`@chenchaolong/plugin-vllm7` provides a model adapter for connecting vLLM inference services to the [XpertAI](https://github.com/xpert-ai/xpert) platform. The plugin communicates with vLLM clusters via an OpenAI-compatible API, enabling agents to invoke c
npm install @chenchaolong/plugin-vllm7@chenchaolong/plugin-vllm7 provides a model adapter for connecting vLLM inference services to the XpertAI platform. The plugin communicates with vLLM clusters via an OpenAI-compatible API, enabling agents to invoke conversational models, embedding models, vision-enhanced models, and reranking models within a unified XpertAI agentic workflow.
VLLMPlugin NestJS module, which automatically registers model providers, lifecycle logging, and configuration validation logic.
LargeLanguageModel via VLLMLargeLanguageModel, supporting function calling, streaming output, and agent token statistics.
VLLMTextEmbeddingModel, reusing LangChain's OpenAIEmbeddings to generate vector representations for knowledge base retrieval.
VLLMRerankModel, leveraging the OpenAI-compatible rerank API to improve retrieval result ranking.
bash
npm install @chenchaolong/plugin-vllm7
`
> Peer Dependencies: The host project must also provide libraries such as @xpert-ai/plugin-sdk, @nestjs/common, @metad/contracts, @langchain/openai, lodash-es, chalk, and zod. Please refer to package.json for version requirements.
Enabling in XpertAI
1. Add the plugin package to your system dependencies and ensure it is resolvable by Node.js.
2. Before starting the service, declare the plugin in your environment variables:
`bash
PLUGINS=@chenchaolong/plugin-vllm7
`
3. Add a new model provider in the XpertAI admin interface or configuration file, and select vllm.
Credentials & Model Configuration
The form fields defined in vllm.yaml cover common deployment scenarios:
| Field | Description |
| --- | --- |
| api_key | vLLM service access token (leave blank if the service does not require authentication). |
| endpoint_url | Required. The base URL of the vLLM OpenAI-compatible API, e.g., https://vllm.example.com/v1. |
| endpoint_model_name | Specify explicitly if the model name on the server differs from the logical model name in XpertAI. |
| mode | Choose between chat or completion inference modes. |
| context_size / max_tokens_to_sample | Control the context window and generation length. |
| agent_though_support, function_calling_type, stream_function_calling, vision_support | Indicate whether the model supports agent thought exposure, function/tool calling, streaming function calling, and multimodal input, to inform UI capability hints. |
| stream_mode_delimiter | Customize the paragraph delimiter for streaming output. |
After saving the configuration, the plugin will call the validateCredentials method in the background, making a minimal request to the vLLM service to ensure the credentials are valid.
Model Capabilities
- Conversational Models: Uses ChatOAICompatReasoningModel to proxy the vLLM OpenAI API, supporting message history, function calling, and streaming output.
- Embedding Models: Relies on LangChain's OpenAIEmbeddings for knowledge base vectorization and retrieval-augmented generation.
- Reranking Models: Wraps OpenAICompatibleReranker to semantically rerank recall results.
- Vision Models: If the vLLM inference service supports multimodal (text+image) input, enable vision_support in the configuration to declare multimodal capabilities to the frontend.
Development & Debugging
From the repository root, enter the xpertai/ directory and use Nx commands to build and test:
`bash
npx nx build @chenchaolong/plugin-vllm7
npx nx test @chenchaolong/plugin-vllm7
`
Build artifacts are output to dist/ by default. Jest configuration is in jest.config.ts for writing and running unit tests.
License
This project follows the AGPL-3.0 License found at the root of the repository.
| Field | Description |
| --- | --- |
| api_key | vLLM service access token (leave blank if the service does not require authentication). |
| endpoint_url | Required. The base URL of the vLLM OpenAI-compatible API, e.g., https://vllm.example.com/v1. |
| endpoint_model_name | Specify explicitly if the model name on the server differs from the logical model name in XpertAI. |
| mode | Choose between chat or completion inference modes. |
| context_size / max_tokens_to_sample | Control the context window and generation length. |
| agent_though_support, function_calling_type, stream_function_calling, vision_support | Indicate whether the model supports agent thought exposure, function/tool calling, streaming function calling, and multimodal input, to inform UI capability hints. |
| stream_mode_delimiter | Customize the paragraph delimiter for streaming output. |
After saving the configuration, the plugin will call the validateCredentials method in the background, making a minimal request to the vLLM service to ensure the credentials are valid.
Model Capabilities
- Conversational Models: Uses ChatOAICompatReasoningModel to proxy the vLLM OpenAI API, supporting message history, function calling, and streaming output.
- Embedding Models: Relies on LangChain's OpenAIEmbeddings for knowledge base vectorization and retrieval-augmented generation.
- Reranking Models: Wraps OpenAICompatibleReranker to semantically rerank recall results.
- Vision Models: If the vLLM inference service supports multimodal (text+image) input, enable vision_support in the configuration to declare multimodal capabilities to the frontend.
Development & Debugging
From the repository root, enter the xpertai/ directory and use Nx commands to build and test:
`bash
npx nx build @xpert-ai/plugin-vllm
npx nx test @xpert-ai/plugin-vllm
`
Build artifacts are output to dist/ by default. Jest configuration is in jest.config.ts for writing and running unit tests.
License
This project follows the AGPL-3.0 License found at the root of the repository.
| Field | Description |
| --- | --- |
| api_key | vLLM service access token (leave blank if the service does not require authentication). |
| endpoint_url | Required. The base URL of the vLLM OpenAI-compatible API, e.g., https://vllm.example.com/v1. |
| endpoint_model_name | Specify explicitly if the model name on the server differs from the logical model name in XpertAI. |
| mode | Choose between chat or completion inference modes. |
| context_size / max_tokens_to_sample | Control the context window and generation length. |
| agent_though_support, function_calling_type, stream_function_calling, vision_support | Indicate whether the model supports agent thought exposure, function/tool calling, streaming function calling, and multimodal input, to inform UI capability hints. |
| stream_mode_delimiter | Customize the paragraph delimiter for streaming output. |
After saving the configuration, the plugin will call the validateCredentials method in the background, making a minimal request to the vLLM service to ensure the credentials are valid.
Model Capabilities
- Conversational Models: Uses ChatOAICompatReasoningModel to proxy the vLLM OpenAI API, supporting message history, function calling, and streaming output.
- Embedding Models: Relies on LangChain's OpenAIEmbeddings for knowledge base vectorization and retrieval-augmented generation.
- Reranking Models: Wraps OpenAICompatibleReranker to semantically rerank recall results.
- Vision Models: If the vLLM inference service supports multimodal (text+image) input, enable vision_support in the configuration to declare multimodal capabilities to the frontend.
Development & Debugging
From the repository root, enter the xpertai/ directory and use Nx commands to build and test:
`bash
npx nx build @xpert-ai/plugin-vllm
npx nx test @xpert-ai/plugin-vllm
`
Build artifacts are output to dist/ by default. Jest configuration is in jest.config.ts` for writing and running unit tests.