fix(js/plugins/ollama): fix ollama embedding config

docs/plugins/ollama.md

@@ -21,21 +21,22 @@ example:
 ollama pull gemma
 ```
 
-To use this plugin, specify it when you call `configureGenkit()`.
+To use the plugin, specify it when you call genkit:
 
-```js
+```typescript
+import { genkit } from 'genkit';
 import { ollama } from 'genkitx-ollama';
 
-export default configureGenkit({
+const ai = genkit({
   plugins: [
     ollama({
       models: [
         {
           name: 'gemma',
-          type: 'generate', // type: 'chat' | 'generate' | undefined
+          type: 'generate', // Options: 'chat' | 'generate' |
         },
       ],
-      serverAddress: 'http://127.0.0.1:11434', // default local address
+      serverAddress: 'http://127.0.0.1:11434', // default serverAddress to use
     }),
   ],
 });
@@ -64,7 +65,7 @@ the Google Auth library:
 ```js
 import { GoogleAuth } from 'google-auth-library';
 import { ollama, OllamaPluginParams } from 'genkitx-ollama';
-import { configureGenkit, isDevEnv } from '@genkit-ai/core';
+import { genkit, isDevEnv } from '@genkit-ai/core';
 
 const ollamaCommon = { models: [{ name: 'gemma:2b' }] };
 
@@ -82,7 +83,7 @@ const ollamaProd = {
   },
 } as OllamaPluginParams;
 
-export default configureGenkit({
+const ai = genkit({
   plugins: [
     ollama(isDevEnv() ? ollamaDev : ollamaProd),
   ],
@@ -117,8 +118,33 @@ This plugin doesn't statically export model references. Specify one of the
 models you configured using a string identifier:
 
 ```js
-const llmResponse = await generate({
-  model: 'ollama/gemma',
+const llmResponse = await ai.generate({
+  model: 'ollama/gemma:2b',
   prompt: 'Tell me a joke.',
 });
 ```
+
+## Embedders
+The Ollama plugin supports embeddings, which can be used for similarity searches and other NLP tasks.
+
+```typescript
+const ai = genkit({
+  plugins: [
+    ollama({
+      serverAddress: 'http://localhost:11434',
+      embedders: [{ name: 'nomic-embed-text', dimensions: 768 }],
+    }),
+  ],
+});
+
+async function getEmbedding() {
+  const embedding = await ai.embed({
+      embedder: 'ollama/nomic-embed-text',
+      content: 'Some text to embed!',
+  })
+
+  return embedding;
+}
+
+getEmbedding().then((e) => console.log(e))
+```

js/plugins/ollama/package.json

@@ -34,6 +34,7 @@
   "devDependencies": {
     "@types/node": "^20.11.16",
     "npm-run-all": "^4.1.5",
+    "ollama": "^0.5.9",
     "rimraf": "^6.0.1",
     "tsup": "^8.0.2",
     "tsx": "^4.7.0",

js/plugins/ollama/src/embeddings.ts

@@ -13,19 +13,50 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-import { Genkit } from 'genkit';
-import { logger } from 'genkit/logging';
-import { OllamaPluginParams } from './index.js';
+import { Document, Genkit } from 'genkit';
+import { EmbedRequest, EmbedResponse } from 'ollama';
+import { DefineOllamaEmbeddingParams, RequestHeaders } from './types.js';
 
-interface OllamaEmbeddingPrediction {
-  embedding: number[];
-}
+async function toOllamaEmbedRequest(
+  modelName: string,
+  dimensions: number,
+  documents: Document[],
+  serverAddress: string,
+  requestHeaders?: RequestHeaders
+): Promise<{
+  url: string;
+  requestPayload: EmbedRequest;
+  headers: Record<string, string>;
+}> {
+  const requestPayload: EmbedRequest = {
+    model: modelName,
+    input: documents.map((doc) => doc.text),
+  };
+
+  // Determine headers
+  const extraHeaders = requestHeaders
+    ? typeof requestHeaders === 'function'
+      ? await requestHeaders({
+          serverAddress,
+          model: {
+            name: modelName,
+            dimensions,
+          },
+          embedRequest: requestPayload,
+        })
+      : requestHeaders
+    : {};
 
-interface DefineOllamaEmbeddingParams {
-  name: string;
-  modelName: string;
-  dimensions: number;
-  options: OllamaPluginParams;
+  const headers = {
+    'Content-Type': 'application/json',
+    ...extraHeaders, // Add any dynamic headers
+  };
+
+  return {
+    url: `${serverAddress}/api/embed`,
+    requestPayload,
+    headers,
+  };
 }
 
 export function defineOllamaEmbedder(
@@ -34,50 +65,47 @@ export function defineOllamaEmbedder(
 ) {
   return ai.defineEmbedder(
     {
-      name,
+      name: `ollama/${name}`,
       info: {
-        label: 'Ollama Embedding - ' + modelName,
+        label: 'Ollama Embedding - ' + name,
         dimensions,
         supports: {
           //  TODO: do any ollama models support other modalities?
           input: ['text'],
         },
       },
     },
-    async (input) => {
-      const serverAddress = options.serverAddress;
-      const responses = await Promise.all(
-        input.map(async (i) => {
-          const requestPayload = {
-            model: modelName,
-            prompt: i.text,
-          };
-          let res: Response;
-          try {
-            res = await fetch(`${serverAddress}/api/embeddings`, {
-              method: 'POST',
-              headers: {
-                'Content-Type': 'application/json',
-              },
-              body: JSON.stringify(requestPayload),
-            });
-          } catch (e) {
-            logger.error('Failed to fetch Ollama embedding');
-            throw new Error(`Error fetching embedding from Ollama: ${e}`);
-          }
-          if (!res.ok) {
-            logger.error('Failed to fetch Ollama embedding');
-            throw new Error(
-              `Error fetching embedding from Ollama: ${res.statusText}`
-            );
-          }
-          const responseData = (await res.json()) as OllamaEmbeddingPrediction;
-          return responseData;
-        })
+    async (input, config) => {
+      const serverAddress = config?.serverAddress || options.serverAddress;
+
+      const { url, requestPayload, headers } = await toOllamaEmbedRequest(
+        modelName,
+        dimensions,
+        input,
+        serverAddress,
+        options.requestHeaders
       );
-      return {
-        embeddings: responses,
-      };
+
+      const response: Response = await fetch(url, {
+        method: 'POST',
+        headers,
+        body: JSON.stringify(requestPayload),
+      });
+
+      if (!response.ok) {
+        throw new Error(
+          `Error fetching embedding from Ollama: ${response.statusText}`
+        );
+      }
+
+      const payload: EmbedResponse = await response.json();
+
+      const embeddings: { embedding: number[] }[] = [];
+
+      for (const embedding of payload.embeddings) {
+        embeddings.push({ embedding });
+      }
+      return { embeddings };
     }
   );
 }

js/plugins/ollama/src/index.ts

@@ -25,40 +25,24 @@ import {
 } from 'genkit/model';
 import { GenkitPlugin, genkitPlugin } from 'genkit/plugin';
 import { defineOllamaEmbedder } from './embeddings';
+import {
+  ApiType,
+  ModelDefinition,
+  OllamaPluginParams,
+  RequestHeaders,
+} from './types';
 
-type ApiType = 'chat' | 'generate';
-
-type RequestHeaders =
-  | Record<string, string>
-  | ((
-      params: { serverAddress: string; model: ModelDefinition },
-      request: GenerateRequest
-    ) => Promise<Record<string, string> | void>);
-
-type ModelDefinition = { name: string; type?: ApiType };
-type EmbeddingModelDefinition = { name: string; dimensions: number };
-
-export interface OllamaPluginParams {
-  models: ModelDefinition[];
-  embeddingModels?: EmbeddingModelDefinition[];
-
-  /**
-   *  ollama server address.
-   */
-  serverAddress: string;
-
-  requestHeaders?: RequestHeaders;
-}
+export { defineOllamaEmbedder };
 
 export function ollama(params: OllamaPluginParams): GenkitPlugin {
   return genkitPlugin('ollama', async (ai: Genkit) => {
-    const serverAddress = params?.serverAddress;
-    params.models.map((model) =>
+    const serverAddress = params.serverAddress;
+    params.models?.map((model) =>
       ollamaModel(ai, model, serverAddress, params.requestHeaders)
     );
-    params.embeddingModels?.map((model) =>
+    params.embedders?.map((model) =>
       defineOllamaEmbedder(ai, {
-        name: `${ollama}/model.name`,
+        name: model.name,
         modelName: model.name,
         dimensions: model.dimensions,
         options: params,
@@ -85,20 +69,20 @@ function ollamaModel(
     },
     async (input, streamingCallback) => {
       const options: Record<string, any> = {};
-      if (input.config?.hasOwnProperty('temperature')) {
-        options.temperature = input.config?.temperature;
+      if (input.config?.temperature !== undefined) {
+        options.temperature = input.config.temperature;
       }
-      if (input.config?.hasOwnProperty('topP')) {
-        options.top_p = input.config?.topP;
+      if (input.config?.topP !== undefined) {
+        options.top_p = input.config.topP;
       }
-      if (input.config?.hasOwnProperty('topK')) {
-        options.top_k = input.config?.topK;
+      if (input.config?.topK !== undefined) {
+        options.top_k = input.config.topK;
       }
-      if (input.config?.hasOwnProperty('stopSequences')) {
-        options.stop = input.config?.stopSequences?.join('');
+      if (input.config?.stopSequences !== undefined) {
+        options.stop = input.config.stopSequences.join('');
       }
-      if (input.config?.hasOwnProperty('maxOutputTokens')) {
-        options.num_predict = input.config?.maxOutputTokens;
+      if (input.config?.maxOutputTokens !== undefined) {
+        options.num_predict = input.config.maxOutputTokens;
       }
       const type = model.type ?? 'chat';
       const request = toOllamaRequest(
@@ -137,13 +121,12 @@ function ollamaModel(
         );
       } catch (e) {
         const cause = (e as any).cause;
-        if (cause) {
-          if (
-            cause instanceof Error &&
-            cause.message?.includes('ECONNREFUSED')
-          ) {
-            cause.message += '. Make sure ollama server is running.';
-          }
+        if (
+          cause &&
+          cause instanceof Error &&
+          cause.message?.includes('ECONNREFUSED')
+        ) {
+          cause.message += '. Make sure the Ollama server is running.';
           throw cause;
         }
         throw e;
@@ -225,11 +208,11 @@ function toOllamaRequest(
   type: ApiType,
   stream: boolean
 ) {
-  const request = {
+  const request: any = {
     model: name,
     options,
     stream,
-  } as any;
+  };
   if (type === 'chat') {
     const messages: Message[] = [];
     input.messages.forEach((m) => {