8f19eaab25
🚀 Generated with Claude Code - Project plan and architecture documentation - Python package structure with core modules - API design and basic usage examples - Development environment configuration - Literature review and research foundation Ready for Phase 1 implementation. Co-Authored-By: Claude <noreply@anthropic.com>
130 lines
7.3 KiB
Markdown
130 lines
7.3 KiB
Markdown
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# PROJECT\_PLAN.md
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## 📘 Title
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**Context‑Aware Hierarchical Context File System (HCFS)**: Unifying file system paths with context blobs for agentic AI cognition
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---
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## 1. Research Motivation & Literature Review 🧠
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* **Semantic and context‑aware file systems**: Gifford et al. (1991) proposed early semantic file systems using directory paths as semantic queries ([Wikipedia][1]). Later work explored tag‑based and ontology‑based systems for richer metadata and context-aware retrieval ([Wikipedia][1]).
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* **LLM‑driven semantic FS (LSFS)**: The recent ICLR 2025 LSFS proposes integrating vector DBs and semantic indexing into a filesystem that supports prompt-driven file operations and semantic rollback ([OpenReview][2]).
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* **Path-structure embeddings**: Recent Transformer-based work shows file paths can be modeled as sequences for semantic anomaly detection—capturing hierarchy and semantics in embeddings ([MDPI][3]).
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* **Context modeling frameworks**: Ontology-driven context models (e.g. OWL/SOCAM) support representing, reasoning about, and sharing context hierarchically ([arXiv][4]).
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Your HCFS merges these prior insights into a hybrid: directory navigation = query scope, backed by semantic context blobs in a DB, enabling agentic systems to zoom in/out contextually.
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---
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## 2. Objectives & Scope
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1. Design a **virtual filesystem layer** that maps hierarchical paths to context blobs.
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2. Build a **context storage system** (DB) to hold context units, versioned and indexed.
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3. Define **APIs and syscalls** for agents to:
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* navigate context scope (`cd`‑style),
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* request context retrieval,
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* push new context,
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* merge or inherit context across levels.
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4. Enable **decentralized context sharing**: agents can publish updates at path-nodes; peer agents subscribe by tree‑paths.
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5. Prototype on a controlled dataset / toy project tree to validate:
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* latency,
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* correct retrieval,
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* hierarchical inheritance semantics.
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---
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## 3. System Architecture Overview
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### 3.1 Virtual Filesystem Layer (e.g. FUSE or AIOS integration)
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* Presents standard POSIX (or AIOS‑style) tree structure.
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* Each directory or file node has metadata pointers into context‑blob IDs.
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* Traversal (e.g., `ls`, `cd`) triggers context lookup for that path.
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### 3.2 Context Database Backend
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* Two possible designs:
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* **Relational/SQLite + versioned tables**: simple, transactional, supports hierarchical inheritance via path parent pointers.
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* **Graph DB (e.g., Neo4j)**: ideal for multi-parent contexts, symlink-like context inheritance.
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* Context blobs include:
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* blob ID,
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* path(s) bound,
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* timestamp/version, author/agent,
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* embedding or semantic tags,
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* content or summary.
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### 3.3 Indexing & Embeddings
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* Generate embeddings of context blobs for semantic similarity retrieval (e.g. for context folding) ([OpenReview][5], [OpenReview][2], [MDPI][3]).
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* Use combination of BM25 + embedding ranking (contextual retrieval) for accurate scope-based retrieval ([TECHCOMMUNITY.MICROSOFT.COM][6]).
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### 3.4 API & Syscalls
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* `context_cd(path)`: sets current context pointer.
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* `context_get(depth=N)`: retrieves cumulative context from current node up N levels.
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* `context_push(path, blob)`: insert new context tied to a path.
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* `context_list(path)`: lists available context blobs at that path.
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* `context_subscribe(path)`: agent registers to receive updates at a path.
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---
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## 4. Project Timeline & Milestones
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| Phase | Duration | Deliverables |
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| ---------------------------------------------- | -------- | -------------------------------------------------------- |
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| **Phase 0: Research & Design** | 2 weeks | Literature review doc, architecture draft |
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| **Phase 1: Prototype FS layer** | 4 weeks | Minimal FUSE‑based path→context mapping, CLI demo |
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| **Phase 2: Backend DB & storage** | 4 weeks | Context blob storage, path linkage, versioning |
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| **Phase 3: Embedding & retrieval integration** | 3 weeks | Embeddings + BM25 hybrid ranking for context relevance |
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| **Phase 4: API/Syscall layer scripting** | 3 weeks | Python (or AIOS) service exposing navigation + push APIs |
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| **Phase 5: Agent integration & simulation** | 3 weeks | Dummy AI agents navigating, querying, publishing context |
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| **Phase 6: Evaluation & refinement** | 2 weeks | Usability, latency, retrieval relevance metrics |
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| **Phase 7: Write-up & publication** | 2 weeks | Report, possible poster/paper submission |
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---
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## 5. Risks & Alternatives
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* **Semantic vs hierarchical mismatch**: Flat tag systems (e.g. Tagsistant) offer semantic tagging but lack path-based inheritance ([research.ijcaonline.org][7], [OpenReview][2], [Wikipedia][1], [arXiv][8], [Anthropic][9], [OpenReview][5], [Wikipedia][10]).
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* **Context explosion**: many small blobs flooding the DB—mitigate via summarization/folding.
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* **Performance trade‑offs**: FS lookups must stay acceptable; versioned graph storage might slow down. Consider caching snapshots at each node.
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---
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## 6. Peer‑Reviewed References
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* David Gifford et al., *Semantic file systems*, ACM Operating Systems Review (1991) ([Wikipedia][1])
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* ICLR 2025: *From Commands to Prompts: LLM-based Semantic File System for AIOS* (LSFS) ([OpenReview][2])
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* Xiaoyu et al., *Transformer-based path sequence modeling for file‑path anomaly detection* ([MDPI][3])
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* Tao Gu et al., *Ontology‑based Context Model in Intelligent Environments* (SOCAM) ([arXiv][4])
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---
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## 7. Next Steps
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* Review cited literature, build an annotated bibliography.
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* Choose backend stack (SQLite vs graph DB) and test embedding pipeline.
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* Begin Phase 1: implementing minimal context‑aware FS mock.
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---
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Let me know if you’d like me to flesh out a proof‑of‑concept scaffold (for example, in Python + SQLite + FUSE), or write a full proposal for funding or conference submission!
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[1]: https://en.wikipedia.org/wiki/Semantic_file_system?utm_source=chatgpt.com "Semantic file system"
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[2]: https://openreview.net/forum?id=2G021ZqUEZ&utm_source=chatgpt.com "From Commands to Prompts: LLM-based Semantic File System for AIOS"
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[3]: https://www.mdpi.com/2079-8954/13/6/403?utm_source=chatgpt.com "Effective Context-Aware File Path Embeddings for Anomaly Detection - MDPI"
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[4]: https://arxiv.org/abs/2003.05055?utm_source=chatgpt.com "An Ontology-based Context Model in Intelligent Environments"
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[5]: https://openreview.net/pdf?id=2G021ZqUEZ&utm_source=chatgpt.com "F COMMANDS TO PROMPTS LLM- S FILE SYSTEM FOR AIOS - OpenReview"
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[6]: https://techcommunity.microsoft.com/blog/azure-ai-services-blog/building-a-contextual-retrieval-system-for-improving-rag-accuracy/4271924?utm_source=chatgpt.com "Building a Contextual Retrieval System for Improving RAG Accuracy"
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[7]: https://research.ijcaonline.org/volume121/number1/pxc3904433.pdf?utm_source=chatgpt.com "A Survey on Different File System Approach - research.ijcaonline.org"
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[8]: https://arxiv.org/abs/1909.10123?utm_source=chatgpt.com "SplitFS: Reducing Software Overhead in File Systems for Persistent Memory"
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[9]: https://www.anthropic.com/news/contextual-retrieval?utm_source=chatgpt.com "Introducing Contextual Retrieval \ Anthropic"
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[10]: https://en.wikipedia.org/wiki/Tagsistant?utm_source=chatgpt.com "Tagsistant"
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