fix: bugs

pkg/rag/knowledge/mgr.py

@@ -6,11 +6,7 @@ import asyncio
 import uuid
 from pkg.rag.knowledge.services.parser import FileParser
 from pkg.rag.knowledge.services.chunker import Chunker
-from pkg.rag.knowledge.services.embedder import Embedder
-from pkg.rag.knowledge.services.retriever import Retriever
 from pkg.rag.knowledge.services.database import create_db_and_tables, SessionLocal, KnowledgeBase, File, Chunk
-from pkg.rag.knowledge.services.embedding_models import EmbeddingModelFactory
-from pkg.rag.knowledge.services.chroma_manager import ChromaIndexManager
 from pkg.core import app
 
 
@@ -20,8 +16,6 @@ class RAGManager:
     def __init__(self, ap: app.Application, logger: logging.Logger = None):
         self.ap = ap
         self.logger = logger or logging.getLogger(__name__)
-        self.embedding_model_type = None
-        self.embedding_model_name = None
         self.chroma_manager = None
         self.parser = FileParser()
         self.chunker = Chunker()
@@ -32,50 +26,13 @@ class RAGManager:
         """Initializes the RAG system by creating database tables."""
         await asyncio.to_thread(create_db_and_tables)
 
-    async def create_specific_model(self, embedding_model_type: str, embedding_model_name: str):
-        """
-        Creates and configures the specific embedding model and ChromaDB manager.
-        This must be called before performing embedding or retrieval operations.
-        """
-        self.embedding_model_type = embedding_model_type
-        self.embedding_model_name = embedding_model_name
-
-        try:
-            model = EmbeddingModelFactory.create_model(
-                model_type=self.embedding_model_type, model_name_key=self.embedding_model_name
-            )
-            self.logger.info(
-                f"Configured embedding model '{self.embedding_model_name}' has dimension: {model.embedding_dimension}"
-            )
-        except Exception as e:
-            self.logger.critical(
-                f"Failed to get dimension for configured embedding model '{self.embedding_model_name}': {e}"
-            )
-            raise RuntimeError('Failed to initialize RAG_Manager due to embedding model issues.')
-
-        self.chroma_manager = ChromaIndexManager(
-            collection_name=f'rag_collection_{self.embedding_model_name.replace("-", "_")}'
-        )
-        self.embedder = Embedder(
-            model_type=self.embedding_model_type,
-            model_name_key=self.embedding_model_name,
-            chroma_manager=self.chroma_manager,
-        )
-        self.retriever = Retriever(
-            model_type=self.embedding_model_type,
-            model_name_key=self.embedding_model_name,
-            chroma_manager=self.chroma_manager,
-        )
-
     async def create_knowledge_base(self, kb_name: str, kb_description: str, embedding_model: str = '', top_k: int = 5):
         """
         Creates a new knowledge base if it doesn't already exist.
         """
         try:
-            if not self.embedding_model_type or not kb_name:
-                raise ValueError(
-                    'Embedding model type and knowledge base name must be set before creating a knowledge base.'
-                )
+            if not kb_name:
+                raise ValueError('Knowledge base name must be set while creating.')
 
             def _create_kb_sync():
                 session = SessionLocal()