docs: add module description

docs/getting_started/concepts.md

@@ -1,3 +1,2 @@
 # Concepts
 
-

docs/getting_started/getting_started.md

@@ -4,4 +4,48 @@ This tutorial gives you a quick walkthrough about use DB-GPT with you environmen
 
 ## Installation
 
-To get started, install DB-GPT with the following command.
+To get started, install DB-GPT with the following steps.
+
+### 1. Hardware Requirements 
+As our project has the ability to achieve ChatGPT performance of over 85%, there are certain hardware requirements. However, overall, the project can be deployed and used on consumer-grade graphics cards. The specific hardware requirements for deployment are as follows:
+
+| GPU  | VRAM Size | Performance                                 |
+| --------- | --------- | ------------------------------------------- |
+| RTX 4090  | 24 GB     | Smooth conversation inference        |
+| RTX 3090  | 24 GB     | Smooth conversation inference, better than V100 |
+| V100      | 16 GB     | Conversation inference possible, noticeable stutter |
+
+### 2. Install
+
+This project relies on a local MySQL database service, which you need to install locally. We recommend using Docker for installation.
+
+```bash
+$ docker run --name=mysql -p 3306:3306 -e MYSQL_ROOT_PASSWORD=aa12345678 -dit mysql:latest
+```
+We use [Chroma embedding database](https://github.com/chroma-core/chroma) as the default for our vector database, so there is no need for special installation. If you choose to connect to other databases, you can follow our tutorial for installation and configuration. 
+For the entire installation process of DB-GPT, we use the miniconda3 virtual environment. Create a virtual environment and install the Python dependencies.
+
+```
+python>=3.10
+conda create -n dbgpt_env python=3.10
+conda activate dbgpt_env
+pip install -r requirements.txt
+```
+
+### 3. Run
+You can refer to this document to obtain the Vicuna weights: [Vicuna](https://github.com/lm-sys/FastChat/blob/main/README.md#model-weights) .
+
+If you have difficulty with this step, you can also directly use the model from [this link](https://huggingface.co/Tribbiani/vicuna-7b) as a replacement.
+
+1. Run server
+```bash
+$ python pilot/server/llmserver.py
+```
+
+Run gradio webui
+
+```bash
+$ python pilot/server/webserver.py
+```
+
+Notice:  the webserver need to connect llmserver,  so you need change the .env file. change the MODEL_SERVER = "http://127.0.0.1:8000" to your address.  It's very important.

docs/modules/connections.md

@@ -1 +1,4 @@
-# Connections
+# Connections
+
+In order to interact more conveniently with users' private environments, the project has designed a connection module, which can support connection to databases, Excel, knowledge bases, and other environments to achieve information and data exchange.
+

docs/modules/embedding.md

@@ -1 +0,0 @@
-# Embedding

docs/modules/index.md

@@ -0,0 +1,3 @@
+# Vector storage and indexing
+
+In order to facilitate the management of knowledge after vectorization, we have built-in multiple vector storage engines, from memory-based Chroma to distributed Milvus. Users can choose different storage engines according to their own scenario needs. The storage of knowledge vectors is the cornerstone of AI capability enhancement. As the intermediate language for interaction between humans and large language models, vectors play a very important role in this project.

docs/modules/knownledge.md

@@ -1 +1,6 @@
-# Knownledge
+# Knownledge
+
+As the knowledge base is currently the most significant user demand scenario, we natively support the construction and processing of knowledge bases. At the same time, we also provide multiple knowledge base management strategies in this project, such as:
+1. Default built-in knowledge base
+2. Custom addition of knowledge bases
+3. Various usage scenarios such as constructing knowledge bases through plugin capabilities and web crawling. Users only need to organize the knowledge documents, and they can use our existing capabilities to build the knowledge base required for the large model.

docs/modules/llms.md

@@ -1 +1,3 @@
-# LLMs
+# LLMs
+
+In the underlying large model integration, we have designed an open interface that supports integration with various large models. At the same time, we have a very strict control and evaluation mechanism for the effectiveness of the integrated models. In terms of accuracy, the integrated models need to align with the capability of ChatGPT at a level of 85% or higher. We use higher standards to select models, hoping to save users the cumbersome testing and evaluation process in the process of use.

docs/modules/plugins.md

@@ -1 +1,3 @@
-# Plugins
+# Plugins
+
+The ability of Agent and Plugin is the core of whether large models can be automated. In this project, we natively support the plugin mode, and large models can automatically achieve their goals. At the same time, in order to give full play to the advantages of the community, the plugins used in this project natively support the Auto-GPT plugin ecology, that is, Auto-GPT plugins can directly run in our project.

docs/modules/prompts.md

@@ -1 +1,3 @@
-# Prompts
+# Prompts
+
+Prompt is a very important part of the interaction between the large model and the user, and to a certain extent, it determines the quality and accuracy of the answer generated by the large model. In this project, we will automatically optimize the corresponding prompt according to user input and usage scenarios, making it easier and more efficient for users to use large language models.

docs/modules/server.md

@@ -1 +1,3 @@
-# Server
+# Server
+
+TODO: In terms of terminal display, we will provide a multi-platform product interface, including PC, mobile phone, command line, Slack and other platforms.