[Feature] Add citations flag in query and chat functions of App to return context along with the answer (#859)

embedchain/vectordb/elasticsearch.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 try:
     from elasticsearch import Elasticsearch
@@ -136,8 +136,13 @@ class ElasticsearchDB(BaseVectorDB):
         self.client.indices.refresh(index=self._get_index())
 
     def query(
-        self, input_query: List[str], n_results: int, where: Dict[str, any], skip_embedding: bool
-    ) -> List[Tuple[str, str, str]]:
+        self,
+        input_query: List[str],
+        n_results: int,
+        where: Dict[str, any],
+        skip_embedding: bool,
+        citations: bool = False,
+    ) -> Union[List[Tuple[str, str, str]], List[str]]:
         """
         query contents from vector data base based on vector similarity
 
@@ -150,8 +155,11 @@ class ElasticsearchDB(BaseVectorDB):
         :param skip_embedding: Optional. If True, then the input_query is assumed to be already embedded.
         :type skip_embedding: bool
         :return: The context of the document that matched your query, url of the source, doc_id
-
-        :rtype: List[Tuple[str,str,str]]
+        :param citations: we use citations boolean param to return context along with the answer.
+        :type citations: bool, default is False.
+        :return: The content of the document that matched your query,
+        along with url of the source and doc_id (if citations flag is true)
+        :rtype: List[str], if citations=False, otherwise List[Tuple[str, str, str]]
         """
         if skip_embedding:
             query_vector = input_query
@@ -175,14 +183,17 @@ class ElasticsearchDB(BaseVectorDB):
         _source = ["text", "metadata.url", "metadata.doc_id"]
         response = self.client.search(index=self._get_index(), query=query, _source=_source, size=n_results)
         docs = response["hits"]["hits"]
-        contents = []
+        contexts = []
         for doc in docs:
             context = doc["_source"]["text"]
-            metadata = doc["_source"]["metadata"]
-            source = metadata["url"]
-            doc_id = metadata["doc_id"]
-            contents.append(tuple((context, source, doc_id)))
-        return contents
+            if citations:
+                metadata = doc["_source"]["metadata"]
+                source = metadata["url"]
+                doc_id = metadata["doc_id"]
+                contexts.append(tuple((context, source, doc_id)))
+            else:
+                contexts.append(context)
+        return contexts
 
     def set_collection_name(self, name: str):
         """