Update app/core/rag_service.py

app/core/rag_service.py

@@ -1,60 +1,6 @@
-# # app/core/rag_service.py
-
-# from app.core.embedding_engine import embedder, COLLECTION_NAME
-# from qdrant_client.http.models import Filter, FieldCondition, MatchValue
-# from qdrant_client import QdrantClient
-# from app.core.config import QDRANT_URL, QDRANT_API_KEY
-
-# qdrant_client = QdrantClient(
-#     url=QDRANT_URL,
-#     api_key=QDRANT_API_KEY,
-#     check_compatibility=False
-# )
-
-# def get_rag_context(query, doc_id, top_k=3):
-    
-#     # ✅ Embed query
-#     query_vector = embedder.encode(query).tolist()
-
-#     # ✅ Query SINGLE collection + filter by doc_id
-#     results = qdrant_client.query_points(
-#         collection_name="smartnotes",   # 🔥 FIXED
-#         query=query_vector,
-#         limit=top_k,
-#         query_filter=Filter(
-#             must=[
-#                 FieldCondition(
-#                     key="doc_id",
-#                     match=MatchValue(value=doc_id)
-#                 )
-#             ]
-#         )
-#     )
-
-#     points = results.points
-
-#     if not points:
-#         return "", [], []
-
-#     context = "\n".join([p.payload["text"] for p in points])
-#     sources = [p.payload.get("source") for p in points]
-#     scores = [p.score for p in points]
-
-#     return context, sources, scores
-
-
-
-
-
-
-
-
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-
-
-
 # app/core/rag_service.py
-from app.core.embedding_engine import embed_query, COLLECTION_NAME  # ✅ use the correct function
+
+from app.core.embedding_engine import embed_query, COLLECTION_NAME
 from qdrant_client.http.models import Filter, FieldCondition, MatchValue
 from qdrant_client import QdrantClient
 from app.core.config import QDRANT_URL, QDRANT_API_KEY
@@ -66,14 +12,15 @@ qdrant_client = QdrantClient(
 )
 
 
-def get_rag_context(query, doc_id, top_k=5):  # ✅ top_k=5 for better recall
-    query_vector = embed_query(query)  # ✅ uses "query: " prefix + returns list
+def get_rag_context(query, doc_id, top_k=5):
+    """Retrieve relevant chunks with BGE embeddings."""
+    query_vector = embed_query(query)
 
     results = qdrant_client.query_points(
         collection_name=COLLECTION_NAME,
         query=query_vector,
         limit=top_k,
-        score_threshold=0.35,  # ✅ filter truly irrelevant results early
+        score_threshold=0.25,  # ✅ LOWERED from 0.35 — better recall
         query_filter=Filter(
             must=[FieldCondition(key="doc_id", match=MatchValue(value=doc_id))]
         )
@@ -84,12 +31,9 @@ def get_rag_context(query, doc_id, top_k=5):  # ✅ top_k=5 for better recall
     if not points:
         return "", [], []
 
-    context = "\n\n---\n\n".join([p.payload["text"] for p in points])  # ✅ clearer separator
+    context = "\n\n---\n\n".join([p.payload["text"] for p in points])
     sources = [p.payload.get("chunk_id", i) for i, p in enumerate(points)]
     scores = [p.score for p in points]
 
     return context, sources, scores
 
-
-
-