> ## Documentation Index
> Fetch the complete documentation index at: https://docs.siliconflow.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Using in DB-GPT

## 1. About DB-GPT

[DB-GPT](https://github.com/eosphoros-ai/DB-GPT) **is an open-source AI native data application development framework (AI Native Data App Development framework with AWEL (Agentic Workflow Expression Language) and Agents).**

Its purpose is to build infrastructure in the field of large models by developing capabilities like multi-model management (SMMF), Text2SQL optimization, RAG framework and enhancements, multi-agent framework collaboration, AWEL (agentic workflow orchestration), and more. This makes building large model applications centered around databases simpler and more convenient.

## 2. Obtain API Key

2.1 Open the [SiliconFlow official website](https://cloud.siliconflow.com/) and register an account (if already registered, simply log in).

2.2 After registration, navigate to [API Key](https://cloud.siliconflow.com/account/ak), create a new API Key, and copy it for later use.

## 3. Deploy DB-GPT

### 3.1 Clone the DB-GPT Source Code

```bash theme={null}
git clone https://github.com/eosphoros-ai/DB-GPT.git
```

### 3.2 Create a Virtual Environment and Install Dependencies

```bash theme={null}
# Navigate to the root directory of the DB-GPT source code
cd DB-GPT

# DB-GPT requires Python >= 3.10
conda create -n dbgpt_env python=3.10
conda activate dbgpt_env

# Install dependencies for proxy model support
pip install -e ".[proxy]"
```

### 3.3 Configure Basic Environment Variables

```bash theme={null}
# Copy the template env file as .env
cp .env.template .env
```

### 3.4 Modify the `.env` Environment Variable File to Configure the SiliconFlow Model

```bash theme={null}
# Use the proxy model from SiliconFlow
LLM_MODEL=siliconflow_proxyllm
# Specify the model name to use
SILICONFLOW_MODEL_VERSION=Qwen/Qwen2.5-Coder-32B-Instruct
SILICONFLOW_API_BASE=https://api.siliconflow.com/v1
# Enter the API Key obtained in Step 2
SILICONFLOW_API_KEY={your-siliconflow-api-key}

# Configure the Embedding model from SiliconFlow
EMBEDDING_MODEL=proxy_http_openapi
PROXY_HTTP_OPENAPI_PROXY_SERVER_URL=https://api.siliconflow.com/v1/embeddings
# Enter the API Key obtained in Step 2
PROXY_HTTP_OPENAPI_PROXY_API_KEY={your-siliconflow-api-key}
# Specify the Embedding model name
PROXY_HTTP_OPENAPI_PROXY_BACKEND=BAAI/bge-large-zh-v1.5

# Configure the rerank model from SiliconFlow
RERANK_MODEL=rerank_proxy_siliconflow
RERANK_PROXY_SILICONFLOW_PROXY_SERVER_URL=https://api.siliconflow.com/v1/rerank
# Enter the API Key obtained in Step 2
RERANK_PROXY_SILICONFLOW_PROXY_API_KEY={your-siliconflow-api-key}
# Specify the rerank model name
RERANK_PROXY_SILICONFLOW_PROXY_BACKEND=BAAI/bge-reranker-v2-m3
```

Note that the `SILICONFLOW_API_KEY`, `PROXY_HTTP_OPENAPI_PROXY_SERVER_URL`, and `RERANK_PROXY_SILICONFLOW_PROXY_API_KEY` environment variables are the SiliconFlow API Keys obtained in Step 2. The language model (`SILICONFLOW_MODEL_VERSION`), embedding model (`PROXY_HTTP_OPENAPI_PROXY_BACKEND`), and rerank model (`RERANK_PROXY_SILICONFLOW_PROXY_BACKEND`) can be found in the [Model List - SiliconFlow](https://docs.siliconflow.com/api-reference/models/get-model-list).

### 3.5 Start the DB-GPT Service

```bash theme={null}
dbgpt start webserver --port 5670
```

Open the browser and navigate to [http://127.0.0.1:5670/](http://127.0.0.1:5670/) to access the deployed DB-GPT.

## 4. Use SiliconFlow Models through the DB-GPT Python SDK

### 4.1 Install the DB-GPT Python Package

```bash theme={null}
pip install "dbgpt>=0.6.3rc2" openai requests numpy
```

Install additional dependencies for testing.

### 4.2 Use SiliconFlow's Large Language Model

```python theme={null}
import asyncio
import os
from dbgpt.core import ModelRequest
from dbgpt.model.proxy import SiliconFlowLLMClient

model = "Qwen/Qwen2.5-Coder-32B-Instruct"
client = SiliconFlowLLMClient(
    api_key=os.getenv("SILICONFLOW_API_KEY"),
    model_alias=model
)

res = asyncio.run(
    client.generate(
        ModelRequest(
            model=model,
            messages=[
                {"role": "system", "content": "You are a helpful AI assistant."},
                {"role": "human", "content": "Hello"},
            ]
        )
    )
)
print(res)
```

### 4.3 Use SiliconFlow's Embedding Model

```python theme={null}
import os
from dbgpt.rag.embedding import OpenAPIEmbeddings

openai_embeddings = OpenAPIEmbeddings(
    api_url="https://api.siliconflow.com/v1/embeddings",
    api_key=os.getenv("SILICONFLOW_API_KEY"),
    model_name="BAAI/bge-large-zh-v1.5",
)

texts = ["Hello, world!", "How are you?"]
res = openai_embeddings.embed_documents(texts)
print(res)
```

### 4.4 Use SiliconFlow's Rerank Model

```python theme={null}
import os
from dbgpt.rag.embedding import SiliconFlowRerankEmbeddings

embedding = SiliconFlowRerankEmbeddings(
    api_key=os.getenv("SILICONFLOW_API_KEY"),
    model_name="BAAI/bge-reranker-v2-m3",
)
res = embedding.predict("Apple", candidates=["苹果", "香蕉", "水果", "蔬菜"])
print(res)
```

## 5. Getting Started Guide

Take the data dialogue feature as an example. The data dialogue capability allows natural language conversations with data, primarily supporting structured and semi-structured data, assisting in data analysis and insights. Below is the specific operation process:

### 1. Add a Data Source

First, select "Data Source" on the left to add a database. DB-GPT currently supports multiple database types. Choose the appropriate database type to add. Here, MySQL is used for demonstration. The test data for this demo can be found in the [test examples](https://github.com/eosphoros-ai/DB-GPT/tree/main/docker/examples/sqls).

### 2. Select Dialogue Type

Choose the ChatData dialogue type.

### 3. Start Data Dialogue

Note: During the conversation, select the corresponding model and database. DB-GPT also provides both preview and edit modes.