fix Update qdrant_impl.py

lightrag/kg/qdrant_impl.py

@@ -275,3 +275,50 @@ async def search_by_prefix(self, prefix: str) -> list[dict[str, Any]]:
         except Exception as e:
             logger.error(f"Error searching for prefix '{prefix}': {e}")
             return []
+
+    async def get_by_id(self, id: str) -> dict[str, Any] | None:
+        """Get vector data by its ID
+
+        Args:
+            id: The unique identifier of the vector
+
+        Returns:
+            The vector data if found, or None if not found
+        """
+        try:
+            qdrant_id = compute_mdhash_id_for_qdrant(id)
+            result = self._client.retrieve(
+                collection_name=self.namespace,
+                ids=[qdrant_id],
+                with_payload=True
+            )
+            if result and len(result) > 0:
+                return {**result[0].payload}
+            return None
+        except Exception as e:
+            logger.error(f"Error getting vector by ID {id}: {e}")
+            return None
+
+    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
+        """Get multiple vector data by their IDs
+
+        Args:
+            ids: List of unique identifiers
+
+        Returns:
+            List of vector data objects that were found
+        """
+        if not ids:
+            return []
+
+        try:
+            qdrant_ids = [compute_mdhash_id_for_qdrant(id) for id in ids]
+            results = self._client.retrieve(
+                collection_name=self.namespace,
+                ids=qdrant_ids,
+                with_payload=True
+            )
+            return [{**point.payload} for point in results if point]
+        except Exception as e:
+            logger.error(f"Error getting vectors by IDs: {e}")
+            return []

lightrag/operate.py

@@ -998,7 +998,7 @@ async def get_kg_context():
 
     async def get_vector_context():
         # Consider conversation history in vector search
-        augmented_query = query
+        augmented_query = f"Instructions: Based on the user query, provide a list of relevant documents that are most likely to contain the answer to the user's query.\nQuery: {query}"
         if history_context:
             augmented_query = f"{history_context}\n{query}"