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SQLiteVec (sqlite-vec) Storage

For common Agent integration, extraction modes, and tool configuration, see Usage and Configuration.

Use case: Local persistence + semantic memory search on a single node

SQLiteVec stores memories in a SQLite file and uses sqlite-vec to do vector similarity search (semantic search). Compared to the plain SQLite backend, it requires an embedder to generate embeddings.

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import (
    "database/sql"

    _ "github.com/mattn/go-sqlite3"
    openaiembedder "trpc.group/trpc-go/trpc-agent-go/knowledge/embedder/openai"
    memorysqlitevec "trpc.group/trpc-go/trpc-agent-go/memory/sqlitevec"
)

db, err := sql.Open("sqlite3", "file:memories_vec.db?_busy_timeout=5000")
if err != nil {
    panic(err)
}

emb := openaiembedder.New(
    openaiembedder.WithModel("text-embedding-3-small"),
)

memoryService, err := memorysqlitevec.NewService(
    db,
    memorysqlitevec.WithEmbedder(emb),
    memorysqlitevec.WithSoftDelete(true),
    memorysqlitevec.WithMemoryLimit(200),
)
if err != nil {
    _ = db.Close()
    panic(err)
}
defer memoryService.Close()

Configuration options:

  • WithTableName(name): Table name (default "memories")
  • WithEmbedder(embedder): Text embedder for vector generation (required)
  • WithIndexDimension(dim): Vector dimension (default is embedder dimension)
  • WithMaxResults(limit): Max search results (default 10)
  • WithSoftDelete(enabled): Enable soft delete (default false)
  • WithMemoryLimit(limit): Memory limit per user
  • WithSkipDBInit(skip): Skip table initialization
  • Auto mode: WithExtractor, WithAsyncMemoryNum, WithMemoryQueueSize, WithMemoryJobTimeout
  • Tools: WithCustomTool, WithToolEnabled

Notes:

  • This backend uses github.com/mattn/go-sqlite3 and requires CGO.
  • The sqlite-vec extension is compiled and registered in-process via Go bindings (no external .so/.dylib download at runtime).