Add python code example

README.md

@@ -2613,11 +2613,15 @@ The Large Language Model (LLM) is not for research and experimentation. We offer
 
 ## How to get embeddings
 
-To get embeddings, you should call API endpoint to send your text. You can send either a single sentence or multiple sentences. The embeddings that correspond to the inputs will be returned.
+Currently, we open the beta version of embedding API v1 and v2. 
+To get embeddings, you should call API endpoint to send your text. 
+You can send either a single sentence or multiple sentences. 
+The embeddings that correspond to the inputs will be returned.
 
 API Endpoint : https://api.sionic.ai/v1/embedding
 
-Example request:
+### Command line Example
+Request:
 ```shell
 curl https://api.sionic.ai/v1/embedding \
   -H "Content-Type: application/json" \
@@ -2626,7 +2630,7 @@ curl https://api.sionic.ai/v1/embedding \
   }'
 ```
 
-Example response:
+Response:
 ```shell
 {
     "embedding": [
@@ -2658,6 +2662,54 @@ Example response:
 }
 ```
 
+### Python code Example
+Get embeddings by directly calling Sionic's embedding API.
+```python
+def get_embedding(queries: List[str], url):
+    response = requests.post(url=url, json={'inputs': queries})
+    return np.asarray(response.json()['embedding'], dtype=np.float32)
+
+url = "https://api.sionic.ai/v1/embedding"
+inputs1 = ["first query", "second query"]
+inputs2 = ["third query", "fourth query"]
+embedding1 = get_embedding(inputs1, url=url)
+embedding2 = get_embedding(inputs2, url=url)
+similarity = embedding1 @ embedding2.T
+print(similarity)
+```
+
+Using pre-defined [SionicEmbeddingModel]() to obtain embeddings.
+
+```python
+import SionicEmbeddingModel
+
+inputs1 = ["first query", "second query"]
+inputs2 = ["third query", "fourth query"]
+model - SionicEmbeddingModel(url="https://api.sionic.ai/v1/embedding",
+                             dimension=2048)
+embedding1 = model.encode(inputs1)
+embedding2 = model.encode(inputs2)
+similarity = embedding1 @ embedding2.T
+print(similarity)
+```
+Inspired by [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding), we also apply instruction to encode short queries for retrieval task.
+By using `encode_queries()`, you can use instruction to encode queries which is added at the beginning of each query.
+The instruction to use for both v1 and v2 models is `"query: "`. 
+
+```python
+import SionicEmbeddingModel
+
+query = ["first query", "second query"]
+passage = ["This is a passage related to the first query", "This is a passage related to the second query"]
+model - SionicEmbeddingModel(url="https://api.sionic.ai/v1/embedding",
+                             instruction="query: ",
+                             dimension=2048)
+query_embedding = model.encode(query)
+passage_embedding = model.encode(passage)
+similarity = query_embedding @ passage_embedding.T
+print(similarity)
+```
+
 ## Massive Text Embedding Benchmark (MTEB) Evaluation
 
 Both versions of Sionic AI's embedding show the state-of-the-art performances on the MTEB!