DB-GPT is an open-source AI-native data framework with AWEL (Agentic Workflow Expression Language) and Agents.The goal is to build infrastructure for the large model domain, through the development of multiple capabilities such as Multi-Model Management (SMMF), Text2SQL performance optimization, RAG framework and optimization, multi-agents framework collaboration, and AWEL (intelligent orchestration). This makes it easier and more convenient to build data model applications around databases.
2.1. Open the SiliconCloud website and register an account (if you have an existing account, log in directly).2.2. After completing the registration, go to the API Keys to create API the future use.
3.4 Modify the environment variables file .env to configure siliconCloud models
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# 使用 SiliconCloud 的代理模型LLM_MODEL=siliconflow_proxyllm# 配置具体使用的模型名称SILICONFLOW_MODEL_VERSION=Qwen/Qwen2.5-Coder-32B-InstructSILICONFLOW_API_BASE=https://api.siliconflow.cn/v1# 记得填写您在步骤2中获取的 API KeySILICONFLOW_API_KEY={your-siliconflow-api-key}# 配置使用 SiliconCloud 的 Embedding 模型EMBEDDING_MODEL=proxy_http_openapiPROXY_HTTP_OPENAPI_PROXY_SERVER_URL=https://api.siliconflow.cn/v1/embeddings# 记得填写您在步骤2中获取的 API KeyPROXY_HTTP_OPENAPI_PROXY_API_KEY={your-siliconflow-api-key}# 配置具体的 Embedding 模型名称PROXY_HTTP_OPENAPI_PROXY_BACKEND=BAAI/bge-large-zh-v1.5# 配置使用 SiliconCloud 的 rerank 模型RERANK_MODEL=rerank_proxy_siliconflowRERANK_PROXY_SILICONFLOW_PROXY_SERVER_URL=https://api.siliconflow.cn/v1/rerank# 记得填写您在步骤2中获取的 API KeyRERANK_PROXY_SILICONFLOW_PROXY_API_KEY={your-siliconflow-api-key}# 配置具体的 rerank 模型名称RERANK_PROXY_SILICONFLOW_PROXY_BACKEND=BAAI/bge-reranker-v2-m3
Note: The SILICONFLOW_API_KEY, PROXY_HTTP_OPENAPI_PROXY_SERVER_URL, and RERANK_PROXY_SILICONFLOW_PROXY_API_KEY environment variables are the API Keys you obtained in step 2. The language model (SILICONFLOW), Embedding model (PROXY_HTTP_OPENAPI_PROXY_BACKEND), and rerank model (RERANK_PROXYICONFLOW_PROXY_BACKEND) can be obtained from Get User Models List - SiliconFlow.
For a data conversation example, data conversation capabilities involve natural language interaction with structured and semi-structured data, which can assist in data analysis and insights. Below are the specific operational steps:
First, select the data on the left side. Add a database, currently, DB-GPT supports various database Choose the corresponding database type to add. Here, we use MySQL as a demonstration. The test data for the demonstration can be found in the [test examples](https://github.com/eosphoros-ai/DB-GPT/tree/main/docker/examples/sqls).