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integrates [RAGFlow](https://ragflow.io/) into the AgentXin plugin system, registering a global NestJS module with the host and exposing validation and retrieval capabilities to help you reuse RAGFlow datasets and knowledge b","maintainers":[{"name":"agentxin-ai","email":"1304040880@qq.com"}],"readme":"# AgentXin Plugin: RAGFlow\n\n## Overview\n\n`@agentxin-ai/plugin-ragflow` integrates [RAGFlow](https://ragflow.io/) into the AgentXin plugin system, registering a global NestJS module with the host and exposing validation and retrieval capabilities to help you reuse RAGFlow datasets and knowledge bases in your workflows.\n\n## Features\n\n- Provides `IntegrationRAGFlowPlugin`, which logs messages on plugin startup and teardown, and mounts the internal `IntegrationRAGFlowModule`.\n- Offers a RAGFlow integration strategy, publishing name, description, icon, and configuration schema (supports `url` and `apiKey` fields) to the AgentXin integration directory.\n- Built-in RAGFlow knowledge strategy, calling the `/api/v1/retrieval` endpoint to retrieve specified datasets and returning fragments as LangChain `Document` + similarity.\n- Exposes a `POST /ragflow/test` validation endpoint for quickly verifying connection info before saving the integration.\n\n## Installation\n\n```bash\nnpm install @agentxin-ai/plugin-ragflow\n# or\npnpm add @agentxin-ai/plugin-ragflow\n```\n\n> **Peer dependencies:** The host project must pre-install dependencies such as `@agentxin-ai/plugin-sdk`, `@nestjs/common`, `@metad/contracts`, `@langchain/core`, `axios`, `chalk`, `lodash-es`, and `zod`. Refer to the repository `package.json` for specific versions.\n\n## Usage\n\n1. Declare the plugin via environment variables when starting the AgentXinAI service:\n\n    ```bash\n    PLUGINS=@agentxin-ai/plugin-ragflow\n    ```\n\n2. When saving the integration, fill in the following configuration fields:\n\n    | Field     | Description                                                                                 |\n    | --------- | ------------------------------------------------------------------------------------------- |\n    | `url`     | RAGFlow service base URL, e.g., `https://ragflow.your-company.com`. The plugin will automatically remove trailing `/`, `/v1`, or `/api`. |\n    | `apiKey`  | API Key for the RAGFlow application, used for request authentication.                       |\n\n3. When calling the knowledge strategy, the payload should include:\n\n    | Field                     | Description                |\n    | ------------------------- | -------------------------- |\n    | `query`                   | User query text.           |\n    | `k`                       | Number of fragments to return. |\n    | `options.knowledgebaseId` | RAGFlow dataset ID.        |\n\nThe plugin returns an array in the form `[Document, similarity]`, which can be directly consumed by downstream LangChain components.\n\n## Connection Test Endpoint\n\nBefore configuring the integration, you can call the test endpoint to verify if the address and credentials are valid:\n\n```bash\ncurl -X POST https://<your-host>/ragflow/test \\\n  -H 'Content-Type: application/json' \\\n  -d '{\n     \"options\": {\n        \"url\": \"https://ragflow.your-company.com\",\n        \"apiKey\": \"ragflow_api_key\"\n     }\n  }'\n```\n\nThe service will request the RAGFlow `/v1` root path and return the raw response. If parameters are missing or the connection fails, a `400 Bad Request` is thrown.\n\n## Development & Debugging\n\nRun Nx commands in the monorepo root directory to build and test:\n\n```bash\nnpx nx build @agentxin-ai/plugin-ragflow\nnpx nx test @agentxin-ai/plugin-ragflow\n```\n\n`nx build` compiles TypeScript source code to `dist/`, and `nx test` runs unit tests with Jest.\n\n## License\n\nThis project follows the [AGPL-3.0 License](../../../LICENSE) in the repository root.\n","readmeFilename":"README.md"}