diff --git a/ar/models/overview.mdx b/ar/models/overview.mdx
index 0c71044d..6cf5b28c 100644
--- a/ar/models/overview.mdx
+++ b/ar/models/overview.mdx
@@ -1,5 +1,6 @@
---
-title: "النماذج"
+title: "كل النماذج"
+sidebarTitle: "كل النماذج"
description: "كتالوج لجميع النماذج المتاحة على Venice API عبر النص والصورة والفيديو والصوت والـ embeddings والكلام، مع القدرات والأسعار ومعرفات النماذج."
"og:title": "النماذج | Venice API Docs"
mode: "wide"
diff --git a/de/guides/features/embeddings.mdx b/de/guides/features/embeddings.mdx
new file mode 100644
index 00000000..b912a00d
--- /dev/null
+++ b/de/guides/features/embeddings.mdx
@@ -0,0 +1,102 @@
+---
+title: "Embeddings"
+description: "Erzeuge Vektor-Embeddings mit Venice für semantische Suche, RAG-Abruf, Clustering und Empfehlungen über den /embeddings-Endpunkt."
+'og:title': "Embeddings | Venice API Docs"
+'og:description': "Erfahre, wie du mit der Venice API Vektor-Embeddings erzeugst."
+---
+
+Embeddings wandeln Text in Vektoren um, die semantische Bedeutung erfassen. Verwende sie für Suche, Retrieval-Augmented Generation (RAG), Clustering, Empfehlungen, Deduplizierung und Ähnlichkeitsbewertung.
+
+Der Embeddings-Endpunkt von Venice ist OpenAI-kompatibel. Sende einen einzelnen String oder ein Array von Strings an `/embeddings` und speichere anschließend die zurückgegebenen Vektoren in deiner Datenbank oder deinem Vektorindex.
+
+## Grundlegende Nutzung
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+response = client.embeddings.create(
+ model="text-embedding-bge-m3",
+ input="Privacy-first AI infrastructure for semantic search",
+)
+
+vector = response.data[0].embedding
+print(len(vector), vector[:5])
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const response = await client.embeddings.create({
+ model: "text-embedding-bge-m3",
+ input: "Privacy-first AI infrastructure for semantic search",
+});
+
+const vector = response.data[0].embedding;
+console.log(vector.length, vector.slice(0, 5));
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/embeddings \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "text-embedding-bge-m3",
+ "input": "Privacy-first AI infrastructure for semantic search",
+ "encoding_format": "float"
+ }'
+```
+
+
+## Batch-Eingaben
+
+Übergib ein Array von Strings, um mehrere Texte in einer einzigen Anfrage einzubetten:
+
+```json
+{
+ "model": "text-embedding-bge-m3",
+ "input": [
+ "Venice supports private chat completions.",
+ "Embeddings help retrieve relevant documents.",
+ "Vector search powers RAG applications."
+ ]
+}
+```
+
+Die Antwort behält die Reihenfolge der Eingaben bei. Speichere jeden Vektor zusammen mit der ID des Quelltexts, den Metadaten und der ID des Embedding-Modells.
+
+## Typischer Ablauf
+
+1. Teile die Ausgangsdokumente in Chunks auf.
+2. Erzeuge Embeddings für jeden Chunk.
+3. Speichere Vektoren und Metadaten in einer Vektordatenbank.
+4. Bette die Anfrage der Nutzer:innen ein.
+5. Rufe die nächstgelegenen Chunks ab.
+6. Sende den abgerufenen Kontext an ein Chat-Modell.
+
+Eine vollständige Implementierung findest du unter [Einen privaten RAG-Bot bauen](/guides/projects/private-rag-bot).
+
+## Modellauswahl
+
+Nutze die Seite [Embedding-Modelle](/models/embeddings), um aktuelle Embedding-Modelle, Dimensionen und Preise zu vergleichen.
+
+
+Verwende beim Indexieren und Abfragen dasselbe Embedding-Modell. Das Mischen von Modellen kann Ähnlichkeitswerte unzuverlässig machen, da Vektorräume nicht austauschbar sind.
+
+
+## Verwandte Ressourcen
+
+- [Embeddings-API](/api-reference/endpoint/embeddings/generate)
+- [Embedding-Modelle](/models/embeddings)
+- [Anleitung: Privater RAG-Bot](/guides/projects/private-rag-bot)
diff --git a/de/guides/features/function-calling.mdx b/de/guides/features/function-calling.mdx
new file mode 100644
index 00000000..b9ded936
--- /dev/null
+++ b/de/guides/features/function-calling.mdx
@@ -0,0 +1,174 @@
+---
+title: "Function Calling"
+description: "Lass Venice-Chatmodelle über OpenAI-kompatibles Function Calling und die Chat-Completions-API die Tools deiner Anwendung aufrufen."
+'og:title': "Function Calling | Venice API Docs"
+'og:description': "Erfahre, wie du Function Calling mit Venice-Chatmodellen verwendest."
+---
+
+Mit Function Calling kann ein Modell strukturierte Tool-Aufrufe auswählen, die deine Anwendung ausführt. Das Modell führt die Funktion nicht selbst aus. Es gibt den Funktionsnamen und die Argumente zurück, dein Code führt die Funktion aus und du sendest das Ergebnis an das Modell zurück.
+
+Verwende Function Calling, wenn das Modell Live-Daten, Anwendungsaktionen, Datenbankabfragen oder deterministische Berechnungen benötigt.
+
+## Grundlegende Tool-Definition
+
+Definiere Tools mit dem OpenAI-kompatiblen `tools`-Array:
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "Get the current weather in a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "City and state, such as San Francisco, CA",
+ }
+ },
+ "required": ["location"],
+ },
+ },
+ }
+]
+
+response = client.chat.completions.create(
+ model="zai-org-glm-5",
+ messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
+ tools=tools,
+)
+
+print(response.choices[0].message.tool_calls)
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const tools = [
+ {
+ type: "function",
+ function: {
+ name: "get_weather",
+ description: "Get the current weather in a location",
+ parameters: {
+ type: "object",
+ properties: {
+ location: {
+ type: "string",
+ description: "City and state, such as San Francisco, CA",
+ },
+ },
+ required: ["location"],
+ },
+ },
+ },
+];
+
+const response = await client.chat.completions.create({
+ model: "zai-org-glm-5",
+ messages: [{ role: "user", content: "What is the weather in San Francisco?" }],
+ tools,
+});
+
+console.log(response.choices[0].message.tool_calls);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/chat/completions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "zai-org-glm-5",
+ "messages": [
+ {"role": "user", "content": "What is the weather in San Francisco?"}
+ ],
+ "tools": [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "Get the current weather in a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "City and state, such as San Francisco, CA"
+ }
+ },
+ "required": ["location"]
+ }
+ }
+ }
+ ]
+ }'
+```
+
+
+## Das Tool ausführen
+
+Wenn das Modell ein Tool auswählt, prüfe `message.tool_calls`, parse die Argumente, führe deine Anwendungsfunktion aus und sende das Ergebnis als `tool`-Nachricht zurück.
+
+```python Python
+import json
+
+message = response.choices[0].message
+tool_call = message.tool_calls[0]
+arguments = json.loads(tool_call.function.arguments)
+
+weather = get_weather(arguments["location"])
+
+follow_up = client.chat.completions.create(
+ model="zai-org-glm-5",
+ messages=[
+ {"role": "user", "content": "What is the weather in San Francisco?"},
+ message.model_dump(),
+ {
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "content": json.dumps(weather),
+ },
+ ],
+ tools=tools,
+)
+
+print(follow_up.choices[0].message.content)
+```
+
+## Ein Modell auswählen
+
+Die Unterstützung für Function Calling ist modellabhängig. Nutze die Seite [Textmodelle](/models/text) oder die [Models-API](/api-reference/endpoint/models/list), um Modelle mit `supportsFunctionCalling` zu finden.
+
+
+Behandle Tool-Argumente als nicht vertrauenswürdige Eingaben. Validiere Argumente, bevor du sie in Datenbankabfragen, Shell-Befehlen, Zahlungen oder anderen Aktionen mit Nebenwirkungen verwendest.
+
+
+## Design-Tipps
+
+- Halte Tool-Namen und Beschreibungen kurz und wörtlich.
+- Verwende JSON Schema, damit das Modell gültige Argumente leichter erzeugen kann.
+- Bevorzuge eng gefasste Tools mit klaren Eingaben gegenüber einem breiten Tool mit vielen optionalen Verhaltensweisen.
+- Gib knappe Tool-Ergebnisse zurück, damit die finale Antwort genügend Kontext hat, ohne Tokens zu verschwenden.
+
+## Verwandte Ressourcen
+
+- [Chat-Completions-API](/api-reference/endpoint/chat/completions)
+- [Textmodelle](/models/text)
+- [Anleitung: Strukturierte Antworten](/guides/features/structured-responses)
+- [LangChain-Integration](/guides/integrations/langchain#function-calling-with-agents)
diff --git a/de/guides/features/vision.mdx b/de/guides/features/vision.mdx
new file mode 100644
index 00000000..fed2bf8e
--- /dev/null
+++ b/de/guides/features/vision.mdx
@@ -0,0 +1,131 @@
+---
+title: "Vision"
+description: "Analysiere Bilder mit den vision-fähigen Chatmodellen von Venice über multimodale Nachrichteninhalte in der OpenAI-kompatiblen Chat-Completions-API."
+'og:title': "Vision | Venice API Docs"
+'og:description': "Erfahre, wie du Bilder an Venice-Vision-Modelle sendest."
+---
+
+Vision-Modelle können Bilder zusammen mit Textprompts analysieren. Verwende sie für Bildverständnis, Extraktion, Klassifikation, visuelle Fragebeantwortung und multimodales Reasoning.
+
+Venice unterstützt OpenAI-kompatible multimodale Chat-Nachrichten. Füge Text- und Bildblöcke in dieselbe Benutzernachricht ein und sende die Anfrage anschließend an ein vision-fähiges Modell.
+
+## Grundlegende Nutzung
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+response = client.chat.completions.create(
+ model="qwen3-vl-235b-a22b",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Describe this image in three bullets."},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://www.gstatic.com/webp/gallery/1.jpg"
+ },
+ },
+ ],
+ }
+ ],
+)
+
+print(response.choices[0].message.content)
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const response = await client.chat.completions.create({
+ model: "qwen3-vl-235b-a22b",
+ messages: [
+ {
+ role: "user",
+ content: [
+ { type: "text", text: "Describe this image in three bullets." },
+ {
+ type: "image_url",
+ image_url: {
+ url: "https://www.gstatic.com/webp/gallery/1.jpg",
+ },
+ },
+ ],
+ },
+ ],
+});
+
+console.log(response.choices[0].message.content);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/chat/completions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "qwen3-vl-235b-a22b",
+ "messages": [
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Describe this image in three bullets."},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://www.gstatic.com/webp/gallery/1.jpg"
+ }
+ }
+ ]
+ }
+ ]
+ }'
+```
+
+
+## Base64-Bilder verwenden
+
+Du kannst auch eine Base64-Data-URL übergeben, wenn das Bild lokal oder privat ist:
+
+```json
+{
+ "type": "image_url",
+ "image_url": {
+ "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
+ }
+}
+```
+
+## Ein Vision-Modell auswählen
+
+Nutze die Seite [Textmodelle](/models/text) oder die [Models-API](/api-reference/endpoint/models/list), um Modelle zu finden, die Vision unterstützen. Die Vision-Unterstützung ist in den Modellfähigkeiten aufgeführt.
+
+
+Verwende bei dokumentähnlichen Eingaben [Datei-Eingaben](/guides/features/file-inputs), wenn Venice Text aus einer Datei extrahieren soll. Verwende Vision, wenn das visuelle Layout oder der Bildinhalt selbst wichtig ist.
+
+
+## Prompt-Tipps
+
+- Sag dem Modell, worauf es sich konzentrieren soll: Objekte, Text, Layout, Sicherheit, Defekte oder Unterschiede.
+- Verlange strukturierte Ausgaben, wenn deine Anwendung Felder benötigt, die du parsen kannst.
+- Achte darauf, dass Bild-URLs für die API zugänglich sind, oder verwende Base64-Data-URLs für private Bilder.
+- Verwende ein Modell mit ausreichend Kontext, wenn du Bilder mit langen Anweisungen kombinierst.
+
+## Verwandte Ressourcen
+
+- [Chat-Completions-API](/api-reference/endpoint/chat/completions)
+- [Textmodelle](/models/text)
+- [Anleitung: Datei-Eingaben](/guides/features/file-inputs)
+- [Anleitung: Strukturierte Antworten](/guides/features/structured-responses)
diff --git a/de/guides/media/image-upscaling.mdx b/de/guides/media/image-upscaling.mdx
new file mode 100644
index 00000000..41df52c4
--- /dev/null
+++ b/de/guides/media/image-upscaling.mdx
@@ -0,0 +1,100 @@
+---
+title: "Bild-Upscaling"
+description: "Verbessere und skaliere Bilder mit der synchronen Image-Upscale-API von Venice mit Base64-Eingabe und binärer Bildausgabe."
+'og:title': "Bild-Upscaling | Venice API Docs"
+'og:description': "Erfahre, wie du mit der Venice API Bilder verbesserst und hochskalierst."
+---
+
+Bild-Upscaling verbessert die Auflösung und visuelle Qualität eines vorhandenen Bildes. Sende ein Base64-kodiertes Bild an `/image/upscale`, wähle einen Skalierungsfaktor und Venice gibt das verbesserte Bild als Binärdaten zurück.
+
+Verwende Bild-Upscaling, wenn du bereits ein Bild hast und eine höher aufgelöste Ausgabe wünschst. Verwende die [Bilderzeugung](/guides/media/image-generation), wenn du ein Bild aus einem Prompt erstellen möchtest, und die [Bildbearbeitung](/guides/media/image-editing), wenn du den Bildinhalt ändern möchtest.
+
+## Grundlegende Nutzung
+
+
+```python Python
+import base64
+import os
+from pathlib import Path
+
+import requests
+
+image_base64 = base64.b64encode(Path("input.jpg").read_bytes()).decode("utf-8")
+
+response = requests.post(
+ "https://api.venice.ai/api/v1/image/upscale",
+ headers={
+ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
+ "Content-Type": "application/json",
+ },
+ json={
+ "image": image_base64,
+ "scale": 2,
+ },
+)
+
+response.raise_for_status()
+Path("upscaled.png").write_bytes(response.content)
+```
+
+```javascript Node.js
+import { readFile, writeFile } from "node:fs/promises";
+
+const image = await readFile("input.jpg");
+
+const response = await fetch("https://api.venice.ai/api/v1/image/upscale", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ image: image.toString("base64"),
+ scale: 2,
+ }),
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+const output = Buffer.from(await response.arrayBuffer());
+await writeFile("upscaled.png", output);
+```
+
+```bash cURL
+IMAGE_BASE64=$(base64 < input.jpg | tr -d '\n')
+
+curl https://api.venice.ai/api/v1/image/upscale \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d "{
+ \"image\": \"$IMAGE_BASE64\",
+ \"scale\": 2
+ }" \
+ --output upscaled.png
+```
+
+
+## Parameter
+
+| Parameter | Typ | Erforderlich | Beschreibung |
+|-----------|------|----------|-------------|
+| `image` | string | Ja | Base64-kodiertes Quellbild. |
+| `scale` | number | Nein | Skalierungsfaktor. Verwende die in der API-Referenz und im Modellkatalog aufgeführten unterstützten Werte. |
+
+
+Die Antwort besteht aus binären Bilddaten, nicht aus JSON. Schreibe den Antwortkörper direkt in eine Datei oder streame ihn in den Speicher.
+
+
+## Tipps zur Eingabe
+
+- Beginne mit dem saubersten Quellbild, das du hast. Upscaling verbessert Details, kann aber nicht vollständig Informationen wiederherstellen, die nicht vorhanden sind.
+- Verwende in Produktions-Workflows moderate Skalierungsfaktoren. Sehr große Ausgaben können Latenz und Dateigröße erhöhen.
+- Behalte das Originalbild, falls du die Qualität vergleichen oder mit anderen Einstellungen erneut versuchen möchtest.
+
+## Verwandte Ressourcen
+
+- [Image-Upscale-API](/api-reference/endpoint/image/upscale)
+- [Bildmodelle](/models/image)
+- [Anleitung: Bildbearbeitung](/guides/media/image-editing)
diff --git a/de/guides/media/speech-to-text.mdx b/de/guides/media/speech-to-text.mdx
new file mode 100644
index 00000000..7b3ee76c
--- /dev/null
+++ b/de/guides/media/speech-to-text.mdx
@@ -0,0 +1,96 @@
+---
+title: "Speech-to-Text"
+description: "Transkribiere Audiodateien mit Venice-Speech-to-Text-Modellen über den OpenAI-kompatiblen /audio/transcriptions-Endpunkt."
+'og:title': "Speech-to-Text | Venice API Docs"
+'og:description': "Erfahre, wie du mit der Venice API Audiodateien transkribierst."
+---
+
+Speech-to-Text transkribiert gesprochene Audioaufnahmen in geschriebenen Text. Sende eine Audiodatei an `/audio/transcriptions`, wähle ein Transkriptionsmodell und lege das gewünschte Antwortformat fest.
+
+## Grundlegende Nutzung
+
+
+```python Python
+import os
+
+import requests
+
+with open("meeting.mp3", "rb") as audio:
+ response = requests.post(
+ "https://api.venice.ai/api/v1/audio/transcriptions",
+ headers={"Authorization": f"Bearer {os.environ['VENICE_API_KEY']}"},
+ files={"file": audio},
+ data={
+ "model": "nvidia/parakeet-tdt-0.6b-v3",
+ "response_format": "json",
+ },
+ )
+
+response.raise_for_status()
+print(response.json()["text"])
+```
+
+```javascript Node.js
+import { createReadStream } from "node:fs";
+import FormData from "form-data";
+
+const form = new FormData();
+form.append("file", createReadStream("meeting.mp3"));
+form.append("model", "nvidia/parakeet-tdt-0.6b-v3");
+form.append("response_format", "json");
+
+const response = await fetch("https://api.venice.ai/api/v1/audio/transcriptions", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ ...form.getHeaders(),
+ },
+ body: form,
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+const transcript = await response.json();
+console.log(transcript.text);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/audio/transcriptions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ --form file=@meeting.mp3 \
+ --form model=nvidia/parakeet-tdt-0.6b-v3 \
+ --form response_format=json
+```
+
+
+## Unterstützte Eingaben
+
+Gängige Audioformate sind `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `wav`, `webm`, `flac` und `ogg`. Aktuelle Modellunterstützung und Preise findest du auf der Seite [Speech-to-Text-Modelle](/models/speech-to-text).
+
+## Antwortformate
+
+| Format | Verwendung |
+|--------|----------|
+| `json` | Wenn du eine einfache `{ "text": "..." }`-Antwort möchtest. |
+| `text` | Wenn du reinen Text ohne JSON-Parsing möchtest. |
+| `srt` | Wenn du SubRip-Untertitel benötigst. |
+| `vtt` | Wenn du WebVTT-Untertitel benötigst. |
+| `verbose_json` | Wenn du umfangreichere Zeitstempel- und Segment-Metadaten benötigst. |
+
+
+Verwende Untertitelformate, wenn das Transkript mit einer Medienwiedergabe kombiniert wird. Verwende `json` oder `text`, wenn das Transkript in Zusammenfassungen, Suche oder nachgelagerte Chat-Prompts einfließt.
+
+
+## Tipps für den Produktiveinsatz
+
+- Halte das Audio klar und vermeide nach Möglichkeit sich überlappende Sprecher.
+- Teile sehr lange Aufnahmen in kleinere Chunks auf, wenn dein Workflow geringere Latenz oder einfachere Wiederholungen benötigt.
+- Speichere den ursprünglichen Audiopfad, die Modell-ID und das Antwortformat mit jedem Transkript, um die Nachvollziehbarkeit zu gewährleisten.
+
+## Verwandte Ressourcen
+
+- [Audio-Transcriptions-API](/api-reference/endpoint/audio/transcriptions)
+- [Speech-to-Text-Modelle](/models/speech-to-text)
+- [Anleitung: Text-to-Speech](/guides/media/text-to-speech)
diff --git a/de/guides/media/text-to-speech.mdx b/de/guides/media/text-to-speech.mdx
new file mode 100644
index 00000000..78dff3d0
--- /dev/null
+++ b/de/guides/media/text-to-speech.mdx
@@ -0,0 +1,102 @@
+---
+title: "Text-to-Speech"
+description: "Erzeuge gesprochenes Audio aus Text mit Venice-Text-to-Speech-Modellen, modellspezifischen Stimmen und dem /audio/speech-Endpunkt."
+'og:title': "Text-to-Speech | Venice API Docs"
+'og:description': "Erfahre, wie du mit der Venice API Text in Sprache umwandelst."
+---
+
+Text-to-Speech wandelt geschriebenen Text in gesprochenes Audio um. Wähle ein TTS-Modell, wähle eine von diesem Modell unterstützte Stimme, sende den Text an `/audio/speech` und speichere die binäre Audioantwort.
+
+Nutze diese Anleitung für die Standard-Sprachgenerierung. Wenn du Sprache aus einer benutzerdefinierten Referenzstimme erzeugen möchtest, siehe [Voice Cloning](/guides/media/voice-cloning).
+
+## Grundlegende Nutzung
+
+
+```python Python
+import os
+from pathlib import Path
+
+import requests
+
+response = requests.post(
+ "https://api.venice.ai/api/v1/audio/speech",
+ headers={
+ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
+ "Content-Type": "application/json",
+ },
+ json={
+ "model": "tts-kokoro",
+ "voice": "af_sky",
+ "input": "Hello, welcome to Venice Voice.",
+ },
+)
+
+response.raise_for_status()
+Path("speech.mp3").write_bytes(response.content)
+```
+
+```javascript Node.js
+import { writeFile } from "node:fs/promises";
+
+const response = await fetch("https://api.venice.ai/api/v1/audio/speech", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ model: "tts-kokoro",
+ voice: "af_sky",
+ input: "Hello, welcome to Venice Voice.",
+ }),
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+await writeFile("speech.mp3", Buffer.from(await response.arrayBuffer()));
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/audio/speech \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "tts-kokoro",
+ "voice": "af_sky",
+ "input": "Hello, welcome to Venice Voice."
+ }' \
+ --output speech.mp3
+```
+
+
+## Modell und Stimme auswählen
+
+Stimmen sind modellspezifisch. Der `voice`-Wert muss zu dem gewählten `model` passen.
+
+Auf der Seite [Text-to-Speech-Modelle](/models/text-to-speech) kannst du verfügbare Modelle und Stimmen durchsuchen. Der Voice-Picker listet die exakten Voice-IDs auf, die du in deiner Anfrage übergeben musst.
+
+
+Voice-IDs unterscheiden zwischen Groß- und Kleinschreibung. Wenn du das TTS-Modell wechselst, aktualisiere gleichzeitig den `voice`-Wert.
+
+
+## Aufbau der Anfrage
+
+| Parameter | Typ | Erforderlich | Beschreibung |
+|-----------|------|----------|-------------|
+| `model` | string | Ja | ID des Text-to-Speech-Modells. |
+| `voice` | string | Ja | Voice-ID, die vom gewählten Modell unterstützt wird. |
+| `input` | string | Ja | Zu synthetisierender Text. |
+
+## Tipps für den Produktiveinsatz
+
+- Zwischenspeichere erzeugtes Audio, wenn derselbe Quelltext und dieselbe Stimme wiederverwendet werden.
+- Normalisiere und korrigiere den Text vor der Synthese. Interpunktion beeinflusst Tempo und Intonation.
+- Speichere die Ausgabe mit der korrekten Dateiendung für das Antwortformat des Modells.
+
+## Verwandte Ressourcen
+
+- [Audio-Speech-API](/api-reference/endpoint/audio/speech)
+- [Text-to-Speech-Modelle](/models/text-to-speech)
+- [Anleitung: Voice Cloning](/guides/media/voice-cloning)
diff --git a/de/guides/overview.mdx b/de/guides/overview.mdx
index bc9f53e7..487c4d46 100644
--- a/de/guides/overview.mdx
+++ b/de/guides/overview.mdx
@@ -1,28 +1,37 @@
---
-title: Leitfäden
-description: "Praktische Venice API-Leitfäden zu API-Schlüsseln, OpenAI-Migration, strukturierten Antworten, Datei-Eingaben, Prompt-Caching, Medien und Agenten-Integrationen."
+title: Anleitungen
+description: Praktische Venice-API-Anleitungen zu API-Keys, OpenAI-Migration, Chat-Funktionen, Embeddings, Medien und Agent-Integrationen.
---
-Verwenden Sie diese Leitfäden, um API-Schlüssel zu generieren, vorhandene OpenAI-Apps zu migrieren, Venice-spezifische Funktionen zu aktivieren und Venice mit Agent-Frameworks, Coding-Tools und Medien-Workflows zu verbinden.
+Nutze diese Anleitungen, um API-Keys zu erstellen, bestehende OpenAI-Anwendungen zu migrieren, Venice-spezifische Funktionen zu aktivieren und Venice mit Agent-Frameworks, Coding-Tools und Medien-Workflows zu verbinden.
-
- Erstellen und verwalten Sie API-Schlüssel über das Venice-Dashboard.
+
+ Erstelle und verwalte API-Keys über das Venice-Dashboard.
- Wechseln Sie OpenAI-kompatible Apps zu Venice durch Ändern der Base-URL.
+ Stelle OpenAI-kompatible Anwendungen auf Venice um, indem du die Base-URL änderst.
- Fordern Sie Antworten an, die einem JSON-Schema entsprechen.
+ Fordere Antworten an, die einem JSON-Schema entsprechen.
-
- Senden Sie Dokumente und Quelldateien an Chat-Modelle.
+
+ Lass Modelle deine Anwendungs-Tools mit strukturierten Argumenten aufrufen.
-
- Reduzieren Sie Latenz und Kosten für wiederholten Prompt-Inhalt.
+
+ Analysiere Bilder mit multimodalen Chatmodellen.
-
- Bauen Sie einen Python-Forschungsagenten, der Quellen sammelt und zitierte Berichte schreibt.
+
+ Erzeuge Vektoren für semantische Suche, RAG und Empfehlungen.
+
+
+ Sende Dokumente und Quelldateien an Chatmodelle.
+
+
+ Reduziere Latenz und Kosten für wiederholte Prompt-Inhalte.
+
+
+ Baue einen Python-Research-Agenten, der Quellen sammelt und Berichte mit Quellenangaben schreibt.
@@ -30,24 +39,24 @@ Verwenden Sie diese Leitfäden, um API-Schlüssel zu generieren, vorhandene Open
- API-Schlüssel, Migration, autonome Schlüsselerstellung und Postman.
+ API-Keys, Migration, autonome Key-Erstellung und Postman.
- Strukturierte Ausgaben, Reasoning-Modelle, File Inputs, Prompt Caching und Modelle mit erhöhtem Datenschutz.
+ Strukturierte Ausgaben, Reasoning-Modelle, Function Calling, Vision, Embeddings, Datei-Eingaben, Prompt-Caching und datenschutzverbesserte Modelle.
-
- Bildgenerierung, Bildbearbeitung, Videogenerierung, Referenzen und Upscaling.
+
+ Bilderzeugung, Bildbearbeitung, Upscaling, Videoerzeugung, Text-to-Speech, Speech-to-Text und Voice Cloning.
-
- Agent-Apps, Assistenz-Tools, Crypto RPC, Wallet-Auth und Community-Integrationen.
+
+ Agent-Apps, Assistenz-Tools, Crypto-RPC, Wallet-Auth und Community-Integrationen.
- Verwenden Sie Venice-Modelle mit Claude Code, Cursor, OpenCode und Codex CLI.
+ Nutze Venice-Modelle mit Claude Code, Cursor, OpenCode und Codex CLI.
- Bauen Sie mit LangChain, Vercel AI SDK und CrewAI.
+ Entwickle mit LangChain, Vercel AI SDK und CrewAI.
- Bauen Sie Ihre eigenen Projekte anhand eines unserer Projekt-Walkthroughs.
+ Baue eigene Projekte mit Hilfe unserer Projekt-Walkthroughs.
diff --git a/de/guides/projects/overview.mdx b/de/guides/projects/overview.mdx
new file mode 100644
index 00000000..08f26939
--- /dev/null
+++ b/de/guides/projects/overview.mdx
@@ -0,0 +1,85 @@
+---
+title: "Demos & Projekte"
+sidebarTitle: "Übersicht"
+description: "Vollständige Demo-Projekte auf Basis der Venice-API, mit lauffähigem Code, den du ausführen, lesen und für deine eigenen Anwendungen anpassen kannst."
+"og:title": "Demos | Venice API Docs"
+---
+
+
+
+
+
+ Python
+
+
Privater RAG-Bot
+
Fundierte, zitierbare Antworten aus deinen eigenen Dokumenten mit neu gewichtetem Retrieval.
diff --git a/es/models/overview.mdx b/es/models/overview.mdx
index 1c393154..b20b163f 100644
--- a/es/models/overview.mdx
+++ b/es/models/overview.mdx
@@ -1,5 +1,6 @@
---
-title: "Modelos"
+title: "Todos los modelos"
+sidebarTitle: "Todos los modelos"
description: "Catálogo de todos los modelos disponibles en la API de Venice para texto, imagen, vídeo, audio, embeddings y voz, con capacidades, precios e IDs de modelo."
"og:title": "Models | Venice API Docs"
mode: "wide"
diff --git a/fr/guides/features/embeddings.mdx b/fr/guides/features/embeddings.mdx
new file mode 100644
index 00000000..36f43df6
--- /dev/null
+++ b/fr/guides/features/embeddings.mdx
@@ -0,0 +1,102 @@
+---
+title: "Embeddings"
+description: "Générez des embeddings vectoriels avec Venice pour la recherche sémantique, la récupération RAG, le clustering et les recommandations à l'aide du point de terminaison /embeddings."
+'og:title': "Embeddings | Documentation de l'API Venice"
+'og:description': "Apprenez à générer des embeddings vectoriels avec l'API Venice."
+---
+
+Les embeddings convertissent le texte en vecteurs qui capturent le sens sémantique. Utilisez-les pour la recherche, la génération augmentée par récupération (RAG), le clustering, les recommandations, la déduplication et le calcul de similarité.
+
+Le point de terminaison d'embeddings de Venice est compatible avec OpenAI. Envoyez une chaîne de caractères ou un tableau de chaînes à `/embeddings`, puis stockez les vecteurs retournés dans votre base de données ou votre index vectoriel.
+
+## Utilisation de base
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+response = client.embeddings.create(
+ model="text-embedding-bge-m3",
+ input="Privacy-first AI infrastructure for semantic search",
+)
+
+vector = response.data[0].embedding
+print(len(vector), vector[:5])
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const response = await client.embeddings.create({
+ model: "text-embedding-bge-m3",
+ input: "Privacy-first AI infrastructure for semantic search",
+});
+
+const vector = response.data[0].embedding;
+console.log(vector.length, vector.slice(0, 5));
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/embeddings \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "text-embedding-bge-m3",
+ "input": "Privacy-first AI infrastructure for semantic search",
+ "encoding_format": "float"
+ }'
+```
+
+
+## Entrées par lots
+
+Passez un tableau de chaînes pour embarquer plusieurs textes en une seule requête :
+
+```json
+{
+ "model": "text-embedding-bge-m3",
+ "input": [
+ "Venice supports private chat completions.",
+ "Embeddings help retrieve relevant documents.",
+ "Vector search powers RAG applications."
+ ]
+}
+```
+
+La réponse conserve l'ordre des entrées. Stockez chaque vecteur avec l'identifiant du texte source, les métadonnées et l'identifiant du modèle d'embedding.
+
+## Flux de travail courant
+
+1. Découpez les documents sources en fragments.
+2. Générez les embeddings pour chaque fragment.
+3. Stockez les vecteurs et les métadonnées dans une base de données vectorielle.
+4. Générez l'embedding de la requête utilisateur.
+5. Récupérez les fragments proches.
+6. Envoyez le contexte récupéré à un modèle de chat.
+
+Pour une implémentation complète, consultez [Créer un bot RAG privé](/guides/projects/private-rag-bot).
+
+## Choix du modèle
+
+Consultez la page [Modèles d'embeddings](/models/embeddings) pour comparer les modèles d'embeddings actuels, leurs dimensions et leurs tarifs.
+
+
+Utilisez le même modèle d'embedding pour l'indexation et l'interrogation. Mélanger les modèles peut rendre les scores de similarité peu fiables car les espaces vectoriels ne sont pas interchangeables.
+
+
+## Ressources connexes
+
+- [API Embeddings](/api-reference/endpoint/embeddings/generate)
+- [Modèles d'embeddings](/models/embeddings)
+- [Guide du bot RAG privé](/guides/projects/private-rag-bot)
diff --git a/fr/guides/features/function-calling.mdx b/fr/guides/features/function-calling.mdx
new file mode 100644
index 00000000..05f6e6e5
--- /dev/null
+++ b/fr/guides/features/function-calling.mdx
@@ -0,0 +1,174 @@
+---
+title: "Appel de fonctions"
+description: "Permettez aux modèles de chat Venice d'appeler les outils de votre application avec l'appel de fonctions compatible OpenAI et l'API de complétions de chat."
+'og:title': "Appel de fonctions | Documentation de l'API Venice"
+'og:description': "Apprenez à utiliser l'appel de fonctions avec les modèles de chat Venice."
+---
+
+L'appel de fonctions permet au modèle de choisir des appels d'outils structurés que votre application peut exécuter. Le modèle n'exécute pas la fonction lui-même. Il retourne le nom de la fonction et ses arguments, votre code exécute la fonction, et vous renvoyez le résultat au modèle.
+
+Utilisez l'appel de fonctions lorsque le modèle a besoin de données en direct, d'actions applicatives, de recherches en base de données ou de calculs déterministes.
+
+## Définition d'outil de base
+
+Définissez les outils avec le tableau `tools` compatible OpenAI :
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "Get the current weather in a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "City and state, such as San Francisco, CA",
+ }
+ },
+ "required": ["location"],
+ },
+ },
+ }
+]
+
+response = client.chat.completions.create(
+ model="zai-org-glm-5",
+ messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
+ tools=tools,
+)
+
+print(response.choices[0].message.tool_calls)
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const tools = [
+ {
+ type: "function",
+ function: {
+ name: "get_weather",
+ description: "Get the current weather in a location",
+ parameters: {
+ type: "object",
+ properties: {
+ location: {
+ type: "string",
+ description: "City and state, such as San Francisco, CA",
+ },
+ },
+ required: ["location"],
+ },
+ },
+ },
+];
+
+const response = await client.chat.completions.create({
+ model: "zai-org-glm-5",
+ messages: [{ role: "user", content: "What is the weather in San Francisco?" }],
+ tools,
+});
+
+console.log(response.choices[0].message.tool_calls);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/chat/completions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "zai-org-glm-5",
+ "messages": [
+ {"role": "user", "content": "What is the weather in San Francisco?"}
+ ],
+ "tools": [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "Get the current weather in a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "City and state, such as San Francisco, CA"
+ }
+ },
+ "required": ["location"]
+ }
+ }
+ }
+ ]
+ }'
+```
+
+
+## Exécuter l'outil
+
+Lorsque le modèle choisit un outil, inspectez `message.tool_calls`, analysez les arguments, exécutez la fonction de votre application, puis renvoyez le résultat sous forme de message `tool`.
+
+```python Python
+import json
+
+message = response.choices[0].message
+tool_call = message.tool_calls[0]
+arguments = json.loads(tool_call.function.arguments)
+
+weather = get_weather(arguments["location"])
+
+follow_up = client.chat.completions.create(
+ model="zai-org-glm-5",
+ messages=[
+ {"role": "user", "content": "What is the weather in San Francisco?"},
+ message.model_dump(),
+ {
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "content": json.dumps(weather),
+ },
+ ],
+ tools=tools,
+)
+
+print(follow_up.choices[0].message.content)
+```
+
+## Choisir un modèle
+
+La prise en charge de l'appel de fonctions dépend du modèle. Consultez la page [Modèles de texte](/models/text) ou l'[API Models](/api-reference/endpoint/models/list) pour trouver les modèles avec `supportsFunctionCalling`.
+
+
+Traitez les arguments d'outil comme une entrée non fiable. Validez les arguments avant de les utiliser dans des requêtes de base de données, des commandes shell, des paiements ou d'autres opérations à effet de bord.
+
+
+## Conseils de conception
+
+- Gardez les noms et descriptions d'outils courts et littéraux.
+- Utilisez JSON Schema pour faciliter la génération d'arguments valides par le modèle.
+- Préférez des outils restreints avec des entrées claires à un outil unique et large avec de nombreux comportements optionnels.
+- Retournez des résultats d'outils concis afin que la réponse finale ait suffisamment de contexte sans gaspiller de jetons.
+
+## Ressources connexes
+
+- [API Chat Completions](/api-reference/endpoint/chat/completions)
+- [Modèles de texte](/models/text)
+- [Guide des réponses structurées](/guides/features/structured-responses)
+- [Intégration LangChain](/guides/integrations/langchain#function-calling-with-agents)
diff --git a/fr/guides/features/vision.mdx b/fr/guides/features/vision.mdx
new file mode 100644
index 00000000..7da858e6
--- /dev/null
+++ b/fr/guides/features/vision.mdx
@@ -0,0 +1,131 @@
+---
+title: "Vision"
+description: "Analysez des images avec les modèles de chat Venice compatibles avec la vision en utilisant du contenu de message multimodal dans l'API de complétions de chat compatible OpenAI."
+'og:title': "Vision | Documentation de l'API Venice"
+'og:description': "Apprenez à envoyer des images aux modèles de vision Venice."
+---
+
+Les modèles de vision peuvent analyser des images en même temps que des invites textuelles. Utilisez-les pour la compréhension d'images, l'extraction, la classification, la réponse aux questions visuelles et le raisonnement multimodal.
+
+Venice prend en charge les messages de chat multimodaux compatibles OpenAI. Placez des blocs de texte et d'image dans le même message utilisateur, puis envoyez la requête à un modèle compatible avec la vision.
+
+## Utilisation de base
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+response = client.chat.completions.create(
+ model="qwen3-vl-235b-a22b",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Describe this image in three bullets."},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://www.gstatic.com/webp/gallery/1.jpg"
+ },
+ },
+ ],
+ }
+ ],
+)
+
+print(response.choices[0].message.content)
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const response = await client.chat.completions.create({
+ model: "qwen3-vl-235b-a22b",
+ messages: [
+ {
+ role: "user",
+ content: [
+ { type: "text", text: "Describe this image in three bullets." },
+ {
+ type: "image_url",
+ image_url: {
+ url: "https://www.gstatic.com/webp/gallery/1.jpg",
+ },
+ },
+ ],
+ },
+ ],
+});
+
+console.log(response.choices[0].message.content);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/chat/completions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "qwen3-vl-235b-a22b",
+ "messages": [
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Describe this image in three bullets."},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://www.gstatic.com/webp/gallery/1.jpg"
+ }
+ }
+ ]
+ }
+ ]
+ }'
+```
+
+
+## Utiliser des images en base64
+
+Vous pouvez également transmettre une URL de données en base64 lorsque l'image est locale ou privée :
+
+```json
+{
+ "type": "image_url",
+ "image_url": {
+ "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
+ }
+}
+```
+
+## Choisir un modèle de vision
+
+Consultez la page [Modèles de texte](/models/text) ou l'[API Models](/api-reference/endpoint/models/list) pour trouver les modèles qui prennent en charge la vision. La prise en charge de la vision est indiquée dans les capacités du modèle.
+
+
+Pour les entrées de type document, utilisez les [Entrées de fichier](/guides/features/file-inputs) lorsque vous souhaitez que Venice extraie le texte d'un fichier. Utilisez la vision lorsque la mise en page visuelle ou le contenu de l'image lui-même est important.
+
+
+## Conseils de rédaction d'invites
+
+- Indiquez au modèle ce sur quoi se concentrer : objets, texte, mise en page, sécurité, défauts ou différences.
+- Demandez une sortie structurée lorsque votre application a besoin de champs analysables.
+- Gardez les URL d'images accessibles à l'API, ou utilisez des URL de données en base64 pour les images privées.
+- Utilisez un modèle avec un contexte suffisant si vous combinez des images avec de longues instructions.
+
+## Ressources connexes
+
+- [API Chat Completions](/api-reference/endpoint/chat/completions)
+- [Modèles de texte](/models/text)
+- [Guide des entrées de fichier](/guides/features/file-inputs)
+- [Guide des réponses structurées](/guides/features/structured-responses)
diff --git a/fr/guides/media/image-upscaling.mdx b/fr/guides/media/image-upscaling.mdx
new file mode 100644
index 00000000..ac185574
--- /dev/null
+++ b/fr/guides/media/image-upscaling.mdx
@@ -0,0 +1,100 @@
+---
+title: "Agrandissement d'image"
+description: "Améliorez et agrandissez des images avec l'API synchrone d'agrandissement d'image de Venice en utilisant une entrée en base64 et une sortie image binaire."
+'og:title': "Agrandissement d'image | Documentation de l'API Venice"
+'og:description': "Apprenez à améliorer et agrandir des images avec l'API Venice."
+---
+
+L'agrandissement d'image améliore la résolution et la qualité visuelle d'une image existante. Envoyez une image encodée en base64 à `/image/upscale`, choisissez un facteur d'échelle, et Venice retourne l'image améliorée sous forme de données binaires.
+
+Utilisez l'agrandissement d'image lorsque vous disposez déjà d'une image et souhaitez obtenir une sortie de plus haute résolution. Utilisez la [génération d'image](/guides/media/image-generation) lorsque vous devez créer une image à partir d'une invite, et l'[édition d'image](/guides/media/image-editing) lorsque vous devez modifier le contenu de l'image.
+
+## Utilisation de base
+
+
+```python Python
+import base64
+import os
+from pathlib import Path
+
+import requests
+
+image_base64 = base64.b64encode(Path("input.jpg").read_bytes()).decode("utf-8")
+
+response = requests.post(
+ "https://api.venice.ai/api/v1/image/upscale",
+ headers={
+ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
+ "Content-Type": "application/json",
+ },
+ json={
+ "image": image_base64,
+ "scale": 2,
+ },
+)
+
+response.raise_for_status()
+Path("upscaled.png").write_bytes(response.content)
+```
+
+```javascript Node.js
+import { readFile, writeFile } from "node:fs/promises";
+
+const image = await readFile("input.jpg");
+
+const response = await fetch("https://api.venice.ai/api/v1/image/upscale", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ image: image.toString("base64"),
+ scale: 2,
+ }),
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+const output = Buffer.from(await response.arrayBuffer());
+await writeFile("upscaled.png", output);
+```
+
+```bash cURL
+IMAGE_BASE64=$(base64 < input.jpg | tr -d '\n')
+
+curl https://api.venice.ai/api/v1/image/upscale \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d "{
+ \"image\": \"$IMAGE_BASE64\",
+ \"scale\": 2
+ }" \
+ --output upscaled.png
+```
+
+
+## Paramètres
+
+| Paramètre | Type | Requis | Description |
+|-----------|------|--------|-------------|
+| `image` | string | Oui | Image source encodée en base64. |
+| `scale` | number | Non | Facteur d'agrandissement. Utilisez les valeurs prises en charge listées dans la référence de l'API et le catalogue de modèles. |
+
+
+La réponse est constituée de données d'image binaires, et non de JSON. Écrivez le corps de la réponse directement dans un fichier ou envoyez-le en flux vers le stockage.
+
+
+## Conseils sur l'entrée
+
+- Commencez avec l'image source la plus nette dont vous disposez. L'agrandissement améliore les détails, mais ne peut pas récupérer entièrement les informations absentes.
+- Utilisez des facteurs d'échelle modérés pour les flux de production. Des sorties très grandes peuvent augmenter la latence et la taille des fichiers.
+- Conservez l'image originale si vous devez comparer la qualité ou réessayer avec d'autres paramètres.
+
+## Ressources connexes
+
+- [API Image Upscale](/api-reference/endpoint/image/upscale)
+- [Modèles d'image](/models/image)
+- [Guide d'édition d'image](/guides/media/image-editing)
diff --git a/fr/guides/media/speech-to-text.mdx b/fr/guides/media/speech-to-text.mdx
new file mode 100644
index 00000000..c34e2910
--- /dev/null
+++ b/fr/guides/media/speech-to-text.mdx
@@ -0,0 +1,96 @@
+---
+title: "Reconnaissance vocale"
+description: "Transcrivez des fichiers audio avec les modèles de reconnaissance vocale de Venice à l'aide du point de terminaison /audio/transcriptions compatible OpenAI."
+'og:title': "Reconnaissance vocale | Documentation de l'API Venice"
+'og:description': "Apprenez à transcrire des fichiers audio avec l'API Venice."
+---
+
+La reconnaissance vocale transcrit l'audio parlé en texte écrit. Envoyez un fichier audio à `/audio/transcriptions`, choisissez un modèle de transcription et sélectionnez le format de réponse que vous souhaitez recevoir.
+
+## Utilisation de base
+
+
+```python Python
+import os
+
+import requests
+
+with open("meeting.mp3", "rb") as audio:
+ response = requests.post(
+ "https://api.venice.ai/api/v1/audio/transcriptions",
+ headers={"Authorization": f"Bearer {os.environ['VENICE_API_KEY']}"},
+ files={"file": audio},
+ data={
+ "model": "nvidia/parakeet-tdt-0.6b-v3",
+ "response_format": "json",
+ },
+ )
+
+response.raise_for_status()
+print(response.json()["text"])
+```
+
+```javascript Node.js
+import { createReadStream } from "node:fs";
+import FormData from "form-data";
+
+const form = new FormData();
+form.append("file", createReadStream("meeting.mp3"));
+form.append("model", "nvidia/parakeet-tdt-0.6b-v3");
+form.append("response_format", "json");
+
+const response = await fetch("https://api.venice.ai/api/v1/audio/transcriptions", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ ...form.getHeaders(),
+ },
+ body: form,
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+const transcript = await response.json();
+console.log(transcript.text);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/audio/transcriptions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ --form file=@meeting.mp3 \
+ --form model=nvidia/parakeet-tdt-0.6b-v3 \
+ --form response_format=json
+```
+
+
+## Entrées prises en charge
+
+Les formats audio courants incluent `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `wav`, `webm`, `flac` et `ogg`. Consultez la page [Modèles de reconnaissance vocale](/models/speech-to-text) pour connaître les modèles actuellement pris en charge et les tarifs.
+
+## Formats de réponse
+
+| Format | À utiliser lorsque |
+|--------|-------------------|
+| `json` | Vous souhaitez une réponse simple `{ "text": "..." }`. |
+| `text` | Vous souhaitez du texte brut sans analyse JSON. |
+| `srt` | Vous avez besoin de sous-titres SubRip. |
+| `vtt` | Vous avez besoin de sous-titres WebVTT. |
+| `verbose_json` | Vous avez besoin de métadonnées de segments et d'horodatages plus riches. |
+
+
+Utilisez les formats de sous-titres lorsque la transcription sera associée à la lecture d'un média. Utilisez `json` ou `text` lorsque la transcription alimente la synthèse, la recherche ou des invites de chat en aval.
+
+
+## Conseils pour la production
+
+- Gardez l'audio clair et évitez les locuteurs qui se chevauchent lorsque possible.
+- Découpez les enregistrements très longs en fragments plus petits si votre flux de travail nécessite une latence plus faible ou des réessais plus faciles.
+- Stockez le chemin audio d'origine, l'identifiant du modèle et le format de réponse avec chaque transcription pour la traçabilité.
+
+## Ressources connexes
+
+- [API Audio Transcriptions](/api-reference/endpoint/audio/transcriptions)
+- [Modèles de reconnaissance vocale](/models/speech-to-text)
+- [Guide de synthèse vocale](/guides/media/text-to-speech)
diff --git a/fr/guides/media/text-to-speech.mdx b/fr/guides/media/text-to-speech.mdx
new file mode 100644
index 00000000..e6526157
--- /dev/null
+++ b/fr/guides/media/text-to-speech.mdx
@@ -0,0 +1,102 @@
+---
+title: "Synthèse vocale"
+description: "Générez de l'audio parlé à partir de texte avec les modèles de synthèse vocale de Venice, des voix spécifiques au modèle et le point de terminaison /audio/speech."
+'og:title': "Synthèse vocale | Documentation de l'API Venice"
+'og:description': "Apprenez à convertir du texte en parole avec l'API Venice."
+---
+
+La synthèse vocale transforme du texte écrit en audio parlé. Choisissez un modèle TTS, sélectionnez une voix prise en charge par ce modèle, envoyez le texte à `/audio/speech` et enregistrez la réponse audio binaire.
+
+Utilisez ce guide pour la génération de voix standard. Si vous souhaitez créer de la parole à partir d'une voix de référence personnalisée, consultez [Clonage de voix](/guides/media/voice-cloning).
+
+## Utilisation de base
+
+
+```python Python
+import os
+from pathlib import Path
+
+import requests
+
+response = requests.post(
+ "https://api.venice.ai/api/v1/audio/speech",
+ headers={
+ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
+ "Content-Type": "application/json",
+ },
+ json={
+ "model": "tts-kokoro",
+ "voice": "af_sky",
+ "input": "Hello, welcome to Venice Voice.",
+ },
+)
+
+response.raise_for_status()
+Path("speech.mp3").write_bytes(response.content)
+```
+
+```javascript Node.js
+import { writeFile } from "node:fs/promises";
+
+const response = await fetch("https://api.venice.ai/api/v1/audio/speech", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ model: "tts-kokoro",
+ voice: "af_sky",
+ input: "Hello, welcome to Venice Voice.",
+ }),
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+await writeFile("speech.mp3", Buffer.from(await response.arrayBuffer()));
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/audio/speech \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "tts-kokoro",
+ "voice": "af_sky",
+ "input": "Hello, welcome to Venice Voice."
+ }' \
+ --output speech.mp3
+```
+
+
+## Choisir un modèle et une voix
+
+Les voix sont spécifiques au modèle. La valeur `voice` doit être valide pour le `model` choisi.
+
+Consultez la page [Modèles de synthèse vocale](/models/text-to-speech) pour parcourir les modèles et les voix disponibles. Le sélecteur de voix indique les identifiants exacts à transmettre dans votre requête.
+
+
+Les identifiants de voix sont sensibles à la casse. Si vous changez de modèle TTS, mettez à jour la valeur `voice` en même temps.
+
+
+## Forme de la requête
+
+| Paramètre | Type | Requis | Description |
+|-----------|------|--------|-------------|
+| `model` | string | Oui | Identifiant du modèle de synthèse vocale. |
+| `voice` | string | Oui | Identifiant de voix pris en charge par le modèle sélectionné. |
+| `input` | string | Oui | Texte à synthétiser. |
+
+## Conseils pour la production
+
+- Mettez en cache l'audio généré lorsque le texte source et la voix sont réutilisés.
+- Normalisez et relisez le texte avant la synthèse. La ponctuation influence le rythme et l'intonation.
+- Stockez la sortie avec l'extension de fichier appropriée au format de réponse du modèle.
+
+## Ressources connexes
+
+- [API Audio Speech](/api-reference/endpoint/audio/speech)
+- [Modèles de synthèse vocale](/models/text-to-speech)
+- [Guide de clonage de voix](/guides/media/voice-cloning)
diff --git a/fr/guides/overview.mdx b/fr/guides/overview.mdx
index 14fbe2d4..8d72c0c1 100644
--- a/fr/guides/overview.mdx
+++ b/fr/guides/overview.mdx
@@ -1,25 +1,34 @@
---
title: Guides
-description: "Guides pratiques de l'API Venice : clés d'API, migration OpenAI, réponses structurées, entrées de fichiers, cache de prompts, médias et agents."
+description: Guides pratiques de l'API Venice pour les clés API, la migration depuis OpenAI, les capacités de chat, les embeddings, les médias et les intégrations d'agents.
---
-Utilisez ces guides pour générer des clés API, migrer des applications OpenAI existantes, activer les fonctionnalités spécifiques à Venice, et connecter Venice à des frameworks d'agents, des outils de codage et des workflows multimédias.
+Utilisez ces guides pour générer des clés API, migrer les applications OpenAI existantes, activer les capacités spécifiques à Venice, et connecter Venice à des frameworks d'agents, des outils de codage et des flux de travail médias.
- Créez et gérez les clés API depuis le tableau de bord Venice.
+ Créez et gérez des clés API depuis le tableau de bord Venice.
Basculez les applications compatibles OpenAI vers Venice en changeant l'URL de base.
- Demandez des réponses correspondant à un schéma JSON.
+ Demandez des réponses qui correspondent à un schéma JSON.
-
- Envoyez des documents et fichiers sources aux modèles de chat.
+
+ Permettez aux modèles d'appeler les outils de votre application avec des arguments structurés.
-
- Réduisez la latence et le coût pour le contenu de prompt répété.
+
+ Analysez des images avec des modèles de chat multimodaux.
+
+
+ Générez des vecteurs pour la recherche sémantique, le RAG et les recommandations.
+
+
+ Envoyez des documents et des fichiers sources aux modèles de chat.
+
+
+ Réduisez la latence et le coût pour le contenu d'invite répété.
Construisez un agent de recherche Python qui collecte des sources et rédige des rapports cités.
@@ -29,25 +38,25 @@ Utilisez ces guides pour générer des clés API, migrer des applications OpenAI
## Explorer par sujet
-
- Clés API, migration, création de clés autonomes et Postman.
+
+ Clés API, migration, création autonome de clés et Postman.
- Sorties structurées, modèles de raisonnement, entrées de fichiers, mise en cache des prompts et modèles à confidentialité renforcée.
+ Sorties structurées, modèles de raisonnement, appel de fonctions, vision, embeddings, entrées de fichier, mise en cache des invites et modèles à confidentialité renforcée.
-
- Génération d'image, édition d'image, génération vidéo, références et agrandissement.
+
+ Génération d'image, édition d'image, agrandissement, génération de vidéo, synthèse vocale, reconnaissance vocale et clonage de voix.
- Applications d'agent, outils d'assistant, RPC crypto, authentification par portefeuille et intégrations communautaires.
+ Applications d'agents, outils d'assistant, RPC crypto, authentification de portefeuille et intégrations communautaires.
Utilisez les modèles Venice avec Claude Code, Cursor, OpenCode et Codex CLI.
- Construisez avec LangChain, Vercel AI SDK et CrewAI.
+ Développez avec LangChain, le SDK IA Vercel et CrewAI.
- Construisez vos propres projets en utilisant l'un de nos tutoriels.
+ Construisez vos propres projets en suivant l'un de nos tutoriels de projets.
diff --git a/fr/guides/projects/overview.mdx b/fr/guides/projects/overview.mdx
new file mode 100644
index 00000000..a6f5efc5
--- /dev/null
+++ b/fr/guides/projects/overview.mdx
@@ -0,0 +1,85 @@
+---
+title: "Démos et projets"
+sidebarTitle: "Aperçu"
+description: "Des projets de démonstration complets construits sur l'API Venice, avec du code fonctionnel que vous pouvez exécuter, lire et adapter à vos propres applications."
+"og:title": "Demos | Venice API Docs"
+---
+
+
+
+
+
+ Python
+
+
Bot RAG privé
+
Des réponses fondées et citables à partir de vos propres documents grâce à une recherche ré-ordonnée.
diff --git a/it/guides/features/embeddings.mdx b/it/guides/features/embeddings.mdx
new file mode 100644
index 00000000..96a3989c
--- /dev/null
+++ b/it/guides/features/embeddings.mdx
@@ -0,0 +1,102 @@
+---
+title: "Embeddings"
+description: "Genera embedding vettoriali con Venice per ricerca semantica, recupero RAG, clustering e raccomandazioni usando l'endpoint /embeddings."
+'og:title': "Embeddings | Documentazione API Venice"
+'og:description': "Scopri come generare embedding vettoriali con l'API di Venice."
+---
+
+Gli embedding convertono il testo in vettori che catturano il significato semantico. Usali per ricerca, generazione aumentata da recupero (RAG), clustering, raccomandazioni, deduplicazione e calcolo della similarità.
+
+L'endpoint embeddings di Venice è compatibile con OpenAI. Invia una singola stringa o un array di stringhe a `/embeddings`, quindi memorizza i vettori restituiti nel tuo database o indice vettoriale.
+
+## Utilizzo di Base
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+response = client.embeddings.create(
+ model="text-embedding-bge-m3",
+ input="Privacy-first AI infrastructure for semantic search",
+)
+
+vector = response.data[0].embedding
+print(len(vector), vector[:5])
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const response = await client.embeddings.create({
+ model: "text-embedding-bge-m3",
+ input: "Privacy-first AI infrastructure for semantic search",
+});
+
+const vector = response.data[0].embedding;
+console.log(vector.length, vector.slice(0, 5));
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/embeddings \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "text-embedding-bge-m3",
+ "input": "Privacy-first AI infrastructure for semantic search",
+ "encoding_format": "float"
+ }'
+```
+
+
+## Input in Batch
+
+Passa un array di stringhe per generare embedding di più testi in una singola richiesta:
+
+```json
+{
+ "model": "text-embedding-bge-m3",
+ "input": [
+ "Venice supports private chat completions.",
+ "Embeddings help retrieve relevant documents.",
+ "Vector search powers RAG applications."
+ ]
+}
+```
+
+La risposta preserva l'ordine dell'input. Memorizza ogni vettore insieme all'ID del testo di origine, ai metadati e all'ID del modello di embedding.
+
+## Flusso di Lavoro Tipico
+
+1. Suddividi i documenti di origine in chunk.
+2. Genera gli embedding per ogni chunk.
+3. Memorizza vettori e metadati in un database vettoriale.
+4. Genera l'embedding della query dell'utente.
+5. Recupera i chunk più vicini.
+6. Invia il contesto recuperato a un modello di chat.
+
+Per un'implementazione completa, consulta [Creare un Bot RAG Privato](/guides/projects/private-rag-bot).
+
+## Scelta del Modello
+
+Usa la pagina [Modelli di Embedding](/models/embeddings) per confrontare i modelli di embedding disponibili, le dimensioni e i prezzi.
+
+
+Usa lo stesso modello di embedding per l'indicizzazione e per le query. Mescolare modelli diversi può rendere i punteggi di similarità inaffidabili perché gli spazi vettoriali non sono interscambiabili.
+
+
+## Risorse Correlate
+
+- [API Embeddings](/api-reference/endpoint/embeddings/generate)
+- [Modelli di Embedding](/models/embeddings)
+- [Guida al Bot RAG Privato](/guides/projects/private-rag-bot)
diff --git a/it/guides/features/function-calling.mdx b/it/guides/features/function-calling.mdx
new file mode 100644
index 00000000..008233b9
--- /dev/null
+++ b/it/guides/features/function-calling.mdx
@@ -0,0 +1,174 @@
+---
+title: "Function Calling"
+description: "Permetti ai modelli di chat Venice di richiamare gli strumenti della tua applicazione con function calling compatibile con OpenAI e l'API chat completions."
+'og:title': "Function Calling | Documentazione API Venice"
+'og:description': "Scopri come usare il function calling con i modelli di chat Venice."
+---
+
+Il function calling permette a un modello di scegliere chiamate strutturate a strumenti che la tua applicazione può eseguire. Il modello non esegue direttamente la funzione. Restituisce il nome della funzione e gli argomenti, il tuo codice esegue la funzione e tu invii il risultato al modello.
+
+Usa il function calling quando il modello ha bisogno di dati in tempo reale, azioni applicative, ricerche in un database o calcoli deterministici.
+
+## Definizione di Base di uno Strumento
+
+Definisci gli strumenti con l'array `tools` compatibile con OpenAI:
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+tools = [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "Get the current weather in a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "City and state, such as San Francisco, CA",
+ }
+ },
+ "required": ["location"],
+ },
+ },
+ }
+]
+
+response = client.chat.completions.create(
+ model="zai-org-glm-5",
+ messages=[{"role": "user", "content": "What is the weather in San Francisco?"}],
+ tools=tools,
+)
+
+print(response.choices[0].message.tool_calls)
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const tools = [
+ {
+ type: "function",
+ function: {
+ name: "get_weather",
+ description: "Get the current weather in a location",
+ parameters: {
+ type: "object",
+ properties: {
+ location: {
+ type: "string",
+ description: "City and state, such as San Francisco, CA",
+ },
+ },
+ required: ["location"],
+ },
+ },
+ },
+];
+
+const response = await client.chat.completions.create({
+ model: "zai-org-glm-5",
+ messages: [{ role: "user", content: "What is the weather in San Francisco?" }],
+ tools,
+});
+
+console.log(response.choices[0].message.tool_calls);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/chat/completions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "zai-org-glm-5",
+ "messages": [
+ {"role": "user", "content": "What is the weather in San Francisco?"}
+ ],
+ "tools": [
+ {
+ "type": "function",
+ "function": {
+ "name": "get_weather",
+ "description": "Get the current weather in a location",
+ "parameters": {
+ "type": "object",
+ "properties": {
+ "location": {
+ "type": "string",
+ "description": "City and state, such as San Francisco, CA"
+ }
+ },
+ "required": ["location"]
+ }
+ }
+ }
+ ]
+ }'
+```
+
+
+## Esecuzione dello Strumento
+
+Quando il modello sceglie uno strumento, analizza `message.tool_calls`, effettua il parsing degli argomenti, esegui la funzione della tua applicazione e poi invia il risultato come messaggio `tool`.
+
+```python Python
+import json
+
+message = response.choices[0].message
+tool_call = message.tool_calls[0]
+arguments = json.loads(tool_call.function.arguments)
+
+weather = get_weather(arguments["location"])
+
+follow_up = client.chat.completions.create(
+ model="zai-org-glm-5",
+ messages=[
+ {"role": "user", "content": "What is the weather in San Francisco?"},
+ message.model_dump(),
+ {
+ "role": "tool",
+ "tool_call_id": tool_call.id,
+ "content": json.dumps(weather),
+ },
+ ],
+ tools=tools,
+)
+
+print(follow_up.choices[0].message.content)
+```
+
+## Scelta di un Modello
+
+Il supporto al function calling dipende dal modello. Usa la pagina [Modelli di Testo](/models/text) o l'[API Modelli](/api-reference/endpoint/models/list) per trovare modelli con `supportsFunctionCalling`.
+
+
+Considera gli argomenti degli strumenti come input non attendibile. Convalida gli argomenti prima di usarli in query al database, comandi shell, pagamenti o altre operazioni con effetti collaterali.
+
+
+## Consigli di Progettazione
+
+- Mantieni i nomi e le descrizioni degli strumenti brevi e letterali.
+- Usa JSON Schema per rendere semplice al modello la produzione di argomenti validi.
+- Preferisci strumenti specifici con input chiari rispetto a un unico strumento generico con molti comportamenti opzionali.
+- Restituisci risultati degli strumenti concisi affinché la risposta finale abbia contesto sufficiente senza sprecare token.
+
+## Risorse Correlate
+
+- [API Chat Completions](/api-reference/endpoint/chat/completions)
+- [Modelli di Testo](/models/text)
+- [Guida alle Risposte Strutturate](/guides/features/structured-responses)
+- [Integrazione LangChain](/guides/integrations/langchain#function-calling-with-agents)
diff --git a/it/guides/features/vision.mdx b/it/guides/features/vision.mdx
new file mode 100644
index 00000000..235fe3f0
--- /dev/null
+++ b/it/guides/features/vision.mdx
@@ -0,0 +1,131 @@
+---
+title: "Vision"
+description: "Analizza immagini con i modelli di chat Venice abilitati alla visione usando contenuti multimodali nei messaggi dell'API chat completions compatibile con OpenAI."
+'og:title': "Vision | Documentazione API Venice"
+'og:description': "Scopri come inviare immagini ai modelli di visione Venice."
+---
+
+I modelli di visione possono analizzare immagini insieme a prompt testuali. Usali per la comprensione delle immagini, l'estrazione, la classificazione, il visual question answering e il ragionamento multimodale.
+
+Venice supporta messaggi di chat multimodali compatibili con OpenAI. Inserisci blocchi di testo e immagini nello stesso messaggio utente, quindi invia la richiesta a un modello abilitato alla visione.
+
+## Utilizzo di Base
+
+
+```python Python
+import os
+from openai import OpenAI
+
+client = OpenAI(
+ api_key=os.environ["VENICE_API_KEY"],
+ base_url="https://api.venice.ai/api/v1",
+)
+
+response = client.chat.completions.create(
+ model="qwen3-vl-235b-a22b",
+ messages=[
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Describe this image in three bullets."},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://www.gstatic.com/webp/gallery/1.jpg"
+ },
+ },
+ ],
+ }
+ ],
+)
+
+print(response.choices[0].message.content)
+```
+
+```javascript Node.js
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ apiKey: process.env.VENICE_API_KEY,
+ baseURL: "https://api.venice.ai/api/v1",
+});
+
+const response = await client.chat.completions.create({
+ model: "qwen3-vl-235b-a22b",
+ messages: [
+ {
+ role: "user",
+ content: [
+ { type: "text", text: "Describe this image in three bullets." },
+ {
+ type: "image_url",
+ image_url: {
+ url: "https://www.gstatic.com/webp/gallery/1.jpg",
+ },
+ },
+ ],
+ },
+ ],
+});
+
+console.log(response.choices[0].message.content);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/chat/completions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "qwen3-vl-235b-a22b",
+ "messages": [
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Describe this image in three bullets."},
+ {
+ "type": "image_url",
+ "image_url": {
+ "url": "https://www.gstatic.com/webp/gallery/1.jpg"
+ }
+ }
+ ]
+ }
+ ]
+ }'
+```
+
+
+## Uso di Immagini Base64
+
+Puoi anche passare una data URL in base64 quando l'immagine è locale o privata:
+
+```json
+{
+ "type": "image_url",
+ "image_url": {
+ "url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
+ }
+}
+```
+
+## Scelta di un Modello di Visione
+
+Usa la pagina [Modelli di Testo](/models/text) o l'[API Modelli](/api-reference/endpoint/models/list) per trovare modelli che supportano la visione. Il supporto alla visione è indicato tra le capacità del modello.
+
+
+Per input di tipo documento, usa gli [Input da File](/guides/features/file-inputs) quando desideri che Venice estragga il testo da un file. Usa la visione quando conta il layout visivo o il contenuto stesso dell'immagine.
+
+
+## Consigli sul Prompting
+
+- Indica al modello su cosa concentrarsi: oggetti, testo, layout, sicurezza, difetti o differenze.
+- Richiedi output strutturato quando la tua applicazione ha bisogno di campi che puoi analizzare.
+- Assicurati che gli URL delle immagini siano accessibili all'API, oppure usa data URL in base64 per immagini private.
+- Usa un modello con contesto sufficiente se combini immagini con istruzioni lunghe.
+
+## Risorse Correlate
+
+- [API Chat Completions](/api-reference/endpoint/chat/completions)
+- [Modelli di Testo](/models/text)
+- [Guida agli Input da File](/guides/features/file-inputs)
+- [Guida alle Risposte Strutturate](/guides/features/structured-responses)
diff --git a/it/guides/media/image-upscaling.mdx b/it/guides/media/image-upscaling.mdx
new file mode 100644
index 00000000..ee8ee561
--- /dev/null
+++ b/it/guides/media/image-upscaling.mdx
@@ -0,0 +1,100 @@
+---
+title: "Upscaling delle Immagini"
+description: "Migliora e aumenta la risoluzione delle immagini con l'API sincrona di upscale immagini di Venice, con input in base64 e output binario dell'immagine."
+'og:title': "Upscaling delle Immagini | Documentazione API Venice"
+'og:description': "Scopri come migliorare e aumentare la risoluzione delle immagini con l'API di Venice."
+---
+
+L'upscaling delle immagini migliora la risoluzione e la qualità visiva di un'immagine esistente. Invia un'immagine codificata in base64 a `/image/upscale`, scegli un fattore di scala e Venice restituirà l'immagine migliorata come dati binari.
+
+Usa l'upscaling quando hai già un'immagine e vuoi ottenerne una versione a maggiore risoluzione. Usa la [generazione di immagini](/guides/media/image-generation) quando devi creare un'immagine da un prompt, e l'[editing di immagini](/guides/media/image-editing) quando devi modificare il contenuto di un'immagine.
+
+## Utilizzo di Base
+
+
+```python Python
+import base64
+import os
+from pathlib import Path
+
+import requests
+
+image_base64 = base64.b64encode(Path("input.jpg").read_bytes()).decode("utf-8")
+
+response = requests.post(
+ "https://api.venice.ai/api/v1/image/upscale",
+ headers={
+ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
+ "Content-Type": "application/json",
+ },
+ json={
+ "image": image_base64,
+ "scale": 2,
+ },
+)
+
+response.raise_for_status()
+Path("upscaled.png").write_bytes(response.content)
+```
+
+```javascript Node.js
+import { readFile, writeFile } from "node:fs/promises";
+
+const image = await readFile("input.jpg");
+
+const response = await fetch("https://api.venice.ai/api/v1/image/upscale", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ image: image.toString("base64"),
+ scale: 2,
+ }),
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+const output = Buffer.from(await response.arrayBuffer());
+await writeFile("upscaled.png", output);
+```
+
+```bash cURL
+IMAGE_BASE64=$(base64 < input.jpg | tr -d '\n')
+
+curl https://api.venice.ai/api/v1/image/upscale \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d "{
+ \"image\": \"$IMAGE_BASE64\",
+ \"scale\": 2
+ }" \
+ --output upscaled.png
+```
+
+
+## Parametri
+
+| Parametro | Tipo | Obbligatorio | Descrizione |
+|-----------|------|--------------|-------------|
+| `image` | string | Sì | Immagine sorgente codificata in base64. |
+| `scale` | number | No | Fattore di upscaling. Usa i valori supportati elencati nel riferimento API e nel catalogo dei modelli. |
+
+
+La risposta contiene dati binari dell'immagine, non JSON. Scrivi il corpo della risposta direttamente in un file o effettuane lo streaming verso uno storage.
+
+
+## Consigli sull'Input
+
+- Parti dall'immagine sorgente più pulita a tua disposizione. L'upscaling migliora il dettaglio, ma non può recuperare completamente informazioni non presenti.
+- Usa fattori di scala moderati nei flussi di produzione. Output molto grandi possono aumentare la latenza e la dimensione dei file.
+- Conserva l'immagine originale nel caso in cui debba confrontare la qualità o riprovare con impostazioni diverse.
+
+## Risorse Correlate
+
+- [API Upscale Immagini](/api-reference/endpoint/image/upscale)
+- [Modelli di Immagini](/models/image)
+- [Guida all'Editing di Immagini](/guides/media/image-editing)
diff --git a/it/guides/media/speech-to-text.mdx b/it/guides/media/speech-to-text.mdx
new file mode 100644
index 00000000..0b3e3477
--- /dev/null
+++ b/it/guides/media/speech-to-text.mdx
@@ -0,0 +1,96 @@
+---
+title: "Speech-to-Text"
+description: "Trascrivi file audio con i modelli speech-to-text di Venice usando l'endpoint /audio/transcriptions compatibile con OpenAI."
+'og:title': "Speech-to-Text | Documentazione API Venice"
+'og:description': "Scopri come trascrivere file audio con l'API di Venice."
+---
+
+Lo speech-to-text trascrive l'audio parlato in testo scritto. Invia un file audio a `/audio/transcriptions`, scegli un modello di trascrizione e seleziona il formato di risposta desiderato.
+
+## Utilizzo di Base
+
+
+```python Python
+import os
+
+import requests
+
+with open("meeting.mp3", "rb") as audio:
+ response = requests.post(
+ "https://api.venice.ai/api/v1/audio/transcriptions",
+ headers={"Authorization": f"Bearer {os.environ['VENICE_API_KEY']}"},
+ files={"file": audio},
+ data={
+ "model": "nvidia/parakeet-tdt-0.6b-v3",
+ "response_format": "json",
+ },
+ )
+
+response.raise_for_status()
+print(response.json()["text"])
+```
+
+```javascript Node.js
+import { createReadStream } from "node:fs";
+import FormData from "form-data";
+
+const form = new FormData();
+form.append("file", createReadStream("meeting.mp3"));
+form.append("model", "nvidia/parakeet-tdt-0.6b-v3");
+form.append("response_format", "json");
+
+const response = await fetch("https://api.venice.ai/api/v1/audio/transcriptions", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ ...form.getHeaders(),
+ },
+ body: form,
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+const transcript = await response.json();
+console.log(transcript.text);
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/audio/transcriptions \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ --form file=@meeting.mp3 \
+ --form model=nvidia/parakeet-tdt-0.6b-v3 \
+ --form response_format=json
+```
+
+
+## Input Supportati
+
+I formati audio comuni includono `mp3`, `mp4`, `mpeg`, `mpga`, `m4a`, `wav`, `webm`, `flac` e `ogg`. Consulta la pagina [Modelli Speech-to-Text](/models/speech-to-text) per il supporto dei modelli e i prezzi aggiornati.
+
+## Formati di Risposta
+
+| Formato | Da usare quando |
+|---------|-----------------|
+| `json` | Vuoi una risposta semplice del tipo `{ "text": "..." }`. |
+| `text` | Vuoi testo semplice senza fare parsing di JSON. |
+| `srt` | Hai bisogno di sottotitoli SubRip. |
+| `vtt` | Hai bisogno di sottotitoli WebVTT. |
+| `verbose_json` | Hai bisogno di timestamp più ricchi e metadati sui segmenti. |
+
+
+Usa i formati per sottotitoli quando la trascrizione verrà abbinata a una riproduzione multimediale. Usa `json` o `text` quando la trascrizione alimenta riassunti, ricerca o prompt di chat a valle.
+
+
+## Consigli per la Produzione
+
+- Mantieni l'audio chiaro ed evita, se possibile, la sovrapposizione tra parlanti.
+- Suddividi registrazioni molto lunghe in chunk più piccoli se il tuo flusso richiede minore latenza o retry più semplici.
+- Memorizza il percorso audio originale, l'ID del modello e il formato di risposta insieme a ogni trascrizione per esigenze di tracciabilità.
+
+## Risorse Correlate
+
+- [API Audio Transcriptions](/api-reference/endpoint/audio/transcriptions)
+- [Modelli Speech-to-Text](/models/speech-to-text)
+- [Guida al Text-to-Speech](/guides/media/text-to-speech)
diff --git a/it/guides/media/text-to-speech.mdx b/it/guides/media/text-to-speech.mdx
new file mode 100644
index 00000000..7d9abdb8
--- /dev/null
+++ b/it/guides/media/text-to-speech.mdx
@@ -0,0 +1,102 @@
+---
+title: "Text-to-Speech"
+description: "Genera audio parlato dal testo con i modelli text-to-speech di Venice, voci specifiche per modello e l'endpoint /audio/speech."
+'og:title': "Text-to-Speech | Documentazione API Venice"
+'og:description': "Scopri come convertire il testo in voce con l'API di Venice."
+---
+
+Il text-to-speech trasforma il testo scritto in audio parlato. Scegli un modello TTS, seleziona una voce supportata da quel modello, invia il testo a `/audio/speech` e salva la risposta audio binaria.
+
+Usa questa guida per la generazione vocale standard. Se vuoi creare audio a partire da una voce di riferimento personalizzata, consulta il [Voice Cloning](/guides/media/voice-cloning).
+
+## Utilizzo di Base
+
+
+```python Python
+import os
+from pathlib import Path
+
+import requests
+
+response = requests.post(
+ "https://api.venice.ai/api/v1/audio/speech",
+ headers={
+ "Authorization": f"Bearer {os.environ['VENICE_API_KEY']}",
+ "Content-Type": "application/json",
+ },
+ json={
+ "model": "tts-kokoro",
+ "voice": "af_sky",
+ "input": "Hello, welcome to Venice Voice.",
+ },
+)
+
+response.raise_for_status()
+Path("speech.mp3").write_bytes(response.content)
+```
+
+```javascript Node.js
+import { writeFile } from "node:fs/promises";
+
+const response = await fetch("https://api.venice.ai/api/v1/audio/speech", {
+ method: "POST",
+ headers: {
+ Authorization: `Bearer ${process.env.VENICE_API_KEY}`,
+ "Content-Type": "application/json",
+ },
+ body: JSON.stringify({
+ model: "tts-kokoro",
+ voice: "af_sky",
+ input: "Hello, welcome to Venice Voice.",
+ }),
+});
+
+if (!response.ok) {
+ throw new Error(await response.text());
+}
+
+await writeFile("speech.mp3", Buffer.from(await response.arrayBuffer()));
+```
+
+```bash cURL
+curl https://api.venice.ai/api/v1/audio/speech \
+ -H "Authorization: Bearer $VENICE_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{
+ "model": "tts-kokoro",
+ "voice": "af_sky",
+ "input": "Hello, welcome to Venice Voice."
+ }' \
+ --output speech.mp3
+```
+
+
+## Scelta di Modello e Voce
+
+Le voci sono specifiche per modello. Il valore di `voice` deve essere valido per il `model` che scegli.
+
+Usa la pagina [Modelli Text-to-Speech](/models/text-to-speech) per esplorare i modelli e le voci disponibili. Il selettore delle voci elenca gli ID esatti da passare nella richiesta.
+
+
+Gli ID delle voci fanno distinzione tra maiuscole e minuscole. Se cambi modello TTS, aggiorna contemporaneamente il valore di `voice`.
+
+
+## Struttura della Richiesta
+
+| Parametro | Tipo | Obbligatorio | Descrizione |
+|-----------|------|--------------|-------------|
+| `model` | string | Sì | ID del modello text-to-speech. |
+| `voice` | string | Sì | ID della voce supportata dal modello selezionato. |
+| `input` | string | Sì | Testo da sintetizzare. |
+
+## Consigli per la Produzione
+
+- Metti in cache l'audio generato quando il testo sorgente e la voce vengono riutilizzati.
+- Normalizza e revisiona il testo prima della sintesi. La punteggiatura influenza ritmo e intonazione.
+- Salva l'output con l'estensione di file corretta in base al formato di risposta del modello.
+
+## Risorse Correlate
+
+- [API Audio Speech](/api-reference/endpoint/audio/speech)
+- [Modelli Text-to-Speech](/models/text-to-speech)
+- [Guida al Voice Cloning](/guides/media/voice-cloning)
diff --git a/it/guides/overview.mdx b/it/guides/overview.mdx
index 31e5529a..ae946b5c 100644
--- a/it/guides/overview.mdx
+++ b/it/guides/overview.mdx
@@ -1,54 +1,62 @@
---
title: Guide
-description: "Guide pratiche per l'API Venice su chiavi API, migrazione da OpenAI, risposte strutturate, file inputs, prompt caching, media e integrazioni con agenti."
+description: Guide pratiche all'API Venice per chiavi API, migrazione da OpenAI, capacità di chat, embedding, media e integrazioni con agenti.
---
-Usa queste guide per generare API key, migrare app OpenAI esistenti, abilitare funzionalità specifiche di Venice e collegare Venice a framework di agenti, strumenti di coding e workflow multimediali.
+Usa queste guide per generare chiavi API, migrare le app OpenAI esistenti, abilitare capacità specifiche di Venice e collegare Venice a framework di agenti, strumenti di coding e flussi di lavoro multimediali.
-
- Crea e gestisci API key dalla dashboard Venice.
+
+ Crea e gestisci le chiavi API dalla dashboard Venice.
- Passa le app compatibili con OpenAI a Venice cambiando il base URL.
+ Passa le app compatibili con OpenAI a Venice cambiando l'URL di base.
-
- Richiedi risposte conformi a uno schema JSON.
+
+ Richiedi risposte che aderiscano a uno schema JSON.
-
- Invia documenti e file sorgente ai modelli chat.
+
+ Permetti ai modelli di richiamare gli strumenti della tua applicazione con argomenti strutturati.
-
+
+ Analizza immagini con modelli di chat multimodali.
+
+
+ Genera vettori per ricerca semantica, RAG e raccomandazioni.
+
+
+ Invia documenti e file sorgente ai modelli di chat.
+
+
Riduci latenza e costi per contenuti di prompt ripetuti.
-
+
Costruisci un agente di ricerca in Python che raccoglie fonti e scrive report con citazioni.
-## Esplora per argomento
+## Esplora per Argomento
-
- API key, migrazione, creazione autonoma di chiavi e Postman.
+
+ Chiavi API, migrazione, creazione autonoma di chiavi e Postman.
-
- Output strutturati, modelli di ragionamento, file inputs, prompt caching e modelli con privacy avanzata.
+
+ Output strutturati, modelli di reasoning, function calling, vision, embedding, input da file, prompt caching e modelli con maggiore privacy.
-
- Generazione di immagini, image editing, generazione video, riferimenti e upscaling.
+
+ Generazione di immagini, editing di immagini, upscaling, generazione video, text-to-speech, speech-to-text e voice cloning.
-
- App agent, strumenti per assistenti, crypto RPC, autenticazione tramite wallet e integrazioni della community.
+
+ App di agenti, strumenti per assistenti, RPC crypto, autenticazione con wallet e integrazioni della community.
-
+
Usa i modelli Venice con Claude Code, Cursor, OpenCode e Codex CLI.
-
+
Costruisci con LangChain, Vercel AI SDK e CrewAI.
- Crea i tuoi progetti seguendo una delle nostre guide pratiche.
+ Costruisci i tuoi progetti seguendo una delle nostre guide passo-passo.
-
diff --git a/it/guides/projects/overview.mdx b/it/guides/projects/overview.mdx
new file mode 100644
index 00000000..d57695bd
--- /dev/null
+++ b/it/guides/projects/overview.mdx
@@ -0,0 +1,85 @@
+---
+title: "Demo e progetti"
+sidebarTitle: "Panoramica"
+description: "Progetti demo completi realizzati sull'API di Venice, con codice funzionante che puoi eseguire, leggere e adattare alle tue applicazioni."
+"og:title": "Demos | Venice API Docs"
+---
+
+
+
+
+
+ Python
+
+
Bot RAG privato
+
Risposte fondate e citabili dai tuoi documenti con recupero ri-ordinato.
diff --git a/ko/models/overview.mdx b/ko/models/overview.mdx
index b379e591..42a6a2f6 100644
--- a/ko/models/overview.mdx
+++ b/ko/models/overview.mdx
@@ -1,5 +1,6 @@
---
-title: "모델"
+title: "전체 모델"
+sidebarTitle: "전체 모델"
description: "텍스트, 이미지, 비디오, 오디오, 임베딩, 음성을 아우르는 Venice API에서 사용 가능한 모든 모델 카탈로그 — 기능, 가격, 모델 ID 포함."
"og:title": "Models | Venice API Docs"
mode: "wide"
diff --git a/model-search.js b/model-search.js
index d55b4e2c..a1833abd 100644
--- a/model-search.js
+++ b/model-search.js
@@ -1,3 +1,74 @@
+// Scroll-position safeguard for the Demos landing page.
+//
+// This site runs with history.scrollRestoration === "auto", so on SPA
+// navigation the browser can asynchronously restore a prior scroll offset onto
+// the newly rendered page. The Demos overview page is short and has its sidebar
+// and "On this page" column hidden, so any restored offset from a taller source
+// page is very visible (the page lands scrolled down). We watch the
+// data-current-path attribute Mintlify sets on and force the window back
+// to the top whenever we land on that route, beating the browser's async
+// restoration. Scoped to this one route so it can't affect anchor links or
+// scroll behavior anywhere else.
+(function() {
+ var DEMOS_PATH = '/guides/projects/overview';
+
+ function resetIfDemos() {
+ if (document.documentElement.getAttribute('data-current-path') === DEMOS_PATH) {
+ window.scrollTo(0, 0);
+ // Re-assert across a few ticks to beat late async browser scroll
+ // restoration, which can fire after the route attribute updates.
+ requestAnimationFrame(function() { window.scrollTo(0, 0); });
+ setTimeout(function() { window.scrollTo(0, 0); }, 60);
+ }
+ }
+
+ new MutationObserver(resetIfDemos).observe(document.documentElement, {
+ attributes: true,
+ attributeFilter: ['data-current-path'],
+ });
+
+ resetIfDemos();
+})();
+
+// Relocate the language selector into the right-hand header actions cluster.
+//
+// Mintlify natively renders the language selector in the left group (next to the
+// logo). We want it on the right, just left of the search / Ask AI / theme icon
+// group. Rather than absolutely positioning it -- which collides whenever
+// Mintlify adds another header button (e.g. the AI assistant) -- we move the
+// node into the right actions cluster so it lays out in natural flex flow. The
+// header is re-rendered on SPA navigation, so we re-run on DOM changes,
+// idempotently and coalesced via requestAnimationFrame to avoid churn.
+(function() {
+ function relocate() {
+ const trigger = document.querySelector('#localization-select-trigger');
+ const search = document.querySelector('#search-bar-entry');
+ if (!trigger || !search) return;
+ const langWrapper = trigger.parentElement; //
wrapping the trigger
+ const iconGroup = search.parentElement; //
holding search + Ask AI
+ const cluster = iconGroup ? iconGroup.parentElement : null; // right actions cluster
+ if (!langWrapper || !iconGroup || !cluster) return;
+ // Already placed immediately before the icon group -> nothing to do (this
+ // guard also stops our own DOM mutation from causing a relocate loop).
+ if (langWrapper.parentElement === cluster && langWrapper.nextElementSibling === iconGroup) return;
+ cluster.insertBefore(langWrapper, iconGroup);
+ }
+
+ let scheduled = false;
+ function schedule() {
+ if (scheduled) return;
+ scheduled = true;
+ requestAnimationFrame(function() { scheduled = false; relocate(); });
+ }
+
+ new MutationObserver(schedule).observe(document.documentElement, {
+ childList: true,
+ subtree: true,
+ });
+
+ relocate();
+})();
+
// Venice AI Model Browser & Pricing Tables - Fetches from API
(function() {
@@ -12,7 +83,7 @@
const CACHE_TTL = 5 * 60 * 1000; // 5 minutes
// Static fallback data for instant pricing page load (updated 2026-07-08)
- const STATIC_MODELS = [{"id":"firered-image-edit","type":"inpaint","model_spec":{"privacy":"private","pricing":{"inpaint":{"usd":0.04,"diem":0.04}},"traits":[],"name":"FireRed Edit"},"created":1774396800},{"id":"flux-2-max-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.19,"diem":0.19}},"traits":[],"name":"Flux 2 Max"},"created":1767571200},{"id":"gpt-image-1-5-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.36,"diem":0.36}},"traits":[],"name":"GPT Image 1.5"},"created":1767555000},{"id":"gpt-image-2-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.36,"diem":0.36},"resolutions":{"1K":{"usd":0.36,"diem":0.36},"2K":{"usd":0.53,"diem":0.53},"4K":{"usd":0.85,"diem":0.85}},"quality":{"1K":{"high":{"usd":0.36,"diem":0.36},"low":{"usd":0.03,"diem":0.03},"medium":{"usd":0.1,"diem":0.1}},"2K":{"high":{"usd":0.53,"diem":0.53},"low":{"usd":0.04,"diem":0.04},"medium":{"usd":0.15,"diem":0.15}},"4K":{"high":{"usd":0.86,"diem":0.86},"low":{"usd":0.06,"diem":0.06},"medium":{"usd":0.22,"diem":0.22}}}},"traits":[],"name":"GPT Image 2"},"created":1776729600},{"id":"grok-imagine-edit","type":"inpaint","model_spec":{"privacy":"private","pricing":{"inpaint":{"usd":0.04,"diem":0.04},"resolutions":{"1K":{"usd":0.04,"diem":0.04},"2K":{"usd":0.06,"diem":0.06}}},"traits":[],"name":"Grok Imagine"},"created":1769644800},{"id":"grok-imagine-quality-edit","type":"inpaint","model_spec":{"privacy":"private","pricing":{"inpaint":{"usd":0.1,"diem":0.1},"resolutions":{"1K":{"usd":0.1,"diem":0.1},"2K":{"usd":0.12,"diem":0.12}}},"traits":[],"name":"Grok Imagine High Quality"},"created":1778198400},{"id":"luma-uni-1-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.06,"diem":0.06}},"traits":[],"name":"Luma Uni-1"},"created":1781654400},{"id":"luma-uni-1-max-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.13,"diem":0.13}},"traits":[],"name":"Luma Uni-1 Max"},"created":1781654400},{"id":"nano-banana-2-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.1,"diem":0.1},"resolutions":{"1K":{"usd":0.1,"diem":0.1},"2K":{"usd":0.14,"diem":0.14},"4K":{"usd":0.19,"diem":0.19}}},"traits":[],"name":"Nano Banana 2"},"created":1772064000},{"id":"nano-banana-2-lite-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.06,"diem":0.06}},"traits":[],"name":"Nano Banana 2 Lite"},"created":1782777600},{"id":"nano-banana-pro-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.18,"diem":0.18},"resolutions":{"1K":{"usd":0.18,"diem":0.18},"2K":{"usd":0.23,"diem":0.23},"4K":{"usd":0.35,"diem":0.35}}},"traits":[],"name":"Nano Banana Pro"},"created":1765584000},{"id":"qwen-edit-uncensored","type":"inpaint","model_spec":{"betaModel":true,"privacy":"private","pricing":{"inpaint":{"usd":0.04,"diem":0.04}},"traits":[],"name":"Qwen Edit Uncensored"},"created":1780531200},{"id":"qwen-image-2-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.05,"diem":0.05}},"traits":[],"name":"Qwen Image 2"},"created":1772582400},{"id":"qwen-image-2-pro-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.1,"diem":0.1}},"traits":[],"name":"Qwen Image 2 Pro"},"created":1772582400},{"id":"seedream-v4-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.05,"diem":0.05}},"traits":[],"name":"Seedream V4.5"},"created":1767484800},{"id":"seedream-v5-lite-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.05,"diem":0.05}},"traits":[],"name":"Seedream V5 Lite"},"created":1771804800},{"id":"wan-2-7-pro-edit","type":"inpaint","model_spec":{"privacy":"anonymized","pricing":{"inpaint":{"usd":0.094,"diem":0.094}},"traits":[],"name":"Wan 2.7 Pro Edit"},"created":1776902400},{"id":"tts-chatterbox-hd","type":"tts","model_spec":{"privacy":"private","pricing":{"input":{"usd":50,"diem":50}},"traits":[],"name":"Chatterbox HD (Resemble AI)","voices":["Aurora","Blade","Britney","Carl","Cliff","Richard","Rico","Siobhan","Vicky"]},"created":1776384000},{"id":"tts-elevenlabs-turbo-v2-5","type":"tts","model_spec":{"privacy":"anonymized","pricing":{"input":{"usd":62.5,"diem":62.5}},"traits":[],"name":"ElevenLabs Turbo v2.5","voices":["Alice","Aria","Bill","Brian","Callum","Charlie","Charlotte","Chris","Daniel","Eric","George","Jessica","Laura","Liam","Lily","Matilda","Rachel","River","Roger","Sarah","Will"]},"created":1776384000},{"id":"tts-gemini-3-1-flash","type":"tts","model_spec":{"privacy":"anonymized","pricing":{"input":{"usd":187.5,"diem":187.5}},"traits":[],"name":"Gemini 3.1 Flash TTS","voices":["Achernar","Achird","Algenib","Algieba","Alnilam","Aoede","Autonoe","Callirrhoe","Charon","Despina","Enceladus","Erinome","Fenrir","Gacrux","Iapetus","Kore","Laomedeia","Leda","Orus","Puck","Pulcherrima","Rasalgethi","Sadachbia","Sadaltager","Schedar","Sulafat","Umbriel","Vindemiatrix","Zephyr","Zubenelgenubi"]},"created":1776643200},{"id":"tts-gradium-v1","type":"tts","model_spec":{"privacy":"anonymized","pricing":{"input":{"usd":47.5,"diem":47.5}},"traits":[],"name":"Gradium TTS","voices":["Alice","Davi","Elise","Emma","Eva","Jack","Kent","Leo","Maximilian","Mia","Sergio","Valentina"]},"created":1780617600},{"id":"tts-inworld-1-5-max","type":"tts","model_spec":{"privacy":"anonymized","pricing":{"input":{"usd":12.5,"diem":12.5}},"traits":[],"name":"Inworld TTS-1.5 Max","voices":["Alex","Ashley","Craig","Edward","Elizabeth","Hades","Luna","Mark","Olivia","Pixie","Priya","Ronald","Sarah","Theodore"]},"created":1776384000},{"id":"tts-kokoro","type":"tts","model_spec":{"privacy":"private","pricing":{"input":{"usd":3.5,"diem":3.5}},"traits":[],"name":"Kokoro Text to 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Pro"},"created":1763653951},{"id":"qwen-image","type":"image","model_spec":{"privacy":"anonymized","pricing":{"generation":{"usd":0.03,"diem":0.03},"upscale":{"2x":{"usd":0.02,"diem":0.02},"4x":{"usd":0.08,"diem":0.08}}},"traits":["highest_quality"],"name":"Qwen Image"},"created":1736635129},{"id":"qwen-image-2","type":"image","model_spec":{"privacy":"anonymized","pricing":{"generation":{"usd":0.05,"diem":0.05},"upscale":{"2x":{"usd":0.02,"diem":0.02},"4x":{"usd":0.08,"diem":0.08}}},"traits":[],"name":"Qwen Image 2"},"created":1772582400},{"id":"qwen-image-2-pro","type":"image","model_spec":{"privacy":"anonymized","pricing":{"generation":{"usd":0.1,"diem":0.1},"upscale":{"2x":{"usd":0.02,"diem":0.02},"4x":{"usd":0.08,"diem":0.08}}},"traits":[],"name":"Qwen Image 2 Pro"},"created":1772582400},{"id":"recraft-v4","type":"image","model_spec":{"privacy":"anonymized","pricing":{"generation":{"usd":0.05,"diem":0.05},"upscale":{"2x":{"usd":0.02,"diem":0.02},"4x":{"usd":0.08,"diem":0.08}}},"traits":[],"name":"Recraft V4"},"created":1770854400},{"id":"recraft-v4-pro","type":"image","model_spec":{"privacy":"anonymized","pricing":{"generation":{"usd":0.29,"diem":0.29},"upscale":{"2x":{"usd":0.02,"diem":0.02},"4x":{"usd":0.08,"diem":0.08}}},"traits":[],"name":"Recraft V4 Pro"},"created":1770854400},{"id":"seedream-v4","type":"image","model_spec":{"privacy":"anonymized","pricing":{"generation":{"usd":0.05,"diem":0.05},"upscale":{"2x":{"usd":0.02,"diem":0.02},"4x":{"usd":0.08,"diem":0.08}}},"traits":[],"name":"Seedream 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// Privacy types that are always private (no API privacy field needed)
const PRIVATE_TYPES = new Set(['upscale']);
@@ -272,16 +343,144 @@
}
}
- // Filter categories
- const CAPABILITY_FILTERS = ['reasoning', 'vision', 'function', 'code'];
-
+ // Capability filters only apply to text/chat models.
function categoryAllowsCapabilityFilters(category) {
return category === 'all' || category === 'text';
}
- const VIDEO_FILTERS = ['text-to-video', 'image-to-video'];
- const IMAGE_FILTERS = ['image-gen', 'image-upscale', 'image-edit', 'image-uncensored'];
- const PRIVACY_FILTERS = ['e2ee', 'tee', 'private', 'anonymized'];
+ // ========== I18N (filter/sort UI chrome) ==========
+ // The model browser UI is rendered by JS, so its labels can't be localized by
+ // Mintlify's per-language content. We detect the locale from the URL prefix
+ // (e.g. /es/models/...) and translate the visible chrome. Keys are the English
+ // source strings; unknown keys fall back to English.
+ const SUPPORTED_LOCALES = ['pt-BR', 'ar', 'it', 'de', 'es', 'fr', 'zh', 'ko'];
+ function detectLocale() {
+ try {
+ const seg = (location.pathname.split('/')[1] || '').toLowerCase();
+ const hit = SUPPORTED_LOCALES.find(l => l.toLowerCase() === seg);
+ if (hit) return hit;
+ const htmlLang = (document.documentElement.getAttribute('lang') || '').trim();
+ const byLang = SUPPORTED_LOCALES.find(l => l.toLowerCase() === htmlLang.toLowerCase());
+ if (byLang) return byLang;
+ } catch (e) {}
+ return 'en';
+ }
+ const LOCALE = detectLocale();
+ const I18N = {
+ 'pt-BR': { 'Type': 'Tipo', 'Kind': 'Categoria', 'Capability': 'Recurso', 'Privacy': 'Privacidade', 'All types': 'Todos os tipos', 'Text': 'Texto', 'Image': 'Imagem', 'Video': 'Vídeo', 'Audio': 'Áudio', 'Embedding': 'Embedding', 'Generation': 'Geração', 'Upscale': 'Ampliação', 'Edit': 'Edição', 'Uncensored': 'Sem censura', 'Text to Video': 'Texto para vídeo', 'Image to Video': 'Imagem para vídeo', 'Reasoning': 'Raciocínio', 'Vision': 'Visão', 'Function Calling': 'Chamada de funções', 'Code': 'Código', 'Private': 'Privado', 'Anonymized': 'Anonimizado', 'Sort': 'Ordenar', 'Sort models': 'Ordenar modelos', 'Search models': 'Buscar modelos', 'Recommended': 'Recomendado', 'Newest': 'Mais recentes', 'Oldest': 'Mais antigos', 'Name (A–Z)': 'Nome (A–Z)', 'Price: Low to High': 'Preço: menor para maior', 'Price: High to Low': 'Preço: maior para menor', 'Clear filters': 'Limpar filtros', 'Search models...': 'Buscar modelos...', 'models': 'modelos', 'closest matches': 'correspondências mais próximas', 'No close model matches': 'Nenhum modelo próximo encontrado', 'No models match your filters': 'Nenhum modelo corresponde aos seus filtros' },
+ 'ar': { 'Type': 'النوع', 'Kind': 'الفئة', 'Capability': 'القدرة', 'Privacy': 'الخصوصية', 'All types': 'كل الأنواع', 'Text': 'نص', 'Image': 'صورة', 'Video': 'فيديو', 'Audio': 'صوت', 'Embedding': 'تضمين', 'Generation': 'توليد', 'Upscale': 'تحسين الدقة', 'Edit': 'تحرير', 'Uncensored': 'بدون رقابة', 'Text to Video': 'نص إلى فيديو', 'Image to Video': 'صورة إلى فيديو', 'Reasoning': 'استدلال', 'Vision': 'رؤية', 'Function Calling': 'استدعاء الدوال', 'Code': 'برمجة', 'Private': 'خاص', 'Anonymized': 'مجهول الهوية', 'Sort': 'ترتيب', 'Sort models': 'ترتيب النماذج', 'Search models': 'بحث في النماذج', 'Recommended': 'موصى به', 'Newest': 'الأحدث', 'Oldest': 'الأقدم', 'Name (A–Z)': 'الاسم (أ–ي)', 'Price: Low to High': 'السعر: من الأقل إلى الأعلى', 'Price: High to Low': 'السعر: من الأعلى إلى الأقل', 'Clear filters': 'مسح عوامل التصفية', 'Search models...': 'بحث في النماذج...', 'models': 'نماذج', 'closest matches': 'أقرب النتائج', 'No close model matches': 'لا توجد نماذج قريبة', 'No models match your filters': 'لا توجد نماذج تطابق عوامل التصفية' },
+ 'it': { 'Type': 'Tipo', 'Kind': 'Categoria', 'Capability': 'Capacità', 'Privacy': 'Privacy', 'All types': 'Tutti i tipi', 'Text': 'Testo', 'Image': 'Immagine', 'Video': 'Video', 'Audio': 'Audio', 'Embedding': 'Embedding', 'Generation': 'Generazione', 'Upscale': 'Upscaling', 'Edit': 'Modifica', 'Uncensored': 'Senza censura', 'Text to Video': 'Testo in video', 'Image to Video': 'Immagine in video', 'Reasoning': 'Ragionamento', 'Vision': 'Visione', 'Function Calling': 'Chiamata di funzioni', 'Code': 'Codice', 'Private': 'Privato', 'Anonymized': 'Anonimizzato', 'Sort': 'Ordina', 'Sort models': 'Ordina modelli', 'Search models': 'Cerca modelli', 'Recommended': 'Consigliati', 'Newest': 'Più recenti', 'Oldest': 'Meno recenti', 'Name (A–Z)': 'Nome (A–Z)', 'Price: Low to High': 'Prezzo: dal più basso', 'Price: High to Low': 'Prezzo: dal più alto', 'Clear filters': 'Cancella filtri', 'Search models...': 'Cerca modelli...', 'models': 'modelli', 'closest matches': 'corrispondenze più vicine', 'No close model matches': 'Nessun modello simile trovato', 'No models match your filters': 'Nessun modello corrisponde ai filtri' },
+ 'de': { 'Type': 'Typ', 'Kind': 'Art', 'Capability': 'Fähigkeit', 'Privacy': 'Datenschutz', 'All types': 'Alle Typen', 'Text': 'Text', 'Image': 'Bild', 'Video': 'Video', 'Audio': 'Audio', 'Embedding': 'Embedding', 'Generation': 'Generierung', 'Upscale': 'Hochskalierung', 'Edit': 'Bearbeiten', 'Uncensored': 'Unzensiert', 'Text to Video': 'Text zu Video', 'Image to Video': 'Bild zu Video', 'Reasoning': 'Reasoning', 'Vision': 'Vision', 'Function Calling': 'Function Calling', 'Code': 'Code', 'Private': 'Privat', 'Anonymized': 'Anonymisiert', 'Sort': 'Sortieren', 'Sort models': 'Modelle sortieren', 'Search models': 'Modelle suchen', 'Recommended': 'Empfohlen', 'Newest': 'Neueste', 'Oldest': 'Älteste', 'Name (A–Z)': 'Name (A–Z)', 'Price: Low to High': 'Preis: aufsteigend', 'Price: High to Low': 'Preis: absteigend', 'Clear filters': 'Filter zurücksetzen', 'Search models...': 'Modelle suchen...', 'models': 'Modelle', 'closest matches': 'nächste Treffer', 'No close model matches': 'Keine ähnlichen Modelle gefunden', 'No models match your filters': 'Keine Modelle entsprechen deinen Filtern' },
+ 'es': { 'Type': 'Tipo', 'Kind': 'Categoría', 'Capability': 'Capacidad', 'Privacy': 'Privacidad', 'All types': 'Todos los tipos', 'Text': 'Texto', 'Image': 'Imagen', 'Video': 'Vídeo', 'Audio': 'Audio', 'Embedding': 'Embedding', 'Generation': 'Generación', 'Upscale': 'Escalado', 'Edit': 'Edición', 'Uncensored': 'Sin censura', 'Text to Video': 'Texto a vídeo', 'Image to Video': 'Imagen a vídeo', 'Reasoning': 'Razonamiento', 'Vision': 'Visión', 'Function Calling': 'Llamada de funciones', 'Code': 'Código', 'Private': 'Privado', 'Anonymized': 'Anonimizado', 'Sort': 'Ordenar', 'Sort models': 'Ordenar modelos', 'Search models': 'Buscar modelos', 'Recommended': 'Recomendado', 'Newest': 'Más recientes', 'Oldest': 'Más antiguos', 'Name (A–Z)': 'Nombre (A–Z)', 'Price: Low to High': 'Precio: de menor a mayor', 'Price: High to Low': 'Precio: de mayor a menor', 'Clear filters': 'Borrar filtros', 'Search models...': 'Buscar modelos...', 'models': 'modelos', 'closest matches': 'coincidencias más cercanas', 'No close model matches': 'No hay modelos parecidos', 'No models match your filters': 'Ningún modelo coincide con tus filtros' },
+ 'fr': { 'Type': 'Type', 'Kind': 'Catégorie', 'Capability': 'Capacité', 'Privacy': 'Confidentialité', 'All types': 'Tous les types', 'Text': 'Texte', 'Image': 'Image', 'Video': 'Vidéo', 'Audio': 'Audio', 'Embedding': 'Embedding', 'Generation': 'Génération', 'Upscale': 'Agrandissement', 'Edit': 'Édition', 'Uncensored': 'Sans censure', 'Text to Video': 'Texte vers vidéo', 'Image to Video': 'Image vers vidéo', 'Reasoning': 'Raisonnement', 'Vision': 'Vision', 'Function Calling': 'Appel de fonctions', 'Code': 'Code', 'Private': 'Privé', 'Anonymized': 'Anonymisé', 'Sort': 'Trier', 'Sort models': 'Trier les modèles', 'Search models': 'Rechercher des modèles', 'Recommended': 'Recommandé', 'Newest': 'Plus récents', 'Oldest': 'Plus anciens', 'Name (A–Z)': 'Nom (A–Z)', 'Price: Low to High': 'Prix : croissant', 'Price: High to Low': 'Prix : décroissant', 'Clear filters': 'Effacer les filtres', 'Search models...': 'Rechercher des modèles...', 'models': 'modèles', 'closest matches': 'correspondances les plus proches', 'No close model matches': 'Aucun modèle proche', 'No models match your filters': 'Aucun modèle ne correspond à vos filtres' },
+ 'zh': { 'Type': '类型', 'Kind': '类别', 'Capability': '能力', 'Privacy': '隐私', 'All types': '全部类型', 'Text': '文本', 'Image': '图像', 'Video': '视频', 'Audio': '音频', 'Embedding': '嵌入', 'Generation': '生成', 'Upscale': '放大', 'Edit': '编辑', 'Uncensored': '无审查', 'Text to Video': '文本转视频', 'Image to Video': '图像转视频', 'Reasoning': '推理', 'Vision': '视觉', 'Function Calling': '函数调用', 'Code': '代码', 'Private': '私有', 'Anonymized': '匿名化', 'Sort': '排序', 'Sort models': '排序模型', 'Search models': '搜索模型', 'Recommended': '推荐', 'Newest': '最新', 'Oldest': '最早', 'Name (A–Z)': '名称 (A–Z)', 'Price: Low to High': '价格：从低到高', 'Price: High to Low': '价格：从高到低', 'Clear filters': '清除筛选', 'Search models...': '搜索模型...', 'models': '个模型', 'closest matches': '最接近的结果', 'No close model matches': '没有相近的模型', 'No models match your filters': '没有符合筛选条件的模型' },
+ 'ko': { 'Type': '유형', 'Kind': '종류', 'Capability': '기능', 'Privacy': '개인정보', 'All types': '모든 유형', 'Text': '텍스트', 'Image': '이미지', 'Video': '비디오', 'Audio': '오디오', 'Embedding': '임베딩', 'Generation': '생성', 'Upscale': '업스케일', 'Edit': '편집', 'Uncensored': '무검열', 'Text to Video': '텍스트→비디오', 'Image to Video': '이미지→비디오', 'Reasoning': '추론', 'Vision': '비전', 'Function Calling': '함수 호출', 'Code': '코드', 'Private': '프라이빗', 'Anonymized': '익명화', 'Sort': '정렬', 'Sort models': '모델 정렬', 'Search models': '모델 검색', 'Recommended': '추천', 'Newest': '최신순', 'Oldest': '오래된순', 'Name (A–Z)': '이름 (A–Z)', 'Price: Low to High': '가격: 낮은순', 'Price: High to Low': '가격: 높은순', 'Clear filters': '필터 지우기', 'Search models...': '모델 검색...', 'models': '개 모델', 'closest matches': '가장 근접한 결과', 'No close model matches': '유사한 모델이 없습니다', 'No models match your filters': '필터와 일치하는 모델이 없습니다' }
+ };
+ function t(s) {
+ if (LOCALE === 'en') return s;
+ const table = I18N[LOCALE];
+ return (table && table[s] != null) ? table[s] : s;
+ }
+
+ // ========== FILTER DROPDOWNS ==========
+ // The model browser filters are grouped into focused dropdowns instead of a
+ // flat wall of pills. Type/Kind/Privacy are single-select; Capability is
+ // multi-select (AND semantics, e.g. Reasoning + Vision).
+ const FILTER_GROUPS = {
+ type: {
+ label: 'Type', mode: 'single', default: 'all',
+ options: [
+ { value: 'all', label: 'All types' },
+ { value: 'text', label: 'Text' },
+ { value: 'image', label: 'Image' },
+ ...(ENABLE_VIDEO ? [{ value: 'video', label: 'Video' }] : []),
+ { value: 'audio', label: 'Audio' },
+ { value: 'embedding', label: 'Embedding' },
+ ],
+ },
+ image: {
+ label: 'Kind', mode: 'single', default: null,
+ options: [
+ { value: 'image-gen', label: 'Generation' },
+ { value: 'image-upscale', label: 'Upscale' },
+ { value: 'image-edit', label: 'Edit' },
+ { value: 'image-uncensored', label: 'Uncensored' },
+ ],
+ },
+ video: {
+ label: 'Kind', mode: 'single', default: null,
+ options: [
+ { value: 'text-to-video', label: 'Text to Video' },
+ { value: 'image-to-video', label: 'Image to Video' },
+ ],
+ },
+ capability: {
+ label: 'Capability', mode: 'multi', default: null,
+ options: [
+ { value: 'reasoning', label: 'Reasoning' },
+ { value: 'vision', label: 'Vision' },
+ { value: 'function', label: 'Function Calling' },
+ { value: 'code', label: 'Code' },
+ ],
+ },
+ privacy: {
+ label: 'Privacy', mode: 'single', default: null,
+ options: [
+ { value: 'e2ee', label: 'E2EE' },
+ { value: 'tee', label: 'TEE' },
+ { value: 'private', label: 'Private' },
+ { value: 'anonymized', label: 'Anonymized' },
+ ],
+ },
+ };
+
+ const FILTER_CHEVRON = '';
+ const FILTER_CHECK = '';
+ const SORT_ICON = '';
+
+ // Sort options (single-select). `default` preserves the API's curated order and
+ // is the natural resting state on preset pages; the overview page defaults to
+ // newest. All values are handled by sortModels().
+ const SORT_OPTIONS = [
+ { value: 'default', label: 'Recommended' },
+ { value: 'newest', label: 'Newest' },
+ { value: 'oldest', label: 'Oldest' },
+ { value: 'name', label: 'Name (A–Z)' },
+ { value: 'price-low', label: 'Price: Low to High' },
+ { value: 'price-high', label: 'Price: High to Low' },
+ ];
+
+ function renderSortDropdown() {
+ const opts = SORT_OPTIONS.map(o =>
+ ``
+ ).join('');
+ return (
+ `