MPNet implementation

.gitignore

@@ -161,4 +161,5 @@ cython_debug/
 #.idea/
 
 gptstew/.env!/gptstew/resources/
-.idea
+.idea
+.vscode

index/conf.py

@@ -10,10 +10,29 @@
 BIOFIND_DICT_SRC = "resources/dictionaries/pd/biofind.csv"
 BIOFIND_EMBEDDINGS_SRC = "resources/embeddings/biofind.csv"
 
-COLORS_AD = {'adni': '#d62728', 'aibl': '#ff7f0e', 'emif': '#8c564b', 'jadni': '#7f7f7f',
-             'a4': '#aec7e8', 'dod-adni': '#ffbb78', 'prevent-ad': '#98df8a', 'arwibo': '#ff9896',
-             'i-adni': '#c5b0d5', 'edsd': '#c49c94', 'pharmacog': '#c7c7c7',
-             'vita': '#bcbd22', 'abvib': '#e0d9e2', 'ad-mapper': '#800000'}
+COLORS_AD = {
+    "adni": "#d62728",
+    "aibl": "#ff7f0e",
+    "emif": "#8c564b",
+    "jadni": "#7f7f7f",
+    "a4": "#aec7e8",
+    "dod-adni": "#ffbb78",
+    "prevent-ad": "#98df8a",
+    "arwibo": "#ff9896",
+    "i-adni": "#c5b0d5",
+    "edsd": "#c49c94",
+    "pharmacog": "#c7c7c7",
+    "vita": "#bcbd22",
+    "abvib": "#e0d9e2",
+    "ad-mapper": "#800000",
+}
 
-COLORS_PD = {'opdc': '#1f77b4', 'tpd': '#e377c2', 'biofind': '#9edae5', 'lrrk2': '#f7b6d2', 'luxpark': '#2ca02c',
-             'ppmi': '#9467bd', 'passionate': '#00ff00'}
+COLORS_PD = {
+    "opdc": "#1f77b4",
+    "tpd": "#e377c2",
+    "biofind": "#9edae5",
+    "lrrk2": "#f7b6d2",
+    "luxpark": "#2ca02c",
+    "ppmi": "#9467bd",
+    "passionate": "#00ff00",
+}

index/embedding.py

@@ -2,10 +2,10 @@
 from abc import ABC
 import numpy as np
 import openai
+from sentence_transformers import SentenceTransformer
 
 
 class EmbeddingModel(ABC):
-
     def get_embedding(self, text: str) -> [float]:
         pass
 
@@ -14,7 +14,6 @@ def get_embeddings(self, messages: [str]) -> [[float]]:
 
 
 class GPT4Adapter(EmbeddingModel):
-
     def __init__(self, api_key: str):
         self.api_key = api_key
         openai.api_key = api_key
@@ -28,19 +27,42 @@ def get_embedding(self, text: str, model="text-embedding-ada-002"):
                 return None
             if isinstance(text, str):
                 text = text.replace("\n", " ")
-            return openai.Embedding.create(input=[text], model=model)['data'][0]['embedding']
+            return openai.Embedding.create(input=[text], model=model)["data"][0][
+                "embedding"
+            ]
         except Exception as e:
             logging.error(f"Error getting embedding for {text}: {e}")
             return None
 
     def get_embeddings(self, messages: [str], model="text-embedding-ada-002"):
         # store index of nan entries
         response = openai.Embedding.create(input=messages, model=model)
-        return [item['embedding'] for item in response['data']]
+        return [item["embedding"] for item in response["data"]]
 
 
-class TextEmbedding:
+class MPNetAdapter(EmbeddingModel):
+    def __init__(self):
+        logging.getLogger().setLevel(logging.INFO)
 
+    def get_embedding(self, text: str, model="sentence-transformers/all-mpnet-base-v2"):
+        mpnet_model = SentenceTransformer(model)
+        logging.info(f"Getting embedding for {text}")
+        try:
+            if text is None or text == "" or text is np.nan:
+                logging.warn(f"Empty text passed to get_embedding")
+                return None
+            if isinstance(text, str):
+                text = text.replace("\n", " ")
+            return mpnet_model.encode(text)
+        except Exception as e:
+            logging.error(f"Error getting embedding for {text}: {e}")
+            return None
+
+    def get_embeddings(self, messages: [str]) -> [[float]]:
+        return [self.get_embedding(msg) for msg in messages]
+
+
+class TextEmbedding:
     def __init__(self, text: str, embedding: [float]):
         self.text = text
         self.embedding = embedding