diff --git a/docs/usage-indexreader.md b/docs/usage-indexreader.md
index 4f5bc0c35..2d96b1eff 100644
--- a/docs/usage-indexreader.md
+++ b/docs/usage-indexreader.md
@@ -72,6 +72,25 @@ print(doc_vector)
 ```
 
 The result is a dictionary where the keys are the analyzed terms and the values are the term frequencies.
+
+If you want to know the positions of each term in the document, you can use `get_term_positions`:
+```python
+term_positions = index_reader.get_term_positions('FBIS4-67701')
+print(term_positions)
+```
+The result is a dictionary where the keys are the analyzed terms and the values are the positions every term occur in the document.
+
+If you want to reconstruct the document using the position information, you can do this:
+```python
+doc = []
+for term, positions in term_positions.items():
+    for p in positions:
+        doc.append((term,p))
+doc = ' '.join([t for t, p in sorted(doc, key=lambda x: x[1])])
+print(doc)
+```
+The reconstructed document contains analyzed terms while [doc.contents()](https://github.com/castorini/pyserini/tree/master#how-do-i-fetch-a-document) contains unanalyzed terms.
+
 To compute the tf-idf representation of a document, do something like this:
 
 ```python
diff --git a/pyserini/index/_base.py b/pyserini/index/_base.py
index 07887d344..02ffff64f 100644
--- a/pyserini/index/_base.py
+++ b/pyserini/index/_base.py
@@ -260,6 +260,30 @@ def get_document_vector(self, docid: str) -> Optional[Dict[str, int]]:
             doc_vector_dict[term] = doc_vector_map.get(JString(term.encode('utf-8')))
         return doc_vector_dict
 
+    def get_term_positions(self, docid: str) -> Optional[Dict[str, int]]:
+        """Return the term position mapping of the document with ``docid``. Note that the term in the document is
+        stemmed and stop words may be removed according to your index settings. Also, requesting the document vector of
+        a ``docid`` that does not exist in the index will return ``None`` (as opposed to an empty dictionary); this
+        forces the caller to handle ``None`` explicitly and guards against silent errors.
+
+        Parameters
+        ----------
+        docid : str
+            Collection ``docid``.
+
+        Returns
+        -------
+        Optional[Dict[str, int]]
+            A tuple contains a dictionary with analyzed terms as keys and corresponding posting list as values
+        """
+        java_term_position_map = self.object.getTermPositions(self.reader, JString(docid))
+        if java_term_position_map is None:
+            return None
+        term_position_map = {}
+        for term in java_term_position_map.keySet().toArray():
+            term_position_map[term] = java_term_position_map.get(JString(term.encode('utf-8'))).toArray()
+        return term_position_map
+
     def doc(self, docid: str) -> Optional[Document]:
         """Return the :class:`Document` corresponding to ``docid``. Returns ``None`` if the ``docid`` does not exist
         in the index.
diff --git a/tests/test_index_reader.py b/tests/test_index_reader.py
index c7c041738..85cf9153e 100644
--- a/tests/test_index_reader.py
+++ b/tests/test_index_reader.py
@@ -183,6 +183,30 @@ def test_doc_vector_matches_index(self):
                 # The tf values should match.
                 self.assertEqual(postings_list[i].tf, 8)
 
+    def test_term_position(self):
+        term_positions = self.index_reader.get_term_positions('CACM-3134')
+        self.assertEqual(len(term_positions), 94)
+        self.assertEqual(term_positions['inform'], [7,24,36,46,60,112,121,159])
+        self.assertEqual(term_positions['retriev'], [10,20,44,132,160,164,172])
+
+    def test_term_position_invalid(self):
+        self.assertTrue(self.index_reader.get_term_positions('foo') is None)
+
+    def test_term_position_matches_index(self):
+        # From the term positions mapping, look up the position list of "information".
+        term_positions = self.index_reader.get_term_positions('CACM-3134')
+        self.assertEqual(term_positions['inform'], [7,24,36,46,60,112,121,159])
+
+        # Now look up the postings list for "information".
+        term = 'information'
+        postings_list = list(self.index_reader.get_postings_list(term))
+
+        for i in range(len(postings_list)):
+            # Go through the postings and find the matching document.
+            if self.index_reader.convert_internal_docid_to_collection_docid(postings_list[i].docid) == 'CACM-3134':
+                # The position list should match.
+                self.assertEqual(postings_list[i].positions, [7,24,36,46,60,112,121,159])
+
     def test_doc_invalid(self):
         self.assertTrue(self.index_reader.doc('foo') is None)
         self.assertTrue(self.index_reader.doc_contents('foo') is None)