[InferenceSnippets] Add endpointUrl option

packages/inference/README.md

@@ -651,9 +651,10 @@ You can use any Chat Completion API-compatible provider with the `chatCompletion
 ```typescript
 // Chat Completion Example
 const MISTRAL_KEY = process.env.MISTRAL_KEY;
-const hf = new InferenceClient(MISTRAL_KEY);
-const ep = hf.endpoint("https://api.mistral.ai");
-const stream = ep.chatCompletionStream({
+const hf = new InferenceClient(MISTRAL_KEY, {
+  endpointUrl: "https://api.mistral.ai",
+});
+const stream = hf.chatCompletionStream({
   model: "mistral-tiny",
   messages: [{ role: "user", content: "Complete the equation one + one = , just the answer" }],
 });

packages/inference/src/snippets/getInferenceSnippets.ts

@@ -18,7 +18,8 @@ export type InferenceSnippetOptions = {
 	streaming?: boolean;
 	billTo?: string;
 	accessToken?: string;
-	directRequest?: boolean;
+	directRequest?: boolean; // to bypass HF routing and call the provider directly
+	endpointUrl?: string; // to call a local endpoint directly
 } & Record<string, unknown>;
 
 const PYTHON_CLIENTS = ["huggingface_hub", "fal_client", "requests", "openai"] as const;
@@ -53,6 +54,7 @@ interface TemplateParams {
 	methodName?: string; // specific to snippetBasic
 	importBase64?: boolean; // specific to snippetImportRequests
 	importJson?: boolean; // specific to snippetImportRequests
+	endpointUrl?: string;
 }
 
 // Helpers to find + load templates
@@ -172,6 +174,7 @@ const snippetGenerator = (templateName: string, inputPreparationFn?: InputPrepar
 			{
 				accessToken: accessTokenOrPlaceholder,
 				provider,
+				endpointUrl: opts?.endpointUrl,
 				...inputs,
 			} as RequestArgs,
 			inferenceProviderMapping,
@@ -217,6 +220,7 @@ const snippetGenerator = (templateName: string, inputPreparationFn?: InputPrepar
 			provider,
 			providerModelId: providerModelId ?? model.id,
 			billTo: opts?.billTo,
+			endpointUrl: opts?.endpointUrl,
 		};
 
 		/// Iterate over clients => check if a snippet exists => generate
@@ -265,7 +269,14 @@ const snippetGenerator = (templateName: string, inputPreparationFn?: InputPrepar
 
 						/// Replace access token placeholder
 						if (snippet.includes(placeholder)) {
-							snippet = replaceAccessTokenPlaceholder(opts?.directRequest, placeholder, snippet, language, provider);
+							snippet = replaceAccessTokenPlaceholder(
+								opts?.directRequest,
+								placeholder,
+								snippet,
+								language,
+								provider,
+								opts?.endpointUrl
+							);
 						}
 
 						/// Snippet is ready!
@@ -444,21 +455,24 @@ function replaceAccessTokenPlaceholder(
 	placeholder: string,
 	snippet: string,
 	language: InferenceSnippetLanguage,
-	provider: InferenceProviderOrPolicy
+	provider: InferenceProviderOrPolicy,
+	endpointUrl?: string
 ): string {
 	// If "opts.accessToken" is not set, the snippets are generated with a placeholder.
 	// Once snippets are rendered, we replace the placeholder with code to fetch the access token from an environment variable.
 
 	// Determine if HF_TOKEN or specific provider token should be used
 	const useHfToken =
-		provider == "hf-inference" || // hf-inference provider => use $HF_TOKEN
-		(!directRequest && // if explicit directRequest => use provider-specific token
-			(!snippet.includes("https://") || // no URL provided => using a client => use $HF_TOKEN
-				snippet.includes("https://router.huggingface.co"))); // explicit routed request => use $HF_TOKEN
-
+		!endpointUrl && // custom endpointUrl => use a generic API_TOKEN
+		(provider == "hf-inference" || // hf-inference provider => use $HF_TOKEN
+			(!directRequest && // if explicit directRequest => use provider-specific token
+				(!snippet.includes("https://") || // no URL provided => using a client => use $HF_TOKEN
+					snippet.includes("https://router.huggingface.co")))); // explicit routed request => use $HF_TOKEN
 	const accessTokenEnvVar = useHfToken
 		? "HF_TOKEN" // e.g. routed request or hf-inference
-		: provider.toUpperCase().replace("-", "_") + "_API_KEY"; // e.g. "REPLICATE_API_KEY"
+		: endpointUrl
+		  ? "API_TOKEN"
+		  : provider.toUpperCase().replace("-", "_") + "_API_KEY"; // e.g. "REPLICATE_API_KEY"
 
 	// Replace the placeholder with the env variable
 	if (language === "sh") {

packages/inference/src/snippets/templates/js/huggingface.js/basic.jinja

@@ -3,6 +3,9 @@ import { InferenceClient } from "@huggingface/inference";
 const client = new InferenceClient("{{ accessToken }}");
 
 const output = await client.{{ methodName }}({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
 	model: "{{ model.id }}",
 	inputs: {{ inputs.asObj.inputs }},
 	provider: "{{ provider }}",

packages/inference/src/snippets/templates/js/huggingface.js/basicAudio.jinja

@@ -5,6 +5,9 @@ const client = new InferenceClient("{{ accessToken }}");
 const data = fs.readFileSync({{inputs.asObj.inputs}});
 
 const output = await client.{{ methodName }}({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
 	data,
 	model: "{{ model.id }}",
 	provider: "{{ provider }}",

packages/inference/src/snippets/templates/js/huggingface.js/basicImage.jinja

@@ -5,6 +5,9 @@ const client = new InferenceClient("{{ accessToken }}");
 const data = fs.readFileSync({{inputs.asObj.inputs}});
 
 const output = await client.{{ methodName }}({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
 	data,
 	model: "{{ model.id }}",
 	provider: "{{ provider }}",

packages/inference/src/snippets/templates/js/huggingface.js/conversational.jinja

@@ -3,6 +3,9 @@ import { InferenceClient } from "@huggingface/inference";
 const client = new InferenceClient("{{ accessToken }}");
 
 const chatCompletion = await client.chatCompletion({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
     provider: "{{ provider }}",
     model: "{{ model.id }}",
 {{ inputs.asTsString }}

packages/inference/src/snippets/templates/js/huggingface.js/conversationalStream.jinja

@@ -5,6 +5,9 @@ const client = new InferenceClient("{{ accessToken }}");
 let out = "";
 
 const stream = client.chatCompletionStream({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
     provider: "{{ provider }}",
     model: "{{ model.id }}",
 {{ inputs.asTsString }}

packages/inference/src/snippets/templates/js/huggingface.js/textToImage.jinja

@@ -3,6 +3,9 @@ import { InferenceClient } from "@huggingface/inference";
 const client = new InferenceClient("{{ accessToken }}");
 
 const image = await client.textToImage({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
     provider: "{{ provider }}",
     model: "{{ model.id }}",
 	inputs: {{ inputs.asObj.inputs }},

packages/inference/src/snippets/templates/js/huggingface.js/textToSpeech.jinja

@@ -3,6 +3,9 @@ import { InferenceClient } from "@huggingface/inference";
 const client = new InferenceClient("{{ accessToken }}");
 
 const audio = await client.textToSpeech({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
     provider: "{{ provider }}",
     model: "{{ model.id }}",
 	inputs: {{ inputs.asObj.inputs }},

packages/inference/src/snippets/templates/js/huggingface.js/textToVideo.jinja

@@ -3,6 +3,9 @@ import { InferenceClient } from "@huggingface/inference";
 const client = new InferenceClient("{{ accessToken }}");
 
 const video = await client.textToVideo({
+{% if endpointUrl %}
+    endpointUrl: "{{ endpointUrl }}",
+{% endif %}
     provider: "{{ provider }}",
     model: "{{ model.id }}",
 	inputs: {{ inputs.asObj.inputs }},

packages/inference/src/snippets/templates/python/huggingface_hub/importInferenceClient.jinja

@@ -1,6 +1,9 @@
 from huggingface_hub import InferenceClient
 
 client = InferenceClient(
+{% if endpointUrl %}
+    base_url="{{ baseUrl }}",
+{% endif %}
     provider="{{ provider }}",
     api_key="{{ accessToken }}",
 {% if billTo %}

packages/tasks-gen/scripts/generate-snippets-fixtures.ts

@@ -95,6 +95,18 @@ const TEST_CASES: {
 		providers: ["hf-inference", "fireworks-ai"],
 		opts: { streaming: true },
 	},
+	{
+		testName: "conversational-llm-custom-endpoint",
+		task: "conversational",
+		model: {
+			id: "meta-llama/Llama-3.1-8B-Instruct",
+			pipeline_tag: "text-generation",
+			tags: ["conversational"],
+			inference: "",
+		},
+		providers: ["hf-inference"],
+		opts: { endpointUrl: "http://localhost:8080/v1" },
+	},
 	{
 		testName: "document-question-answering",
 		task: "document-question-answering",

packages/tasks-gen/snippets-fixtures/conversational-llm-custom-endpoint/js/huggingface.js/0.hf-inference.js

@@ -0,0 +1,17 @@
+import { InferenceClient } from "@huggingface/inference";
+
+const client = new InferenceClient(process.env.API_TOKEN);
+
+const chatCompletion = await client.chatCompletion({
+    endpointUrl: "http://localhost:8080/v1",
+    provider: "hf-inference",
+    model: "meta-llama/Llama-3.1-8B-Instruct",
+    messages: [
+        {
+            role: "user",
+            content: "What is the capital of France?",
+        },
+    ],
+});
+
+console.log(chatCompletion.choices[0].message);

packages/tasks-gen/snippets-fixtures/conversational-llm-custom-endpoint/js/openai/0.hf-inference.js

@@ -0,0 +1,18 @@
+import { OpenAI } from "openai";
+
+const client = new OpenAI({
+	baseURL: "http://localhost:8080/v1",
+	apiKey: process.env.API_TOKEN,
+});
+
+const chatCompletion = await client.chat.completions.create({
+	model: "meta-llama/Llama-3.1-8B-Instruct",
+    messages: [
+        {
+            role: "user",
+            content: "What is the capital of France?",
+        },
+    ],
+});
+
+console.log(chatCompletion.choices[0].message);

packages/tasks-gen/snippets-fixtures/conversational-llm-custom-endpoint/python/huggingface_hub/0.hf-inference.py

@@ -0,0 +1,20 @@
+import os
+from huggingface_hub import InferenceClient
+
+client = InferenceClient(
+    base_url="http://localhost:8080/v1",
+    provider="hf-inference",
+    api_key=os.environ["API_TOKEN"],
+)
+
+completion = client.chat.completions.create(
+    model="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[
+        {
+            "role": "user",
+            "content": "What is the capital of France?"
+        }
+    ],
+)
+
+print(completion.choices[0].message)

packages/tasks-gen/snippets-fixtures/conversational-llm-custom-endpoint/python/openai/0.hf-inference.py

@@ -0,0 +1,19 @@
+import os
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="http://localhost:8080/v1",
+    api_key=os.environ["API_TOKEN"],
+)
+
+completion = client.chat.completions.create(
+    model="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[
+        {
+            "role": "user",
+            "content": "What is the capital of France?"
+        }
+    ],
+)
+
+print(completion.choices[0].message)

packages/tasks-gen/snippets-fixtures/conversational-llm-custom-endpoint/python/requests/0.hf-inference.py

@@ -0,0 +1,23 @@
+import os
+import requests
+
+API_URL = "http://localhost:8080/v1/chat/completions"
+headers = {
+    "Authorization": f"Bearer {os.environ['API_TOKEN']}",
+}
+
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+
+response = query({
+    "messages": [
+        {
+            "role": "user",
+            "content": "What is the capital of France?"
+        }
+    ],
+    "model": "meta-llama/Llama-3.1-8B-Instruct"
+})
+
+print(response["choices"][0]["message"])

packages/tasks-gen/snippets-fixtures/conversational-llm-custom-endpoint/sh/curl/0.hf-inference.sh

@@ -0,0 +1,13 @@
+curl http://localhost:8080/v1/chat/completions \
+    -H "Authorization: Bearer $API_TOKEN" \
+    -H 'Content-Type: application/json' \
+    -d '{
+        "messages": [
+            {
+                "role": "user",
+                "content": "What is the capital of France?"
+            }
+        ],
+        "model": "meta-llama/Llama-3.1-8B-Instruct",
+        "stream": false
+    }'