Update README.md

README.md

@@ -1,16 +1,70 @@
-## Typesense Public Embedding Models
-We maintain a repository of currently supported embedding models, and we welcome contributions from the community. If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it.
+---
+license: gpl-3.0
+tags:
+- typesense
+- semantic search
+- vector search
+---
+
+# Typesense Built-in Embedding Models
+
+This repository holds all the built-in ML models supported by [Typesense](https://typesense.org) for semantic search currently. 
+
+If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it. (See below for instructions).
+
+## Usage
+
+Here's an example of how to specify the model to use for auto-embedding generation when creating a collection in Typesense:
+
+```bash
+curl -X POST \
+  'http://localhost:8108/collections' \
+  -H 'Content-Type: application/json' \
+  -H "X-TYPESENSE-API-KEY: ${TYPESENSE_API_KEY}" \
+  -d '{
+        "name": "products",
+        "fields": [
+          {
+            "name": "product_name",
+            "type": "string"
+          },
+          {
+            "name": "embedding",
+            "type": "float[]",
+            "embed": {
+              "from": [
+                "product_name"
+              ],
+              "model_config": {
+                "model_name": "ts/all-MiniLM-L12-v2"
+              }
+            }
+          }
+        ]
+      }'
+```
+
+Replace `all-MiniLM-L12-v2` with any model name from this repository. 
+
+Here's a detailed step-by-step article with more information: https://typesense.org/docs/guide/semantic-search.html
+
+## Contributing
+
+If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it. (See below for instructions).
 
 ### Convert a model to ONNX format
 
 #### Converting a Hugging Face Transformers Model
 To convert any model from Hugging Face to ONNX format, you can follow the instructions in [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) using the ```optimum-cli```.
+
 #### Converting a PyTorch Model
 If you have a PyTorch model, you can use the ```torch.onnx``` APIs to convert it to the ONNX format. More information on the conversion process can be found  [here](https://pytorch.org/docs/stable/onnx.html).
+
 #### Converting a Tensorflow Model
 For Tensorflow models, you can utilize the tf2onnx tool to convert them to the ONNX format. Detailed guidance on this conversion can be found [here](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx). 
 
-### Creating model config 
+#### Creating model config 
+
 Before submitting your ONNX model through a PR, you need to organize the necessary files under a folder with the model's name. Ensure that your model configuration adheres to the following structure:
 
   - **Model File**: The ONNX model file.
@@ -28,4 +82,4 @@ Before submitting your ONNX model through a PR, you need to organize the necessa
 
 Please make sure that the information in the configuration file is accurate and complete before submitting your PR.
 
-We appreciate your contributions to expand our collection of supported embedding models!
+We appreciate your contributions to expand our collection of supported embedding models!