RAG & memory: bring your own

Decision: Bide does not ship a vector store, an embedder, or a memory backend. It provides the seam (a Retriever port and thin glue) and you plug in the store you already run. This is a deliberate scope boundary, not a gap.

Why

• It's orthogonal to the moat. Our differentiator is durable, side-effect-safe resume. Retrieval is a separate concern; owning it wouldn't strengthen the moat, it would dilute focus and pull us into a fast-churning, commoditized space (pgvector / Pinecone / Weaviate / the embedded-DB-of-the-month).
• It would break the dependency story. We just made the core zero-dep and split adapters into their own modules so consumers don't inherit infra they don't use. Bundling a vector store + embedding client (and their transitive trees) into the core would blow a hole straight through that.
• Teams already have a store. Most run pgvector / Pinecone / their own index. Forcing our memory abstraction on them is friction; respecting their infrastructure is a feature. The positioning: "we don't ship a vector DB you'll outgrow and fight; we give you a clean retrieval seam that works with the store you already run."

The seam (in core, zero-dep)

type Doc struct { ID string; Text string; Score float64; Metadata map[string]any }

type Retriever interface {
    Retrieve(ctx context.Context, query string, k int) ([]Doc, error)
}

func RetrievalTool(r Retriever, k int) Tool        // agentic: the model searches on demand
func WithRetrieval(r Retriever, k int) Middleware  // classic: top-k auto-injected each user turn

Implement Retriever against your store (~20 lines), then wire it in one of two ways:

• Agentic RAG: agent.New(model, store, agent.RetrievalTool(myStore, 5)). The model decides when to search and with what query; results come back as a tool result.
• Classic RAG: a.Use(agent.WithRetrieval(myStore, 5)). On each fresh user turn the middleware retrieves top-k for the user message and prepends them as a system message; it does not retrieve on mid-loop tool-result turns. A retrieval error aborts the call; return (nil, nil) from your Retriever if you prefer to degrade to no context.

Memory, in layers

• Conversational memory is already built in: Session / Session.Send carry the Q&A transcript across turns, durably (see the sessions docs). No retriever needed.
• Dynamic context (current time, tenant, retrieved summaries) goes through WithSystemPromptFunc(func(ctx) string).
• Semantic / long-term memory is the Retriever seam above, backed by your store.

If demand appears

Concrete store adapters (e.g. a pgvector Retriever) would ship as separate modules (like store/postgres and the other adapters), never in the core, preserving the zero-dep core. Until then, the seam + your ~20-line Retriever is the whole story.