# vstash Project Review

## Overview
**vstash** is a blazing-fast, local-first document memory system built for instant semantic search. It functions as a lightweight Retrieval-Augmented Generation (RAG) tool that works natively from the CLI without requiring heavy servers or cloud dependencies for storage.

**Key Characteristics:**
* **Storage Layer:** Uses standard SQLite enhanced with `sqlite-vec` (for vector storage) and `FTS5` (for keyword search). Everything lives in a portable, single `.db` file.
* **Embedding Layer:** Utilizes `FastEmbed` (ONNX on CPU) and `mlx-embeddings` (Apple Silicon GPU) to embed document chunks 100% locally with high throughput (up to 1,200 chunks/s).
* **Search Strategy:** Employs a Hybrid Search mechanism combining vector distance (cosine similarity) and exact keyword matching (BM25), merged via Reciprocal Rank Fusion (RRF).
* **Inference Layer:** Highly configurable. Can use blazing-fast external APIs like Cerebras or OpenAI for generation, or fallback to entirely local generation using Ollama, providing users with absolute privacy.
* **Tooling:** Python 3.10+, `Typer` + `Rich` for CLI, `markitdown` for universal document parsing, and `Pydantic v2` for data modeling.

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## Opportunities for Improvement

Based on the project's adherence to SOLID principles, strict typing, and robust architecture, the following improvements are recommended:

### 1. Architecture & Design Patterns (SOLID)
* **Strategy Pattern for Backends:** The codebase heavily relies on multiple backends for both embeddings (ONNX vs MLX) and inference (Cerebras, Ollama, OpenAI). Implementing a formal `Strategy` pattern with abstract base classes (e.g., `InferenceProvider`, `EmbeddingProvider`) will eliminate complex conditional branching in core modules (`chat.py` and `embed.py`).
* **Dependency Injection:** Decouple the CLI layer (`cli.py`) from the underlying business logic (`store.py`, `ingest.py`, etc.). Inject the active configuration, database connection, and selected providers into the core services. This promotes the Dependency Inversion Principle and makes unit testing much cleaner.

### 2. Error Handling & Resilience
* **Domain-Specific Exceptions:** Replace generic Python exceptions with a clear hierarchy of custom domain exceptions:
  * `VStashDatabaseError`: Wrapper around SQLite failures.
  * `VStashInferenceError`: Wrapper around remote LLM timeouts or failures.
  * `VStashIngestError`: File parsing or encoding failures.
* **API Resilience:** Remote inference APIs (Cerebras, OpenAI) are susceptible to rate limits and network latency. Implement robust retry mechanisms using exponential backoff (e.g., integrating the `tenacity` library) and catch specific HTTP exceptions carefully, preventing the CLI from crashing abruptly on transient issues.

### 3. Data Flow & Pydantic Validation
* **CLI Input Contracts:** While `models.py` uses `Pydantic v2` extensively to track results and stats, ensure that all incoming CLI arguments and JSON payloads are instantly mapped into `Pydantic` models before hitting the business layer. This guarantees a type-safe boundary at the edge of the application.

### 4. Testing & Verification
* **Property-Based Testing:** Tools like `hypothesis` can be introduced to test the robustness of the chunking logic (`ingest.py`) across a vast array of erratic strings, edge-case token lengths, and language variations.
* **Deterministic Core Tests:** Create robust mock implementations of the `InferenceProvider` and `EmbeddingProvider` interfaces so that tests covering logic (like prompt construction or RRF algorithms) can run instantly without requiring actual MLX/ONNX binaries or local Ollama servers.

### 5. Roadmap Continuation
* **Semantic Chunking:** The constitution outlines an objective for semantic chunking. Currently, chunking is token-based (1024 tokens). Advancing this to split at paragraph or Markdown section boundaries will drastically improve the context quality delivered to the LLMs.
* **Export/Import Commands:** Develop the roadmap's `vstash export` / `import` tasks. These should output strictly-typed JSON (validated by Pydantic models) representing the memory state, satisfying the goal of database portability.
