diff --git a/elm/wizard.py b/elm/wizard.py
index 7e0424c..8f9472d 100644
--- a/elm/wizard.py
+++ b/elm/wizard.py
@@ -407,7 +407,8 @@ class EnergyWizardPostgres(EnergyWizardBase):
     def __init__(self, db_host, db_port, db_name,
                  db_schema, db_table, meta_columns=None,
                  cursor=None, boto_client=None,
-                 model=None, token_budget=3500):
+                 model=None, token_budget=3500,
+                 tag=False):
         """
         Parameters
         ----------
@@ -435,6 +436,9 @@ def __init__(self, db_host, db_port, db_name,
             Number of tokens that can be embedded in the prompt. Note that the
             default budget for GPT-3.5-Turbo is 4096, but you want to subtract
             some tokens to account for the response budget.
+        tag: bool
+            Flag to add tag/metadata to text chunks before sending query to
+            GPT.
         """
         boto3 = try_import('boto3')
         psycopg2 = try_import('psycopg2')
@@ -461,6 +465,8 @@ def __init__(self, db_host, db_port, db_name,
         else:
             self.cursor = cursor
 
+        self.tag = tag
+
         if boto_client is None:
             access_key = os.getenv('AWS_ACCESS_KEY_ID')
             secret_key = os.getenv('AWS_SECRET_ACCESS_KEY')
@@ -521,7 +527,33 @@ def get_embedding(self, text):
 
         return embedding
 
-    def query_vector_db(self, query, limit=100):
+    @staticmethod
+    def _add_tag(meta):
+        """Function to add tag/metadata to text strings before
+        sending query to GPT.
+
+        Parameters
+        ----------
+        meta : tuple
+            Text values to include in tag (title, authors, year)
+
+        Returns
+        -------
+        tag : str
+            Text string containing provided metadata.
+        """
+        title, authors, year = meta
+        if authors and year:
+            tag = (f"Title: {title}\n"
+                   f"Authors: {authors}\n"
+                   f"Publication Year: {year}\n\n"
+                   )
+        else:
+            tag = f"Title: {title}\n\n"
+
+        return tag
+
+    def query_vector_db(self, query, probes=25, limit=100):
         """Returns a list of strings and relatednesses, sorted from most
         related to least.
 
@@ -529,6 +561,8 @@ def query_vector_db(self, query, limit=100):
         ----------
         query : str
             Question being asked of GPT
+        probes: int
+            Number of lists to search in vector database index.
         limit : int
             Number of top results to return.
 
@@ -545,17 +579,25 @@ def query_vector_db(self, query, limit=100):
 
         query_embedding = self.get_embedding(query)
 
-        self.cursor.execute(f"SELECT {self.db_table}.id, "
+        self.cursor.execute(f"SET LOCAL ivfflat.probes = {probes};"
+                            f"SELECT {self.db_table}.id, "
                             f"{self.db_table}.chunks, "
                             f"{self.db_table}.embedding "
-                            "<=> %s::vector as score "
+                            "<=> %s::vector as score, "
+                            f"{self.db_table}.title, "
+                            f"{self.db_table}.authors, "
+                            f"{self.db_table}.year "
                             f"FROM {self.db_schema}.{self.db_table} "
                             "ORDER BY embedding <=> %s::vector LIMIT %s;",
                             (query_embedding, query_embedding, limit,), )
 
         result = self.cursor.fetchall()
 
-        strings = [s[1] for s in result]
+        if self.tag:
+            strings = [self._add_tag(s[3:]) + s[1] for s in result]
+        else:
+            strings = [s[1] for s in result]
+
         scores = [s[2] for s in result]
         best = [s[0] for s in result]