rony-llm-agent/pkg/rag

pkg/rag

Retrieval-Augmented Generation: memory, embeddings, and semantic search.

Responsibility

Provide persistent memory and semantic search over agent content.

Components

Type	What it stores	Persistence
Working	Current session messages	RAM
Episodic	Past events: "what I did on 2026-06-20"	Vector DB
Semantic	Consolidated knowledge: "how is the architecture"	Vector DB (curated)
Procedural	How to do things: user workflows	Vector DB (auto-learned)

Public API

type Memory interface {
    Add(ctx context.Context, fragment Fragment) error
    Search(ctx context.Context, query string, topK int) ([]Fragment, error)
    Forget(ctx context.Context, id string) error
}

type Fragment struct {
    ID         string
    Content    string
    Vector     []float32
    Metadata   map[string]string
    Timestamp  time.Time
    ProjectID  string
}

type Embedder interface {
    Embed(ctx context.Context, text string) ([]float32, error)
    Dimensions() int
}

Backends

Backend	When to use
ChromaDB embedded	Default. Simple, sufficient for <100k docs
Qdrant embedded	If you need >100k docs or very fast queries
SQLite + sqlite-vec	If you want zero-dependency (no CGO with modernc.org/sqlite)

Embeddings

Provider	Model	Dimensions
Ollama	nomic-embed-text	768
Ollama	bge-m3	1024
Local ONNX	all-MiniLM-L6-v2	384

Usage

import "github.com/VictorVargas/rony-llm-agent/pkg/rag"
import "github.com/VictorVargas/rony-llm-agent/pkg/rag/backends/chroma"

backend, _ := chroma.New(chroma.Config{
    Path: "~/.local/share/rony/chroma",
})

memory := rag.New(rag.Config{
    Backend: backend,
    Embedder: ollamaEmbedder,
})

err := memory.Add(ctx, rag.Fragment{
    Content: "Refactored auth.go using hexagonal",
    ProjectID: "rony",
})

See also

• pkg/agent — Injects memory into the loop
• pkg/llm — For summarization in compaction