Update utils/qa.py

utils/qa.py

@@ -1,24 +1,23 @@
 import logging
-from ingestion import DocumentProcessor
-from llm import LLMProcessor
+from utils.ingestion import DocumentProcessor
+from utils.llm import LLMProcessor
 
 
 class QAEngine:
     def __init__(self):
         self.processor = DocumentProcessor()
         self.llm_processor = LLMProcessor()
+        self.collection = self.processor.client.get_or_create_collection("document_chunks")  # Fix
 
     def query(self, question: str, k: int = 5) -> str:
         """Query the document using semantic search and generate an answer"""
         query_embedding = self.llm_processor.embed_model.encode(question)
 
-        # Corrected ChromaDB query syntax
-        results = self.processor.index.query(
+        results = self.collection.query(
             query_embeddings=[query_embedding],
             n_results=k
         )
 
-        # Extracting results properly
         chunks = []
         for i in range(len(results["documents"][0])):  # Iterate over top-k results
             chunks.append({
@@ -35,24 +34,3 @@ class QAEngine:
         print(context)
 
         return self.llm_processor.generate_answer(context, question)
-
-
-# def main():
-#     logging.basicConfig(level=logging.INFO)
-
-#     processor = DocumentProcessor()
-    
-#     pdf_path = "sample/InternLM.pdf"
-#     processor.process_document(pdf_path)
-
-#     qa_engine = QAEngine()
-#     question = "What are the main features of InternLM-XComposer-2.5?"
-#     answer = qa_engine.query(question)
-
-#     print("\nAnswer:")
-#     print("=" * 80)
-#     print(answer)
-
-
-# if __name__ == "__main__":
-#     main()