🤖 Auto-update model configuration

- Generated from EMD Python model definitions
- Updated at: $(date -u '+%Y-%m-%d %H:%M:%S UTC')
- Triggered by: workflow_dispatch
- Source commit: 937cac4 937cac4

Author: yanbasic

en/api.md

@@ -0,0 +1,381 @@
+# API Documentation
+
+> **Getting Started**: To obtain the base URL and API key for your deployed models, run `emd status` in your terminal. The command will display a table with your deployed models and their details, including a link to retrieve the API key from AWS Secrets Manager. The base URL is shown at the bottom of the output.
+>
+> Example output:
+> ```
+> Models
+> ┌────────────────────────┬───────────────────────────────────────────────────────────────────────┐
+> │ Model ID               │ Qwen2.5-0.5B-Instruct/dev                                             │
+> │ Status                 │ CREATE_COMPLETE                                                       │
+> │ Service Type           │ Amazon SageMaker AI Real-time inference with OpenAI Compatible API    │
+> │ Instance Type          │ ml.g5.2xlarge                                                         │
+> │ Create Time            │ 2025-05-08 12:27:05 UTC                                               │
+> │ Query Model API Key    │ https://console.aws.amazon.com/secretsmanager/secret?name=EMD-APIKey- │
+> │                        │ Secrets&region=us-east-1                                              │
+> │ SageMakerEndpointName  │ EMD-Model-qwen2-5-0-5b-instruct-endpoint                              │
+> └────────────────────────┴───────────────────────────────────────────────────────────────────────┘
+>
+> Base URL
+> http://your-emd-endpoint.region.elb.amazonaws.com/v1
+> ```
+
+## List Models
+
+Returns a list of available models.
+
+**Endpoint:** `GET /v1/models`
+
+**Curl Example:**
+```bash
+curl https://BASE_URL/v1/models
+```
+
+**Python Example:**
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    # No API key needed for listing models
+    base_url="https://BASE_URL"
+)
+
+# List available models
+models = client.models.list()
+for model in models.data:
+    print(model.id)
+```
+
+## Chat Completions
+
+Create a model response for a conversation.
+
+**Endpoint:** `POST /v1/chat/completions`
+
+**Parameters:**
+
+- `model` (required): ID of the model to use (e.g., "Qwen2.5-7B-Instruct/dev", "Llama-3.3-70B-Instruct/dev")
+- `messages` (required): Array of message objects with `role` and `content`
+- `temperature`: Sampling temperature (0-2, default: 1)
+- `top_p`: Nucleus sampling parameter (0-1, default: 1)
+- `n`: Number of chat completion choices to generate (default: 1)
+- `stream`: Whether to stream partial progress (default: false)
+- `stop`: Sequences where the API will stop generating
+- `max_tokens`: Maximum number of tokens to generate
+- `presence_penalty`: Penalty for new tokens based on presence (-2.0 to 2.0)
+- `frequency_penalty`: Penalty for new tokens based on frequency (-2.0 to 2.0)
+- `function_call`: Controls how the model responds to function calls
+- `functions`: List of functions the model may generate JSON inputs for
+
+**Curl Example:**
+```bash
+curl https://BASE_URL/v1/chat/completions \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Qwen2.5-7B-Instruct/dev",
+    "messages": [
+      {"role": "system", "content": "You are a helpful assistant."},
+      {"role": "user", "content": "Hello!"}
+    ],
+    "temperature": 0.7
+  }'
+```
+
+**Python Example:**
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="YOUR_API_KEY",
+    base_url="https://BASE_URL"
+)
+
+# Create a chat completion
+response = client.chat.completions.create(
+    model="Qwen2.5-7B-Instruct/dev",  # Model ID with tag
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Hello!"}
+    ],
+    temperature=0.7,
+    stream=False
+)
+
+# Print the response
+print(response.choices[0].message.content)
+```
+
+**Streaming Example:**
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="YOUR_API_KEY",
+    base_url="https://BASE_URL"
+)
+
+# Create a streaming chat completion
+stream = client.chat.completions.create(
+    model="Llama-3.3-70B-Instruct/dev",  # Model ID with tag
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Write a short poem about AI."}
+    ],
+    stream=True
+)
+
+# Process the stream
+for chunk in stream:
+    if chunk.choices[0].delta.content is not None:
+        print(chunk.choices[0].delta.content, end="")
+print()
+```
+
+## Embeddings
+
+> Some embedding models may have additional parameters or usage guidelines specified in their official documentation. For model-specific details, please refer to the provider's documentation.
+
+Get vector representations of text.
+
+**Endpoint:** `POST /v1/embeddings`
+
+**Parameters:**
+
+- `model` (required): ID of the model to use (e.g., "bge-m3/dev")
+- `input` (required): Input text to embed or array of texts
+- `user`: A unique identifier for the end-user
+
+**Curl Example:**
+```bash
+curl https://BASE_URL/v1/embeddings \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "bge-m3/dev",
+    "input": "The food was delicious and the waiter..."
+  }'
+```
+
+**Python Example:**
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="YOUR_API_KEY",
+    base_url="https://BASE_URL"
+)
+
+# Get embeddings for a single text
+response = client.embeddings.create(
+    model="bge-m3/dev",  # Embedding model ID with tag
+    input="The food was delicious and the service was excellent."
+)
+
+# Print the embedding vector
+print(response.data[0].embedding)
+
+# Get embeddings for multiple texts
+response = client.embeddings.create(
+    model="bge-m3/dev",  # Embedding model ID with tag
+    input=[
+        "The food was delicious and the service was excellent.",
+        "The restaurant was very expensive and the food was mediocre."
+    ]
+)
+
+# Print the number of embeddings
+print(f"Generated {len(response.data)} embeddings")
+```
+
+## Rerank
+
+> Some reranking models may have additional parameters or usage guidelines specified in their official documentation. For model-specific details, please refer to the provider's documentation.
+
+Rerank a list of documents based on their relevance to a query.
+
+**Endpoint:** `POST /v1/rerank`
+
+**Parameters:**
+
+- `model` (required): ID of the model to use (e.g., "bge-reranker-v2-m3/dev")
+- `query` (required): The search query
+- `documents` (required): List of documents to rerank
+- `max_rerank`: Maximum number of documents to rerank (default: all)
+- `return_metadata`: Whether to return metadata (default: false)
+
+**Curl Example:**
+```bash
+curl https://BASE_URL/v1/rerank \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "bge-reranker-v2-m3/dev",
+    "query": "What is the capital of France?",
+    "documents": [
+      "Paris is the capital of France.",
+      "Berlin is the capital of Germany.",
+      "London is the capital of England."
+    ]
+  }'
+```
+
+**Python Example:**
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="YOUR_API_KEY",
+    base_url="https://BASE_URL"
+)
+
+# Rerank documents based on a query
+response = client.reranking.create(
+    model="bge-reranker-v2-m3/dev",  # Reranking model ID with tag
+    query="What is the capital of France?",
+    documents=[
+        "Paris is the capital of France.",
+        "Berlin is the capital of Germany.",
+        "London is the capital of England."
+    ],
+    max_rerank=3
+)
+
+# Print the reranked documents
+for result in response.data:
+    print(f"Document: {result.document}")
+    print(f"Relevance Score: {result.relevance_score}")
+    print("---")
+```
+
+## Invocations
+
+General-purpose endpoint for model invocations.
+
+**Endpoint:** `POST /v1/invocations`
+
+**Parameters:**
+
+- `model` (required): ID of the model to use
+- `input`: Input data for the model
+- `parameters`: Additional parameters for the model
+
+**Curl Example:**
+```bash
+curl https://BASE_URL/v1/invocations \
+  -H "Authorization: Bearer YOUR_API_KEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "Qwen2.5-7B-Instruct/dev",
+    "input": {
+      "query": "What is machine learning?"
+    },
+    "parameters": {
+      "max_tokens": 100
+    }
+  }'
+```
+
+**Python Example:**
+```python
+import requests
+import json
+
+# Set up the API endpoint and headers
+url = "https://BASE_URL/v1/invocations"
+headers = {
+    "Authorization": "Bearer YOUR_API_KEY",
+    "Content-Type": "application/json"
+}
+
+# Prepare the payload
+payload = {
+    "model": "Qwen2.5-7B-Instruct/dev",  # Model ID with tag
+    "input": {
+        "query": "What is machine learning?"
+    },
+    "parameters": {
+        "max_tokens": 100
+    }
+}
+
+# Make the API call
+response = requests.post(url, headers=headers, data=json.dumps(payload))
+
+# Print the response
+print(response.json())
+```
+
+## Vision Models
+
+Process images along with text prompts.
+
+**Endpoint:** `POST /v1/chat/completions`
+
+**Parameters:**
+Same as Chat Completions, but with messages that include image content.
+
+**Python Example:**
+```python
+from openai import OpenAI
+import base64
+
+# Function to encode the image
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Path to your image
+image_path = "path/to/your/image.jpg"
+base64_image = encode_image(image_path)
+
+client = OpenAI(
+    api_key="YOUR_API_KEY",
+    base_url="https://BASE_URL"
+)
+
+response = client.chat.completions.create(
+    model="Qwen2-VL-7B-Instruct/dev",  # Vision model ID with tag
+    messages=[
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What's in this image?"},
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": f"data:image/jpeg;base64,{base64_image}"
+                    }
+                }
+            ]
+        }
+    ]
+)
+
+print(response.choices[0].message.content)
+```
+
+## Audio Transcription
+
+Transcribe audio files to text.
+
+**Endpoint:** `POST /v1/audio/transcriptions`
+
+**Python Example:**
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    api_key="YOUR_API_KEY",
+    base_url="https://BASE_URL"
+)
+
+audio_file_path = "path/to/audio.mp3"
+with open(audio_file_path, "rb") as audio_file:
+    response = client.audio.transcriptions.create(
+        model="whisper-large-v3/dev",  # ASR model ID with tag
+        file=audio_file
+    )
+
+print(response.text)  # Transcribed text
+```