Update README.md

Author: woodthom2

README.md

@@ -50,7 +50,7 @@ harmony.download_models()
 
 ```
 instruments = harmony.example_instruments["CES_D English"], harmony.example_instruments["GAD-7 Portuguese"]
-questions, similarity, query_similarity = harmony.match_instruments(instruments)
+questions, similarity, query_similarity, new_vectors_dict = harmony.match_instruments(instruments)
 ```
 
 ## How to load a PDF, Excel or Word into an instrument
@@ -91,6 +91,30 @@ all_questions, similarity, query_similarity = match_instruments(instruments)
 * `all_questions` is a list of the questions passed to Harmony, in order.
 * `similarity` is the similarity matrix returned by Harmony.
 * `query_similarity` is the degree of similarity of each item to an optional query passed as argument to `match_instruments`.
+
+## Using a different vectorisation function
+
+Harmony defaults to `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2` ([HuggingFace link](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)). However you can use other sentence transformers from HuggingFace by setting the environment `HARMONY_SENTENCE_TRANSFORMER_PATH` before importing Harmony:
+
+```
+export HARMONY_SENTENCE_TRANSFORMER_PATH=sentence-transformers/distiluse-base-multilingual-cased-v2
+```
+
+## Using OpenAI or other LLMs for vectorisation
+
+Any word vector representation can be used by Harmony. The below example works for OpenAI's [text-embedding-ada-002](https://openai.com/blog/new-and-improved-embedding-model) model as of July 2023, provided you have create a paid OpenAI account. However, since LLMs are progressing rapidly, we have chosen not to integrate Harmony directly into the OpenAI client libraries, but instead allow you to pass Harmony any vectorisation function of your choice.
+
+```
+import openai
+import numpy as np
+from harmony import match_instruments_with_function, example_instruments
+model_name = "text-embedding-ada-002"
+def convert_texts_to_vector(texts):
+    vectors = openai.Embedding.create(input = texts, model=model_name)['data']
+    return [vectors[i]["embedding"] for i in range(len(vectors))]
+instruments = example_instruments["CES_D English"], example_instruments["GAD-7 Portuguese"]
+all_questions, similarity, query_similarity, new_vectors_dict = match_instruments_with_function(instruments, None, convert_texts_to_vector)
+```
 
 ## Do you want to run Harmony in your browser locally?