Update layout and tips

RapidAI · Aug 11, 2023 · a28d299 · a28d299
1 parent ed92a00
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diff --git a/README.md b/README.md
@@ -1,6 +1,6 @@
-English | [简体中文](https://github.com/RapidAI/Knowledge-QA-LLM/blob/main/docs/README_zh.md)
+[简体中文](https://github.com/RapidAI/Knowledge-QA-LLM/blob/main/docs/README_zh.md) | English
 
-## Knowledge QA LLM
+## 🧐 Knowledge QA LLM
 <p>
      <a href=""><img src="https://img.shields.io/badge/Python->=3.8,<3.12-aff.svg"></a>
      <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
@@ -74,8 +74,9 @@ English | [简体中文](https://github.com/RapidAI/Knowledge-QA-LLM/blob/main/d
 │ └── raw_upload_files
 ├── knowledge_qa_llm
 │ ├── __init__.py
-│ ├── config.yaml         # configuration file
+│ ├── config.yaml       # configuration file
 │ ├── file_loader       # Handle documents in various formats
+│ ├── encoder           # Extract embeddings
 │ ├── llm               # Large model interface, the large model needs to be deployed separately and called by interface
 │ ├── utils
 │ └── vector_utils      # embedding access and search
@@ -87,7 +88,12 @@ English | [简体中文](https://github.com/RapidAI/Knowledge-QA-LLM/blob/main/d
 └── webui.py            # UI implementation based on streamlit
 ```
 
-#### Update Log
+#### Change Log
+- 2023-08-11 v0.0.7 update:
+  - Optimize layout, remove the plugin option, and put the extract vector model option on the home page.
+  - The tips are translated into English for easy communication.
+  - Add project logo:🧐
+  - Update CLI module code.
 - 2023-08-05 v0.0.6 update:
   - Adapt more llm_api, include online llm api, such ad ERNIE-Bot-Turbo.
   - Add the status of extracting embeddings.

diff --git a/cli.py b/cli.py
@@ -1,10 +1,11 @@
 # -*- encoding: utf-8 -*-
 # @Author: SWHL
 # @Contact: [email protected]
+from knowledge_qa_llm.encoder import EncodeText
 from knowledge_qa_llm.file_loader import FileLoader
-from knowledge_qa_llm.llm import ChatGLM26B
+from knowledge_qa_llm.llm import Qwen7B_Chat
 from knowledge_qa_llm.utils import make_prompt, read_yaml
-from knowledge_qa_llm.vector_utils import DBUtils, EncodeText
+from knowledge_qa_llm.vector_utils import DBUtils
 
 config = read_yaml("knowledge_qa_llm/config.yaml")
 
@@ -16,18 +17,20 @@
 # sentences = text[0][1]
 
 # 提取特征
-embedding_model = EncodeText(config.get("encoder_model_path"))
+embedding_model = EncodeText(config.get("Encoder")["m3e-small"])
 # embeddings = embedding_model(sentences)
 
 # 插入数据到数据库中
 db_tools = DBUtils(config.get("vector_db_path"))
 # db_tools.insert(file_path, embeddings, sentences)
 
-llm_engine = ChatGLM26B(api_url=config.get("llm_api_url"))
+llm_engine = Qwen7B_Chat(api_url=config.get("LLM_API")["Qwen7B_Chat"])
 
-print("欢迎使用 Knowledge QA LLM，输入内容即可进行对话，stop 终止程序")
+print(
+    "Welcom to 🧐 Knowledge QA LLM，enter the content to start the conversation, enter stop to terminate the program."
+)
 while True:
-    query = input("\n用户：")
+    query = input("\n😀 User：")
     if query.strip() == "stop":
         break
 
@@ -37,4 +40,4 @@
 
     prompt = make_prompt(query, context, custom_prompt=config.get("DEFAULT_PROMPT"))
     response = llm_engine(prompt, history=None)
-    print(response)
+    print(f"🤖 LLM:{response}")
diff --git a/docs/README_zh.md b/docs/README_zh.md
@@ -1,6 +1,6 @@
-[English](https://github.com/RapidAI/Knowledge-QA-LLM) | 简体中文
+简体中文 | [English](https://github.com/RapidAI/Knowledge-QA-LLM)
 
-## Knowledge QA LLM
+## 🧐 Knowledge QA LLM
 <p>
     <a href=""><img src="https://img.shields.io/badge/Python->=3.8,<3.12-aff.svg"></a>
     <a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
@@ -79,6 +79,7 @@
 │   ├── __init__.py
 │   ├── config.yaml         # 配置文件
 │   ├── file_loader         # 处理各种格式的文档
+│   ├── encoder             # 提取特征向量
 │   ├── llm                 # 大模型接口，大模型需要单独部署，以接口方式调用
 │   ├── utils
 │   └── vector_utils        # embedding的存取和搜索
@@ -91,6 +92,11 @@
 ```
 
 #### 更新日志
+- 2023-08-11 v0.0.7 update:
+  - 优化布局，去掉插件选项，将提取向量模型选项放到主页部分
+  - 将提示语英语化，便于交流使用。
+  - 添加项目logo: 🧐
+  - 更新CLI使用代码
 - 2023-08-05 v0.0.6 update:
   - 适配更多模型接口，包括在线大模型接口，例如文心一言
   - 添加提取特征向量的状态提示

diff --git a/knowledge_qa_llm/config.yaml b/knowledge_qa_llm/config.yaml
@@ -1,5 +1,5 @@
-title: Knowledge QA LLM
-version: 0.0.6
+title: 🧐 Knowledge QA LLM
+version: 0.0.7
 
 LLM_API:
   Qwen7B_Chat: your_api
@@ -34,16 +34,16 @@ Parameter:
     max_value: 4096
     default: 1024
     step: 1
-    tip: 输入input_ids的最大长度
+    tip: The longest length of input_ids
   top_p:
     min_value: 0.0
     max_value: 1.0
     default: 0.7
     step: 0.01
-    tip: 限制模型为仅考虑最可能的前p个标记
+    tip: Limit the model to only consider the most likely first p markers.
   temperature:
     min_value: 0.01
     max_value: 1.0
     default: 0.01
     step: 0.01
-    tip: 控制模型输出的随机性，温度越低将导致输出更加可预测和重复，越高将更富创意和自发的输出。
+    tip: Control the randomness of the model output. The smaller the value, the more standardized it is, and vice versa, the more creative it is.
diff --git a/webui.py b/webui.py
@@ -23,7 +23,7 @@
 
 
 def init_sidebar():
-    st.sidebar.markdown("### 🛶 参数设置")
+    st.sidebar.markdown("### 🛶 Parameter Settings")
     param = config.get("Parameter")
 
     param_max_length = param.get("max_length")
@@ -61,42 +61,35 @@ def init_sidebar():
     )
     st.session_state["params"]["temperature"] = temperature
 
-    st.sidebar.markdown("### 🧻 知识库")
+    st.sidebar.markdown("### 🧻 Knowledge Base")
     uploaded_files = st.sidebar.file_uploader(
         "default",
         accept_multiple_files=True,
         label_visibility="hidden",
-        help="支持多选",
+        help="Support for multiple selections",
     )
 
     upload_dir = config.get("upload_dir")
-
-    ENCODER_OPTIONS = config.get("Encoder")
-    select_encoder = st.sidebar.selectbox("🧬提取向量模型：", ENCODER_OPTIONS.keys())
-    tips(f"初始化{select_encoder}...")
-    embedding_extract = init_encoder(ENCODER_OPTIONS[select_encoder])
-    tips("初始化完成！")
-
-    btn_upload = st.sidebar.button("上传文档并加载数据库", use_container_width=True)
+    btn_upload = st.sidebar.button("Upload and load database", use_container_width=True)
     if btn_upload:
         time_stamp = get_timestamp()
         save_dir = Path(upload_dir) / time_stamp
         st.session_state["upload_dir"] = save_dir
 
-        tips("正在上传文件到平台...", icon="⏳")
+        tips("Uploading files to platform...", icon="⏳")
         for file in uploaded_files:
             bytes_data = file.getvalue()
 
             mkdir(save_dir)
             save_path = save_dir / file.name
             with open(save_path, "wb") as f:
                 f.write(bytes_data)
-        tips("上传完毕！")
+        tips("Upload completed！")
 
         doc_dir = st.session_state["upload_dir"]
         all_doc_contents = file_loader(doc_dir)
 
-        pro_text = "正在提取特征向量..."
+        pro_text = "Extracting embeddings..."
         batch_size = config.get("encoder_batch_size", 32)
         for file_path, one_doc_contents in all_doc_contents.items():
             my_bar = st.sidebar.progress(0, text=pro_text)
@@ -111,23 +104,21 @@ def init_sidebar():
 
                 my_bar.progress(
                     end_idx / content_nums,
-                    f"提取{file_path}数据: [{end_idx}/{content_nums}]",
+                    f"Extract {file_path} datas: [{end_idx}/{content_nums}]",
                 )
             my_bar.empty()
             all_embeddings = np.vstack(all_embeddings)
             db_tools.insert(file_path, all_embeddings, one_doc_contents)
         my_bar.empty()
 
         shutil.rmtree(doc_dir.resolve())
-        tips("已经加载并存入数据库中，可以提问了！")
+        tips("You can now ask a question!")
 
     had_files = db_tools.get_files()
     if had_files:
-        st.sidebar.markdown("仓库已有文档：")
+        st.sidebar.markdown("Existing documents:")
         st.sidebar.markdown("\n".join([f" - {v}" for v in had_files]))
 
-    return embedding_extract
-
 
 def init_state():
     if "history" not in st.session_state:
@@ -153,26 +144,26 @@ def predict(
     logger.info(f"Using {type(model).__name__}")
 
     query_embedding = embedding_extract(text)
-    with st.spinner("从文档中搜索相关内容"):
+    with st.spinner("Search for relevant contents from docs..."):
         search_res, search_elapse = db_tools.search_local(
             query_embedding, top_k=config.get("top_k")
         )
     if search_res is None:
-        bot_print("从文档中搜索相关内容为空，暂不能回答该问题")
+        bot_print("The results of searching from docs is empty.")
     else:
         context = "\n".join(sum(search_res.values(), []))
-        res_cxt = f"**从文档中检索到的相关内容Top5\n(相关性从高到低，耗时:{search_elapse:.5f}s):** \n"
+        res_cxt = f"**Find Top{search_top}\n(Scores from high to low，cost:{search_elapse:.5f}s):** \n"
         bot_print(res_cxt)
 
         for file, content in search_res.items():
             content = "\n".join(content)
-            one_context = f"**来自文档：《{file}》** \n{content}"
+            one_context = f"**From：《{file}》** \n{content}"
             bot_print(one_context)
 
-            logger.info(f"上下文：\n{one_context}\n")
+            logger.info(f"Context：\n{one_context}\n")
 
         response, elapse = get_model_response(text, context, custom_prompt, model)
-        print_res = f"**使用模型：{select_model}**\n**模型推理耗时：{elapse:.5f}s**"
+        print_res = f"**Use：{select_model}**\n**Infer model cost：{elapse:.5f}s**"
         bot_print(print_res)
         bot_print(response)
 
@@ -193,14 +184,14 @@ def get_model_response(text, context, custom_prompt, model):
 
     s_model = time.perf_counter()
     prompt_msg = make_prompt(text, context, custom_prompt)
-    logger.info(f"最终拼接后的文本：\n{prompt_msg}\n")
+    logger.info(f"Final prompt: \n{prompt_msg}\n")
 
     response = model(prompt_msg, history=None, **params_dict)
     elapse = time.perf_counter() - s_model
 
-    logger.info(f"模型回答: \n{response}\n")
+    logger.info(f"Reponse of LLM: \n{response}\n")
     if not response:
-        response = "抱歉，未能正确回答该问题"
+        response = "Sorry, I didn't answer the question correctly"
     return response, elapse
 
 
@@ -222,7 +213,7 @@ def tips(txt: str, wait_time: int = 2, icon: str = "🎉"):
     db_path = config.get("vector_db_path")
     db_tools = DBUtils(db_path)
 
-    embedding_extract = init_sidebar()
+    init_sidebar()
     init_state()
 
     llm_module = importlib.import_module("knowledge_qa_llm.llm")
@@ -240,22 +231,24 @@ def tips(txt: str, wait_time: int = 2, icon: str = "🎉"):
             }
         )
 
-    PLUGINS_OPTIONS = {
-        "文档": 0,
-    }
     TOP_OPTIONS = [5, 10, 15]
+    ENCODER_OPTIONS = config.get("Encoder")
 
     menu_col1, menu_col2, menu_col3 = st.columns([1, 1, 1])
-    select_model = menu_col1.selectbox("🎨基础模型：", MODEL_OPTIONS.keys())
-    select_plugin = menu_col2.selectbox("🛠Plugin：", PLUGINS_OPTIONS.keys())
-    search_top = menu_col3.selectbox("🔍查找Top_K", TOP_OPTIONS)
+    select_model = menu_col1.selectbox("🎨Base model：", MODEL_OPTIONS.keys())
+    select_encoder = menu_col2.selectbox(
+        "🧬Extract Embedding Model：", ENCODER_OPTIONS.keys()
+    )
+    search_top = menu_col3.selectbox("🔍Search Top_K", TOP_OPTIONS)
+
+    embedding_extract = init_encoder(ENCODER_OPTIONS[select_encoder])
 
     input_prompt_container = st.container()
     with input_prompt_container:
         with st.expander("💡Prompt", expanded=False):
             text_area = st.empty()
             input_prompt = text_area.text_area(
-                label="输入",
+                label="Input",
                 max_chars=500,
                 height=200,
                 label_visibility="hidden",
@@ -268,15 +261,13 @@ def tips(txt: str, wait_time: int = 2, icon: str = "🎉"):
         with st.chat_message("user", avatar="😀"):
             st.markdown(input_txt)
 
-        plugin_id = PLUGINS_OPTIONS[select_plugin]
         llm = MODEL_OPTIONS[select_model]
 
         if not input_prompt:
             input_prompt = config.get("DEFAULT_PROMPT")
 
-        if plugin_id == 0:
-            predict(
-                input_txt,
-                llm,
-                input_prompt,
-            )
+        predict(
+            input_txt,
+            llm,
+            input_prompt,
+        )