Dev local vector store

The Dev Local Vector Store provides a local, file-based vector store for development and testing purposes. It is not intended for production use.

Installation

The local vector store functionality is built into Genkit Go. You need to import the localvec package:

import "github.com/firebase/genkit/go/plugins/localvec"

Configuration

To use the local vector store, initialize it and define a retriever with an embedder:

import (
    "context"
    "log"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    "github.com/firebase/genkit/go/plugins/googlegenai"
    "github.com/firebase/genkit/go/plugins/localvec"
)

ctx := context.Background()

g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.VertexAI{}))

if err := localvec.Init(); err != nil {
    log.Fatal(err)
}

myDocStore, myRetriever, err := localvec.DefineRetriever(
    g, "my_vectorstore", localvec.Config{
        Embedder: googlegenai.VertexAIEmbedder(g, "text-embedding-004"),
    },
    nil,
)
if err != nil {
    log.Fatal(err)
}

Configuration Options

• name (string): A unique name for this vector store instance. This is used as the retriever reference.
• Embedder (ai.Embedder): The embedding model to use. Must be a configured embedder in your Genkit project.

Usage

Indexing Documents

The Dev Local Vector Store automatically creates indexes. To populate with data, you need to implement your own indexing logic using the ai.Document format:

import (
    "github.com/firebase/genkit/go/ai"
)

// Create documents from text
data := []string{
    "This is the first document.",
    "This is the second document.",
    "This is the third document.",
    "This is the fourth document.",
}

var docs []*ai.Document
for _, text := range data {
    docs = append(docs, ai.DocumentFromText(text, nil))
}

// Index the documents using the DocStore returned by DefineRetriever
err := localvec.Index(ctx, docs, myDocStore)
if err != nil {
    log.Fatal(err)
}

Retrieving Documents

Use ai.Retrieve with the retriever you defined:

// Retrieve documents relevant to a query
resp, err := ai.Retrieve(ctx, myRetriever, ai.WithTextDocs("search query"))
if err != nil {
    log.Fatal(err)
}

// Process the retrieved documents
for _, doc := range resp.Documents {
    fmt.Println(doc.Content)
}