🧠 Retrieval-Augmented Generation server - MO5 Knowledge API

📌 Overview

This project aims to provide an AI-powered knowledge base and development assistant for building software in C and Assembly for the Thomson MO5 (retro computing platform).

The goal is to enhance developer productivity by allowing AI tools (such as GitHub Copilot or ChatGPT) to have full contextual knowledge about the MO5 architecture, including:

• Motorola 6809 CPU instructions and quirks
• Memory layout and I/O mapping
• ROM/RAM organization, video memory, and hardware registers
• Development toolchain setup, build formats, and best practices

This will make it easier for anyone to build MO5 applications while benefiting from AI-assisted coding with domain-specific knowledge.

🚀 Quick Start

Choose your deployment environment:

• Raspberry Pi NAS - Optimized for ARM64 (Raspberry Pi 4/5)
• Azure Cloud - Production deployment on Azure Container Instances
• Local Development - Docker Compose for local development
• Portainer - Web UI for Docker management

See the Deployment Guide for detailed instructions.

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⚖️ Solutions Considered

1. .ai.md or Static Context Files

• Pros: Simple to create and version; no infrastructure required
• Cons: Static, not easily searchable or updatable; hard to maintain as knowledge grows

2. Embedding the Knowledge Base Directly into AI Prompts

• Pros: No backend needed; quick to prototype
• Cons: Context size is very limited; not scalable; not reusable by others

3. (Chosen) Knowledge API + Plugins (RAG Architecture)

• Pros:
  • Centralized and reusable for any developer
  • Dynamic and easily updateable knowledge base
  • Allows semantic search and fine-grained answers
  • Can be plugged into multiple IDEs or ChatGPT custom GPTs
• Cons:
  • Requires hosting and initial setup
  • More complex than static files

✅ This is the chosen approach.

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🧩 Architecture Overview

The solution will be based on a Retrieval-Augmented Generation (RAG) pattern:

1. Knowledge ingestion

   • Collect MO5-related documents (text, manuals, code samples, PDFs)
   • Split them into chunks and store them with vector embeddings in a vector database

2. Knowledge API

   • Provide a REST API that receives a user query, performs a semantic search on the knowledge base, and returns the most relevant documents

3. IDE/ChatGPT Plugins

   • Create plugins or custom GPTs that call the API to retrieve MO5 context
   • The retrieved content is used by the AI as additional context to answer questions

This enables any developer to benefit from a shared, centralized knowledge base accessible by AI tools.

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⚙️ Implementation Steps

Phase 1 — Knowledge Base Setup

• Collect and clean MO5-related documents (hardware docs, tutorials, sample code)
• Write a script to:
  • Chunk the documents into small pieces
  • Generate vector embeddings for each chunk (using OpenAI text-embedding-3-small or a local model)
  • Store them in a vector database (Qdrant, Weaviate, Milvus, or pgvector)

Phase 2 — Knowledge API

• Build a REST API in C#/.NET with the following endpoints:
  • POST /ask — accepts a query and returns the top-N most relevant knowledge chunks
  • POST /ingest — (optional) to add or update documents
• The API should:
  • Compute the embedding of the incoming question
  • Perform a semantic similarity search against the database
  • Return the results as context text

Phase 3 — Plugin Integration

• Create one or both of the following:
  • Custom GPT in ChatGPT with an OpenAPI schema pointing to your API
  • Visual Studio / VS Code extension that calls the API when the user asks MO5-related questions
• The plugin will forward the retrieved context along with the user’s code or question to the AI assistant (Copilot or GPT)

Phase 4 — Documentation & Publishing

• Document the API with an openapi.json file
• Provide example queries and expected responses
• Optionally deploy the API publicly so other developers can use it

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📚 References

• OpenAI Docs: Retrieval-Augmented Generation (RAG)
• OpenAPI Specification
• LangChain .NET
• Qdrant Vector Database
• Milvus Vector Database

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✅ Summary

Step	Description
1️⃣ Knowledge Base	Gather MO5 technical documents and code samples
2️⃣ API	Build a REST API that returns relevant MO5 info from the knowledge base
3️⃣ Plugins	Create IDE/ChatGPT plugins that query the API
4️⃣ Integration	Assist developers with AI powered by your MO5 knowledge

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📌 Project Goals

• Centralize all knowledge about MO5 development
• Make it accessible via an API
• Allow AI assistants to leverage this API to help developers write C/Assembly code for the MO5
• Encourage community contributions to the knowledge base

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This repository will host the source code of the MO5 Knowledge API, its plugins, and all related documentation.