[Feature] Update db.query to return source of context (#831)

embedchain/vectordb/qdrant.py

@@ -1,7 +1,7 @@
 import copy
 import os
 import uuid
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 try:
     from qdrant_client import QdrantClient
@@ -160,7 +160,9 @@ class QdrantDB(BaseVectorDB):
                 ),
             )
 
-    def query(self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool) -> List[str]:
+    def query(
+        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
+    ) -> List[Tuple[str, str, str]]:
         """
         query contents from vector database based on vector similarity
         :param input_query: list of query string
@@ -172,8 +174,8 @@ class QdrantDB(BaseVectorDB):
         :param skip_embedding: A boolean flag indicating if the embedding for the documents to be added is to be
         generated or not
         :type skip_embedding: bool
-        :return: Database contents that are the result of the query
-        :rtype: List[str]
+        :return: The context of the document that matched your query, url of the source, doc_id
+        :rtype: List[Tuple[str,str,str]]
         """
         if not skip_embedding:
             query_vector = self.embedder.embedding_fn([input_query])[0]
@@ -199,9 +201,14 @@ class QdrantDB(BaseVectorDB):
             query_vector=query_vector,
             limit=n_results,
         )
+
         response = []
         for result in results:
-            response.append(result.payload.get("text", ""))
+            context = result.payload["text"]
+            metadata = result.payload["metadata"]
+            source = metadata["url"]
+            doc_id = metadata["doc_id"]
+            response.append(tuple((context, source, doc_id)))
         return response
 
     def count(self) -> int:
@@ -211,3 +218,15 @@ class QdrantDB(BaseVectorDB):
     def reset(self):
         self.client.delete_collection(collection_name=self.collection_name)
         self._initialize()
+
+    def set_collection_name(self, name: str):
+        """
+        Set the name of the collection. A collection is an isolated space for vectors.
+
+        :param name: Name of the collection.
+        :type name: str
+        """
+        if not isinstance(name, str):
+            raise TypeError("Collection name must be a string")
+        self.config.collection_name = name
+        self.collection_name = self._get_or_create_collection()