0.1.187

PyPI/setup.cfg

@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.186
+version = 0.1.187
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package

PyPI/src/guan.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 2.4
 Name: guan
-Version: 0.1.186
+Version: 0.1.187
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan

PyPI/src/guan.egg-info/SOURCES.txt

@@ -2,6 +2,7 @@ LICENSE
 README.md
 pyproject.toml
 setup.cfg
+src/guan/AI_chat.py
 src/guan/Fourier_transform.py
 src/guan/Green_functions.py
 src/guan/Hamiltonian_of_examples.py

PyPI/src/guan/AI_chat.py

@@ -0,0 +1,217 @@
+# Module: AI_chat
+
+# AI 对话
+def chat(prompt='你好', model=1, stream=1, stream_label=0):
+    import requests
+    url = "http://api.guanjihuan.com/chat"
+    data = {
+        "prompt": prompt, 
+        "model": model,
+    }
+    if stream == 1:
+        if stream_label == 1:
+            print('\n--- Start Chat Stream Message ---\n')
+    requests_response = requests.post(url, json=data, stream=True)
+    response = ''
+    if requests_response.status_code == 200:
+        for line in requests_response.iter_lines():
+            if line:
+                if stream == 1:
+                    print(line.decode('utf-8'), end='', flush=True)
+                response += line.decode('utf-8')
+        print()
+    else:
+        pass
+    if stream == 1:
+        if stream_label == 1:
+            print('\n--- End Chat Stream Message ---\n')
+    return response
+
+# 加上函数代码的 AI 对话
+def chat_with_function_code(function_name, prompt='', model=1, stream=1):
+    import guan
+    function_source = guan.get_source(function_name)
+    if prompt == '':
+        response = guan.chat(prompt=function_source, model=model, stream=stream)
+    else:
+        response = guan.chat(prompt=function_source+'\n\n'+prompt, model=model, stream=stream)
+    return response
+
+# 机器人自动对话
+def auto_chat(prompt='你好', round=2, model=1, stream=1):
+    import guan
+    response0 = prompt
+    for i0 in range(round):
+        print(f'\n【对话第 {i0+1} 轮】\n')
+        print('机器人 1: ')
+        response1 = guan.chat(prompt=response0, model=model, stream=stream)
+        print('机器人 2: ')
+        response0 = guan.chat(prompt=response1, model=model, stream=stream)
+
+# 机器人自动对话（引导对话）
+def auto_chat_with_guide(prompt='你好', guide_message='（回答字数少于30个字，最后反问我一个问题）', round=5, model=1, stream=1):
+    import guan
+    response0 = prompt
+    for i0 in range(round):
+        print(f'\n【对话第 {i0+1} 轮】\n')
+        print('机器人 1: ')
+        response1 = guan.chat(prompt=response0+guide_message, model=model, stream=stream)
+        print('机器人 2: ')
+        response0 = guan.chat(prompt=response1+guide_message, model=model, stream=stream)
+
+# 使用 LangChain 无记忆对话（需要 API Key)
+def langchain_chat_without_memory(prompt="你好", temperature=0.7, system_message=None, print_show=1):
+    from langchain_openai import ChatOpenAI
+    from langchain_core.prompts import ChatPromptTemplate
+    import dotenv
+    import os
+    dotenv.load_dotenv()
+    llm = ChatOpenAI(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        base_url=os.getenv("DASHSCOPE_BASE_URL"),
+        model="qwen-plus",
+        temperature=temperature,
+        streaming=True,
+    )
+    if system_message == None:
+        langchain_prompt = ChatPromptTemplate.from_messages([
+            ("human", "{question}")
+        ])
+    else:
+        langchain_prompt = ChatPromptTemplate.from_messages([
+            ("system", system_message),
+            ("human", "{question}")
+        ])
+    chain = langchain_prompt | llm
+    response = ''
+    for chunk in chain.stream({"question": prompt}):
+        response += chunk.content
+        if print_show:
+            print(chunk.content, end="", flush=True)
+    if print_show:
+        print()
+    return response
+
+# 使用 LangChain 有记忆对话（记忆临时保存在函数的属性上，需要 API Key)
+def langchain_chat_with_memory(prompt="你好", temperature=0.7, system_message=None, session_id="default", print_show=1):
+    from langchain_openai import ChatOpenAI
+    from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+    from langchain_core.runnables.history import RunnableWithMessageHistory
+    from langchain_community.chat_message_histories import ChatMessageHistory
+    from dotenv import load_dotenv
+    import os
+    load_dotenv()
+    llm = ChatOpenAI(
+        api_key=os.getenv("OPENAI_API_KEY"),
+        base_url=os.getenv("DASHSCOPE_BASE_URL"),
+        model="qwen-plus",
+        temperature=temperature,
+        streaming=True,
+    )
+    if system_message == None:
+        langchain_prompt = ChatPromptTemplate.from_messages([
+            MessagesPlaceholder("history"),
+            ("human", "{question}") 
+        ])
+    else:
+        langchain_prompt = ChatPromptTemplate.from_messages([
+            ("system", system_message),
+            MessagesPlaceholder("history"),
+            ("human", "{question}") 
+        ])
+    chain = langchain_prompt | llm
+    if not hasattr(langchain_chat_with_memory, "store"):
+        langchain_chat_with_memory.store = {}
+    
+    def get_session_history(sid: str):
+        if sid not in langchain_chat_with_memory.store:
+            langchain_chat_with_memory.store[sid] = ChatMessageHistory()
+        return langchain_chat_with_memory.store[sid]
+    
+    chatbot = RunnableWithMessageHistory(
+        chain,
+        lambda sid: get_session_history(sid),
+        input_messages_key="question",
+        history_messages_key="history",
+    )
+    response = ''
+    for chunk in chatbot.stream({"question": prompt}, config={"configurable": {"session_id": session_id}}):
+        response += chunk.content
+        if print_show:
+            print(chunk.content, end="", flush=True)
+    if print_show:
+        print()
+    return response
+
+# 使用 Ollama 本地模型对话（需要运行 Ollama 和下载对应的模型）
+def ollama_chat(prompt='你好/no_think', model="qwen3:0.6b", temperature=0.8, print_show=1):
+    import ollama
+    response_stream = ollama.chat(model=model, messages=[{"role": "user", "content": prompt}], stream=True, options={"temperature": temperature})
+    response = ''
+    start_thinking = 1
+    for part in response_stream:
+        response += part['message']['content']
+        if print_show == 1:
+            thinking = part['message'].get('thinking')
+            if thinking is not None:
+                if start_thinking == 1:
+                    print('<think>')
+                    start_thinking = 0
+                print(f"{thinking}", end='', flush=True)
+            else:
+                if start_thinking == 0:
+                    print('</think>')
+                    start_thinking = 1
+                print(part['message']['content'], end='', flush=True)
+    if print_show == 1:
+        print()
+    return response
+
+# ModelScope 加载本地模型和分词器（只加载一次）
+def load_modelscope_model(model_name="D:/models/Qwen/Qwen3-0.6B"):
+    from modelscope import AutoModelForCausalLM, AutoTokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return model, tokenizer
+
+# 使用 ModelScope 本地模型聊天
+def modelscope_chat(model, tokenizer, prompt='你好 /no_think', history=[], temperature=0.7, top_p=0.8):
+    messages = history + [{"role": "user", "content": prompt}]
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    inputs = tokenizer(text, return_tensors="pt")
+    response_ids = model.generate(**inputs, max_new_tokens=32768, temperature=temperature, top_p=top_p, do_sample=True)[0][len(inputs.input_ids[0]):].tolist()
+    response = tokenizer.decode(response_ids, skip_special_tokens=True)
+    new_history = history + [
+        {"role": "user", "content": prompt},
+        {"role": "assistant", "content": response}
+    ]
+    return response, new_history
+
+# LLaMA 加载本地模型（只加载一次）
+def load_llama_model(model_path="D:/models/Qwen/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf"):
+    from llama_cpp import Llama
+    llm = Llama(
+        model_path=model_path,
+        n_ctx=32768,
+        verbose=False,
+        chat_format="chatml",
+        logits_all=False
+    )
+    return llm
+
+# 使用 LLaMA 本地模型聊天
+def llama_chat(llm, prompt, history=[], temperature=0.7, top_p=0.8):
+    new_history = history + [{"role": "user", "content": prompt}]
+    llm_response = llm.create_chat_completion(
+        messages=new_history,
+        temperature=temperature,
+        top_p=top_p,
+        repeat_penalty=1.5,
+    )
+    response = llm_response["choices"][0]["message"]["content"].strip()
+    new_history.append({"role": "assistant", "content": response})
+    return response, new_history

PyPI/src/guan/__init__.py

@@ -12,6 +12,7 @@ from .machine_learning import *
 from .file_reading_and_writing import *
 from .figure_plotting import *
 from .data_processing import *
+from .AI_chat import *
 from .decorators import *
 from .others import *
 statistics_of_guan_package()

PyPI/src/guan/others.py

@@ -1,64 +1,5 @@
 # Module: others
 
-# AI 对话
-def chat(prompt='你好', model=1, stream=1, stream_label=0):
-    import requests
-    url = "http://api.guanjihuan.com/chat"
-    data = {
-        "prompt": prompt, 
-        "model": model,
-    }
-    if stream == 1:
-        if stream_label == 1:
-            print('\n--- Start Chat Stream Message ---\n')
-    requests_response = requests.post(url, json=data, stream=True)
-    response = ''
-    if requests_response.status_code == 200:
-        for line in requests_response.iter_lines():
-            if line:
-                if stream == 1:
-                    print(line.decode('utf-8'), end='', flush=True)
-                response += line.decode('utf-8')
-        print()
-    else:
-        pass
-    if stream == 1:
-        if stream_label == 1:
-            print('\n--- End Chat Stream Message ---\n')
-    return response
-
-# 加上函数代码的 AI 对话
-def chat_with_function_code(function_name, prompt='', model=1, stream=1):
-    import guan
-    function_source = guan.get_source(function_name)
-    if prompt == '':
-        response = guan.chat(prompt=function_source, model=model, stream=stream)
-    else:
-        response = guan.chat(prompt=function_source+'\n\n'+prompt, model=model, stream=stream)
-    return response
-
-# 机器人自动对话
-def auto_chat(prompt='你好', round=2, model=1, stream=1):
-    import guan
-    response0 = prompt
-    for i0 in range(round):
-        print(f'\n【对话第 {i0+1} 轮】\n')
-        print('机器人 1: ')
-        response1 = guan.chat(prompt=response0, model=model, stream=stream)
-        print('机器人 2: ')
-        response0 = guan.chat(prompt=response1, model=model, stream=stream)
-
-# 机器人自动对话（引导对话）
-def auto_chat_with_guide(prompt='你好', guide_message='（回答字数少于30个字，最后反问我一个问题）', round=5, model=1, stream=1):
-    import guan
-    response0 = prompt
-    for i0 in range(round):
-        print(f'\n【对话第 {i0+1} 轮】\n')
-        print('机器人 1: ')
-        response1 = guan.chat(prompt=response0+guide_message, model=model, stream=stream)
-        print('机器人 2: ')
-        response0 = guan.chat(prompt=response1+guide_message, model=model, stream=stream)
-
 # CPU性能测试（十亿次循环的浮点加法运算的时间，约30秒左右）
 def cpu_test_with_addition(print_show=1):
     import time

README.md

@@ -1,6 +1,6 @@
 ## Guan package
 
-Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about (Ji-Huan Guan, 关济寰). With this package, you can calculate band structures, density of states, quantum transport and topological invariant of tight-binding models by invoking the functions you need. Other frequently used functions are also integrated, such as machine learning, file reading and writing, figure plotting, and data processing.
+Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about (Ji-Huan Guan, 关济寰). With this package, you can calculate band structures, density of states, quantum transport and topological invariant of tight-binding models by invoking the functions you need. Other frequently used functions are also integrated, such as machine learning, file reading and writing, figure plotting, data processing and AI chat.
 
 The primary location of this package is on https://py.guanjihuan.com.
 
@@ -26,6 +26,7 @@ import guan
 + file reading and writing
 + figure plotting
 + data processing
++ AI chat
 + decorators
 + others