fix: remove heavy local embedding model

apps/api/src/db/organization_config.py

@@ -21,8 +21,8 @@ class AIConfig(BaseModel):
     enabled : bool = True
     limits: AILimitsSettings = AILimitsSettings()
     embeddings: Literal[
-        "text-embedding-ada-002", "all-MiniLM-L6-v2"
-    ] = "all-MiniLM-L6-v2"
+        "text-embedding-ada-002",
+    ] = "text-embedding-ada-002"
     ai_model: Literal["gpt-3.5-turbo", "gpt-4-1106-preview"] = "gpt-3.5-turbo"
     features: AIEnabledFeatures = AIEnabledFeatures()
 

apps/api/src/services/ai/base.py

@@ -2,7 +2,6 @@ from typing import Optional
 from uuid import uuid4
 from langchain.agents import AgentExecutor
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
 from langchain_community.vectorstores import Chroma
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
 from langchain.prompts import MessagesPlaceholder
@@ -45,7 +44,6 @@ def ask_ai(
     texts = text_splitter.split_documents(documents)
 
     embedding_models = {
-        "all-MiniLM-L6-v2": SentenceTransformerEmbeddings,
         "text-embedding-ada-002": OpenAIEmbeddings,
     }
 
@@ -53,11 +51,11 @@ def ask_ai(
 
     if embedding_model_name in embedding_models:
         if embedding_model_name == "text-embedding-ada-002":
-            embedding_function = embedding_models[embedding_model_name](model=embedding_model_name, api_key=openai_api_key)
-        if embedding_model_name == "all-MiniLM-L6-v2":
-            embedding_function = embedding_models[embedding_model_name](model_name=embedding_model_name)
+            embedding_function = embedding_models[embedding_model_name](
+                model=embedding_model_name, api_key=openai_api_key
+            )
     else:
-        embedding_function = embedding_models[embedding_model_name](model_name=embedding_model_name)
+        raise Exception("Embedding model not found")
 
     # load it into Chroma and use it as a retriever
     db = Chroma.from_documents(texts, embedding_function)
@@ -75,7 +73,10 @@ def ask_ai(
     memory_key = "history"
 
     memory = AgentTokenBufferMemory(
-        memory_key=memory_key, llm=llm, chat_memory=message_history, max_token_limit=1000
+        memory_key=memory_key,
+        llm=llm,
+        chat_memory=message_history,
+        max_token_limit=1000,
     )
 
     system_message = SystemMessage(content=(message_for_the_prompt))

apps/api/src/services/orgs/orgs.py

@@ -169,7 +169,7 @@ async def create_org(
                 limits_enabled=False,
                 max_asks=0,
             ),
-            embeddings="all-MiniLM-L6-v2",
+            embeddings="text-embedding-ada-002",
             ai_model="gpt-3.5-turbo",
             features=AIEnabledFeatures(
                 editor=False,
@@ -531,8 +531,6 @@ async def get_org_join_mechanism(
 
     return signup_mechanism
 
-    
-
 
 ## 🔒 RBAC Utils ##