---
created: 2026-05-20 15:01
modified: 2026-05-20 15:01
type: note
tags: 
  - AI
  - Coding
  - Development
  - Pi
  - Qdrant
  - Fastembed
  - Docker
  - Neovim
  - RAG
aliases:
  - Local AI Coding Harness
  - Pi-Qdrant Workflow
id: 1779253376-HGNC
---
# [[Local AI Coding Harness Architecture]]

This note details the high-level architecture for building a private, cost-optimised, and highly customizable local alternative to Cursor IDE using Neovim, Pi.dev, Qdrant, and Fastembed.

## System Architecture

The environment relies on a structured, three-tier local loop that tightly integrates your editor, your agent orchestrator, and your vector pipeline:

### 1. The Interaction Tier (Neovim & Terminal)
*   **Editor Layer**: Your primary workspace runs inside Neovim buffers, navigating the filesystem and editing source files.
*   **Execution Layer**: A terminal window hosts the active Pi.dev agent instance, responding to commands and monitoring your developer workspace.

### 2. The Agent Tier (Pi.dev Engine)
*   **Context Request**: When a debugging or coding task requires deep structural knowledge, the Pi agent runs a registered tool called `search_codebase`.
*   **Context Return**: The tool returns the top 3 most relevant structural code chunks directly into Pi's active context window, preventing token bloat.

### 3. The Retrieval Tier (Local Docker RAG)
*   **Save Hook Handler**: A Git hook or save utility uses Tree-sitter AST parsers to divide your source code into cleanly isolated function and class scopes.
*   **Fastembed Vectorizer**: The chunks are processed locally via CPU-optimised embedding models, converting plain text code blocks into multi-dimensional vectors.
*   **Qdrant Storage Node**: A lightweight Qdrant vector database running inside a background Docker container indexing payloads, ready for instant semantic retrieval.



## Workflow Overview

Instead of constantly streaming entire code repositories to expensive proprietary cloud servers, this setup routes contextual sub-tasks through a local pipeline. You retain full control over context boundaries, model selections, and operational expenses.

### 1. Ingestion & Chunking (AST Scope-Splitting)
*   **Trigger**: A file-save event or a Git pre-commit hook triggers a local TypeScript indexing script.
*   **Parsing**: The script uses lightweight abstract syntax tree (AST) parsing via Tree-sitter to slice code by logical boundaries (classes, methods, functions) rather than arbitrary text character counts.
*   **Context Wrapping**: Each chunk is decorated with metadata including file path, line numbers, and its parent structural scope.

### 2. Private Local Embeddings
*   **Execution**: The chunked code blocks pass into `@qdrant/fastembed` entirely on your local machine.
*   **Model**: Uses a fast, CPU-optimized model (e.g., `bge-small-en-v1.5`) to transform code into vector representations.
*   **Cost**: Fully local execution results in \$0.00 infrastructure or token costs for repository indexing.

### 3. Vector Storage via Qdrant
*   **Infrastructure**: A lightweight Qdrant vector database runs silently in a background Docker container.
*   **Storage**: Vector points are stored locally, paired with their code payloads and metadata, allowing for near-instant semantic lookup.

### 4. Pi.dev Agent Automation
*   **The Bridge**: A custom TypeScript tool (`search_codebase`) is registered in your Pi profile.
*   **Agent Flow**: When the Pi agent encounters an execution error or needs architectural context, it queries the local Qdrant collection.
*   **Parallelization**: Pi orchestrates low-cost sub-agents in parallel tabs to lint code, run tests, and check types, preserving expensive frontier models purely for complex engineering decisions.

## Key Advantages Over Out-of-the-Box IDEs

*   **Asymmetric Cost Efficiency**: Saves hundreds of dollars monthly by using tiered model routing and semantic RAG filtering over global code dumps.
*   **Deterministic Control**: The agent's action loop is managed via local scripts and shell guardrails—no unexpected cloud system prompt updates.
*   **Zero-Trust Security**: Private codebases, environment variables, and proprietary logic never leave your machine during index cycles.