dify中agent的提示词 text2sql
时间: 2025-06-28 16:21:17 浏览: 12
### Dify Platform Agent Configuration for Text2SQL
In the context of configuring an Agent within the Dify platform specifically for Text2SQL conversions, several key components and configurations are essential to ensure effective operation. The setup involves defining tools that interact with databases such as PostgreSQL or MySQL through Docker containers[^2]. For instance, modifying `docker-compose.yml` allows setting up these database environments properly.
The core aspect lies in creating a tool definition file which starts API services providing functionalities necessary for Dify's use, including obtaining table information (`table_info`) from configured databases and executing SQL statements[^3].
For integrating Agents into this workflow, one must configure the environment on the Dify Agent page where both LLM (Large Language Model) models like those mentioned in advanced applications using Flux.1 and Qwen2.5(7B)[^4], alongside specific tools designed for interacting with relational databases, can be set up. This integration enables natural language queries to translate accurately into executable SQL commands against target databases.
To achieve efficient text-to-SQL conversion via prompts:
- **Prompt Template Method**: Utilize predefined templates tailored towards structuring user input so it aligns closely with expected SQL syntax patterns.
Example Prompt Structure:
```plaintext
Given the following schema: {schema details}, please generate a query to find all records matching "{search criteria}".
```
- **Configuration Guidelines**:
- Ensure access credentials for connecting to your chosen RDBMS (e.g., Postgres/MySQL).
- Define clear mappings between natural language elements and corresponding SQL constructs.
```json
{
"database": {
"type": "postgres",
"host": "localhost",
"port": 5432,
"username": "your_username",
"password": "your_password"
},
"prompts": [
{"intent":"select","template":"Find me {{columns}} from {{tables}} where {{conditions}}" }
]
}
```
This JSON snippet demonstrates how connection parameters along with basic intent-based prompt structures could look when configuring agents for performing text-to-SQL tasks efficiently.
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