraph.prebuilt import ToolNode
-from langchain_community.agent_toolkits import SQLDatabaseToolkit
-from config.llm import llm,llmThink
-from langgraph.graph import StateGraph, END
-from langchain.prompts import PromptTemplate
-from config.llm import llm
-from typing import Annotated
-from typing_extensions import TypedDict
-from langgraph.graph import StateGraph, START, END
-from langgraph.graph.message import add_messages
-from langchain_community.agent_toolkits import create_sql_agent
-from langchain.prompts import PromptTemplate
-from config.llm import llm
-from typing import Annotated
-from langgraph.graph.message import add_messages
-import os
-from langchain_tavily import TavilySearch
-from langgraph.prebuilt import ToolNode, tools_condition
-from llm.chatLLM import get_chat_response
 from typing import TypedDict
+
+from langchain_core.prompts import PromptTemplate
 from langgraph.graph import StateGraph, END
-from llm.summarizeLLM import getSummary
+
-import db.postgres as pgdb
-import db.sqlserver as sqlserver
-from typing import List, Dict
 import db.milvus as milvus
+import db.postgres as pgdb
+from config.llm import llm
+from config.llm import llmThink
 
 
 # -------- 定义状态 --------
@@ -48,6 +27,7 @@ class State(TypedDict):
     userInput: str  # 用户输入
     reply: str  # 最终回复
 
+
 # -------- 定义节点 --------
 # ------------------------------------------------------------------------ 向量数据库查询 --------
 
@@ -65,23 +45,28 @@ gen_sql_prompt = PromptTemplate(
 4. 确保关键词之间相互独立，不包含其他关键词。
 关键词之间用空格分隔。
 你的回答是：
-"""
+""",
 )
 sqlChain = gen_sql_prompt | llm
+
+
 def db_search(state: State):
-    key_words = sqlChain.invoke({
+    key_words = sqlChain.invoke(
-        "userInput": state['userInput'],
+        {
-    }).content
+            "userInput": state["userInput"],
+        }
+    ).content
     print("关键词是：", key_words)
-    knowledge = milvus.get_knowledge_by_key_words(key_words, state['kn_bases'])
+    knowledge = milvus.get_knowledge_by_key_words(key_words, state["kn_bases"])
     print("知识库内容是：", knowledge)
     state["knowledge"] = knowledge
-    ai_ids = [state['ai_id']]
+    ai_ids = [state["ai_id"]]
     memory = milvus.get_memory_by_key_words(key_words, ai_ids)
     print("记忆是：", memory)
     state["memory"] = memory
     return state
 
+
 # ------------------------------------------------------------------------ 意图分析 --------
 
 pathSelectPrompt = PromptTemplate(
@@ -103,17 +88,25 @@ pathSelectPrompt = PromptTemplate(
 判断规则如下：
 如果用户最新回复与你的负责工作相关，需要去查知识库，输出“kn”；如果不相关，则输出“chat”，不要包含任何标点符号以及空格。
 你生成的结果：
-    """
+    """,
 )
 pathSelectChain = pathSelectPrompt | llmThink
+
+
 def decide_source(state: State, max_retry=3):
     """根据用户输入选择数据来源"""
     for _ in range(max_retry):
-        choice = pathSelectChain.invoke({
+        choice = (
+            pathSelectChain.invoke(
+                {
                     "userInput": state["userInput"],
                     "ai_service": state["ai_service"],
                     "history": state["history"],
-        }).content.strip().lower()
+                }
+            )
+            .content.strip()
+            .lower()
+        )
         print("根据用户输入选择数据来源，路径是：", choice)
         if choice in ["kn", "chat"]:
             state["path"] = choice
@@ -123,6 +116,7 @@ def decide_source(state: State, max_retry=3):
         state["path"] = "chat"
     return state
 
+
 # ------------------------------------------------------------------------ !普通聊天 --------
 noChatPrompt = PromptTemplate(
     input_variables=["ai_name", "ai_service", "ai_role", "history"],
@@ -140,21 +134,26 @@ noChatPrompt = PromptTemplate(
 4. 回复要简洁明了，避免冗长和复杂的表述。
 
 你的回答：
-    """
+    """,
 )
 
 noChatChain = noChatPrompt | llm
+
+
 def chat(state: State):
-    state["reply"] = noChatChain.invoke({
+    state["reply"] = noChatChain.invoke(
+        {
             "ai_name": state["ai_name"],
             "ai_service": state["ai_service"],
             "ai_role": state["ai_role"],
             "history": state["history"],
-        "userStr": state["userInput"]
+            "userStr": state["userInput"],
-    }).content
+        }
+    ).content
     print("直接回复")
     return state
 
+
 # ------------------------------------------------------------------------ 整理结果 --------
 
 summarizePrompt = PromptTemplate(
@@ -177,32 +176,40 @@ summarizePrompt = PromptTemplate(
 2. 回复的语气要结合你的性格特点。
 3. 确保回复内容清晰、简洁、有针对性。
 请生成你的回复：
-    """
+    """,
 )
 summarizeChain = summarizePrompt | llm
+
+
 def summarize_ai(state: State):
     """AI 总结输出"""
-    mem = state['memory']
+    mem = state["memory"]
     if mem != "":
-        memStr = """
+        memStr = (
+            """
         这是给你参考的相关历史记忆：
         <memory>
         %s
         </memory>
-    """ % mem   # 这里用 % 把 mem 填进去
+    """
+            % mem
+        )  # 这里用 % 把 mem 填进去
     else:
         memStr = "没有记忆内容"
     print("历史记录是：", state["history"])
-    state["reply"] = summarizeChain.invoke({
+    state["reply"] = summarizeChain.invoke(
+        {
             "ai_role": state["ai_role"],
             "ai_name": state["ai_name"],
             "history": state["history"],
-        "ai_service":state['ai_service'],
+            "ai_service": state["ai_service"],
             "knowledge": state["knowledge"],
             "memory": memStr,
-    }).content
+        }
+    ).content
     return state
 
+
 # ------------------------------------------------------------------------ 构建有向图 --------
 workflow = StateGraph(State)
 workflow.add_node("decide", decide_source)
@@ -217,15 +224,18 @@ workflow.add_conditional_edges(
     {
         "kn": "db_search",
         "chat": "chat",
-    }
+    },
 )
 workflow.add_edge("db_search", "summarize")
 workflow.add_edge("summarize", END)
 workflow.add_edge("chat", END)
 graph = workflow.compile()
 
+
 # 执行函数
-def get_service_agent_reply(aiId:str, userInput: str,history:str, kn_bases:List[str]) :
+def get_service_agent_reply(
+    aiId: str, userInput: str, history: str, kn_bases: List[str]
+):
     json = pgdb.get_ai_personality(aiId)
     ai_service = json["业务"]
     ai_role = json["性格"]
@@ -233,7 +243,8 @@ def get_service_agent_reply(aiId:str, userInput: str,history:str, kn_bases:List[
     print("AI Name:", ai_name)
     print("AI Service:", ai_service)
 
-    final_state = graph.invoke({
+    final_state = graph.invoke(
+        {
             "ai_service": ai_service,
             "ai_role": ai_role,
             "ai_name": ai_name,
@@ -242,5 +253,6 @@ def get_service_agent_reply(aiId:str, userInput: str,history:str, kn_bases:List[
             "table_info": pgdb.get_available_tables_str(aiId),
             "userInput": userInput,
             "ai_id": aiId,
-    })
+        }
+    )
     return final_state["reply"]

bbit_ai/app/config/emqx.py

@@ -40,17 +40,14 @@ if sys.platform.lower() == "win32" or os.name.lower() == "nt":
 
 
 def get_device_id_simple():
-    try:
+    hostname = os.getenv("HOST_NAME")
-        with open("/etc/machine-id") as f:
+    if not hostname:
-            mid = f.read().strip()
-            if mid:
-                return mid
-    except Exception:
-        pass
         hostname = socket.gethostname()
         mac = uuid.getnode()
         mac_str = ":".join(f"{(mac >> ele) & 0xff:02x}" for ele in range(40, -1, -8))
         return f"{hostname}|{mac_str}"
+    else:
+        return hostname
 
 
 # todo  这里需要订阅状态信息 设备发送信息 这里回复 vue前端发送指令 后端发送指令 设备接收指令

bbit_ai/app/db/milvus.py

@@ -1,9 +1,10 @@
-from config.milvus import knVectorstore,memVectorstore
-from langchain.schema import Document
 from datetime import datetime
 from typing import List
 
-from typing import List, Dict, Any
+from langchain_core.documents import Document
+
+from config.milvus import knVectorstore, memVectorstore
+
 
 def get_knowledge_by_key_words(key_words: str, kn_ids: List[str]) -> str:
     """
@@ -17,9 +18,7 @@ def get_knowledge_by_key_words(key_words: str, kn_ids: List[str]) -> str:
         return "未找到相关的知识。"
 
     result = knVectorstore.similarity_search(
-        query=key_words,
+        query=key_words, k=3, expr=expr  # 可调节返回条数
-        k=3,  # 可调节返回条数
-        expr=expr
     )
 
     # 整理成字符串
@@ -48,9 +47,7 @@ def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:
         expr = ""  # 不限制 kn_id todo 实际上应该不反悔任何内容
 
     result = memVectorstore.similarity_search(
-        query=key_words,
+        query=key_words, k=5, expr=expr  # 可调节返回条数
-        k=5,  # 可调节返回条数
-        expr=expr
     )
 
     # 整理成字符串
@@ -64,12 +61,12 @@ def get_memory_by_key_words(key_words: str, ai_ids: List[str]) -> str:
     # 拼成一个大字符串，用换行隔开
     combined_text = "\n\n".join(doc_texts)
     return combined_text
+
+
 def get_knowledge_by_base_id(base_id: str):
     expr = f'kn_id == "{base_id}"'  # base_id 会被替换
     result = knVectorstore.similarity_search(
-        query="",  # 如果只想用过滤条件，可以传空字符串
+        query="", k=100, expr=expr  # 如果只想用过滤条件，可以传空字符串
-        k=100,
-        expr=expr
     )
     return [
         {
@@ -80,6 +77,7 @@ def get_knowledge_by_base_id(base_id: str):
         for doc in result
     ]
 
+
 def add_knowledge(text: str, is_active: bool, base_id: str, user_id: str):
     docs = [
         Document(
@@ -89,11 +87,12 @@ def add_knowledge(text: str, is_active: bool, base_id: str, user_id: str):
                 "created_by": str(user_id),
                 "created_at": datetime.now().isoformat(),
                 "is_active": is_active,
-            }
+            },
         )
     ]
     return knVectorstore.add_documents(docs)
 
+
 def add_memory(ai_id: str, mem: str, user_id: str, is_active: bool):
     docs = [
         Document(
@@ -103,7 +102,7 @@ def add_memory(ai_id:str,mem: str, user_id: str,is_active: bool):
                 "created_by": str(user_id),
                 "created_at": datetime.now().isoformat(),
                 "is_active": is_active,
-            }
+            },
         )
     ]
     return memVectorstore.add_documents(docs)

bbit_ai/app/llm/chatLLM.py

@@ -1,6 +1,6 @@
+from langchain_core.prompts import PromptTemplate
 
 from config.llm import llm
-from langchain.prompts import PromptTemplate
 
 chatPrompt = PromptTemplate(
     input_variables=["aiRole", "history", "userInput"],
@@ -15,13 +15,12 @@ chatPrompt = PromptTemplate(
         {userInput}
 
         最后，请注意，不要编造数据，不知道就说不知道，现在，请生成你的回复：
-    """
+    """,
 )
 chatChain = chatPrompt | llm
 
+
 def get_chat_response(aiRole: str, history: str, userInput: str) -> str:
-    return chatChain.invoke({
+    return chatChain.invoke(
-            "aiRole": aiRole,
+        {"aiRole": aiRole, "history": history, "userInput": userInput}
-            "history": history,
+    )
-            "userInput": userInput
-        })

bbit_ai/app/llm/memLLM.py

@@ -1,8 +1,8 @@
-from langchain.prompts import PromptTemplate
+from langchain_core.prompts import PromptTemplate
-from config.llm import llm,llmThink
+
 import db.milvus as milvus
 import db.postgres as pg
-import json
+from config.llm import llmThink
 
 memPathPrompt = PromptTemplate(
     input_variables=["ai_role", "CHAT_RECORD"],
@@ -31,7 +31,7 @@ no：用户最新回复价值有限或几乎不会在未来业务中使用。
 
 回复不要带任何标点符号以及空格、换行符。
 请给出你的判断结果：
-    """
+    """,
 )
 memPathChain = memPathPrompt | llmThink
 memPrompt = PromptTemplate(
@@ -48,9 +48,11 @@ memPrompt = PromptTemplate(
 4. 总结内容应包含时间，并确保时间是准确的。
 5. 你需要针对你的业务场景{ai_role}，展开对用户最后回复的总结。
 请生成你的总结，以用户、时间开头：
-    """
+    """,
 )
 memChain = memPrompt | llmThink
+
+
 def take_memory(ai_id: str, sessionId: str, user_id: str, max_retry=3):
     """根据用户输入选择数据来源"""
     history = pg.get_history_with_time(sessionId, 10)
@@ -66,17 +68,29 @@ def take_memory(ai_id:str,sessionId: str,user_id:str, max_retry=3):
         else:
             ai_service = json["业务"]
     print("获取的描述是：", ai_service)
-    choice = memPathChain.invoke({
+    choice = (
+        memPathChain.invoke(
+            {
                 "ai_role": ai_service,
                 "CHAT_RECORD": history,
-    }).content.strip().lower()
+            }
+        )
+        .content.strip()
+        .lower()
+    )
     print("记忆判断器判断的结果是：", choice)
     if choice == "yes":
         # 对对话进行总结
-        memory = memChain.invoke({
+        memory = (
+            memChain.invoke(
+                {
                     "CHAT_RECORD": history,
                     "ai_role": ai_service,
-        }).content.strip().lower()
+                }
+            )
+            .content.strip()
+            .lower()
+        )
         print("记忆生成结果是：", memory)
         milvus.add_memory(mem=memory, user_id=user_id, is_active=True, ai_id=ai_id)
         return

bbit_ai/app/llm/sqlLLM.py

@@ -1,8 +1,8 @@
+from langchain_community.agent_toolkits import create_sql_agent
+from langchain_core.prompts import PromptTemplate
 
 from config.llm import llm
-from langchain.prompts import PromptTemplate
 from config.ssDb import ssDBLC
-from langchain_community.agent_toolkits import create_sql_agent
 
 # ______________________________________________________________SQL描述_____________________________________________________________________
 sqlDescriptionPrompt = PromptTemplate(
@@ -16,14 +16,14 @@ sqlDescriptionPrompt = PromptTemplate(
         3. 不能有markdown语法
         4. 要用业务语言描述，不能有专业语句例如SQL表名等
         请生成你认为合适的标题，：
-    """
+    """,
 )
 sqlDescriptionChain = sqlDescriptionPrompt | llm
 
+
 def get_sql_description_response(sql: str) -> str:
-    return sqlDescriptionChain.invoke({
+    return sqlDescriptionChain.invoke({"sql": sql})
-            "sql": sql
+
-        })
 
 # ______________________________________________________________第一次生成SQL_____________________________________________________________________
 sqlPrompt = PromptTemplate(
@@ -34,15 +34,12 @@ sqlPrompt = PromptTemplate(
         只需要返回SQL语句，不要任何解释。
         用户需求：{userInput}
         请生成SQL语句：
-    """
+    """,
 )
 sqlChain = sqlPrompt | llm
-agent = create_sql_agent(
+agent = create_sql_agent(llm=llm, db=ssDBLC, agent_type="tool-calling", verbose=True)
-    llm=llm,
+
-    db=ssDBLC,
+
-    agent_type="tool-calling",  
-    verbose=True
-)
 # def get_chat_sql_response2( userInput: str) -> str:
 #     return sqlChain.invoke({
 #             "userInput": userInput
@@ -50,6 +47,7 @@ agent = create_sql_agent(
 def get_chat_sql_response(userInput: str) -> str:
     return agent.invoke({"input": userInput})["output"]
 
+
 # ______________________________________________________________改进SQL_____________________________________________________________________
 sqlImprovePrompt = PromptTemplate(
     input_variables=["userInput", "sql"],
@@ -59,11 +57,10 @@ sqlImprovePrompt = PromptTemplate(
         只需要返回改进后的SQL语句，不要任何解释。
         已有SQL：{sql}
         用户需求：{userInput}
-    """
+    """,
 )
 sqlImproveChain = sqlImprovePrompt | llm
 
+
 def get_chat_sql_improve_response(userInput: str) -> str:
-    return sqlImproveChain.invoke({
+    return sqlImproveChain.invoke({"userInput": userInput})
-            "userInput": userInput
-        })

bbit_ai/app/llm/summarizeLLM.py

@@ -1,5 +1,5 @@
+from langchain_core.prompts import PromptTemplate
 
-from langchain.prompts import PromptTemplate
 from config.llm import llm
 
 summarizePrompt = PromptTemplate(
@@ -21,14 +21,17 @@ summarizePrompt = PromptTemplate(
         {infomation}
         ···
         如果参考内容明显有问题，你要请用户重新描述问题，现在请生成你的回复：
-    """
+    """,
 )
 summarizeChain = summarizePrompt | llm
 
+
 def getSummary(aiRole: str, history: str, userInput: str, infomation: str) -> str:
-    return summarizeChain.invoke({
+    return summarizeChain.invoke(
+        {
             "aiRole": aiRole,
             "history": history,
             "userStr": userInput,
-        "infomation": infomation
+            "infomation": infomation,
-    }).content
+        }
+    ).content

bbit_ai/app/llm/ticketLLM.py

@@ -1,7 +1,7 @@
 import json
 import re
 
-from langchain.schema import HumanMessage
+from langchain_core.messages import HumanMessage
 
 from config.llm import *
 

bbit_ai/app/llm/ticketLLMv2.py

@@ -1,7 +1,7 @@
 import json
 import re
 
-from langchain.schema import HumanMessage
+from langchain_core.messages import HumanMessage
 
 from config.llm import *
 from llm.ticketLLM import decode_barcode

bbit_ai/app/llm/titleChain.py

@@ -1,5 +1,5 @@
+from langchain_core.prompts import PromptTemplate
 
-from langchain.prompts import PromptTemplate
 from config.llm import llm
 
 titlePrompt = PromptTemplate(
@@ -12,9 +12,10 @@ titlePrompt = PromptTemplate(
         4. 保持自然、易懂、专业或有趣（可根据场景调整风格）。
         5. 不能出现标点符号。
         用户原话："{userStr}"
-    """
+    """,
 )
 titleChain = titlePrompt | llm
 
+
 def get_title(userInput: str):
     return titleChain.invoke({"userStr": userInput}).content

bbit_ai/app/requirements.txt

@@ -23,6 +23,7 @@ python-multipart==0.0.20
 aio_pika==9.5.7
 ultralytics==8.3.227
 redis==7.1.0
+aiomqtt==2.4.0
 # MCP服务
 python-dotenv>=1.0.0
 websockets>=11.0.3