Update pgvector docs to include Go (using weave)

docs-go/Makefile

@@ -1,6 +1,6 @@
 WEAVE=$(HOME)/go/bin/weave 
 
-all: $(WEAVE) get-started-go.md flows.md models.md prompts.md dotprompt.md
+all: $(WEAVE) get-started-go.md flows.md models.md prompts.md dotprompt.md pgvector.md
 
 $(WEAVE): ../go/internal/cmd/weave/*.go
 	go -C ../go install ./internal/cmd/weave

docs-go/pgvector.md

@@ -0,0 +1,84 @@
+<!-- Autogenerated by weave; DO NOT EDIT -->
+
+# pgvector retriever template
+
+You can use PostgreSQL and `pgvector` as your retriever implementation. Use the
+following examples as a starting point and modify it to work with your database
+schema.
+
+We use [database/sql](https://pkg.go.dev/database/sql) to connect to the Postgres server, but you may use another client library of your choice.
+
+- {Go}
+
+  ```go
+  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)
+  }
+  ```
+
+And here's how to use the retriever in a flow:
+
+- {Go}
+
+  ```go
+  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
+  })
+  ```

docs-go/pgvector.src

@@ -0,0 +1,17 @@
+# pgvector retriever template
+
+You can use PostgreSQL and `pgvector` as your retriever implementation. Use the
+following examples as a starting point and modify it to work with your database
+schema.
+
+We use [database/sql](https://pkg.go.dev/database/sql) to connect to the Postgres server, but you may use another client library of your choice.
+
+- {Go}
+
+  %include ../go/samples/pgvector/main.go retr
+
+And here's how to use the retriever in a flow:
+
+- {Go}
+
+  %include ../go/samples/pgvector/main.go use-retr

go/samples/pgvector/main.go

@@ -84,6 +84,7 @@ func run() error {
 		}
 	}
 
+	// !+use-retr
 	retriever := defineRetriever(db, embedder)
 
 	type input struct {
@@ -105,12 +106,14 @@ func run() error {
 		// Use documents in RAG prompts.
 		return "", nil
 	})
+	// !-use-retr
 
 	return genkit.Init(ctx, nil)
 }
 
 const provider = "pgvector"
 
+// !+retr
 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}})
@@ -154,6 +157,8 @@ func defineRetriever(db *sql.DB, embedder *ai.Embedder) *ai.Retriever {
 	return ai.DefineRetriever(provider, "shows", f)
 }
 
+// !-retr
+
 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.