diff --git a/docs/templates/pgvector.md b/docs/templates/pgvector.md
index be8cf3b0cb..44b68f3002 100644
--- a/docs/templates/pgvector.md
+++ b/docs/templates/pgvector.md
@@ -1,74 +1,206 @@
 # pgvector retriever template
 
 You can use PostgreSQL and `pgvector` as your retriever implementation. Use the
-following example as a starting point and modify it to work with your database
+following examples as a starting point and modify it to work with your database
 schema.
 
-```js
-import { embed } from '@genkit-ai/ai/embedder';
-import { Document, defineRetriever, retrieve } from '@genkit-ai/ai/retriever';
-import { defineFlow } from '@genkit-ai/flow';
-import { textEmbeddingGecko } from '@genkit-ai/vertexai';
-import { toSql } from 'pgvector';
-import postgres from 'postgres';
-import { z } from 'zod';
-
-const sql = postgres({ ssl: false, database: 'recaps' });
-
-const QueryOptions = z.object({
-  show: z.string(),
-  k: z.number().optional(),
-});
-
-const sqlRetriever = defineRetriever(
-  {
-    name: 'pgvector-myTable',
-    configSchema: QueryOptions,
-  },
-  async (input, options) => {
-    const embedding = await embed({
-      embedder: textEmbeddingGecko,
-      content: input,
-    });
-    const results = await sql`
-      SELECT episode_id, season_number, chunk as content
-        FROM embeddings
-        WHERE show_id = ${options.show}
-        ORDER BY embedding <#> ${toSql(embedding)} LIMIT ${options.k ?? 3}
-      `;
-    return {
-      documents: results.map((row) => {
-        const { content, ...metadata } = row;
-        return Document.fromText(content, metadata);
-      }),
-    };
-  }
-);
-```
+For the Golang snippet, we use [pgx](https://github.com/jackc/pgx) as the
+Postgres client, but you may use another client libaray of your choice.
+
+<section>
+  <devsite-selector>
+    <section>
+      <h3>Node.js (Typescript)</h3>
+      <p>```js
+            import { embed } from '@genkit-ai/ai/embedder';
+            import { Document, defineRetriever, retrieve } from '@genkit-ai/ai/retriever';
+            import { defineFlow } from '@genkit-ai/flow';
+            import { textEmbeddingGecko } from '@genkit-ai/vertexai';
+            import { toSql } from 'pgvector';
+            import postgres from 'postgres';
+            import { z } from 'zod';
+
+            const sql = postgres({ ssl: false, database: 'recaps' });
+
+            const QueryOptions = z.object({
+              show: z.string(),
+              k: z.number().optional(),
+            });
+
+            const sqlRetriever = defineRetriever(
+              {
+                name: 'pgvector-myTable',
+                configSchema: QueryOptions,
+              },
+              async (input, options) => {
+                const embedding = await embed({
+                  embedder: textEmbeddingGecko,
+                  content: input,
+                });
+                const results = await sql
+                  SELECT episode_id, season_number, chunk as content
+                    FROM embeddings
+                    WHERE show_id = ${options.show}
+                    ORDER BY embedding <#> ${toSql(embedding)} LIMIT ${options.k ?? 3}
+                 ;
+                return {
+                  documents: results.map((row) => {
+                    const { content, ...metadata } = row;
+                    return Document.fromText(content, metadata);
+                  }),
+                };
+              }
+            );
+            ```
+      </p>
+    </section>
+    <section>
+      <h3>Go</h3>
+      <p>
+      ```go
+      package main
+
+      import (
+        "context"
+        "fmt"
+        "os"
+
+      )
+
+      import (
+        "context"
+        "fmt"
+        "log"
+        "os"
+
+        "github.com/firebase/genkit/go/ai"
+        "github.com/firebase/genkit/go/plugins/googleai"
+        "github.com/jackc/pgx/v5"
+        "github.com/pgvector/pgvector-go"
+      )
+
+      func retrieve(ctx context.Context, req *ai.RetrieverRequest) (*ai.RetrieverResponse, error) {
+        // urlExample := "postgres://username:password@localhost:5432/database_name"
+        conn, err := pgx.Connect(context.Background(), os.Getenv("DATABASE_URL"))
+        if err != nil {
+          return nil, fmt.Errorf( "Unable to connect to database: %v\n", err)
+        }
+        defer conn.Close(context.Background())
+
+        // Use the embedder to convert the document we want to
+        // retrieve into a vector.
+        ereq := &ai.EmbedRequest{
+          Document: req.Document,
+        }
+        embedder := googleai.Embedder("embedding-001")
+        vals, err := embedder.Embed(ctx, ereq)
+        if err != nil {
+          return nil, fmt.Errorf("embedding failed: %v", err)
+        }
+
+        k := 3
+        if options, _ := req.Options.(*RetrieverOptions); options != nil {
+          k = options.K
+        }
+
+        rows, err := conn.Query(context.Background(), "SELECT chunk as content FROM embeddings WHERE show_id = $1 ORDER BY embedding <#> $2 LIMIT $3", "game_of_thrones", pgvector.NewVector(vals), k)
+        if err != nil {
+          return nil, fmt.Errorf("Query failed: %v\n", err)
+        }
+
+        docs := make([]*ai.Document, 0, 3)
+
+        for rows.Next() {
+          var content string
+          rows.Scan(&content)
+          docs = append(docs, ai.DocumentFromText(content, nil))
+        }
+
+        resp := &ai.RetrieverResponse{
+          Documents: docs,
+        }
+        return resp, nil
+      }
+
+
+      func main() {
+        // ... Configure genkit
+
+        provider := "pgvector"
+        name := "tvShows"
+        retriever := ai.DefineRetriever(provider, name, retrieve)
+      }
+      ```
+      </p>
+    </section>
+
+  </devsite-selector>
+</section>
 
 And here's how to use the retriever in a flow:
 
-```js
-// Simple flow to use the sqlRetriever
-export const askQuestionsOnGoT = defineFlow(
-  {
-    name: 'askQuestionsOnGoT',
-    inputSchema: z.string(),
-    outputSchema: z.string(),
-  },
-  async (inputQuestion) => {
-    const docs = await retrieve({
-      retriever: sqlRetriever,
-      query: inputQuestion,
-      options: {
-        show: 'Game of Thrones',
-      },
-    });
-    console.log(docs);
-
-    // Continue with using retrieved docs
-    // in RAG prompts.
-    //...
-  }
-);
-```
+<section>
+  <devsite-selector>
+    <section>
+      <h3>Node.js (Typescript)</h3>
+      <p>
+       ```js
+        // Simple flow to use the sqlRetriever
+        export const askQuestionsOnGoT = defineFlow(
+          {
+            name: 'askQuestionsOnGoT',
+            inputSchema: z.string(),
+            outputSchema: z.string(),
+          },
+          async (inputQuestion) => {
+            const docs = await retrieve({
+              retriever: sqlRetriever,
+              query: inputQuestion,
+              options: {
+                show: 'Game of Thrones',
+              },
+            });
+            console.log(docs);
+
+            // Continue with using retrieved docs
+            // in RAG prompts.
+            //...
+          }
+        );
+       ```
+      </p>
+    </section>
+    <section>
+      <h3>Go</h3>
+      <p>
+        ```go
+        func main() {
+          // ...
+          // Setup genkit and get the pgvector retriever as shown above
+
+          // Use your retriever.
+          tvShowSuggestionFlow := genkit.DefineFlow(
+          "tvShowSuggestionFlow",
+          func(ctx context.Context, inputQuery string) (string, error) {
+            dRequest := ai.DocumentFromText(inputQuery, nil)
+            retrieverReq := &ai.RetrieverRequest{
+              Document: dRequest,
+            }
+            response, err := retriever.Retrieve(ctx, retrieverReq)
+            if err != nil {
+              return "", err
+            }
+
+            // Use retrieved docs in `response` for RAG
+            // ...
+
+            return suggestion, nil
+          },
+        )
+        }
+        ```
+      </p>
+    </section>
+
+  </devsite-selector>
+</section>