[Go] pgvector sample

go/go.mod

@@ -49,7 +49,9 @@ require (
 	github.com/googleapis/enterprise-certificate-proxy v0.3.2 // indirect
 	github.com/googleapis/gax-go/v2 v2.12.4 // indirect
 	github.com/kr/text v0.2.0 // indirect
+	github.com/lib/pq v1.10.9 // indirect
 	github.com/mailru/easyjson v0.7.7 // indirect
+	github.com/pgvector/pgvector-go v0.2.0 // indirect
 	github.com/xeipuuv/gojsonpointer v0.0.0-20180127040702-4e3ac2762d5f // indirect
 	github.com/xeipuuv/gojsonreference v0.0.0-20180127040603-bd5ef7bd5415 // indirect
 	go.opencensus.io v0.24.0 // indirect

go/go.sum

@@ -100,8 +100,12 @@ github.com/kr/pretty v0.3.1 h1:flRD4NNwYAUpkphVc1HcthR4KEIFJ65n8Mw5qdRn3LE=
 github.com/kr/pretty v0.3.1/go.mod h1:hoEshYVHaxMs3cyo3Yncou5ZscifuDolrwPKZanG3xk=
 github.com/kr/text v0.2.0 h1:5Nx0Ya0ZqY2ygV366QzturHI13Jq95ApcVaJBhpS+AY=
 github.com/kr/text v0.2.0/go.mod h1:eLer722TekiGuMkidMxC/pM04lWEeraHUUmBw8l2grE=
+github.com/lib/pq v1.10.9 h1:YXG7RB+JIjhP29X+OtkiDnYaXQwpS4JEWq7dtCCRUEw=
+github.com/lib/pq v1.10.9/go.mod h1:AlVN5x4E4T544tWzH6hKfbfQvm3HdbOxrmggDNAPY9o=
 github.com/mailru/easyjson v0.7.7 h1:UGYAvKxe3sBsEDzO8ZeWOSlIQfWFlxbzLZe7hwFURr0=
 github.com/mailru/easyjson v0.7.7/go.mod h1:xzfreul335JAWq5oZzymOObrkdz5UnU4kGfJJLY9Nlc=
+github.com/pgvector/pgvector-go v0.2.0 h1:NZdW4NxUxdSCzaev3LVHb9ORf+LdX+uZOQVqQ6s2Zyg=
+github.com/pgvector/pgvector-go v0.2.0/go.mod h1:OQpvU5QZGQOPI9quIXAyHaRZ5yGk/RGUDbs9C3DPUNE=
 github.com/pmezard/go-difflib v1.0.0 h1:4DBwDE0NGyQoBHbLQYPwSUPoCMWR5BEzIk/f1lZbAQM=
 github.com/pmezard/go-difflib v1.0.0/go.mod h1:iKH77koFhYxTK1pcRnkKkqfTogsbg7gZNVY4sRDYZ/4=
 github.com/prometheus/client_model v0.0.0-20190812154241-14fe0d1b01d4/go.mod h1:xMI15A0UPsDsEKsMN9yxemIoYk6Tm2C1GtYGdfGttqA=

go/samples/pgvector/main.go

@@ -0,0 +1,218 @@
+// Copyright 2024 Google LLC
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// This program shows how to use Postgres's pgvector extension with Genkit.
+
+// This program can be manually tested like so:
+//
+// In development mode (with the environment variable GENKIT_ENV="dev"):
+// Start the server listening on port 3100:
+//
+//	go run . -dbconn "$DBCONN" -apikey $API_KEY &
+//
+// Ask a question:
+//
+//	curl -d '{"Show": "Best Friends", "Question": "Who does Alice love?"}' http://localhost:3400/askQuestion
+package main
+
+import (
+	"context"
+	"database/sql"
+	"errors"
+	"flag"
+	"fmt"
+	"log"
+
+	"github.com/firebase/genkit/go/ai"
+	"github.com/firebase/genkit/go/genkit"
+	"github.com/firebase/genkit/go/plugins/googleai"
+	_ "github.com/lib/pq"
+	pgv "github.com/pgvector/pgvector-go"
+)
+
+var (
+	connString = flag.String("dbconn", "", "database connection string")
+	apiKey     = flag.String("apikey", "", "Gemini API key")
+	index      = flag.Bool("index", false, "index the existing data")
+)
+
+func main() {
+	flag.Parse()
+	if err := run(); err != nil {
+		log.Fatal(err)
+	}
+}
+
+func run() error {
+	if *connString == "" {
+		return errors.New("need -dbconn")
+	}
+	if *apiKey == "" {
+		return errors.New("need -apikey")
+	}
+	ctx := context.Background()
+	if err := googleai.Init(ctx, &googleai.Config{APIKey: *apiKey}); err != nil {
+		return err
+	}
+	const embedderName = "embedding-001"
+	embedder := googleai.Embedder(embedderName)
+	if embedder == nil {
+		return fmt.Errorf("embedder %s is not known to the googleai plugin", embedderName)
+	}
+
+	db, err := sql.Open("postgres", *connString)
+	if err != nil {
+		return err
+	}
+	defer db.Close()
+
+	if *index {
+		indexer := defineIndexer(db, embedder)
+		if err := indexExistingRows(ctx, db, indexer); err != nil {
+			return err
+		}
+	}
+
+	retriever := defineRetriever(db, embedder)
+
+	type input struct {
+		Question string
+		Show     string
+	}
+
+	genkit.DefineFlow("askQuestion", func(ctx context.Context, in input) (string, error) {
+		res, err := retriever.Retrieve(ctx, &ai.RetrieverRequest{
+			Document: &ai.Document{Content: []*ai.Part{ai.NewTextPart(in.Question)}},
+			Options:  in.Show,
+		})
+		if err != nil {
+			return "", err
+		}
+		for _, doc := range res.Documents {
+			fmt.Printf("%+v %q\n", doc.Metadata, doc.Content[0].Text)
+		}
+		// Use documents in RAG prompts.
+		return "", nil
+	})
+
+	return genkit.Init(ctx, nil)
+}
+
+const provider = "pgvector"
+
+func defineRetriever(db *sql.DB, embedder *ai.Embedder) *ai.Retriever {
+	f := func(ctx context.Context, req *ai.RetrieverRequest) (*ai.RetrieverResponse, error) {
+		eres, err := embedder.Embed(ctx, &ai.EmbedRequest{Documents: []*ai.Document{req.Document}})
+		if err != nil {
+			return nil, err
+		}
+		rows, err := db.QueryContext(ctx, `
+			SELECT episode_id, season_number, chunk as content
+			FROM embeddings
+			WHERE show_id = $1
+		  	ORDER BY embedding <#> $2
+		  	LIMIT 2`,
+			req.Options, pgv.NewVector(eres.Embeddings[0].Embedding))
+		if err != nil {
+			return nil, err
+		}
+		defer rows.Close()
+
+		res := &ai.RetrieverResponse{}
+		for rows.Next() {
+			var eid, sn int
+			var content string
+			if err := rows.Scan(&eid, &sn, &content); err != nil {
+				return nil, err
+			}
+			meta := map[string]any{
+				"episode_id":    eid,
+				"season_number": sn,
+			}
+			doc := &ai.Document{
+				Content:  []*ai.Part{ai.NewTextPart(content)},
+				Metadata: meta,
+			}
+			res.Documents = append(res.Documents, doc)
+		}
+		if err := rows.Err(); err != nil {
+			return nil, err
+		}
+		return res, nil
+	}
+	return ai.DefineRetriever(provider, "shows", f)
+}
+
+func defineIndexer(db *sql.DB, embedder *ai.Embedder) *ai.Indexer {
+	// The indexer assumes that each Document has a single part, to be embedded, and metadata fields
+	// for the table primary key: show_id, season_number, episode_id.
+	const query = `
+			UPDATE embeddings
+			SET embedding = $4
+			WHERE show_id = $1 AND season_number = $2 AND episode_id = $3
+		`
+	return ai.DefineIndexer(provider, "shows", func(ctx context.Context, req *ai.IndexerRequest) error {
+		res, err := embedder.Embed(ctx, &ai.EmbedRequest{Documents: req.Documents})
+		if err != nil {
+			return err
+		}
+		// You may want to use your database's batch functionality to insert the embeddings
+		// more efficiently.
+		for i, emb := range res.Embeddings {
+			doc := req.Documents[i]
+			args := make([]any, 4)
+			for j, k := range []string{"show_id", "season_number", "episode_id"} {
+				if a, ok := doc.Metadata[k]; ok {
+					args[j] = a
+				} else {
+					return fmt.Errorf("doc[%d]: missing metadata key %q", i, k)
+				}
+			}
+			args[3] = pgv.NewVector(emb.Embedding)
+			if _, err := db.ExecContext(ctx, query, args...); err != nil {
+				return err
+			}
+		}
+		return nil
+	})
+}
+
+func indexExistingRows(ctx context.Context, db *sql.DB, indexer *ai.Indexer) error {
+	rows, err := db.QueryContext(ctx, `SELECT show_id, season_number, episode_id, chunk FROM embeddings`)
+	if err != nil {
+		return err
+	}
+	defer rows.Close()
+
+	req := &ai.IndexerRequest{}
+	for rows.Next() {
+		var sid, chunk string
+		var sn, eid int
+		if err := rows.Scan(&sid, &sn, &eid, &chunk); err != nil {
+			return err
+		}
+		req.Documents = append(req.Documents, &ai.Document{
+			Content: []*ai.Part{ai.NewTextPart(chunk)},
+			Metadata: map[string]any{
+				"show_id":       sid,
+				"season_number": sn,
+				"episode_id":    eid,
+			},
+		})
+	}
+	if err := rows.Err(); err != nil {
+		return err
+	}
+	return indexer.Index(ctx, req)
+}

go/samples/pgvector/pgvector.sql

@@ -0,0 +1,22 @@
+-- This SQL enables the vector extension and creates the table and data used
+-- in the accompanying sample.
+
+CREATE EXTENSION IF NOT EXISTS vector;
+
+CREATE TABLE embeddings (
+    show_id TEXT NOT NULL,
+    season_number INTEGER NOT NULL,
+    episode_id INTEGER NOT NULL,
+    chunk TEXT,
+    embedding vector(768),
+    PRIMARY KEY (show_id, season_number, episode_id)
+);
+
+INSERT INTO embeddings (show_id, season_number, episode_id, chunk) VALUES 
+	('La Vie', 1,  1,  'Natasha confesses her love for Pierre.'),
+	('La Vie', 1,  2,  'Pierre and Natasha become engaged.'),
+	('La Vie', 1,  3,  'Margot and Henri divorce.'),
+	('Best Friends', 1,  1,  'Alice confesses her love for Oscar.'),
+	('Best Friends', 1,  2,  'Oscar and Alice become engaged.'),
+	('Best Friends', 1,  3,  'Bob and Pat divorce.')
+;