82 lines
2 KiB
Markdown
82 lines
2 KiB
Markdown
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# pkg/rag
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> Retrieval-Augmented Generation: memoria, embeddings, y búsqueda semántica.
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## Responsabilidad
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Proveer memoria persistente y búsqueda semántica sobre el contenido del agente.
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## Componentes
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| Tipo | Qué guarda | Persistencia |
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|---|---|---|
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| **Working** | Mensajes de la sesión actual | RAM |
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| **Episodic** | Eventos pasados (qué hice el día X) | Vector DB |
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| **Semantic** | Conocimiento consolidado | Vector DB (curado) |
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| **Procedural** | Patrones de uso (workflows) | Vector DB (auto-aprendido) |
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## API pública
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```go
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type Memory interface {
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Add(ctx context.Context, fragment Fragment) error
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Search(ctx context.Context, query string, topK int) ([]Fragment, error)
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Forget(ctx context.Context, id string) error
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}
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type Fragment struct {
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ID string
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Content string
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Vector []float32
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Metadata map[string]string
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Timestamp time.Time
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ProjectID string
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}
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type Embedder interface {
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Embed(ctx context.Context, text string) ([]float32, error)
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Dimensions() int
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}
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```
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## Backends
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| Backend | Cuándo usar |
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|---|---|
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| ChromaDB embedded | Default. Simple, suficiente para <100k docs |
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| Qdrant embedded | Si necesitas >100k docs o queries muy rápidas |
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| SQLite + sqlite-vec | Si quieres zero-dependency (sin CGO con `modernc.org/sqlite`) |
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## Embeddings
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| Provider | Modelo | Dimensiones |
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|---|---|---|
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| Ollama | `nomic-embed-text` | 768 |
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| Ollama | `bge-m3` | 1024 |
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| Local ONNX | `all-MiniLM-L6-v2` | 384 |
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## Uso
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```go
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import "github.com/VictorVargas/go-llm-agent/pkg/rag"
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import "github.com/VictorVargas/go-llm-agent/pkg/rag/backends/chroma"
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backend, _ := chroma.New(chroma.Config{
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Path: "~/.local/share/rony/chroma",
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})
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memory := rag.New(rag.Config{
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Backend: backend,
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Embedder: ollamaEmbedder,
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})
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err := memory.Add(ctx, rag.Fragment{
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Content: "Refactoricé auth.go usando hexagonal",
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ProjectID: "rony",
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})
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```
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## Ver también
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- [pkg/agent](../agent/README.md) — Inyecta memoria al loop
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- [pkg/llm](../llm/README.md) — Para summarization en compaction
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