Haypile

io.github.BenyD由 GitHub 账号 BenyD 发布v0.3.1更新于 Oct 25, 2026

Hybrid search over your local documents, with file and page citations. One binary, fully local.

已验证STDIO仅桌面文件与存储知识与记忆

概览

AI 生成的概览

Haypile 是一个本地文档搜索服务器,为文件夹建立索引,并通过 MCP 让助手从你自己的文件中获得带引用的段落。

可以做什么
对已索引文档进行语义与关键词混合检索,返回带文件和页码引用的排序段落(search_documents)。列出当前已索引的文件夹和文档(list_sources)。索引文件夹与文件,并通过文件监视保持更新。借助本地或自选的 OpenAI 兼容模型,基于检索到的段落回答问题。
适用场景
当助手需要根据个人或工作文档(如合同、案卷、论文、项目笔记)回答问题,并应引用来源而不是猜测时使用。适合希望检索留在本机、不把文档上传到云端服务的用户。
运行要求
以发布二进制或包管理器安装的本地进程,运行在桌面环境。守护进程只监听 localhost,通过 MCP Streamable HTTP 或 stdio 访问。文本索引完全离线可用;扫描版 PDF 需要本地视觉模型做 OCR。生成答案需要本地 OpenAI 兼容服务器,或你提供的端点和密钥。
安装前请注意
索引与检索都在本地,但若选择用云端端点生成答案,该问题的检索段落会发送到你指定的端点并使用你的密钥。对非 localhost 的纯 http 地址会拒绝密钥。索引、移除文件夹和重新同步会改变本地索引状态。付费版本被描述为未来计划,目前并不存在。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Haypile,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

[图片]

Haypile

Private document search, for you and your agents. One binary that watches your folders, indexes every document, and hands your agent the right passages over MCP, each with a file and page citation. Nothing ever leaves your machine.

Everyone says finding information in your files is like finding a needle in a haystack. Haypile is the haystack that finds its own needles.

[hay demo: add a folder, search by meaning, verify zero outbound connections]

sh
brew install BenyD/tap/hayhay add ~/Documentsclaude mcp add --transport http haypile http://localhost:11500/mcp

That is the whole setup. Claude Code (or Cursor, or anything that speaks MCP) can now search everything you indexed: search_documents returns ranked passages with citations, and the agent answers from them instead of guessing.

It is a full standalone CLI too:

sh
hay search "termination clause"hay ask "what did the Meridian contract say about termination?"

Search understands meaning, not just words: "agreement cancellation" finds termination clauses. Exact identifiers still match exactly. Every result cites its source file and page.

Install

sh
brew install BenyD/tap/hay

Or grab a binary from releases, or run the install script:

sh
curl -fsSL haypile.sh | sh

On Windows, run this in PowerShell:

powershell
irm https://haypile.sh/install.ps1 | iex

One binary, around 56MB, with the embedding model inside. No Python, no Docker, no vector database, no model downloads, no network. Text documents index fully offline; scanned PDFs need a local vision model for OCR (hay llm setup).

Why

An agent can open any file you point it at. Finding the right one is the problem: grep matches words, not meaning, and a question about termination clauses does not contain the words the contract used. Reading whole documents to find one passage spends the context window the task needed. Cloud document tools fix this by uploading everything to someone else's computer, and self-hosted RAG stacks fix it with Python environments, Docker, and a vector database to babysit.

Haypile is the missing option: one binary, point it at folders, done.

  • Agent-ready. MCP and REST on localhost:11500. search_documents gives Claude Code, Cursor, or your own scripts cited passages from your documents.
  • Hybrid search. Semantic and keyword, merged. Paraphrases match by meaning; case numbers match exactly.
  • Citations always. Every result and every answer points to the source file and page.
  • Always fresh. Folders are watched. Save a file and it is searchable in seconds.
  • Verifiably private. See below.

Trust commitments

These are versioned with the code and will not be quietly redrawn:

  1. The open/paid boundary is declared upfront. Open forever (AGPL-3.0): indexing, search, ask, REST API, MCP, CLI, and the upcoming single-user web UI. The full single-user product, no feature hostages. Paid (later): team features such as auth, roles, audit logs, shared indexes, and a hosted version.
  2. "Zero external connections" is a verifiable feature, not a claim. hay status reports outbound connections; the target is 0. No telemetry. If that ever changes it will be opt-in, documented loudly, and off by default.
  3. No silent network behavior. A local-first tool must never phone home to keep working.
  4. All development happens in this public repo. No private-repo surprises.

How it works

folders -> watcher -> extract (pdf/docx/pptx/md/txt/html/mbox) -> chunk -> embed                                                            |        you <- citations <- RRF merge <- FTS5 + vector search <- SQLite (one file)

Everything lives in a single SQLite database on your disk, and search is fully self-contained: the embedding model ships inside the binary. Answers (hay ask) are generated by whatever OpenAI-compatible local server you already run (Ollama, LM Studio, llama.cpp, Jan). Haypile itself ships no LLM and makes no network calls.

Set up a folder properly: hay init

For a folder you work in (a case folder, a project, a paper archive), hay init writes a per-folder config and wires everything up in one go:

sh
cd ~/cases/acme-litigationhay init          # three short questions, all with sensible defaults

It creates .haypile.yml (tag and exclude patterns), indexes the folder, optionally writes .mcp.json so Claude Code and Cursor can search these docs, and offers hay llm setup if you do not have a local LLM yet. hay init --yes runs unattended.

Edit .haypile.yml by hand anytime. The daemon notices and re-syncs the index within seconds:

yaml
tag: acme-litigationexclude:  - drafts/**  - "*.bak"

Ask questions (bring your own LLM)

hay ask retrieves the most relevant passages and has a local LLM answer from them, with citations:

sh
hay ask "what did the Meridian contract say about termination?"

Generation uses any OpenAI-compatible server you already run (Ollama, LM Studio, llama.cpp, Jan), auto-detected on their usual ports, or set explicitly with --endpoint and --model. Without one, hay ask explains and shows the top passages instead. Search never needs an LLM.

Prefer a cloud model for answers? Bring your own key:

sh
hay ask --endpoint https://api.example.com/v1 --key sk-... "what changed in the lease?"

The boundary stays sharp: your documents are indexed locally, always. Opting in sends only the retrieved passages for that one question, to an endpoint you chose, with your key (HAYPILE_LLM_API_KEY works too). Keys are refused over plain http to anything that is not localhost.

No local LLM yet? One guided command gets you there:

sh
hay llm setup    # installs and starts Ollama, pulls a model, asks before every download

Use from Claude Code, Cursor, or your own tools

The daemon exposes MCP (Streamable HTTP) and REST on localhost:11500:

sh
# Claude Codeclaude mcp add --transport http haypile http://localhost:11500/mcp
# Anything that prefers launching a process (stdio transport)#   command: hay   args: ["mcp-stdio"]
# Plain RESTcurl -X POST localhost:11500/api/query -d '{"query": "termination clause"}'

Tools exposed: search_documents (hybrid search with citations) and list_sources. The daemon starts automatically on hay add and only ever listens on localhost.

Commands

hay init [folder]        per-folder setup: config, index, editor wiringhay add <path>           index a folder or file and watch it for changeshay search "<query>"     hybrid retrieval, results with citationshay ask "<question>"     answer from your documents, with cited sourceshay list                 indexed folders and document countshay remove <path>        un-index a folderhay status               daemon state, model info, outbound connections (target: 0)hay web                  open the local web UI in your browserhay serve                run the daemon (REST API + MCP on localhost:11500)hay llm setup            guided local LLM setup for hay ask

Roadmap

VersionScope
v0.x (now)CLI, REST API, MCP server, hay web local UI. Markdown, text, PDF, docx, pptx, HTML, mbox email. Scanned-PDF OCR via your local vision LLM.
v1.xBundled OCR (no LLM required), Windows installer polish
v2Optional larger embedding models, ANN index for very large corpora
ProTeam layer for offices: auth, roles, audit logs, shared indexes (paid)

Roadmap, decisions, and trust commitments in detail: docs/ROADMAP.md.

Development

sh
go build ./cmd/hay     # build the binarygo test ./... -race    # run tests (green before any merge)

Semantic search uses an embedding model that release builds carry inside the binary. Dev builds load it from disk instead, so the weights stay out of git:

sh
./hack/fetch-model.sh                    # one-time download (also quantizes)go build -tags bundled ./cmd/hay         # release-style: model in the binaryHAYPILE_MODEL_PATH=internal/embed/bundled/model.safetensors ./hay   # dev

Without the model, everything still works in keyword-only mode.

The web UI (hay web) lives in webui/ as a small Vite + Preact app; its built output is committed under internal/webui/dist and embedded in the binary, so go build alone always ships the current UI. Touch the UI with:

sh
cd webui && npm installnpm run dev      # live dev server, proxies /api to a running daemonnpm run build    # writes internal/webui/dist (commit the result)

Retrieval quality is measured, not vibes: eval/ holds a query set with expected results that runs on every retrieval-affecting change.

Contributing

Contributions are welcome. Please read CONTRIBUTING.md first. It covers the dev workflow, the rule that the privacy contract (zero outbound, bundled model, localhost only) must stay intact, how contributions are licensed, and the DCO sign-off (git commit -s) that CI enforces.

Security

Found a vulnerability? Please report it privately, not as a public issue. See SECURITY.md for the disclosure process and what is in scope.

License

AGPL-3.0. Free forever for individuals. The AGPL keeps it free for every actual user while requiring anyone offering Haypile as a service to open-source their changes.

来源:README.md,提交 45faf84

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

1
  1. v0.3.1最新Sep 16, 2026

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