后端docker 部署文件大改

bbit_ai/test/other/dspyTest.py

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+import dspy
+lm = dspy.LM("openai/deepseek-chat", api_key="sk-6129a200ae294b9f86553505191fa477", api_base="https://api.deepseek.com")
+dspy.configure(lm=lm)
+
+# print(lm("Say this is a test!", temperature=0.7))  # => ['This is a test!']
+# print(lm(messages=[{"role": "user", "content": "Say this is a test!"}]))  # => ['This is a test!']
+
+from typing import Literal
+
+class Classify(dspy.Signature):
+    """Classify sentiment of a given sentence."""
+
+    sentence: str = dspy.InputField()
+    sentiment: Literal["positive", "negative", "neutral"] = dspy.OutputField()
+    confidence: float = dspy.OutputField()
+
+classify = dspy.Predict(Classify)
+print(classify(sentence="This book was super fun to read, though not the last chapter."))

bbit_ai/test/other/sqlserver.py

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+from sqlalchemy import create_engine, text  # ✅ 注意这里导入 text
+uri = "mssql+pyodbc://f8_db_test:APN^QPr!K9@122.114.58.23/f8_db_test?driver=ODBC+Driver+18+for+SQL+Server&TrustServerCertificate=yes"
+
+engine = create_engine(uri)
+with engine.connect() as conn:
+    result = conn.execute(text("SELECT count(*) FROM dbo.NONGHU_INFO"))
+    print(result.scalar())

bbit_ai/test/other/test.py

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+from langchain_community.chat_models.tongyi import ChatTongyi
+from langchain_postgres import PostgresChatMessageHistory
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+import uuid
+import psycopg
+from utils.tools import all_tools
+# ------------------ 配置 PostgreSQL 聊天记录 ------------------
+conn = sync_connection=psycopg.connect("postgresql://postgres:123456@10.10.10.9/ktor2")
+database_name = "ai_chat_history"
+PostgresChatMessageHistory.create_tables(conn, database_name)
+
+history = PostgresChatMessageHistory(
+    database_name,  
+    str(uuid.uuid4()),          # session_id
+    sync_connection=conn
+)
+# ------------------ 配置 LLM ------------------
+prompt = PromptTemplate(
+    input_variables=["question"],
+    template="""
+        请基于上下文，保持中文简洁回答用户：
+        上下文：{history},
+        用户：{userStr}
+    """
+)
+llm = ChatTongyi(streaming=False, api_key="sk-fb46eefb6b404382a0a5325202e923a6") 
+llm_with_tools = llm.bind_tools(all_tools)
+# 建立链
+chain = prompt | llm_with_tools
+# chain = prompt | llm
+
+# ------------------ 循环聊天 ------------------
+while True:
+    userStr = input("用户: ")
+    history.add_user_message(userStr)
+    answer = chain.invoke({"history":history.messages,"userStr": userStr})
+    print("AI:", answer.content)
+    history.add_ai_message(answer)
+
+