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osFoundry 上的知识库、记忆与 RAG
自动提取实体、知识图谱、自定义检索流水线和经重排的 RAG——全部可在 osStudio 中配置。
osFoundry 是围绕 AI 构建的知识平台：每一条笔记、文档和上传的文件都会被自动分块、嵌入并索引。工作空间知识图谱会跨您所做过的一切提取实体与关系。可在 osStudio 中按每个对话路径配置自定义检索流水线（含重排、MMR 多样性、多阶段处理）——无需修改源码。
快速解答
- 自动分块流水线在笔记、知识库和文件创建时即为其建立索引。
- 知识图谱持续提取实体 + 关系，可通过 Graph 页面查询。
- 自定义 RAG 流水线——按 Path 配置定义阶段（检索、过滤、重排、MMR）。
- 通过托管代理使用 Voyage 嵌入 + 重排（无需单独 API 密钥）。
简介
A knowledge base is only useful if the right passage surfaces at the right moment. osFoundry treats that as a pipeline problem: every note, document, and uploaded file is chunked, embedded, and indexed as it is created, so retrieval is ready the moment content exists — there is no separate “ingest” step to remember and no vector database to operate.
Retrieval is hybrid and configurable. Under the hood a query runs through embedding search and BM25 keyword search, then optional reranking and MMR diversity stages; each Path in osStudio can define its own stages, models, and thresholds. This matters because stuffing whole documents into a prompt hits context-window limits and degrades answer quality — retrieving only the relevant chunks keeps the context focused.
Two layers accumulate on top. The workspace knowledge graph extracts entities and relationships from every chunk continuously, queryable from the Graph screen. And when long conversations compact, memory extraction distills them into durable facts — so decisions made in chat three months ago remain findable, not buried in scrollback.
主要功能
- 笔记支持丰富 Markdown、版本历史和三向合并。
- 知识库——带版本、多作者、可导出。
- 工作空间知识图谱，对每个分块做实体提取。
- 对话压缩时提取记忆——将冗长对话提炼为事实。
- 可配置的检索流水线（Voyage 嵌入 → tantivy BM25 → 重排 → MMR）。
- 工作空间级搜索覆盖笔记、知识库、对话和代码。
在 osFoundry 中如何操作
- Add sources — Write notes, create knowledge bases, or upload files. Chunking, embedding, and indexing happen automatically on creation.
- Ask with grounding — Chat normally — the retrieval pipeline attached to your Path pulls the relevant chunks into context and the model answers against them.
- Tune retrieval when needed — In osStudio, adjust the pipeline per Path: swap embedding models, add a reranker, tighten thresholds, or add MMR diversity — no source-code changes.
使用场景
- Support teams: Load product docs and past tickets into knowledge bases; agents answer new tickets grounded in the actual documentation.
- Researchers and writers: Digest long reports without pasting them into chat — retrieval brings in the relevant sections, avoiding context-window overflow.
- Teams with long-running projects: Decisions distilled from compacted chats plus the knowledge graph answer “when did we decide this and why” in seconds.
常见问题
Does osFoundry use my data to train its models?
No. We don’t train on your data. Embeddings stay in your workspace; retrieval queries are scoped to your workspace; nothing crosses tenant boundaries.
What embedding model does osFoundry use?
Managed Voyage AI by default (no separate API key). You can swap to a local embedding model or BYOK to OpenAI/Cohere via the embedding pipeline config in osStudio.
Can I customise the RAG pipeline per chat?
Yes. Each "Path" in osStudio has its own retrieval pipeline — different stages, models, thresholds, rerankers per use case.
Is the knowledge graph generated automatically?
Yes — entities and relationships are extracted from every note, KB doc, and chat. Browse and edit it in the Graph screen.
Do I need to run a vector database?
No. Chunking, embedding, BM25 indexing, and reranking are built into the platform — there is no separate database to deploy or operate.
How is this different from pasting a document into the chat?
Pasting consumes the context window and degrades on long inputs. Retrieval selects only the chunks relevant to the question, which keeps context focused and works no matter how large the corpus grows.
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