fixed type issues

rag_pipeline/eval_ragas/metrics.py

@@ -105,7 +105,7 @@ def run_grounded_eval(
     Returns:
         metrics dict (keys → list of per-query scores).
     """
-    metrics = defaultdict(list)
+    metrics: defaultdict[str, list[float]] = defaultdict(list) #fixed type issues
     failed  = []
  
     for item in benchmark:

rag_pipeline/reranking/rerank.py

@@ -1,7 +1,8 @@
 #/rag_pipeline/reranking/rerank.py
 import numpy as np
 from sentence_transformers import CrossEncoder
- 
+import rag_pipeline.retrieval.bm25 as _bm25
+import rag_pipeline.retrieval.semantic as _sem
 from rag_pipeline.config import (
     CROSS_ENCODER_MODEL,
     TOP_N_RERANK,
@@ -76,7 +77,7 @@ def retrieve_and_grade(
         from rag_pipeline.config import get_groq_client
         groq_client = get_groq_client()
  
-    raw = hybrid_search_expanded(query, top_k=fetch_k)
+    raw = hybrid_search_expanded(query,retriever=_bm25._retriever,client=_sem._qdrant,bi_encoder=_sem._bi_encoder,all_chunks=_bm25._all_chunks, top_k=fetch_k)
     pre_confidence, _ = confidence_score(raw[:top_k])
  
     if pre_confidence >= confidence_threshold:

rag_pipeline/retrieval/bm25.py

@@ -3,6 +3,9 @@
 import bm25s
 from rag_pipeline.config import BM25_INDEX_PATH, TOP_K_RETRIEVE
 
+from typing import Optional
+_retriever = None
+_all_chunks: list[dict] =[]
 def create_bm25(all_chunks: list[dict]) -> None:
     corpus_texts = [c["text"] for c in all_chunks]
     retriever = bm25s.BM25()

rag_pipeline/retrieval/semantic.py

@@ -6,6 +6,9 @@ from qdrant_client.models import VectorParams, Distance, PointStruct
 from sentence_transformers import SentenceTransformer
 from rag_pipeline.config import BI_ENCODER_MODEL,TOP_K_RETRIEVE,COLLECTION_NAME,VECTOR_SIZE    
 
+_bi_encoder = None
+_qdrant = None
+_all_chunks:list[dict]=[]
 def load_qdrant() -> QdrantClient:
     client = QdrantClient(host="localhost",port=6333)
     print("[INFO] Qdrant loaded")