Update learning_platform.py

learning_platform.py

@@ -5,11 +5,9 @@ from datetime import datetime
 from typing import List, Dict, Any, Literal
 
 import faiss  # Example for vector store
-from langchain.chains import LLMChain  # Example for LLMChain
 from langchain.chat_models import ChatOpenAI
 from langchain.embeddings import OpenAIEmbeddings  # Example for embeddings
-from langchain.prompts import PromptTemplate  # Example for PromptTemplate
-from langgraph.graph import StateGraph, START, END
+from langgraph.graph import StateGraph, END
 
 
 @dataclass
@@ -65,16 +63,8 @@ class Config:
             "max_retries": 3,
             "timeout": 300
         }
-        self.rag_config = {
-            "chunk_size": 500,
-            "chunk_overlap": 50,
-            "distance_metric": "cosine",
-            "k_similar": 3
-        }
         self.course_defaults = {
-            "max_modules": 5,
-            "sections_per_module": 4,
-            "questions_per_quiz": 3
+            "max_modules": 5
         }
 
 
@@ -141,8 +131,8 @@ class CourseBuilder:
                              temperature=self.config.llm_config["temperature"],
                              max_tokens=self.config.llm_config["max_tokens"])
 
-        # Example OpenAI Embeddings usage (if needed)
-        self.embeddings = OpenAIEmbeddings(self.api_key)
+        # Corrected instantiation of OpenAIEmbeddings
+        self.embeddings = OpenAIEmbeddings(openai_api_key=self.api_key)
 
         # Example FAISS initialization for vector store (if needed)
         self.vector_store = faiss.IndexFlatL2(128)
@@ -241,7 +231,6 @@ class LearningPlatform:
     async def create_course(self, topic: str, difficulty: str) -> LearningPath:
         try:
             state = CourseState(topic=topic, difficulty=difficulty)
-            # Corrected to use 'arun'
             result = await self.course_builder.graph.arun(
                 state,
                 config={"recursion_limit": self.config.graph_config["recursion_limit"]}