Merge branch 'main' into TY_07_DEV

2025-08-17 15:58:25 +00:00 · 2023-07-27 17:57:49 +08:00 · 2023-07-27 17:57:49 +08:00 · 0ed51b5ee4
commit 0ed51b5ee4
parent 1547c3aff1 b613947124
82 changed files with 1086 additions and 170 deletions
--- a/.env.template
+++ b/.env.template
@ -31,6 +31,9 @@ QUANTIZE_QLORA=True
 ## FAST_LLM_MODEL - Fast language model (Default: chatglm-6b)
 # SMART_LLM_MODEL=vicuna-13b
 # FAST_LLM_MODEL=chatglm-6b
+## Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm", When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, 
+## "PROXYLLM_BACKEND" is the model they actually deploy. We can use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene. 
+# PROXYLLM_BACKEND=


 #*******************************************************************#
--- a/README.md
+++ b/README.md
@ -1,5 +1,10 @@
 # DB-GPT: Revolutionizing Database Interactions with Private LLM Technology
 
+<p align="left">
+  <img src="./assets/LOGO.png" width="100%" />
+</p>
+
+
 <div align="center">
  <p>
    <a href="https://github.com/csunny/DB-GPT">
@ -55,39 +60,6 @@ https://github.com/csunny/DB-GPT/assets/13723926/55f31781-1d49-4757-b96e-7ef6d3d
      <source id="mp4" src="https://github.com/csunny/DB-GPT/assets/17919400/654b5a49-5ea4-4c02-b5b2-72d089dcc1f0" type="video/mp4">
 </videos> -->

-
-#### Chat with data, and figure charts.
-
-<p align="left">
-  <img src="./assets/dashboard.png" width="800px" />
-</p>
-
-#### Text2SQL, generate SQL from chat
-<p align="left">
-  <img src="./assets/chatSQL.png" width="800px" />
-</p>
-
-#### Chat with database meta information.
-<p align="left">
-  <img src="./assets/chatdb.png" width="800px" />
-</p>
-
-#### Chat with data, and execute results.
-<p align="left">
-  <img src="./assets/chatdata.png" width="800px" />
-</p>
-
-#### Knownledge space to manage docs.
-<p align="left">
-  <img src="./assets/ks.png" width="800px" />
-</p>
-
-#### Chat with knowledge, such as txt、pdf、csv、words. etc
-<p align="left">
-  <img src="./assets/chat_knowledge.png" width="800px" />
-</p>
-
-
 ## Features

 Currently, we have released multiple key features, which are listed below to demonstrate our current capabilities:
@ -99,6 +71,9 @@ Currently, we have released multiple key features, which are listed below to dem
  -   Knowledge Management(We currently support many document formats: txt, pdf, md, html, doc, ppt, and url.)
  -  Database knowledge Q&A
  - knowledge Embedding
+
+- ChatDB
+- ChatDashboard
 - Plugins
  -  Support custom plugin execution tasks and natively support the Auto-GPT plugin, such as:
    - Automatic execution of SQL and retrieval of query results
@ -106,13 +81,14 @@ Currently, we have released multiple key features, which are listed below to dem
 - Unified vector storage/indexing of knowledge base
  - Support for unstructured data such as PDF, TXT, Markdown, CSV, DOC, PPT, and WebURL

- Multi LLMs Support
-  - Supports multiple large language models, currently supporting Vicuna (7b, 13b), ChatGLM-6b (int4, int8), guanaco(7b,13b,33b), Gorilla(7b,13b), 🔥 llama-2(7b, 13b, 70b) 
-  - TODO: baichuan(7b, 13b)
-
-
-[![Star History Chart](https://api.star-history.com/svg?repos=csunny/DB-GPT)](https://star-history.com/#csunny/DB-GPT)
-
+- Multi LLMs Support, Supports multiple large language models, currently supporting
+  - 🔥 llama-2(7b,13b,70b)
+  - Vicuna (7b,13b)
+  - ChatGLM-6b (int4,int8)
+  - ChatGLM2-6b (int4,int8)
+  - guanaco(7b,13b,33b)
+  - Gorilla(7b,13b)
+  - baichuan(7b,13b)

 ## Introduction 
 DB-GPT creates a vast model operating system using [FastChat](https://github.com/lm-sys/FastChat) and offers a large language model powered by [Vicuna](https://huggingface.co/Tribbiani/vicuna-7b). In addition, we provide private domain knowledge base question-answering capability. Furthermore, we also provide support for additional plugins, and our design natively supports the Auto-GPT plugin.Our vision is to make it easier and more convenient to build  applications around databases and llm.
--- a/README.zh.md
+++ b/README.zh.md
@ -1,4 +1,10 @@
 # DB-GPT: 用私有化LLM技术定义数据库下一代交互方式
+
+<p align="left">
+  <img src="./assets/LOGO.png" width="100%" />
+</p>
+
+
 <div align="center">
  <p>
    <a href="https://github.com/csunny/DB-GPT">
@ -50,7 +56,6 @@ DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地

 示例通过 RTX 4090 GPU 演示

-
 https://github.com/csunny/DB-GPT/assets/13723926/55f31781-1d49-4757-b96e-7ef6d3dbcf80

 #### 根据自然语言对话生成分析图表
@ -96,6 +101,8 @@ https://github.com/csunny/DB-GPT/assets/13723926/55f31781-1d49-4757-b96e-7ef6d3d
  - 知识库管理(目前支持 txt, pdf, md, html, doc, ppt, and url)
  - 数据库知识问答
  - 数据处理
+- 数据库对话
+- Chat2Dashboard
 - 插件模型
  - 支持自定义插件执行任务，原生支持Auto-GPT插件。如:
    - SQL自动执行，获取查询结果
@ -104,11 +111,13 @@ https://github.com/csunny/DB-GPT/assets/13723926/55f31781-1d49-4757-b96e-7ef6d3d
  - 非结构化数据支持包括PDF、MarkDown、CSV、WebURL

 - 多模型支持
-  - 支持多种大语言模型, 当前已支持Vicuna(7b,13b), ChatGLM-6b(int4, int8), guanaco(7b,13b,33b), Gorilla(7b,13b), 🔥 llama-2(7b, 13b, 70b) 
-  - TODO: baichuan(7b, 13b)
-
-
-[![Star History Chart](https://api.star-history.com/svg?repos=csunny/DB-GPT)](https://star-history.com/#csunny/DB-GPT)
+  - 支持多种大语言模型, 当前已支持如下模型: 
+    - Vicuna(7b,13b)
+    - ChatGLM-6b(int4,int8)
+    - guanaco(7b,13b,33b)
+    - Gorilla(7b,13b)
+    - 🔥 llama-2(7b,13b,70b)
+    - baichuan(7b,13b)

 ## 架构方案
 DB-GPT基于 [FastChat](https://github.com/lm-sys/FastChat) 构建大模型运行环境，并提供 vicuna 作为基础的大语言模型。此外，我们通过LangChain提供私域知识库问答能力。同时我们支持插件模式, 在设计上原生支持Auto-GPT插件。我们的愿景是让围绕数据库和LLM构建应用程序更加简便和便捷。
@ -195,5 +204,3 @@ The MIT License (MIT)
  <img src="./assets/wechat.jpg" width="300px" />
 </p>

-
-
--- a/assets/LOGO.png
+++ b/assets/LOGO.png
--- a/assets/wechat.jpg
+++ b/assets/wechat.jpg
--- a/docker-compose.yml
+++ b/docker-compose.yml
@ -2,57 +2,45 @@ version: '3.10'

 services:
  db:
-    image: mysql:8.0.33
+    image: mysql/mysql-server
    environment:
-      MYSQL_DATABASE: 'db'
      MYSQL_USER: 'user'
      MYSQL_PASSWORD: 'password'
      MYSQL_ROOT_PASSWORD: 'aa123456'
    ports:
      - 3306:3306
    volumes:
-      - my-db:/var/lib/mysql
+      - dbgpt-myql-db:/var/lib/mysql
+      - ./docker/examples/my.cnf:/etc/my.cnf
+      - ./docker/examples/sqls:/docker-entrypoint-initdb.d
+      - ./assets/schema/knowledge_management.sql:/docker-entrypoint-initdb.d/knowledge_management.sql
    restart: unless-stopped
+    networks:
+      - dbgptnet
  webserver:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    command: python3 pilot/server/webserver.py
+    image: db-gpt:latest
+    command: python3 pilot/server/dbgpt_server.py
    environment:
-      - MODEL_SERVER=http://llmserver:8000
      - LOCAL_DB_HOST=db
-      - WEB_SERVER_PORT=7860
+      - LOCAL_DB_PASSWORD=aa123456
      - ALLOWLISTED_PLUGINS=db_dashboard
-    depends_on:
-      - db
-      - llmserver
-    volumes:
-      - ./models:/app/models
-      - ./plugins:/app/plugins
-      - data:/app/pilot/data
-    env_file:
-      - .env.template
-    ports:
-      - 7860:7860/tcp
-    expose:
-      - 7860/tcp
-    restart: unless-stopped
-  llmserver:
-    build:
-      context: .
-      dockerfile: Dockerfile
-    command: python3 pilot/server/llmserver.py
-    environment:
-      - LOCAL_DB_HOST=db
+      - LLM_MODEL=vicuna-13b
    depends_on:
      - db
    volumes:
-      - ./models:/app/models
+      - /data:/data
+      # Please modify it to your own model directory
+      - /data/models:/app/models
+      - dbgpt-data:/app/pilot/data
+      - dbgpt-message:/app/pilot/message
    env_file:
      - .env.template
    ports:
-      - 8000:8000
+      - 5000:5000/tcp
+    # webserver may be failed, it must wait all sqls in /docker-entrypoint-initdb.d execute finish.
    restart: unless-stopped
+    networks:
+      - dbgptnet
    deploy:
      resources:
        reservations:
@ -60,17 +48,11 @@ services:
            - driver: nvidia
              device_ids: ['0']
              capabilities: [gpu]
-  tunnel:
-    image: cloudflare/cloudflared:latest
-    container_name: cloudflared-tunnel
-    restart: unless-stopped
-    environment:
-      - TUNNEL_URL=http://webserver:7860
-    command: tunnel --no-autoupdate
-    depends_on:
-      - webserver
-
-
 volumes:
-  my-db:
-  data:
+  dbgpt-myql-db:
+  dbgpt-data:
+  dbgpt-message:
+networks:
+  dbgptnet:
+    driver: bridge
+    name: dbgptnet
--- a/docker/allinone/Dockerfile
+++ b/docker/allinone/Dockerfile
@ -0,0 +1,30 @@
+ARG BASE_IMAGE="db-gpt:latest"
+
+FROM ${BASE_IMAGE}
+
+RUN apt-get update && apt-get install -y wget gnupg lsb-release net-tools
+
+RUN apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 467B942D3A79BD29
+
+RUN wget https://dev.mysql.com/get/mysql-apt-config_0.8.17-1_all.deb
+RUN dpkg -i mysql-apt-config_0.8.17-1_all.deb
+
+RUN apt-get update && apt-get install -y mysql-server && apt-get clean
+
+# Remote access
+RUN sed -i 's/bind-address\s*=\s*127.0.0.1/bind-address = 0.0.0.0/g' /etc/mysql/mysql.conf.d/mysqld.cnf \
+    && echo "[mysqld]\ncharacter_set_server=utf8mb4\ncollation-server=utf8mb4_unicode_ci\ninit_connect='SET NAMES utf8mb4'\n[mysql]\ndefault-character-set=utf8mb4\n[client]\ndefault-character-set=utf8mb4\n" >> /etc/mysql/my.cnf
+
+# Init sql
+RUN mkdir /docker-entrypoint-initdb.d \
+    && echo "USE mysql;\nUPDATE user SET Host='%' WHERE User='root';\nFLUSH PRIVILEGES;" > /docker-entrypoint-initdb.d/init.sql
+
+ENV MYSQL_ROOT_PASSWORD=aa123456
+ENV LOCAL_DB_PASSWORD="$MYSQL_ROOT_PASSWORD"
+
+RUN cp /app/assets/schema/knowledge_management.sql /docker-entrypoint-initdb.d/
+
+COPY docker/allinone/allinone-entrypoint.sh /usr/local/bin/allinone-entrypoint.sh
+COPY docker/examples/sqls/ /docker-entrypoint-initdb.d/
+
+ENTRYPOINT ["/usr/local/bin/allinone-entrypoint.sh"]
--- a/docker/allinone/allinone-entrypoint.sh
+++ b/docker/allinone/allinone-entrypoint.sh
@ -0,0 +1,17 @@
+#!/bin/bash
+
+service mysql start
+
+# execute all mysql init script
+for file in /docker-entrypoint-initdb.d/*.sql
+do
+    echo "execute sql file: $file"
+    mysql -u root -p${MYSQL_ROOT_PASSWORD} < "$file"
+done
+
+mysql -u root -p${MYSQL_ROOT_PASSWORD} -e "
+ALTER USER 'root'@'%' IDENTIFIED WITH mysql_native_password BY '$MYSQL_ROOT_PASSWORD';
+FLUSH PRIVILEGES;
+"
+
+python3 pilot/server/dbgpt_server.py
--- a/docker/allinone/build_image.sh
+++ b/docker/allinone/build_image.sh
@ -0,0 +1,9 @@
+#!/bin/bash
+
+SCRIPT_LOCATION=$0
+cd "$(dirname "$SCRIPT_LOCATION")"
+WORK_DIR=$(pwd)
+
+IMAGE_NAME="db-gpt-allinone"
+
+docker build -f Dockerfile -t $IMAGE_NAME $WORK_DIR/../../
--- a/docker/allinone/run.sh
+++ b/docker/allinone/run.sh
@ -0,0 +1,13 @@
+#!/bin/bash
+
+docker run --gpus "device=0" -d -p 3306:3306 \
+    -p 5000:5000 \
+    -e LOCAL_DB_HOST=127.0.0.1 \
+    -e LOCAL_DB_PASSWORD=aa123456 \
+    -e MYSQL_ROOT_PASSWORD=aa123456 \
+    -e LLM_MODEL=vicuna-13b \
+    -e LANGUAGE=zh \
+    -v /data:/data \
+    -v /data/models:/app/models \
+    --name db-gpt-allinone \
+    db-gpt-allinone
--- a/docker/allinone/run_proxyllm.sh
+++ b/docker/allinone/run_proxyllm.sh
@ -0,0 +1,19 @@
+#!/bin/bash
+
+# Your api key
+PROXY_API_KEY="$PROXY_API_KEY"
+PROXY_SERVER_URL="${PROXY_SERVER_URL-'https://api.openai.com/v1/chat/completions'}"
+
+docker run --gpus "device=0" -d -p 3306:3306 \
+    -p 5000:5000 \
+    -e LOCAL_DB_HOST=127.0.0.1 \
+    -e LOCAL_DB_PASSWORD=aa123456 \
+    -e MYSQL_ROOT_PASSWORD=aa123456 \
+    -e LLM_MODEL=proxyllm \
+    -e PROXY_API_KEY=$PROXY_API_KEY \
+    -e PROXY_SERVER_URL=$PROXY_SERVER_URL \
+    -e LANGUAGE=zh \
+    -v /data:/data \
+    -v /data/models:/app/models \
+    --name db-gpt-allinone \
+    db-gpt-allinone
--- a/docker/base/Dockerfile
+++ b/docker/base/Dockerfile
@ -0,0 +1,25 @@
+FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
+
+RUN apt-get update && apt-get install -y git python3 pip wget \
+    && apt-get clean
+
+# download code from githu: https://github.com/csunny/DB-GPT
+# ENV DBGPT_VERSION="v0.3.3"
+# RUN wget https://github.com/csunny/DB-GPT/archive/refs/tags/$DBGPT_VERSION.zip
+
+# clone latest code, and rename to /app
+RUN git clone https://github.com/csunny/DB-GPT.git /app
+
+WORKDIR /app
+
+RUN pip3 install --upgrade pip \
+    && pip3 install --no-cache-dir -r requirements.txt \
+    && pip3 install seaborn mpld3 \
+    && wget https://github.com/explosion/spacy-models/releases/download/zh_core_web_sm-3.5.0/zh_core_web_sm-3.5.0-py3-none-any.whl -O /tmp/zh_core_web_sm-3.5.0-py3-none-any.whl \
+    && pip3 install /tmp/zh_core_web_sm-3.5.0-py3-none-any.whl \
+    && rm /tmp/zh_core_web_sm-3.5.0-py3-none-any.whl \
+    && rm -rf `pip3 cache dir` 
+
+# RUN python3 -m spacy download zh_core_web_sm
+
+EXPOSE 5000
--- a/docker/base/build_image.sh
+++ b/docker/base/build_image.sh
@ -0,0 +1,8 @@
+#!/bin/bash
+
+SCRIPT_LOCATION=$0
+cd "$(dirname "$SCRIPT_LOCATION")"
+WORK_DIR=$(pwd)
+
+IMAGE_NAME="db-gpt"
+docker build -f Dockerfile -t $IMAGE_NAME $WORK_DIR/../../
--- a/docker/build_all_images.sh
+++ b/docker/build_all_images.sh
@ -0,0 +1,9 @@
+#!/bin/bash
+
+SCRIPT_LOCATION=$0
+cd "$(dirname "$SCRIPT_LOCATION")"
+WORK_DIR=$(pwd)
+
+bash $WORK_DIR/base/build_image.sh
+
+bash $WORK_DIR/allinone/build_image.sh
--- a/docker/examples/my.cnf
+++ b/docker/examples/my.cnf
@ -0,0 +1,44 @@
+# For advice on how to change settings please see
+# http://dev.mysql.com/doc/refman/8.0/en/server-configuration-defaults.html
+
+[mysqld]
+#
+# Remove leading # and set to the amount of RAM for the most important data
+# cache in MySQL. Start at 70% of total RAM for dedicated server, else 10%.
+# innodb_buffer_pool_size = 128M
+#
+# Remove leading # to turn on a very important data integrity option: logging
+# changes to the binary log between backups.
+# log_bin
+#
+# Remove leading # to set options mainly useful for reporting servers.
+# The server defaults are faster for transactions and fast SELECTs.
+# Adjust sizes as needed, experiment to find the optimal values.
+# join_buffer_size = 128M
+# sort_buffer_size = 2M
+# read_rnd_buffer_size = 2M
+
+# Remove leading # to revert to previous value for default_authentication_plugin,
+# this will increase compatibility with older clients. For background, see:
+# https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_default_authentication_plugin
+# default-authentication-plugin=mysql_native_password
+skip-host-cache
+skip-name-resolve
+datadir=/var/lib/mysql
+socket=/var/lib/mysql/mysql.sock
+secure-file-priv=/var/lib/mysql-files
+user=mysql
+
+pid-file=/var/run/mysqld/mysqld.pid
+
+# add example config
+
+default-authentication-plugin=mysql_native_password
+character_set_server=utf8mb4
+collation-server=utf8mb4_unicode_ci
+init_connect='SET NAMES utf8mb4'
+
+[mysql]
+default-character-set=utf8mb4
+[client]
+default-character-set=utf8mb4
--- a/docker/examples/sqls/case_1_student_manager.sql
+++ b/docker/examples/sqls/case_1_student_manager.sql
@ -0,0 +1,63 @@
+create database case_1_student_manager character set utf8;
+use case_1_student_manager;
+
+CREATE TABLE students (
+    student_id INT PRIMARY KEY,
+    student_name VARCHAR(100) COMMENT '学生姓名',
+    major VARCHAR(100) COMMENT '专业',
+    year_of_enrollment INT COMMENT '入学年份',
+    student_age INT COMMENT '学生年龄'
+) COMMENT '学生信息表';
+
+CREATE TABLE courses (
+    course_id INT PRIMARY KEY,
+    course_name VARCHAR(100) COMMENT '课程名称',
+    credit FLOAT COMMENT '学分'
+) COMMENT '课程信息表';
+
+CREATE TABLE scores (
+    student_id INT,
+    course_id INT,
+    score INT COMMENT '得分',
+    semester VARCHAR(50) COMMENT '学期',
+    PRIMARY KEY (student_id, course_id),
+    FOREIGN KEY (student_id) REFERENCES students(student_id),
+    FOREIGN KEY (course_id) REFERENCES courses(course_id)
+) COMMENT '学生成绩表';
+
+
+INSERT INTO students (student_id, student_name, major, year_of_enrollment, student_age) VALUES
+(1, '张三', '计算机科学', 2020, 20),
+(2, '李四', '计算机科学', 2021, 19),
+(3, '王五', '物理学', 2020, 21),
+(4, '赵六', '数学', 2021, 19),
+(5, '周七', '计算机科学', 2022, 18),
+(6, '吴八', '物理学', 2020, 21),
+(7, '郑九', '数学', 2021, 19),
+(8, '孙十', '计算机科学', 2022, 18),
+(9, '刘十一', '物理学', 2020, 21),
+(10, '陈十二', '数学', 2021, 19);
+
+INSERT INTO courses (course_id, course_name, credit) VALUES
+(1, '计算机基础', 3),
+(2, '数据结构', 4),
+(3, '高等物理', 3),
+(4, '线性代数', 4),
+(5, '微积分', 5),
+(6, '编程语言', 4),
+(7, '量子力学', 3),
+(8, '概率论', 4),
+(9, '数据库系统', 4),
+(10, '计算机网络', 4);
+
+INSERT INTO scores (student_id, course_id, score, semester) VALUES
+(1, 1, 90, '2020年秋季'),
+(1, 2, 85, '2021年春季'),
+(2, 1, 88, '2021年秋季'),
+(2, 2, 90, '2022年春季'),
+(3, 3, 92, '2020年秋季'),
+(3, 4, 85, '2021年春季'),
+(4, 3, 88, '2021年秋季'),
+(4, 4, 86, '2022年春季'),
+(5, 1, 90, '2022年秋季'),
+(5, 2, 87, '2023年春季');
--- a/docker/examples/sqls/case_2_ecom.sql
+++ b/docker/examples/sqls/case_2_ecom.sql
@ -0,0 +1,63 @@
+create database case_2_ecom character set utf8;
+use case_2_ecom;
+
+CREATE TABLE users (
+    user_id INT PRIMARY KEY,
+    user_name VARCHAR(100) COMMENT '用户名',
+    user_email VARCHAR(100) COMMENT '用户邮箱',
+    registration_date DATE COMMENT '注册日期',
+    user_country VARCHAR(100) COMMENT '用户国家'
+) COMMENT '用户信息表';
+
+CREATE TABLE products (
+    product_id INT PRIMARY KEY,
+    product_name VARCHAR(100) COMMENT '商品名称',
+    product_price FLOAT COMMENT '商品价格'
+) COMMENT '商品信息表';
+
+CREATE TABLE orders (
+    order_id INT PRIMARY KEY,
+    user_id INT,
+    product_id INT,
+    quantity INT COMMENT '数量',
+    order_date DATE COMMENT '订单日期',
+    FOREIGN KEY (user_id) REFERENCES users(user_id),
+    FOREIGN KEY (product_id) REFERENCES products(product_id)
+) COMMENT '订单信息表';
+
+
+INSERT INTO users (user_id, user_name, user_email, registration_date, user_country) VALUES
+(1, 'John', 'john@gmail.com', '2020-01-01', 'USA'),
+(2, 'Mary', 'mary@gmail.com', '2021-01-01', 'UK'),
+(3, 'Bob', 'bob@gmail.com', '2020-01-01', 'USA'),
+(4, 'Alice', 'alice@gmail.com', '2021-01-01', 'UK'),
+(5, 'Charlie', 'charlie@gmail.com', '2020-01-01', 'USA'),
+(6, 'David', 'david@gmail.com', '2021-01-01', 'UK'),
+(7, 'Eve', 'eve@gmail.com', '2020-01-01', 'USA'),
+(8, 'Frank', 'frank@gmail.com', '2021-01-01', 'UK'),
+(9, 'Grace', 'grace@gmail.com', '2020-01-01', 'USA'),
+(10, 'Helen', 'helen@gmail.com', '2021-01-01', 'UK');
+
+INSERT INTO products (product_id, product_name, product_price) VALUES
+(1, 'iPhone', 699),
+(2, 'Samsung Galaxy', 599),
+(3, 'iPad', 329),
+(4, 'Macbook', 1299),
+(5, 'Apple Watch', 399),
+(6, 'AirPods', 159),
+(7, 'Echo', 99),
+(8, 'Kindle', 89),
+(9, 'Fire TV Stick', 39),
+(10, 'Echo Dot', 49);
+
+INSERT INTO orders (order_id, user_id, product_id, quantity, order_date) VALUES
+(1, 1, 1, 1, '2022-01-01'),
+(2, 1, 2, 1, '2022-02-01'),
+(3, 2, 3, 2, '2022-03-01'),
+(4, 2, 4, 1, '2022-04-01'),
+(5, 3, 5, 2, '2022-05-01'),
+(6, 3, 6, 3, '2022-06-01'),
+(7, 4, 7, 2, '2022-07-01'),
+(8, 4, 8, 1, '2022-08-01'),
+(9, 5, 9, 2, '2022-09-01'),
+(10, 5, 10, 3, '2022-10-01');
--- a/docker/examples/sqls/test_case.md
+++ b/docker/examples/sqls/test_case.md
@ -0,0 +1,87 @@
+# 测试问题
+
+## 场景一 
+
+学校管理系统，主要测试SQL助手的联合查询，条件查询和排序功能。
+
+我们的数据库有三个表：学生表、课程表和成绩表。我们要测试SQL助手能否处理复杂的SQL查询，包括连接多个表，按照一定的条件筛选数据，以及对结果进行排序。
+
+### Q1
+
+查询所有学生的姓名，专业和成绩，按成绩降序排序
+
+SQL：
+```sql
+SELECT students.student_name, students.major, scores.score
+FROM students
+JOIN scores ON students.student_id = scores.student_id
+ORDER BY scores.score DESC;
+```
+
+### Q2
+
+查询 "计算机科学" 专业的学生的平均成绩
+
+SQL：
+```sql
+SELECT AVG(scores.score) as avg_score
+FROM students
+JOIN scores ON students.student_id = scores.student_id
+WHERE students.major = '计算机科学';
+```
+
+### Q3
+
+查询哪些学生在 "2023年春季" 学期的课程学分总和超过2学分
+
+```sql
+SELECT students.student_name
+FROM students
+JOIN scores ON students.student_id = scores.student_id
+JOIN courses ON scores.course_id = courses.course_id
+WHERE scores.semester = '2023年春季'
+GROUP BY students.student_id
+HAVING SUM(courses.credit) > 2;
+```
+
+## 场景二：电商系统，主要测试SQL助手的数据聚合和分组功能。
+
+我们的数据库有三个表：用户表、商品表和订单表。我们要测试SQL助手能否处理复杂的SQL查询，包括对数据进行聚合和分组。
+
+### Q1
+
+查询每个用户的总订单数量
+
+SQL:
+
+```sql
+SELECT users.user_name, COUNT(orders.order_id) as order_count
+FROM users
+JOIN orders ON users.user_id = orders.user_id
+GROUP BY users.user_id;
+```
+
+### Q2
+
+查询每种商品的总销售额
+
+```sql
+SELECT products.product_name, SUM(products.product_price * orders.quantity) as total_sales
+FROM products
+JOIN orders ON products.product_id = orders.product_id
+GROUP BY products.product_id;
+```
+
+### Q3
+
+查询2023年最受欢迎的商品（订单数量最多的商品）
+
+```sql
+SELECT products.product_name
+FROM products
+JOIN orders ON products.product_id = orders.product_id
+WHERE YEAR(orders.order_date) = 2023
+GROUP BY products.product_id
+ORDER BY COUNT(orders.order_id) DESC
+LIMIT 1;
+```
--- a/docker/examples/sqls/test_case_info.sql
+++ b/docker/examples/sqls/test_case_info.sql
@ -0,0 +1,19 @@
+create database test_case_info character set utf8;
+use test_case_info;
+
+CREATE TABLE test_cases (
+    case_id INT AUTO_INCREMENT PRIMARY KEY,
+    scenario_name VARCHAR(100) COMMENT '场景名称',
+    scenario_description TEXT COMMENT '场景描述',
+    test_question VARCHAR(500) COMMENT '测试问题',
+    expected_sql TEXT COMMENT '预期SQL',
+    correct_output TEXT COMMENT '正确输出'
+) COMMENT '测试用例表';
+
+INSERT INTO test_cases (scenario_name, scenario_description, test_question, expected_sql, correct_output) VALUES
+('学校管理系统', '测试SQL助手的联合查询，条件查询和排序功能', '查询所有学生的姓名，专业和成绩，按成绩降序排序', 'SELECT students.student_name, students.major, scores.score FROM students JOIN scores ON students.student_id = scores.student_id ORDER BY scores.score DESC;', '返回所有学生的姓名，专业和成绩，按成绩降序排序的结果'),
+('学校管理系统', '测试SQL助手的联合查询，条件查询和排序功能', '查询计算机科学专业的学生的平均成绩', 'SELECT AVG(scores.score) as avg_score FROM students JOIN scores ON students.student_id = scores.student_id WHERE students.major = ''计算机科学'';', '返回计算机科学专业学生的平均成绩'),
+('学校管理系统', '测试SQL助手的联合查询，条件查询和排序功能', '查询哪些学生在2023年秋季学期的课程学分总和超过15', 'SELECT students.student_name FROM students JOIN scores ON students.student_id = scores.student_id JOIN courses ON scores.course_id = courses.course_id WHERE scores.semester = ''2023年秋季'' GROUP BY students.student_id HAVING SUM(courses.credit) > 15;', '返回在2023年秋季学期的课程学分总和超过15的学生的姓名'),
+('电商系统', '测试SQL助手的数据聚合和分组功能', '查询每个用户的总订单数量', 'SELECT users.user_name, COUNT(orders.order_id) as order_count FROM users JOIN orders ON users.user_id = orders.user_id GROUP BY users.user_id;', '返回每个用户的总订单数量'),
+('电商系统', '测试SQL助手的数据聚合和分组功能', '查询每种商品的总销售额', 'SELECT products.product_name, SUM(products.product_price * orders.quantity) as total_sales FROM products JOIN orders ON products.product_id = orders.product_id GROUP BY products.product_id;', '返回每种商品的总销售额'),
+('电商系统', '测试SQL助手的数据聚合和分组功能', '查询2023年最受欢迎的商品（订单数量最多的商品）', 'SELECT products.product_name FROM products JOIN orders ON products.product_id = orders.product_id WHERE YEAR(orders.order_date) = 2023 GROUP BY products.product_id ORDER BY COUNT(orders.order_id) DESC LIMIT 1;', '返回2023年最受欢迎的商品（订单数量最多的商品）的名称');
--- a/docker/examples/sqls/user_config.sql
+++ b/docker/examples/sqls/user_config.sql
@ -0,0 +1,3 @@
+USE mysql;
+UPDATE user SET Host='%' WHERE User='root';
+FLUSH PRIVILEGES;
--- a/docs/getting_started/getting_started.md
+++ b/docs/getting_started/getting_started.md
@ -86,3 +86,98 @@ $ python pilot/server/dbgpt_server.py --light

 If you want to learn about dbgpt-webui, read https://github.com/csunny/DB-GPT/tree/new-page-framework/datacenter

+### 4. Docker (Experimental)
+
+#### 4.1 Building Docker image
+
+```bash
+$ bash docker/build_all_images.sh
+```
+
+Review images by listing them:
+
+```bash
+$ docker images|grep db-gpt
+```
+
+Output should look something like the following:
+
+```
+db-gpt-allinone    latest     e1ffd20b85ac   45 minutes ago   14.5GB
+db-gpt             latest     e36fb0cca5d9   3 hours ago      14GB
+```
+
+#### 4.2. Run all in one docker container
+
+**Run with local model**
+
+```bash
+$ docker run --gpus "device=0" -d -p 3306:3306 \
+    -p 5000:5000 \
+    -e LOCAL_DB_HOST=127.0.0.1 \
+    -e LOCAL_DB_PASSWORD=aa123456 \
+    -e MYSQL_ROOT_PASSWORD=aa123456 \
+    -e LLM_MODEL=vicuna-13b \
+    -e LANGUAGE=zh \
+    -v /data/models:/app/models \
+    --name db-gpt-allinone \
+    db-gpt-allinone
+```
+
+Open http://localhost:5000 with your browser to see the product.
+
+
+- `-e LLM_MODEL=vicuna-13b`, means we use vicuna-13b as llm model, see /pilot/configs/model_config.LLM_MODEL_CONFIG
+- `-v /data/models:/app/models`, means we mount the local model file directory `/data/models` to the docker container directory `/app/models`, please replace it with your model file directory.
+
+You can see log with command:
+
+```bash
+$ docker logs db-gpt-allinone -f
+```
+
+**Run with openai interface**
+
+```bash
+$ PROXY_API_KEY="You api key"
+$ PROXY_SERVER_URL="https://api.openai.com/v1/chat/completions"
+$ docker run --gpus "device=0" -d -p 3306:3306 \
+    -p 5000:5000 \
+    -e LOCAL_DB_HOST=127.0.0.1 \
+    -e LOCAL_DB_PASSWORD=aa123456 \
+    -e MYSQL_ROOT_PASSWORD=aa123456 \
+    -e LLM_MODEL=proxyllm \
+    -e PROXY_API_KEY=$PROXY_API_KEY \
+    -e PROXY_SERVER_URL=$PROXY_SERVER_URL \
+    -e LANGUAGE=zh \
+    -v /data/models/text2vec-large-chinese:/app/models/text2vec-large-chinese \
+    --name db-gpt-allinone \
+    db-gpt-allinone
+```
+
+- `-e LLM_MODEL=proxyllm`, means we use proxy llm(openai interface, fastchat interface...)
+- `-v /data/models/text2vec-large-chinese:/app/models/text2vec-large-chinese`, means we mount the local text2vec model to the docker container.
+
+#### 4.2. Run with docker compose
+
+```bash
+$ docker compose up -d
+```
+
+Output should look something like the following:
+```
+[+] Building 0.0s (0/0)
+[+] Running 2/2
+ ✔ Container db-gpt-db-1         Started                                                                                                                                                                                          0.4s
+ ✔ Container db-gpt-webserver-1  Started
+```
+
+You can see log with command:
+
+```bash
+$ docker logs db-gpt-webserver-1 -f
+```
+
+Open http://localhost:5000 with your browser to see the product.
+
+You can open docker-compose.yml in the project root directory to see more details.
--- a/docs/modules/llms.md
+++ b/docs/modules/llms.md
@ -11,7 +11,7 @@ cp .env.template .env
 LLM_MODEL=vicuna-13b
 MODEL_SERVER=http://127.0.0.1:8000
 ```
-now we support models vicuna-13b, vicuna-7b, chatglm-6b, flan-t5-base, guanaco-33b-merged, falcon-40b, gorilla-7b, llama-2-7b, llama-2-13b.
+now we support models vicuna-13b, vicuna-7b, chatglm-6b, flan-t5-base, guanaco-33b-merged, falcon-40b, gorilla-7b, llama-2-7b, llama-2-13b, baichuan-7b, baichuan-13b

 if you want use other model, such as chatglm-6b, you just need update .env config file.
 ```
--- a/pilot/common/formatting.py
+++ b/pilot/common/formatting.py
@ -36,7 +36,19 @@ class StrictFormatter(Formatter):
        super().format(format_string, **dummy_inputs)


+class NoStrictFormatter(StrictFormatter):
+    def check_unused_args(
+        self,
+        used_args: Sequence[Union[int, str]],
+        args: Sequence,
+        kwargs: Mapping[str, Any],
+    ) -> None:
+        """Not check unused args"""
+        pass
+
+
 formatter = StrictFormatter()
+no_strict_formatter = NoStrictFormatter()


 class MyEncoder(json.JSONEncoder):
--- a/pilot/configs/config.py
+++ b/pilot/configs/config.py
@ -131,6 +131,13 @@ class Config(metaclass=Singleton):

        ### LLM Model Service Configuration
        self.LLM_MODEL = os.getenv("LLM_MODEL", "vicuna-13b")
+        ### Proxy llm backend, this configuration is only valid when "LLM_MODEL=proxyllm"
+        ### When we use the rest API provided by deployment frameworks like fastchat as a proxyllm, "PROXYLLM_BACKEND" is the model they actually deploy.
+        ### We need to use "PROXYLLM_BACKEND" to load the prompt of the corresponding scene.
+        self.PROXYLLM_BACKEND = None
+        if self.LLM_MODEL == "proxyllm":
+            self.PROXYLLM_BACKEND = os.getenv("PROXYLLM_BACKEND")
+
        self.LIMIT_MODEL_CONCURRENCY = int(os.getenv("LIMIT_MODEL_CONCURRENCY", 5))
        self.MAX_POSITION_EMBEDDINGS = int(os.getenv("MAX_POSITION_EMBEDDINGS", 4096))
        self.MODEL_PORT = os.getenv("MODEL_PORT", 8000)
--- a/pilot/configs/model_config.py
+++ b/pilot/configs/model_config.py
@ -50,6 +50,9 @@ LLM_MODEL_CONFIG = {
    "llama-2-7b": os.path.join(MODEL_PATH, "Llama-2-7b-chat-hf"),
    "llama-2-13b": os.path.join(MODEL_PATH, "Llama-2-13b-chat-hf"),
    "llama-2-70b": os.path.join(MODEL_PATH, "Llama-2-70b-chat-hf"),
+    "baichuan-13b": os.path.join(MODEL_PATH, "Baichuan-13B-Chat"),
+    # please rename "fireballoon/baichuan-vicuna-chinese-7b" to "baichuan-7b"
+    "baichuan-7b": os.path.join(MODEL_PATH, "baichuan-7b"),
 }

 # Load model config
--- a/pilot/embedding_engine/docx_loader.py
+++ b/pilot/embedding_engine/docx_loader.py
@ -0,0 +1,28 @@
+from typing import List, Optional
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+import docx
+
+
+class DocxLoader(BaseLoader):
+    """Load docx files."""
+
+    def __init__(self, file_path: str, encoding: Optional[str] = None):
+        """Initialize with file path."""
+        self.file_path = file_path
+        self.encoding = encoding
+
+    def load(self) -> List[Document]:
+        """Load from file path."""
+        docs = []
+        doc = docx.Document(self.file_path)
+        content = []
+        for i in range(len(doc.paragraphs)):
+            para = doc.paragraphs[i]
+            text = para.text
+            content.append(text)
+        docs.append(
+            Document(page_content="".join(content), metadata={"source": self.file_path})
+        )
+        return docs
--- a/pilot/embedding_engine/ppt_embedding.py
+++ b/pilot/embedding_engine/ppt_embedding.py
@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 from typing import List, Optional

-from langchain.document_loaders import UnstructuredPowerPointLoader
 from langchain.schema import Document
 from langchain.text_splitter import (
    SpacyTextSplitter,
@ -11,6 +10,7 @@ from langchain.text_splitter import (
 )

 from pilot.embedding_engine import SourceEmbedding, register
+from pilot.embedding_engine.ppt_loader import PPTLoader


 class PPTEmbedding(SourceEmbedding):
@ -36,7 +36,7 @@ class PPTEmbedding(SourceEmbedding):
    def read(self):
        """Load from ppt path."""
        if self.source_reader is None:
-            self.source_reader = UnstructuredPowerPointLoader(self.file_path)
+            self.source_reader = PPTLoader(self.file_path)
        if self.text_splitter is None:
            try:
                self.text_splitter = SpacyTextSplitter(
--- a/pilot/embedding_engine/ppt_loader.py
+++ b/pilot/embedding_engine/ppt_loader.py
@ -0,0 +1,28 @@
+from typing import List, Optional
+
+from langchain.docstore.document import Document
+from langchain.document_loaders.base import BaseLoader
+from pptx import Presentation
+
+
+class PPTLoader(BaseLoader):
+    """Load PPT files."""
+
+    def __init__(self, file_path: str, encoding: Optional[str] = None):
+        """Initialize with file path."""
+        self.file_path = file_path
+        self.encoding = encoding
+
+    def load(self) -> List[Document]:
+        """Load from file path."""
+        pr = Presentation(self.file_path)
+        docs = []
+        for slide in pr.slides:
+            for shape in slide.shapes:
+                if hasattr(shape, "text") and shape.text is not "":
+                    docs.append(
+                        Document(
+                            page_content=shape.text, metadata={"source": slide.slide_id}
+                        )
+                    )
+        return docs
--- a/pilot/embedding_engine/word_embedding.py
+++ b/pilot/embedding_engine/word_embedding.py
@ -2,7 +2,6 @@
 # -*- coding: utf-8 -*-
 from typing import List, Optional

-from langchain.document_loaders import UnstructuredWordDocumentLoader
 from langchain.schema import Document
 from langchain.text_splitter import (
    SpacyTextSplitter,
@ -11,6 +10,7 @@ from langchain.text_splitter import (
 )

 from pilot.embedding_engine import SourceEmbedding, register
+from pilot.embedding_engine.docx_loader import DocxLoader


 class WordEmbedding(SourceEmbedding):
@ -36,7 +36,7 @@ class WordEmbedding(SourceEmbedding):
    def read(self):
        """Load from word path."""
        if self.source_reader is None:
-            self.source_reader = UnstructuredWordDocumentLoader(self.file_path)
+            self.source_reader = DocxLoader(self.file_path)
        if self.text_splitter is None:
            try:
                self.text_splitter = SpacyTextSplitter(
--- a/pilot/model/adapter.py
+++ b/pilot/model/adapter.py
@ -276,6 +276,25 @@ class Llama2Adapter(BaseLLMAdaper):
        return model, tokenizer


+class BaichuanAdapter(BaseLLMAdaper):
+    """The model adapter for Baichuan models (e.g., baichuan-inc/Baichuan-13B-Chat)"""
+
+    def match(self, model_path: str):
+        return "baichuan" in model_path.lower()
+
+    def loader(self, model_path: str, from_pretrained_kwargs: dict):
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_path, trust_remote_code=True, use_fast=False
+        )
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            trust_remote_code=True,
+            low_cpu_mem_usage=True,
+            **from_pretrained_kwargs,
+        )
+        return model, tokenizer
+
+
 register_llm_model_adapters(VicunaLLMAdapater)
 register_llm_model_adapters(ChatGLMAdapater)
 register_llm_model_adapters(GuanacoAdapter)
@ -283,6 +302,7 @@ register_llm_model_adapters(FalconAdapater)
 register_llm_model_adapters(GorillaAdapter)
 register_llm_model_adapters(GPT4AllAdapter)
 register_llm_model_adapters(Llama2Adapter)
+register_llm_model_adapters(BaichuanAdapter)
 # TODO Default support vicuna, other model need to tests and Evaluate

 # just for test_py, remove this later
--- a/pilot/model/conversation.py
+++ b/pilot/model/conversation.py
@ -284,6 +284,21 @@ def get_conv_template(name: str) -> Conversation:
    return conv_templates[name].copy()


+# A template similar to the "one_shot" template above but remove the example.
+register_conv_template(
+    Conversation(
+        name="zero_shot",
+        system="A chat between a curious human and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+        roles=("Human", "Assistant"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.ADD_COLON_SINGLE,
+        sep="\n### ",
+        stop_str="###",
+    )
+)
+
 # llama2 template
 # reference: https://github.com/facebookresearch/llama/blob/cfc3fc8c1968d390eb830e65c63865e980873a06/llama/generation.py#L212
 register_conv_template(
@ -305,4 +320,21 @@ register_conv_template(
    )
 )

+# Baichuan-13B-Chat template
+register_conv_template(
+    # source: https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/f5f47be2adbbdceb784f334d6fa1ca2c73e65097/modeling_baichuan.py#L507
+    # https://huggingface.co/baichuan-inc/Baichuan-13B-Chat/blob/main/generation_config.json
+    Conversation(
+        name="baichuan-chat",
+        system="",
+        roles=(" <reserved_102> ", " <reserved_103> "),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.NO_COLON_TWO,
+        sep="",
+        sep2="</s>",
+        stop_token_ids=[2, 195],
+    )
+)
+
 # TODO Support other model conversation template
--- a/pilot/out_parser/base.py
+++ b/pilot/out_parser/base.py
@ -54,17 +54,19 @@ class BaseOutputParser(ABC):
        """ TODO Multi mode output handler,  rewrite this for multi model, use adapter mode.
        """
        model_context = data.get("model_context")
+        has_echo = True
        if model_context and "prompt_echo_len_char" in model_context:
            prompt_echo_len_char = int(model_context.get("prompt_echo_len_char", -1))
+            has_echo = bool(model_context.get("echo", True))
            if prompt_echo_len_char != -1:
                skip_echo_len = prompt_echo_len_char

        if data.get("error_code", 0) == 0:
-            if "vicuna" in CFG.LLM_MODEL or "llama-2" in CFG.LLM_MODEL:
+            if has_echo and ("vicuna" in CFG.LLM_MODEL or "llama-2" in CFG.LLM_MODEL):
                # TODO Judging from model_context
                # output = data["text"][skip_echo_len + 11:].strip()
                output = data["text"][skip_echo_len:].strip()
-            elif "guanaco" in CFG.LLM_MODEL:
+            elif has_echo and "guanaco" in CFG.LLM_MODEL:
                # NO stream output
                # output = data["text"][skip_echo_len + 2:].replace("<s>", "").strip()

--- a/pilot/prompts/prompt_new.py
+++ b/pilot/prompts/prompt_new.py
@ -4,7 +4,7 @@ from typing import Any, Callable, Dict, List, Mapping, Optional, Set, Union
 from pydantic import BaseModel, Extra, Field, root_validator


-from pilot.common.formatting import formatter
+from pilot.common.formatting import formatter, no_strict_formatter
 from pilot.out_parser.base import BaseOutputParser
 from pilot.common.schema import SeparatorStyle
 from pilot.prompts.example_base import ExampleSelector
@ -24,8 +24,10 @@ def jinja2_formatter(template: str, **kwargs: Any) -> str:


 DEFAULT_FORMATTER_MAPPING: Dict[str, Callable] = {
-    "f-string": formatter.format,
-    "jinja2": jinja2_formatter,
+    "f-string": lambda is_strict: formatter.format
+    if is_strict
+    else no_strict_formatter.format,
+    "jinja2": lambda is_strict: jinja2_formatter,
 }


@ -38,6 +40,8 @@ class PromptTemplate(BaseModel, ABC):
    template: Optional[str]
    """The prompt template."""
    template_format: str = "f-string"
+    """strict template will check template args"""
+    template_is_strict: bool = True
    """The format of the prompt template. Options are: 'f-string', 'jinja2'."""
    response_format: Optional[str]
    """default use stream out"""
@ -68,10 +72,12 @@ class PromptTemplate(BaseModel, ABC):
        """Format the prompt with the inputs."""
        if self.template:
            if self.response_format:
-                kwargs["response"] = json.dumps(self.response_format, indent=4)
+                kwargs["response"] = json.dumps(
+                    self.response_format, ensure_ascii=False, indent=4
+                )
            return DEFAULT_FORMATTER_MAPPING[self.template_format](
-                self.template, **kwargs
-            )
+                self.template_is_strict
+            )(self.template, **kwargs)

    def add_goals(self, goal: str) -> None:
        self.goals.append(goal)
--- a/pilot/prompts/prompt_registry.py
+++ b/pilot/prompts/prompt_registry.py
@ -58,13 +58,26 @@ class PromptTemplateRegistry:
                scene_registry, prompt_template, language, [_DEFAULT_MODEL_KEY]
            )

-    def get_prompt_template(self, scene_name: str, language: str, model_name: str):
-        """Get prompt template with scene name, language and model name"""
+    def get_prompt_template(
+        self,
+        scene_name: str,
+        language: str,
+        model_name: str,
+        proxyllm_backend: str = None,
+    ):
+        """Get prompt template with scene name, language and model name
+        proxyllm_backend: see CFG.PROXYLLM_BACKEND
+        """
        scene_registry = self.registry[scene_name]
-        registry = scene_registry.get(model_name)
+
        print(
-            f"Get prompt template of scene_name: {scene_name} with model_name: {model_name} language: {language}"
+            f"Get prompt template of scene_name: {scene_name} with model_name: {model_name}, proxyllm_backend: {proxyllm_backend}, language: {language}"
        )
+        registry = None
+        if proxyllm_backend:
+            registry = scene_registry.get(proxyllm_backend)
+        if not registry:
+            registry = scene_registry.get(model_name)
        if not registry:
            registry = scene_registry.get(_DEFAULT_MODEL_KEY)
            if not registry:
--- a/pilot/scene/base_chat.py
+++ b/pilot/scene/base_chat.py
@ -69,6 +69,7 @@ class BaseChat(ABC):
        self.chat_mode = chat_mode
        self.current_user_input: str = current_user_input
        self.llm_model = CFG.LLM_MODEL
+        self.llm_echo = False
        ### can configurable storage methods
        self.memory = DuckdbHistoryMemory(chat_session_id)

@ -78,7 +79,10 @@ class BaseChat(ABC):
        # ]
        self.prompt_template: PromptTemplate = (
            CFG.prompt_template_registry.get_prompt_template(
-                self.chat_mode.value(), language=CFG.LANGUAGE, model_name=CFG.LLM_MODEL
+                self.chat_mode.value(),
+                language=CFG.LANGUAGE,
+                model_name=CFG.LLM_MODEL,
+                proxyllm_backend=CFG.PROXYLLM_BACKEND,
            )
        )
        self.history_message: List[OnceConversation] = self.memory.messages()
@ -128,6 +132,7 @@ class BaseChat(ABC):
            "temperature": float(self.prompt_template.temperature),
            "max_new_tokens": int(self.prompt_template.max_new_tokens),
            "stop": self.prompt_template.sep,
+            "echo": self.llm_echo,
        }
        return payload

--- a/pilot/scene/chat_db/auto_execute/example.py
+++ b/pilot/scene/chat_db/auto_execute/example.py
@ -9,10 +9,7 @@ EXAMPLES = [
            {
                "type": "ai",
                "data": {
-                    "content": """{
-							\"thoughts\": \"thought text\",
-							\"sql\": \"SELECT city FROM user where user_name='test1'\",
-						}""",
+                    "content": """{\n\"thoughts\": \"直接查询用户表中用户名为'test1'的记录即可\",\n\"sql\": \"SELECT city FROM user where user_name='test1'\"}""",
                    "example": True,
                },
            },
@ -24,10 +21,7 @@ EXAMPLES = [
            {
                "type": "ai",
                "data": {
-                    "content": """{
-							\"thoughts\": \"thought text\",
-							\"sql\": \"SELECT b.* FROM user a  LEFT JOIN tran_order b ON a.user_name=b.user_name  where a.city='成都'\",
-						}""",
+                    "content": """{\n\"thoughts\": \"根据订单表的用户名和用户表的用户名关联用户表和订单表，再通过用户表的城市为'成都'的过滤即可\",\n\"sql\": \"SELECT b.* FROM user a  LEFT JOIN tran_order b ON a.user_name=b.user_name  where a.city='成都'\"}""",
                    "example": True,
                },
            },
--- a/pilot/scene/chat_db/auto_execute/prompt.py
+++ b/pilot/scene/chat_db/auto_execute/prompt.py
@ -43,7 +43,7 @@ PROMPT_TEMPERATURE = 0.5
 prompt = PromptTemplate(
    template_scene=ChatScene.ChatWithDbExecute.value(),
    input_variables=["input", "table_info", "dialect", "top_k", "response"],
-    response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, indent=4),
+    response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
    template_define=PROMPT_SCENE_DEFINE,
    template=_DEFAULT_TEMPLATE,
    stream_out=PROMPT_NEED_NEED_STREAM_OUT,
@ -54,3 +54,4 @@ prompt = PromptTemplate(
    temperature=PROMPT_TEMPERATURE,
 )
 CFG.prompt_template_registry.register(prompt, is_default=True)
+from . import prompt_baichuan
--- a/pilot/scene/chat_db/auto_execute/prompt_baichuan.py
+++ b/pilot/scene/chat_db/auto_execute/prompt_baichuan.py
@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import json
+from pilot.prompts.prompt_new import PromptTemplate
+from pilot.configs.config import Config
+from pilot.scene.base import ChatScene
+from pilot.scene.chat_db.auto_execute.out_parser import DbChatOutputParser, SqlAction
+from pilot.common.schema import SeparatorStyle
+from pilot.scene.chat_db.auto_execute.example import sql_data_example
+
+CFG = Config()
+
+PROMPT_SCENE_DEFINE = None
+
+_DEFAULT_TEMPLATE = """
+你是一个 SQL 专家，给你一个用户的问题，你会生成一条对应的 {dialect} 语法的 SQL 语句。
+
+如果用户没有在问题中指定 sql 返回多少条数据，那么你生成的 sql 最多返回 {top_k} 条数据。 
+你应该尽可能少地使用表。
+
+已知表结构信息如下：
+{table_info}
+
+注意：
+1. 只能使用表结构信息中提供的表来生成 sql，如果无法根据提供的表结构中生成 sql ，请说：“提供的表结构信息不足以生成 sql 查询。” 禁止随意捏造信息。
+2. 不要查询不存在的列，注意哪一列位于哪张表中。
+3. 使用 json 格式回答，确保你的回答是必须是正确的 json 格式，并且能被 python 语言的 `json.loads` 库解析, 格式如下：
+{response}
+"""
+
+RESPONSE_FORMAT_SIMPLE = {
+    "thoughts": "对用户说的想法摘要",
+    "sql": "生成的将被执行的 SQL",
+}
+
+PROMPT_SEP = SeparatorStyle.SINGLE.value
+
+PROMPT_NEED_NEED_STREAM_OUT = False
+
+# Temperature is a configuration hyperparameter that controls the randomness of language model output.
+# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
+# For example, if you adjust the temperature to 0.5, the model will usually generate text that is more predictable and less creative than if you set the temperature to 1.0.
+PROMPT_TEMPERATURE = 0.5
+
+prompt = PromptTemplate(
+    template_scene=ChatScene.ChatWithDbExecute.value(),
+    input_variables=["input", "table_info", "dialect", "top_k", "response"],
+    response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
+    template_is_strict=False,
+    template_define=PROMPT_SCENE_DEFINE,
+    template=_DEFAULT_TEMPLATE,
+    stream_out=PROMPT_NEED_NEED_STREAM_OUT,
+    output_parser=DbChatOutputParser(
+        sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
+    ),
+    # example_selector=sql_data_example,
+    temperature=PROMPT_TEMPERATURE,
+)
+
+CFG.prompt_template_registry.register(
+    prompt,
+    language=CFG.LANGUAGE,
+    is_default=False,
+    model_names=["baichuan-13b", "baichuan-7b"],
+)
--- a/pilot/server/chat_adapter.py
+++ b/pilot/server/chat_adapter.py
@ -19,17 +19,20 @@ class BaseChatAdpter:
        """Return the generate stream handler func"""
        pass

-    def get_conv_template(self) -> Conversation:
+    def get_conv_template(self, model_path: str) -> Conversation:
        return None

-    def model_adaptation(self, params: Dict) -> Tuple[Dict, Dict]:
+    def model_adaptation(self, params: Dict, model_path: str) -> Tuple[Dict, Dict]:
        """Params adaptation"""
-        conv = self.get_conv_template()
+        conv = self.get_conv_template(model_path)
        messages = params.get("messages")
        # Some model scontext to dbgpt server
        model_context = {"prompt_echo_len_char": -1}
        if not conv or not messages:
            # Nothing to do
+            print(
+                f"No conv from model_path {model_path} or no messages in params, {self}"
+            )
            return params, model_context
        conv = conv.copy()
        system_messages = []
@ -62,7 +65,12 @@ class BaseChatAdpter:
        # TODO remote bos token and eos token from tokenizer_config.json of model
        prompt_echo_len_char = len(new_prompt.replace("</s>", "").replace("<s>", ""))
        model_context["prompt_echo_len_char"] = prompt_echo_len_char
+        model_context["echo"] = params.get("echo", True)
        params["prompt"] = new_prompt
+
+        # Overwrite model params:
+        params["stop"] = conv.stop_str
+
        return params, model_context


@ -79,6 +87,7 @@ def get_llm_chat_adapter(model_path: str) -> BaseChatAdpter:
    """Get a chat generate func for a model"""
    for adapter in llm_model_chat_adapters:
        if adapter.match(model_path):
+            print(f"Get model path: {model_path} adapter {adapter}")
            return adapter

    raise ValueError(f"Invalid model for chat adapter {model_path}")
@ -186,7 +195,7 @@ class Llama2ChatAdapter(BaseChatAdpter):
    def match(self, model_path: str):
        return "llama-2" in model_path.lower()

-    def get_conv_template(self) -> Conversation:
+    def get_conv_template(self, model_path: str) -> Conversation:
        return get_conv_template("llama-2")

    def get_generate_stream_func(self):
@ -195,6 +204,21 @@ class Llama2ChatAdapter(BaseChatAdpter):
        return generate_stream


+class BaichuanChatAdapter(BaseChatAdpter):
+    def match(self, model_path: str):
+        return "baichuan" in model_path.lower()
+
+    def get_conv_template(self, model_path: str) -> Conversation:
+        if "chat" in model_path.lower():
+            return get_conv_template("baichuan-chat")
+        return get_conv_template("zero_shot")
+
+    def get_generate_stream_func(self):
+        from pilot.model.inference import generate_stream
+
+        return generate_stream
+
+
 register_llm_model_chat_adapter(VicunaChatAdapter)
 register_llm_model_chat_adapter(ChatGLMChatAdapter)
 register_llm_model_chat_adapter(GuanacoChatAdapter)
@ -202,6 +226,7 @@ register_llm_model_chat_adapter(FalconChatAdapter)
 register_llm_model_chat_adapter(GorillaChatAdapter)
 register_llm_model_chat_adapter(GPT4AllChatAdapter)
 register_llm_model_chat_adapter(Llama2ChatAdapter)
+register_llm_model_chat_adapter(BaichuanChatAdapter)

 # Proxy model for test and develop, it's cheap for us now.
 register_llm_model_chat_adapter(ProxyllmChatAdapter)
--- a/pilot/server/llmserver.py
+++ b/pilot/server/llmserver.py
@ -78,7 +78,9 @@ class ModelWorker:
    def generate_stream_gate(self, params):
        try:
            # params adaptation
-            params, model_context = self.llm_chat_adapter.model_adaptation(params)
+            params, model_context = self.llm_chat_adapter.model_adaptation(
+                params, self.ml.model_path
+            )
            for output in self.generate_stream_func(
                self.model, self.tokenizer, params, DEVICE, CFG.MAX_POSITION_EMBEDDINGS
            ):
@ -136,6 +138,7 @@ class PromptRequest(BaseModel):
    max_new_tokens: int
    model: str
    stop: str = None
+    echo: bool = True


 class StreamRequest(BaseModel):
@ -178,6 +181,7 @@ def generate(prompt_request: PromptRequest) -> str:
        "temperature": prompt_request.temperature,
        "max_new_tokens": prompt_request.max_new_tokens,
        "stop": prompt_request.stop,
+        "echo": prompt_request.echo,
    }

    rsp_str = ""
--- a/pilot/server/static/404.html
+++ b/pilot/server/static/404.html
@ -1 +1 @@
-<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-81b9e46a3f1e5c68.js" defer=""></script><script src="/_next/static/chunks/framework-43665103d101a22d.js" defer=""></script><script src="/_next/static/chunks/main-c6e90425c3eeb90a.js" defer=""></script><script src="/_next/static/chunks/pages/_app-1f2755172264764d.js" defer=""></script><script src="/_next/static/chunks/pages/_error-f5357f382422dd96.js" defer=""></script><script src="/_next/static/Cow4Dk3Cb5ywOteYPWBYm/_buildManifest.js" defer=""></script><script src="/_next/static/Cow4Dk3Cb5ywOteYPWBYm/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"Cow4Dk3Cb5ywOteYPWBYm","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
+<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-e0b549c3ec4ce91b.js" defer=""></script><script src="/_next/static/chunks/framework-43665103d101a22d.js" defer=""></script><script src="/_next/static/chunks/main-c6e90425c3eeb90a.js" defer=""></script><script src="/_next/static/chunks/pages/_app-1f2755172264764d.js" defer=""></script><script src="/_next/static/chunks/pages/_error-f5357f382422dd96.js" defer=""></script><script src="/_next/static/AVF7sR15c1tF8wuv8mGBK/_buildManifest.js" defer=""></script><script src="/_next/static/AVF7sR15c1tF8wuv8mGBK/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"AVF7sR15c1tF8wuv8mGBK","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
--- a/pilot/server/static/404/index.html
+++ b/pilot/server/static/404/index.html
@ -1 +1 @@
-<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-81b9e46a3f1e5c68.js" defer=""></script><script src="/_next/static/chunks/framework-43665103d101a22d.js" defer=""></script><script src="/_next/static/chunks/main-c6e90425c3eeb90a.js" defer=""></script><script src="/_next/static/chunks/pages/_app-1f2755172264764d.js" defer=""></script><script src="/_next/static/chunks/pages/_error-f5357f382422dd96.js" defer=""></script><script src="/_next/static/Cow4Dk3Cb5ywOteYPWBYm/_buildManifest.js" defer=""></script><script src="/_next/static/Cow4Dk3Cb5ywOteYPWBYm/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"Cow4Dk3Cb5ywOteYPWBYm","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
+<!DOCTYPE html><html><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width"/><title>404: This page could not be found</title><meta name="next-head-count" content="3"/><noscript data-n-css=""></noscript><script defer="" nomodule="" src="/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js"></script><script src="/_next/static/chunks/webpack-e0b549c3ec4ce91b.js" defer=""></script><script src="/_next/static/chunks/framework-43665103d101a22d.js" defer=""></script><script src="/_next/static/chunks/main-c6e90425c3eeb90a.js" defer=""></script><script src="/_next/static/chunks/pages/_app-1f2755172264764d.js" defer=""></script><script src="/_next/static/chunks/pages/_error-f5357f382422dd96.js" defer=""></script><script src="/_next/static/AVF7sR15c1tF8wuv8mGBK/_buildManifest.js" defer=""></script><script src="/_next/static/AVF7sR15c1tF8wuv8mGBK/_ssgManifest.js" defer=""></script></head><body><div id="__next"><div style="font-family:system-ui,&quot;Segoe UI&quot;,Roboto,Helvetica,Arial,sans-serif,&quot;Apple Color Emoji&quot;,&quot;Segoe UI Emoji&quot;;height:100vh;text-align:center;display:flex;flex-direction:column;align-items:center;justify-content:center"><div style="line-height:48px"><style>body{color:#000;background:#fff;margin:0}.next-error-h1{border-right:1px solid rgba(0,0,0,.3)}@media (prefers-color-scheme:dark){body{color:#fff;background:#000}.next-error-h1{border-right:1px solid rgba(255,255,255,.3)}}</style><h1 class="next-error-h1" style="display:inline-block;margin:0 20px 0 0;padding-right:23px;font-size:24px;font-weight:500;vertical-align:top">404</h1><div style="display:inline-block"><h2 style="font-size:14px;font-weight:400;line-height:28px">This page could not be found<!-- -->.</h2></div></div></div></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"statusCode":404}},"page":"/_error","query":{},"buildId":"AVF7sR15c1tF8wuv8mGBK","nextExport":true,"isFallback":false,"gip":true,"scriptLoader":[]}</script></body></html>
--- a/pilot/server/static/LOGO.png
+++ b/pilot/server/static/LOGO.png
--- a/pilot/server/static/LOGO_1.png
+++ b/pilot/server/static/LOGO_1.png
--- a/pilot/server/static/_next/static/AVF7sR15c1tF8wuv8mGBK/_buildManifest.js
+++ b/pilot/server/static/_next/static/AVF7sR15c1tF8wuv8mGBK/_buildManifest.js
@ -0,0 +1 @@
+self.__BUILD_MANIFEST={__rewrites:{beforeFiles:[],afterFiles:[],fallback:[]},"/_error":["static/chunks/pages/_error-f5357f382422dd96.js"],sortedPages:["/_app","/_error"]},self.__BUILD_MANIFEST_CB&&self.__BUILD_MANIFEST_CB();
--- a/pilot/server/static/_next/static/AVF7sR15c1tF8wuv8mGBK/_ssgManifest.js
+++ b/pilot/server/static/_next/static/AVF7sR15c1tF8wuv8mGBK/_ssgManifest.js
@ -0,0 +1 @@
+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()
--- a/pilot/server/static/_next/static/chunks/341-c3312a204c5835b8.js
+++ b/pilot/server/static/_next/static/chunks/341-c3312a204c5835b8.js
--- a/pilot/server/static/_next/static/chunks/394-0ffa189aa535d3eb.js
+++ b/pilot/server/static/_next/static/chunks/394-0ffa189aa535d3eb.js
--- a/pilot/server/static/_next/static/chunks/775-224c8c8f5ee3fd65.js
+++ b/pilot/server/static/_next/static/chunks/775-224c8c8f5ee3fd65.js
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--- a/pilot/vector_store/connector.py
+++ b/pilot/vector_store/connector.py
@ -1,8 +1,9 @@
 from pilot.vector_store.chroma_store import ChromaStore

 from pilot.vector_store.milvus_store import MilvusStore
+from pilot.vector_store.weaviate_store import WeaviateStore

-connector = {"Chroma": ChromaStore, "Milvus": MilvusStore}
+connector = {"Chroma": ChromaStore, "Milvus": MilvusStore, "Weaviate": WeaviateStore}


 class VectorStoreConnector:
--- a/requirements.txt
+++ b/requirements.txt
@ -28,6 +28,7 @@ pyyaml==6.0
 tokenizers==0.13.2
 tqdm==4.64.1
 transformers==4.30.0
+transformers_stream_generator
 timm==0.6.13
 spacy==3.5.3
 webdataset==0.2.48
@ -68,7 +69,7 @@ distro
 pypdf
 weaviate-client

-# databse
+# database

 pymysql
 duckdb
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