Morph updates

core/control-plane/schema.ts

@@ -20,7 +20,7 @@ const modelDescriptionSchema = z.object({
     "nebius",
     "siliconflow",
     "scaleway",
-    "watsonx"
+    "watsonx",
   ]),
   model: z.string(),
   apiKey: z.string().optional(),
@@ -88,13 +88,14 @@ const embeddingsProviderSchema = z.object({
     "ollama",
     "openai",
     "cohere",
+    "morph",
     "free-trial",
     "gemini",
     "ovhcloud",
     "nebius",
     "siliconflow",
     "scaleway",
-    "watsonx"
+    "watsonx",
   ]),
   apiBase: z.string().optional(),
   apiKey: z.string().optional(),
@@ -116,7 +117,7 @@ const embeddingsProviderSchema = z.object({
 });
 
 const rerankerSchema = z.object({
-  name: z.enum(["cohere", "voyage", "llm", "watsonx"]),
+  name: z.enum(["cohere", "morph", "voyage", "llm", "watsonx"]),
   params: z.record(z.any()).optional(),
 });
 

core/llm/llms/Morph.ts

@@ -0,0 +1,35 @@
+import { Chunk, LLMOptions } from "../../index.js";
+import OpenAI from "./OpenAI.js";
+
+class Morph extends OpenAI {
+  static providerName = "morph";
+  static defaultOptions: Partial<LLMOptions> = {
+    apiBase: "https://api.morphllm.com/v1",
+    maxEmbeddingBatchSize: 96,
+  };
+  static maxStopSequences = 5;
+
+  async rerank(query: string, chunks: Chunk[]): Promise<number[]> {
+    const resp = await this.fetch(new URL("rerank", this.apiBase), {
+      method: "POST",
+      headers: {
+        Authorization: `Bearer ${this.apiKey}`,
+        "Content-Type": "application/json",
+      },
+      body: JSON.stringify({
+        model: this.model,
+        query,
+        documents: chunks.map((chunk) => chunk.content),
+      }),
+    });
+
+    if (!resp.ok) {
+      throw new Error(await resp.text());
+    }
+
+    const data = (await resp.json()) as any;
+    const results = data.results.sort((a: any, b: any) => a.index - b.index);
+    return results.map((result: any) => result.relevance_score);
+  }
+}
+export default Morph;

core/llm/llms/index.ts

@@ -18,6 +18,7 @@ import BedrockImport from "./BedrockImport";
 import Cerebras from "./Cerebras";
 import Cloudflare from "./Cloudflare";
 import Cohere from "./Cohere";
+import Morph from "./Morph"
 import DeepInfra from "./DeepInfra";
 import Deepseek from "./Deepseek";
 import Docker from "./Docker";
@@ -116,6 +117,7 @@ export const LLMClasses = [
   SiliconFlow,
   Scaleway,
   Relace,
+  Morph,
   Inception,
   Voyage,
 ];

docs/docs/customize/model-providers/more/morph.mdx

@@ -9,7 +9,7 @@ Morph provides a fast apply model that helps you quickly and accurately apply co
    <TabItem value="yaml" label="YAML">
    ```yaml title="config.yaml"
    models:
-     - uses: morphllm/morph-v0
+     - uses: morphllm/morph-v2
        with:
          MORPH_API_KEY: ${{ secrets.MORPH_API_KEY }}
    ```
@@ -21,7 +21,7 @@ Morph provides a fast apply model that helps you quickly and accurately apply co
        {
          "title": "Morph Fast Apply",
          "provider": "openai",
-         "model": "morph-v0",
+         "model": "morph-v2",
          "apiKey": "<YOUR_MORPH_API_KEY>",
          "apiBase": "https://api.morphllm.com/v1/",
          "roles": ["apply", "chat"],
@@ -34,3 +34,4 @@ Morph provides a fast apply model that helps you quickly and accurately apply co
    ```
    </TabItem>
 </Tabs>
+

docs/docs/customize/model-roles/embeddings.mdx

@@ -152,3 +152,8 @@ See [here](../model-providers/more/watsonx.mdx#embeddings-model) for instruction
 ### LMStudio
 
 See [here](../model-providers/more/lmstudio.mdx#embeddings-model) for instructions on how to use LMStudio for embeddings.
+
+### Morph
+
+See [here](../model-providers/more/morph.mdx#embeddings-model) for instructions on how to use Morph for embeddings.
+

docs/docs/customize/model-roles/reranking.mdx

@@ -85,6 +85,8 @@ See Cohere's documentation for rerankers [here](https://docs.cohere.com/docs/rer
   </TabItem>
 </Tabs>
 
+
+
 ### LLM
 
 If you only have access to a single LLM, then you can use it as a reranker. This is discouraged unless truly necessary, because it will be much more expensive and still less accurate than any of the above models trained specifically for the task. Note that this will not work if you are using a local model, for example with Ollama, because too many parallel requests need to be made.
@@ -154,3 +156,7 @@ The `"modelTitle"` field must match one of the models in your "models" array in
     ```
     </TabItem>
 </Tabs>
+
+### Morph
+
+See the [Morph guide](../model-providers/more/morph.mdx) for details on how to use MorphLLM for reranking.

extensions/vscode/config_schema.json

@@ -900,6 +900,23 @@
             }
           }
         },
+        {
+          "if": {
+            "properties": {
+              "provider": {
+                "enum": ["morph"]
+              }
+            },
+            "required": ["provider"]
+          },
+          "then": {
+            "properties": {
+              "model": {
+                "enum": ["morph-embedding-v2", "morph-rerank-v2", "morph-v2"]
+              }
+            }
+          }
+        },
         {
           "if": {
             "properties": {
@@ -1515,7 +1532,11 @@
           "then": {
             "properties": {
               "model": {
-                "enum": ["llama3.1-8b", "llama3.1-70b", "llama-4-scout-17b-16e-instruct"]
+                "enum": [
+                  "llama3.1-8b",
+                  "llama3.1-70b",
+                  "llama-4-scout-17b-16e-instruct"
+                ]
               }
             }
           }

gui/src/pages/AddNewModel/configs/providers.ts

@@ -353,6 +353,28 @@ Select the \`GPT-4o\` model below to complete your provider configuration, but n
     packages: [models.commandR, models.commandRPlus],
     apiKeyUrl: "https://docs.cohere.com/v2/docs/rate-limits",
   },
+  morph: {
+    title: "Morph",
+    provider: "morph",
+    refPage: "morph",
+    description: "Fast Apply, Embed, and Rerank models",
+    icon: "morph.png",
+    tags: [ModelProviderTags.RequiresApiKey],
+    longDescription:
+      "To use Morph, visit the [Morph dashboard](https://morphllm.com/dashboard) to create an API key.",
+    collectInputFor: [
+      {
+        inputType: "text",
+        key: "apiKey",
+        label: "API Key",
+        placeholder: "Enter your Morph API key",
+        required: true,
+      },
+      ...completionParamsInputsConfigs,
+    ],
+    packages: [models.morphFastApply, models.morphEmbed, models.morphRerank],
+    apiKeyUrl: "https://morphllm.com/dashboard",
+  },
   groq: {
     title: "Groq",
     provider: "groq",
@@ -797,7 +819,7 @@ To get started, [register](https://dataplatform.cloud.ibm.com/registration/stepo
       },
       ...completionParamsInputsConfigs,
     ],
-    packages:[
+    packages: [
       models.llama4Scout,
       models.llama4Maverick,
       models.llama3370BInstruct,
@@ -808,7 +830,7 @@ To get started, [register](https://dataplatform.cloud.ibm.com/registration/stepo
       models.qwq32B,
       models.deepseekR1DistillLlama70B,
       models.deepseekR1,
-      models.deepseekV3
+      models.deepseekV3,
     ],
     apiKeyUrl: "https://cloud.sambanova.ai/apis",
   },
@@ -1011,9 +1033,7 @@ To get started, [register](https://dataplatform.cloud.ibm.com/registration/stepo
         required: true,
       },
     ],
-    packages: [
-      {...models.AUTODETECT}
-    ],
-    apiKeyUrl: "https://venice.ai/chat"
-  }
+    packages: [{ ...models.AUTODETECT }],
+    apiKeyUrl: "https://venice.ai/chat",
+  },
 };

packages/config-types/src/index.ts

@@ -46,6 +46,7 @@ export const modelDescriptionSchema = z.object({
     "openai",
     "anthropic",
     "cohere",
+    "morph",
     "ollama",
     "huggingface-tgi",
     "huggingface-inference-api",
@@ -60,7 +61,7 @@ export const modelDescriptionSchema = z.object({
     "continue-proxy",
     "nebius",
     "scaleway",
-    "watsonx"
+    "watsonx",
   ]),
   model: z.string(),
   apiKey: z.string().optional(),
@@ -109,13 +110,14 @@ export const embeddingsProviderSchema = z.object({
     "ollama",
     "openai",
     "cohere",
+    "morph",
     "free-trial",
     "gemini",
     "ovhcloud",
     "continue-proxy",
     "nebius",
     "scaleway",
-    "watsonx"
+    "watsonx",
   ]),
   apiBase: z.string().optional(),
   apiKey: z.string().optional(),
@@ -178,7 +180,14 @@ export const contextProviderSchema = z.object({
 export type ContextProvider = z.infer<typeof contextProviderSchema>;
 
 export const rerankerSchema = z.object({
-  name: z.enum(["cohere", "voyage", "watsonx", "llm", "continue-proxy"]),
+  name: z.enum([
+    "cohere",
+    "morph",
+    "voyage",
+    "watsonx",
+    "llm",
+    "continue-proxy",
+  ]),
   params: z.record(z.any()).optional(),
 });
 export type Reranker = z.infer<typeof rerankerSchema>;

packages/llm-info/src/index.ts

@@ -2,6 +2,7 @@ import { Anthropic } from "./providers/anthropic.js";
 import { Azure } from "./providers/azure.js";
 import { Bedrock } from "./providers/bedrock.js";
 import { Cohere } from "./providers/cohere.js";
+import { Morph } from "./providers/morph.js";
 import { Gemini } from "./providers/gemini.js";
 import { Mistral } from "./providers/mistral.js";
 import { Ollama } from "./providers/ollama.js";
@@ -22,6 +23,7 @@ export const allModelProviders: ModelProvider[] = [
   Vllm,
   Bedrock,
   Cohere,
+  Morph,
   xAI,
 ];
 

packages/llm-info/src/providers/morph.ts

@@ -0,0 +1,27 @@
+import { ModelProvider } from "../types.js";
+
+export const Morph: ModelProvider = {
+  models: [
+    {
+      model: "morph-rerank-v2",
+      displayName: "Morph Rerank v2",
+      // contextLength: 128000,
+      // maxCompletionTokens: 4000,
+      // recommendedFor: ["rerank"],
+    },
+    {
+      model: "morph-v2",
+      displayName: "Morph Fast Apply v2",
+      // contextLength: 128000,
+      // maxCompletionTokens: 4000,
+    },
+    {
+      model: "morph-embedding-v2",
+      displayName: "Morph Embedding v2",
+      // recommendedFor: ["embed"],
+      // contextLength: 512,
+    },
+  ],
+  id: "morph",
+  displayName: "Morph",
+};

packages/openai-adapters/README.md

@@ -21,7 +21,7 @@ They are concerned with:
 - Cache behavior
 - max stop words
 - use legacy completions endpoint?
-- anything else that couldn't possibly be guess by the client since it won't know the endpoint behind the proxy
+- anything else that couldn't possibly be guess by the client ssince it won't know the endpoint behind the proxy
 
 ## Supported APIs
 
@@ -33,6 +33,7 @@ They are concerned with:
 - [x] Cerebras
 - [ ] Cloudflare
 - [x] Cohere
+- [x] Morph
 - [x] DeepInfra
 - [x] Deepseek
 - [ ] Flowise

packages/openai-adapters/src/test/main.test.ts

@@ -85,6 +85,12 @@ const TESTS: Omit<ModelConfig, "name">[] = [
   //   roles: ["embed"],
   // },
   // {
+  //   provider: "morph",
+  //   model: "morph-embedding-v2",
+  //   apiKey: process.env.MORPH_API_KEY!,
+  //   roles: ["embed"],
+  // },
+  // {
   //   provider: "gemini",
   //   model: "models/text-embedding-004",
   //   apiKey: process.env.GEMINI_API_KEY!,
@@ -102,6 +108,12 @@ const TESTS: Omit<ModelConfig, "name">[] = [
   //   apiKey: process.env.COHERE_API_KEY!,
   //   roles: ["rerank"],
   // },
+  // {
+  //   provider: "morph",
+  //   model: "morph-rerank-v2",
+  //   apiKey: process.env.MORPH_API_KEY!,
+  //   roles: ["rerank"],
+  // },
 ];
 
 TESTS.forEach((config) => {