refactor: fix typo

Changed all occurrences of "embeder" to "embedder".

.env.template

@@ -50,8 +50,8 @@ SMART_TOKEN_LIMIT=8000
 
 # MEMORY_BACKEND - Memory backend type (Default: local)
 MEMORY_BACKEND=local
-# MEMORY_EMBEDER - Embeddings model to use (Default: ada)
-MEMORY_EMBEDER=ada
+# MEMORY_EMBEDDER - Embeddings model to use (Default: ada)
+MEMORY_EMBEDDER=ada
 
 ### PINECONE
 # PINECONE_API_KEY - Pinecone API Key (Example: my-pinecone-api-key)

scripts/config.py

@@ -82,7 +82,7 @@ class Config(metaclass=Singleton):
         # Note that indexes must be created on db 0 in redis, this is not configurable.
 
         self.memory_backend = os.getenv("MEMORY_BACKEND", 'local')
-        self.memory_embeder = os.getenv("MEMORY_EMBEDER", 'ada')
+        self.memory_embedder = os.getenv("MEMORY_EMBEDDER", 'ada')
         # Initialize the OpenAI API client
         openai.api_key = self.openai_api_key
 

scripts/memory/base.py

@@ -8,25 +8,25 @@ try:
     from sentence_transformers import SentenceTransformer
 except ImportError:
     SentenceTransformer = None
-    if cfg.memory_embeder == "sbert":
+    if cfg.memory_embedder == "sbert":
         print("Error: Sentence Transformers is not installed. Please install sentence_transformers"
-            " to use BERT as an embeder. Defaulting to Ada.")
-        cfg.memory_embeder = "ada"
+            " to use sBERT as an embedder. Defaulting to Ada.")
+        cfg.memory_embedder = "ada"
 
 
 cfg = Config()
-# Dimension of embeddings encoded by models
+# Dimension of embeddings encoded by embedders
 EMBED_DIM = {
     "ada": 1536,
     "sbert": 768
-}.get(cfg.memory_embeder, default=1536)
+}.get(cfg.memory_embedder, default=1536)
 
 
 def get_embedding(text):
     text = text.replace("\n", " ")
 
-    # use the embeder specified in the config
-    if cfg.memory_embeder == "sbert":
+    # use the embedder specified in the config
+    if cfg.memory_embedder == "sbert":
         embedding = SentenceTransformer("sentence-transformers/all-mpnet-base-v2", device="cpu").encode(text, show_progress_bar=False)
     else:
         embedding = openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]