Update README.md to add information to access embedding api v2

README.md

@@ -2606,15 +2606,120 @@ model-index:
 
 Sionic AI delivers more accessible and cost-effective AI technology addressing the various needs to boost productivity and drive innovation.
 
-The Large Language Model (LLM) is not for research and experimentation. We offer solutions that leverage LLM to add value to your business. Anyone can easily train and control AI.
+The Large Language Model (LLM) is not for research and experimentation. 
+We offer solutions that leverage LLM to add value to your business. 
+Anyone can easily train and control AI.
 
 ## How to get embeddings
 
-We are working on releasing v2 API. In the meantime, you can try our embedding API v1. Please visit [here](https://huggingface.co/sionic-ai/sionic-ai-v1).
+Currently, we open the beta version of embedding APIs. 
+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/v2/embedding
+
+### Command line Example
+Request:
+```shell
+curl https://api.sionic.ai/v2/embedding \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": ["first query", "second query", "third query"]
+  }'
+```
+
+Response:
+```shell
+{
+    "embedding": [
+        [
+            0.5567971,
+            -1.1578958,
+            -0.7148851,
+            -0.2326297,
+            0.4394867,
+            ...
+        ],
+        [
+            0.5049863,
+            -0.8253384, 
+            -1.0041373, 
+            -0.6503708, 
+            0.5007141, 
+            ...
+        ],
+        [
+            0.6059823,
+            -1.0369557,
+            -0.6705063,
+            -0.4467056,
+            0.8618057,
+            ...
+        ]
+    ]
+}
+```
+
+### Python code Example
+Get embeddings by directly calling embedding API. 
+
+```python
+from typing import List
+import numpy as np
+import requests
+
+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/v2/embedding"
+inputs1 = ["first query", "second query"]
+inputs2 = ["third query", "fourth query"]
+embedding1 = get_embedding(inputs1, url=url)
+embedding2 = get_embedding(inputs2, url=url)
+cos_similarity = (embedding1 / np.linalg.norm(embedding1)) @ (embedding2 / np.linalg.norm(embedding1)).T
+print(cos_similarity)
+```
+
+Using pre-defined [SionicEmbeddingModel](https://huggingface.co/sionic-ai/sionic-ai-v2/blob/main/model_api.py) to obtain embeddings.
+
+```python
+from model_api import SionicEmbeddingModel
+import numpy as np
+
+inputs1 = ["first query", "second query"]
+inputs2 = ["third query", "fourth query"]
+model = SionicEmbeddingModel(url="https://api.sionic.ai/v2/embedding",
+                             dimension=3072)
+embedding1 = model.encode(inputs1)
+embedding2 = model.encode(inputs2)
+cos_similarity = (embedding1 / np.linalg.norm(embedding1)) @ (embedding2 / np.linalg.norm(embedding1)).T
+print(cos_similarity)
+```
+We apply the instruction to encode short queries for retrieval tasks.
+By using `encode_queries()`, you can use the instruction to encode queries which is prefixed to each query as the following example.
+The recommended instruction for both v1 and v2 models is `"query: "`.
+
+```python
+from model_api import SionicEmbeddingModel
+import numpy as np
+
+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/v2/embedding",
+                             instruction="query: ",
+                             dimension=3072)
+query_embedding = model.encode_queries(query)
+passage_embedding = model.encode_corpus(passage)
+cos_similarity = (query_embedding / np.linalg.norm(query_embedding)) @ (passage_embedding / np.linalg.norm(passage_embedding)).T
+print(cos_similarity)
+``` 
 
 ## Massive Text Embedding Benchmark (MTEB) Evaluation
 
 Both versions of Sionic AI's embedding show the state-of-the-art performances on the MTEB!
+You can find a code to evaluate MTEB datasets using Sionic embedding APIs [here](https://huggingface.co/sionic-ai/sionic-ai-v2/blob/main/mteb_evaluate.py).
 
 |                               Model Name                                | Dimension | Sequence Length | Average (56) | 
 |:-----------------------------------------------------------------------:|:---------:|:---------------:|:------------:|
@@ -2623,3 +2728,4 @@ Both versions of Sionic AI's embedding show the state-of-the-art performances on
 |   [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)    |   1024    |       512       |    64.23     |  
 |      [gte-large-en](https://huggingface.co/barisaydin/gte-large)      |   1024    |       512       |    63.13     |
 |      [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings/types-of-embedding-models)      |   1536    |      8191       |    60.99     |
+