[Dify] 知识库在 Agent 模式中的应用策略:让智能体更懂知识、更懂业务

在使用 Dify 构建 AI 应用的过程中,我们经常会使用两种模式:

  • 工作流(Workflow)模式:流程明确、逻辑可控;

  • 智能体(Agent)模式:任务自主规划、工具自动调用。

在多数企业级应用中,仅仅让 Agent “会调用工具” 还不够,它还要 懂公司业务、懂制度、懂内容上下文
这就需要让 Agent 拥有“知识库”这一关键能力。

本文将深入讲解 Dify 中 知识库在 Agent 模式下的使用策略,包括架构逻辑、调用方式、提示词策略、性能优化与实际案例。


一、Agent 模式回顾:从执行型到认知型

Dify 的 Agent 模式 是一种基于 LLM 的“智能执行体”设计理念:

Agent 能根据用户输入,自主决定是否调用工具、执行任务或检索知识。

它不同于固定流程的 Workflow,而是具备以下特征:

特征 说明
### 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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