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| Framework | Go | 1.24 |
| Framework | Next.js | 14.1.0 |
| Gateway | OpenResty | 1.27.1.2 |
| Database | PostgreSQL + pgvector | N/A |
| Cache | Redis | N/A |
| Model (Local) | HuggingFace Hub + Ollama | N/A |
| Model (Online) | Chutes AskAI + CodePRobot | N/A |
| Database | PostgreSQL + pgvector | 14.18 |
| Cache | Redis | 8.2.0 |
| Model | ollama/chutes.ai| baai/bge-m3, llama2:13b, moonshotai/Kimi-K2-Instruct |
## LangChainGo 核心功能一览
XControl 通过 LangChainGo 统一接入多种大模型，并为 AskAI、CLI 与 Server 提供链式调用能力：
- **LLM 接口层（Model I/O）**：统一调用 OpenAI、Hugging Face、Ollama、Google AI、Cohere 等模型接口。
- **LLM 接口层（Model I/O）**：统一调用 Hugging Face、Ollama、OpenAI 兼容模型接口。
- **Chains（链式流程）**：将 prompt、检索结果、工具调用等组合成完整流程，支持 RAG、聊天、代码生成等场景。
- **工具与 Agent 体系**：定义 Web 搜索、Scraper、SQL 查询等工具，并集成到 LLM Agent，实现 ReAct 风格的工具调用。
- **向量检索与数据接入**：适配 PGVector、Weaviate、Qdrant、MongoDB Atlas Vector Search、Chroma、Pinecone、Redis Vector 等向量存储。
- **工具与 Agent 体系**：定义 Web 搜索、实现 ReAct 风格的工具调用。
- **向量检索与数据接入**：适配 PGVector 向量存储。
- **文档加载与分块**：提供 Document Loaders 与 Text Splitters，用于处理长文本与构建向量检索块。
- **Memory 与历史追踪**：支持 Conversation Buffer 等对话记忆机制，增强交互体验。
## Supported Platforms
Tested on **Ubuntu 22.04 x64** and **macOS 26 arm64**.
@ -108,32 +108,6 @@ See [docs/changelog.md](./docs/changelog.md) for a list of completed changes, in
The roadmap below is also available in [docs/Roadmap.md](./docs/Roadmap.md).
### Milestone 1: MVP (Completed)
- Use default Redis port (#98) and establish PostgreSQL & Redis baseline.
- Stream RAG sync progress for GitHub repository synchronization (#100).
- Add client-side Markdown parsing to the CLI (#104).
- Refactor RAG ingestion into the CLI with a server upsert endpoint (#103).
- RAG API functional tests and per-file ingestion workflow (#115).
- Allow RAG upsert to migrate embedding dimensions (#119) and document pgvector initialization (#120).
- Ingest files automatically (#123).
### Milestone 2: Hybrid Search
- CLI and server dynamically support 1024-dimensional embeddings.
- Update docs and configs to vector(1024) (#130).
- Add embedding configuration fields (#131).
- Add RAG API integration tests for vectors (#132).
- Add allama support (#136).
- Deploy homepage via rsync from CI and fix SSH directory creation (#18, #19).
- Deploy XControl panel via GitHub Actions (#20).
- Fix yarn lock context concatenation (#21).
### Milestone 3: Production Monitoring & Optimization
- Switch server and CLI to Cobra (#133).
- Add repo sync proxy configuration (#135).
- Allow custom AskAI timeout (#141).
- Add log level support to CLI and server and log AskAI errors (#125, #140).
- Continue performance optimization, error handling, multi-model support, permission control, hot reload, and improve RAG upsert docs (#129).
## License
This project is licensed under the terms of the [MIT License](./LICENSE).

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# Changelog
## Milestone 1: MVP
- Use default Redis port (#98) and establish PostgreSQL & Redis baseline.
- Stream RAG sync progress for GitHub repository synchronization (#100).
- Add client-side Markdown parsing to the CLI (#104).
- Refactor RAG ingestion into the CLI with a server upsert endpoint (#103).
- Perform RAG API functional tests.
- Support per-file ingestion workflow in the CLI (#115).
- Allow RAG upsert to migrate embedding dimensions (#119).
- Add pgvector database initialization guide (#120).
- Ingest files automatically (#123).
## Milestone 1: MVP (Completed)
Use default Redis port (#98) and establish PostgreSQL & Redis baseline.
Stream RAG sync progress for GitHub repository synchronization (#100).
Add client-side Markdown parsing to the CLI (#104).
Refactor RAG ingestion into the CLI with a server upsert endpoint (#103).
Perform RAG API functional tests and support per-file ingestion workflow in the CLI (#115).
Allow RAG upsert to migrate embedding dimensions (#119) and document pgvector database initialization (#120).
Ingest files automatically (#123).
## Milestone 2: Hybrid Search
## Milestone 2: Hybrid Search (In Progress)
- Rename RAG 第二阶段优化规划为 `docs/Milestone-2.md` 并新增子任务列表。
- AskAI 接口与 CLI 规划使用 LangChainGo 框架以支持多模型与链式调用。
- Document local and Chutes model configurations for AskAI.
- Document local and Chutes model configurations for AskAI.
- CLI and server dynamically support 1024-dimensional embeddings.
- Update docs and configs to vector(1024) (#130).
- Add embedding configuration fields (#131).
- Add RAG API integration tests for vectors (#132).
- Add allama support (#136).
- Deploy homepage via rsync from CI and fix SSH directory creation (#18, #19).
- Deploy XControl panel via GitHub Actions (#20).
- Fix yarn lock context concatenation (#21).
## Milestone 3: Production Monitoring & Optimization
- Switch server and CLI to Cobra (#133).
- Add repo sync proxy configuration (#135).
- Allow custom AskAI timeout (#141).
- Add log level support to CLI and server and log AskAI errors (#125, #140).
- Continue performance optimization, error handling, multi-model support, permission control, hot reload, and improve RAG upsert docs (#129).