Add response model to /vectorize endpoint and update README with API documentation

README.md

@@ -7,4 +7,102 @@ sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Vector Endpoint
+
+A simple API that converts text into vector embeddings using the [LaBSE](https://huggingface.co/sentence-transformers/LaBSE) sentence transformer model.
+
+## API Reference
+
+### Endpoint
+
+```
+POST /vectorize
+```
+
+### Request Format
+
+```json
+{
+  "text": "Your text to be vectorized"
+}
+```
+
+### Response Format
+
+```json
+{
+  "embedding": [0.123, 0.456, ...]  // Vector representation of your text
+}
+```
+
+## Usage Examples
+
+### cURL
+
+```bash
+curl -X 'POST' \
+  'https://placingholocaust-vector-endpoint.hf.space/vectorize' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "text": "This is a text"
+}'
+```
+
+### Python
+
+```python
+import requests
+import json
+
+url = "https://placingholocaust-vector-endpoint.hf.space/vectorize"
+headers = {
+    "accept": "application/json",
+    "Content-Type": "application/json"
+}
+data = {
+    "text": "This is a text"
+}
+
+response = requests.post(url, headers=headers, json=data)
+result = response.json()
+embedding = result["embedding"]
+
+print(f"Embedding length: {len(embedding)}")
+print(f"First few values: {embedding[:5]}")
+```
+
+### JavaScript
+
+```javascript
+// Using fetch
+async function getEmbedding(text) {
+  const response = await fetch(
+    "https://placingholocaust-vector-endpoint.hf.space/vectorize", 
+    {
+      method: "POST",
+      headers: {
+        "accept": "application/json",
+        "Content-Type": "application/json"
+      },
+      body: JSON.stringify({ text })
+    }
+  );
+  
+  const data = await response.json();
+  return data.embedding;
+}
+
+// Example usage
+getEmbedding("This is a text")
+  .then(embedding => {
+    console.log(`Embedding length: ${embedding.length}`);
+    console.log(`First few values: ${embedding.slice(0, 5)}`);
+  })
+  .catch(error => console.error("Error:", error));
+```
+
+## Model Information
+
+This endpoint uses the [sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE) model, which produces 768-dimensional embeddings that capture semantic meaning of text across multiple languages.
+

app.py

@@ -9,7 +9,11 @@ model = SentenceTransformer('sentence-transformers/LaBSE')
 class EmbedRequest(BaseModel):
     text: str
 
-@app.post("/vectorize")
+class EmbedResponse(BaseModel):
+    embedding: list
+
+@app.post("/vectorize", response_model=EmbedResponse)
 async def embed_text(request: EmbedRequest):
-    return model.encode(request.text).tolist()
+    embedding = model.encode(request.text).tolist()
+    return {"embedding": embedding}