From fd6f65c3be7a408a70eb5f327cd138612befc9ad Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Mon, 1 Dec 2025 16:07:39 +0800 Subject: [PATCH] 0.1.188 --- PyPI/setup.cfg | 2 +- PyPI/src/guan.egg-info/PKG-INFO | 2 +- PyPI/src/guan/AI_chat.py | 67 +++++++++++++++++++++++++++------ 3 files changed, 57 insertions(+), 14 deletions(-) diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg index b76f728..adf42de 100644 --- a/PyPI/setup.cfg +++ b/PyPI/setup.cfg @@ -1,7 +1,7 @@ [metadata] # replace with your username: name = guan -version = 0.1.187 +version = 0.1.188 author = guanjihuan author_email = guanjihuan@163.com description = An open source python package diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO index cbd8104..b5dec04 100644 --- a/PyPI/src/guan.egg-info/PKG-INFO +++ b/PyPI/src/guan.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.4 Name: guan -Version: 0.1.187 +Version: 0.1.188 Summary: An open source python package Home-page: https://py.guanjihuan.com Author: guanjihuan diff --git a/PyPI/src/guan/AI_chat.py b/PyPI/src/guan/AI_chat.py index e69501e..6075454 100644 --- a/PyPI/src/guan/AI_chat.py +++ b/PyPI/src/guan/AI_chat.py @@ -60,12 +60,19 @@ def auto_chat_with_guide(prompt='你好', guide_message='（回答字数少于30 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): +def langchain_chat_without_memory(prompt="你好", temperature=0.7, system_message=None, print_show=1, load_env=1): from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate - import dotenv import os - dotenv.load_dotenv() + if load_env: + import dotenv + from pathlib import Path + import inspect + caller_frame = inspect.stack()[1] + caller_dir = Path(caller_frame.filename).parent + env_path = caller_dir / ".env" + if env_path.exists(): + dotenv.load_dotenv(env_path) llm = ChatOpenAI( api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("DASHSCOPE_BASE_URL"), @@ -93,14 +100,21 @@ def langchain_chat_without_memory(prompt="你好", temperature=0.7, system_messa 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): +def langchain_chat_with_memory(prompt="你好", temperature=0.7, system_message=None, session_id="default", print_show=1, load_env=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() + if load_env: + import dotenv + from pathlib import Path + import inspect + caller_frame = inspect.stack()[1] + caller_dir = Path(caller_frame.filename).parent + env_path = caller_dir / ".env" + if env_path.exists(): + dotenv.load_dotenv(env_path) llm = ChatOpenAI( api_key=os.getenv("OPENAI_API_KEY"), base_url=os.getenv("DASHSCOPE_BASE_URL"), @@ -175,7 +189,9 @@ def load_modelscope_model(model_name="D:/models/Qwen/Qwen3-0.6B"): 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): +def modelscope_chat(model, tokenizer, prompt='你好 /no_think', history=[], temperature=0.7, top_p=0.8, print_show=1): + from threading import Thread + from transformers import TextIteratorStreamer messages = history + [{"role": "user", "content": prompt}] text = tokenizer.apply_chat_template( messages, @@ -183,8 +199,25 @@ def modelscope_chat(model, tokenizer, prompt='你好 /no_think', history=[], tem 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) + streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) + generation_kwargs = dict( + **inputs, + streamer=streamer, + max_new_tokens=32768, + temperature=temperature, + top_p=top_p, + do_sample=True, + repetition_penalty=1.2 + ) + thread = Thread(target=model.generate, kwargs=generation_kwargs) + thread.start() + response = "" + for new_text in streamer: + if print_show: + print(new_text, end="", flush=True) + response += new_text + if print_show: + print() new_history = history + [ {"role": "user", "content": prompt}, {"role": "assistant", "content": response} @@ -204,14 +237,24 @@ def load_llama_model(model_path="D:/models/Qwen/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0. return llm # 使用 LLaMA 本地模型聊天 -def llama_chat(llm, prompt, history=[], temperature=0.7, top_p=0.8): +def llama_chat(llm, prompt='你好 /no_think', history=[], temperature=0.7, top_p=0.8, print_show=1): 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, + stream=True, ) - response = llm_response["choices"][0]["message"]["content"].strip() + response = '' + for chunk in llm_response: + delta = chunk['choices'][0]['delta'] + if 'content' in delta: + token = delta['content'] + response += token + if print_show: + print(token, end="", flush=True) + if print_show: + print() new_history.append({"role": "assistant", "content": response}) - return response, new_history + return response, new_history \ No newline at end of file