Update README.md

README.md

@@ -91,7 +91,7 @@ import json
 
 # --- Configuration ---
 API_KEY = "your_api_key"  # Replace with your API key or "mcinext" for testing
-API_URL = "http://your-server-address/api/embedding-model"
+API_URL = "http://mcinext.ai/api/embedding-model"
 
 # --- Request Details ---
 headers = {
@@ -127,33 +127,36 @@ except Exception as err:
 ```
 
 3. Handling Special Tasks
-STS (Semantic Textual Similarity)
-For STS tasks, you need to compare the similarity between two pieces of text. To do this:
+### STS (Semantic Textual Similarity)
+For STS tasks, you need to compare the similarity between two pieces of text. You can send one or more sentences for comparison. To do this:
 
-Send the first sentence with the sts.sent1 prompt type.
-
-Send the second sentence with the sts.sent2 prompt type.
+1. Send the first sentence(s) with the `sts.sent1` prompt type.
+2. Send the second sentence(s) with the `sts.sent2` prompt type.
 
 Here’s how to do this:
 
-Request 1: First sentence (sts.sent1):
+**Request 1:** First sentence(s) (`sts.sent1`):
 
 ```json
 {
     "model": "Hakim",
     "input": [
-        "This is the first sentence."
+        "This is the first sentence.",
+        "This is another first sentence."
     ],
     "prompt_type": "sts.sent1"
 }
 ```
-Request 2: Second sentence (sts.sent2):
+
+Request 2: Second sentence(s) (sts.sent2):
+
 
 ```json
 {
     "model": "Hakim",
     "input": [
-        "This is the second sentence."
+        "This is the second sentence.",
+        "This is another second sentence."
     ],
     "prompt_type": "sts.sent2"
 }
@@ -163,13 +166,15 @@ Both requests will return embeddings for the respective sentences. You can then
 Retrieval
 For retrieval tasks, you need to compare a query to multiple documents. You need to send two different types of requests:
 
+
 Query Embedding (retrieval.query):
 
 ```json
 {
     "model": "Hakim",
     "input": [
-        "What is the capital of France?"
+        "What is the capital of France?",
+        "What is the population of France?"
     ],
     "prompt_type": "retrieval.query"
 }
@@ -181,13 +186,14 @@ Document Embedding (retrieval.passage):
 {
     "model": "Hakim",
     "input": [
-        "Paris is the capital of France."
+        "Paris is the capital of France.",
+        "Paris has a population of over 2 million."
     ],
     "prompt_type": "retrieval.passage"
 }
 ```
 
-This way, you can compare the query embedding to the document embedding to check if they are related or similar. The model will return embeddings for both the query and the document, and you can compute their similarity.
+This way, you can compare the query embeddings to the document embeddings to check if they are related or similar. The model will return embeddings for both the query and the document, and you can compute their similarity.
 
 Cross Task
 The cross task is used when you want to perform a binary classification or categorization based on the embeddings of two related texts. For example, given two sentences, you might want to categorize them into different categories (e.g., "similar" or "dissimilar").
@@ -198,14 +204,14 @@ For this, you provide both texts in a specific format:
 {
     "model": "Hakim",
     "input": [
-        "[text1]: This is the first text, [text2]: This is the second text"
+        "[text1]: This is the first text, [text2]: This is the second text",
+        "[text1]: A new sentence, [text2]: Another different sentence"
     ],
     "prompt_type": "cross"
 }
 ```
 
-The model will process both texts, compute their embeddings, and then categorize or classify them into predefined categories based on the similarity or relationship between the two texts.
-
+The model will process both pairs of texts, compute their embeddings, and then you can use these embeddings to train a model to categorize or classify them into predefined categories based on the similarity or relationship between the two texts.
 4. Error Handling
 If the input is incorrect or the prompt type is invalid, the API will return a 400 Bad Request with a detailed error message. For example: