Upload rag_service.py

app/core/rag_service.py

@@ -0,0 +1,98 @@
+# 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(question: str, doc_id: str):
+#     question_vector = embedder.encode([question])[0].tolist()
+
+#     hits = qdrant_client.query_points(
+#         collection_name=COLLECTION_NAME,
+#         query=question_vector,
+#         query_filter=Filter(
+#             must=[FieldCondition(key="doc_id", match=MatchValue(value=doc_id))]
+#         ),
+#         limit=5,
+#     ).points
+
+#     # context = "\n".join([hit.payload["text"] for hit in hits])
+
+#     contexts = []
+#     sources = []
+
+#     for hit in hits:
+#         text = hit.payload.get("text", "")
+#         contexts.append(text)
+
+#         sources.append({
+#             "text": text[:300],   # limit for UI
+#             # add page if you have it later
+#         })
+
+#     context = "\n".join(contexts)
+
+#     return context, sources
+
+
+# def get_rag_context(query, doc_id, top_k=3):
+#     query_vector = embedder.encode(query).tolist()
+
+#     results = qdrant_client.query_points(
+#         collection_name=doc_id,
+#         query=query_vector,
+#         limit=top_k
+#     )
+
+#     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
+
+
+
+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
+