Boost Dify’s RAG Performance with Higress AI Gateway: Two Integration Strategies

Background

Dify is an open‑source AI application development platform. Its native Retrieval‑Augmented Generation (RAG) engine suffers from limited complex‑document chunking, weak retrieval relevance, and verbose configuration, which reduces accuracy and reliability in production.

Limitations of Dify’s Built‑in RAG

Inadequate handling of non‑textual content such as images, charts, and PDFs.

Poor relevance ranking and recall quality in large knowledge bases.

Configuration requires many parameters and manual tuning, lacking self‑optimization.

Solution Overview

Higress AI Gateway provides a bridge that lets Dify invoke external, mature RAG engines without changing Dify’s workflow or agent orchestration. Two integration patterns are supported:

RAG Retrieval Agent – the gateway performs the retrieval and returns the raw chunks to Dify, where the application can apply custom processing.

Automatic Retrieval Injection – the gateway automatically retrieves relevant knowledge and injects it into the LLM request context (e.g., as a system prompt or into a user‑defined template).

Integration Pattern 1 – RAG Retrieval Agent

Create a Bailei (Alibaba Cloud) knowledge‑base service in the AI Gateway.

Define a custom Agent API route whose path ends with /retrieval and bind it to the Bailei service.

Install the AI RAG Retrieval Agent plugin in the gateway, configure the Bailei API key, enable the plugin, and save.

In Dify, add an external knowledge‑base pointing to the gateway endpoint and provide the Bailei knowledge‑base ID.

Run a recall test in Dify; successful chunk return confirms the bridge works.

Integration Pattern 2 – Automatic Retrieval Injection

Deploy an external RAG engine (e.g., open‑source RagFlow or a SaaS offering) and obtain its fully qualified domain name (FQDN), port, and API key.

Create an AI Service in the gateway for the RagFlow endpoint and a Model API that forwards LLM calls.

Install the AI Retrieval‑Enhanced Generation (Enhanced) plugin, fill in the RagFlow FQDN, port, and API key, enable the plugin, and save.

Debug the Model API in the gateway console to verify that retrieved knowledge is injected into the LLM request context.

Call the Model API from Dify; the response now includes RAG‑enhanced content without any code changes in the Dify application.

Benefits

Significant improvement in chunk quality and retrieval accuracy by leveraging professional RAG engines.

Zero‑code enhancement: Dify applications gain advanced RAG capabilities through configuration alone.

Flexible selection of open‑source or SaaS RAG solutions to meet diverse enterprise scenarios.

Availability and Outlook

The integration is available on Alibaba Cloud’s Cloud‑Native AI Gateway. Future work will add multimodal support, broader ecosystem extensions, and continued reliability improvements to evolve Dify into a high‑precision, enterprise‑grade knowledge hub.

Key References

AI RAG Retrieval Agent documentation: https://help.aliyun.com/zh/api-gateway/ai-gateway/user-guide/ai-retrieval-agent

AI Retrieval‑Enhanced Generation (Enhanced) documentation: https://help.aliyun.com/zh/api-gateway/ai-gateway/user-guide/ai-retrieval-enhanced-generation-enhanced-version

Bailei API‑Key management: https://bailian.console.aliyun.com/?tab=model#/api-key

RagFlow Serverless deployment guide: https://saenext.console.aliyun.com/cn-hangzhou/scene-market/market/detail/service-611f1d5343924329a69e?tab=document&name=RAGFlow%E7%A4%BE%E5%8C%BA%E7%89%88-Serverless%E9%83%A8%E7%BD%B2&dataSource=computeNest

Alibaba Cloud AI Gateway product page: https://www.aliyun.com/product/apigate