rag

Overview ¶

Package rag constants.go defines shared constants, types, and configuration for RAG operations.

Contents:

• Source type constants (SourceTypeConversation, SourceTypeFile, SourceTypeSystem)
• Table schema constants for documents table
• NewDocStoreConfig factory for consistent DocStore configuration

Package rag implements Retrieval-Augmented Generation (RAG) for Koopa.

The rag package provides document indexing and knowledge base integration for LLM applications. It uses Firebase Genkit's PostgreSQL plugin for vector storage and retrieval.

Overview ¶

RAG enhances LLM responses by augmenting prompts with relevant context from a knowledge base. The rag package manages:

• System knowledge indexing (IndexSystemKnowledge function)
• Integration with Genkit's PostgreSQL DocStore

Architecture ¶

System Knowledge Docs
     |
     v
IndexSystemKnowledge()
     |
     v
Genkit PostgreSQL DocStore
     |
     +-- Vector embedding (via AI Embedder)
     +-- Vector storage (PostgreSQL + pgvector)
     |
     v
Genkit Retriever (ai.Retriever interface)
     |
     +-- source_type filtering (conversation, file, system)
     +-- Semantic search
     |
     v
LLM (with augmented context)

Key Components ¶

IndexSystemKnowledge: Package-level function that indexes built-in knowledge:

• Go best practices and coding standards
• Agent capabilities and tool usage
• Architecture principles

Source Types ¶

Documents are categorized by source_type:

• SourceTypeConversation: Chat message history
• SourceTypeFile: Indexed file content
• SourceTypeSystem: Built-in system knowledge

Thread Safety ¶

DocStore handles concurrent operations safely. IndexSystemKnowledge is called once at startup.

Package rag provides RAG (Retrieval-Augmented Generation) functionality. This file implements IndexSystemKnowledge for managing built-in knowledge about Agent capabilities, Golang best practices, and architecture principles.