Upload Flask API README.md

README.md

@@ -3,9 +3,8 @@ title: BAAI Vector Api
 emoji: 🚀
 colorFrom: blue
 colorTo: purple
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
+sdk: docker
+app_port: 7860
 pinned: false
 license: mit
 models:
@@ -16,11 +15,13 @@ tags:
 - multilingual
 - retrieval
 - bge-m3
+- flask
+- api
 ---
 
-# BGE-M3 Vector API Demo 🚀
+# BGE-M3 Vector API 🚀
 
-This Hugging Face Space demonstrates the powerful capabilities of the **BGE-M3** embedding model, featuring multi-functionality, multi-linguality, and multi-granularity text processing.
+A Flask-based REST API for the **BGE-M3** embedding model, featuring multi-functionality, multi-linguality, and multi-granularity text processing.
 
 ## 🌟 Features
 
@@ -39,13 +40,110 @@ This Hugging Face Space demonstrates the powerful capabilities of the **BGE-M3**
 - Handle up to **8192 tokens** in a single input
 - Consistent performance across different text lengths
 
-## 🎯 Use Cases
+## 🔧 API Endpoints
 
-1. **Semantic Search**: Find relevant documents using natural language queries
-2. **Text Similarity**: Compare semantic similarity between texts
-3. **Multilingual Retrieval**: Search across different languages
-4. **Document Clustering**: Group similar documents together
-5. **Question Answering**: Retrieve relevant passages for questions
+### Base Information
+- `GET /` - API information and available endpoints
+- `GET /health` - Health check endpoint
+
+### Core Functionality
+- `POST /embed` - Generate embeddings for text(s)
+- `POST /similarity` - Compute similarity between text pairs
+- `POST /search` - Search through documents using semantic similarity
+
+## 📚 API Usage Examples
+
+### 1. Generate Embeddings
+
+```bash
+curl -X POST https://huggingface.co/spaces/Noblhyon/BAAI_Vector_Api/embed \
+  -H "Content-Type: application/json" \
+  -d '{
+    "texts": ["Hello world", "How are you?"],
+    "return_dense": true,
+    "return_sparse": false,
+    "max_length": 512
+  }'
+```
+
+**Response:**
+```json
+{
+  "success": true,
+  "num_texts": 2,
+  "processing_time": 0.123,
+  "dense_embeddings": [[0.1, 0.2, ...], [0.3, 0.4, ...]],
+  "dense_shape": [2, 1024]
+}
+```
+
+### 2. Compute Similarity
+
+```bash
+curl -X POST https://huggingface.co/spaces/Noblhyon/BAAI_Vector_Api/similarity \
+  -H "Content-Type: application/json" \
+  -d '{
+    "pairs": [["Hello world", "Hi there"], ["Cat", "Dog"]],
+    "method": "all"
+  }'
+```
+
+**Response:**
+```json
+{
+  "success": true,
+  "method": "all",
+  "num_pairs": 2,
+  "processing_time": 0.234,
+  "scores": {
+    "dense": [0.8234, 0.4567],
+    "sparse": [0.1234, 0.0567],
+    "colbert": [0.7890, 0.5432],
+    "combined": [0.7456, 0.4123]
+  }
+}
+```
+
+### 3. Document Search
+
+```bash
+curl -X POST https://huggingface.co/spaces/Noblhyon/BAAI_Vector_Api/search \
+  -H "Content-Type: application/json" \
+  -d '{
+    "query": "machine learning",
+    "documents": [
+      "Deep learning is a subset of machine learning",
+      "Cats are cute animals",
+      "Neural networks are used in AI"
+    ],
+    "top_k": 2
+  }'
+```
+
+**Response:**
+```json
+{
+  "success": true,
+  "query": "machine learning",
+  "num_documents": 3,
+  "top_k": 2,
+  "processing_time": 0.345,
+  "results": [
+    {
+      "rank": 1,
+      "document_index": 0,
+      "document": "Deep learning is a subset of machine learning",
+      "similarity_score": 0.8765
+    },
+    {
+      "rank": 2,
+      "document_index": 2,
+      "document": "Neural networks are used in AI",
+      "similarity_score": 0.6543
+    }
+  ]
+}
+```
 
 ## 🔧 Model Details
 
@@ -55,6 +153,53 @@ This Hugging Face Space demonstrates the powerful capabilities of the **BGE-M3**
 - **Max Sequence Length**: 8192 tokens
 - **Languages**: 100+ supported
 
+## 🚀 Python Client Example
+
+```python
+import requests
+import json
+
+# API base URL
+BASE_URL = "https://huggingface.co/spaces/Noblhyon/BAAI_Vector_Api"
+
+def get_embeddings(texts):
+    response = requests.post(
+        f"{BASE_URL}/embed",
+        json={
+            "texts": texts,
+            "return_dense": True,
+            "max_length": 512
+        }
+    )
+    return response.json()
+
+def compute_similarity(text1, text2):
+    response = requests.post(
+        f"{BASE_URL}/similarity",
+        json={
+            "pairs": [[text1, text2]],
+            "method": "all"
+        }
+    )
+    return response.json()
+
+def search_documents(query, documents, top_k=5):
+    response = requests.post(
+        f"{BASE_URL}/search",
+        json={
+            "query": query,
+            "documents": documents,
+            "top_k": top_k
+        }
+    )
+    return response.json()
+
+# Example usage
+embeddings = get_embeddings(["Hello world", "How are you?"])
+similarity = compute_similarity("Hello", "Hi")
+search_results = search_documents("AI", ["Machine learning", "Cooking", "Neural networks"])
+```
+
 ## �� Performance
 
 BGE-M3 achieves state-of-the-art performance on various benchmarks:
@@ -63,30 +208,6 @@ BGE-M3 achieves state-of-the-art performance on various benchmarks:
 - **MLDR**: Long document retrieval
 - **NarritiveQA**: Long text understanding
 
-## 🚀 Quick Start
-
-Try the different tabs in this Space:
-
-1. **Text Embeddings**: Generate dense, sparse, or multi-vector embeddings
-2. **Similarity Comparison**: Compare semantic similarity between texts
-3. **Document Search**: Search through your documents using natural language
-4. **Model Info**: Learn more about BGE-M3 capabilities
-
-## 💻 Code Usage
-
-```python
-from FlagEmbedding import BGEM3FlagModel
-
-# Load the model
-model = BGEM3FlagModel('Noblhyon/BAAI_Vector_Api', use_fp16=True)
-
-# Generate embeddings
-embeddings = model.encode(["Your text here"], max_length=8192)['dense_vecs']
-
-# Compute similarity
-scores = model.compute_score([["text1", "text2"]])
-```
-
 ## 📚 Citation
 
 ```bibtex
@@ -108,4 +229,4 @@ scores = model.compute_score([["text1", "text2"]])
 
 ---
 
-*Built with ❤️ using Gradio and Hugging Face Spaces*
+*Built with ❤️ using Flask and Docker*