diff --git a/haystack/reader/transformers.py b/haystack/reader/transformers.py
index 5dd725e224..6706ac4cb9 100644
--- a/haystack/reader/transformers.py
+++ b/haystack/reader/transformers.py
@@ -76,22 +76,22 @@ def predict(self, question, paragraphs, meta_data_paragraphs=None, top_k=None):
 
         # get top-answers for each candidate passage
         answers = []
-        for p in paragraphs:
-            query = {"context": p, "question": question}
+        for para, meta in zip(paragraphs, meta_data_paragraphs):
+            query = {"context": para, "question": question}
             predictions = self.model(query, topk=self.n_best_per_passage)
             # assemble and format all answers
             for pred in predictions:
                 if pred["answer"]:
                     context_start = max(0, pred["start"] - self.context_size)
-                    context_end = min(len(p), pred["end"] + self.context_size)
+                    context_end = min(len(para), pred["end"] + self.context_size)
                     answers.append({
                         "answer": pred["answer"],
-                        "context": p[context_start:context_end],
+                        "context": para[context_start:context_end],
                         "offset_answer_start": pred["start"],
                         "offset_answer_end": pred["end"],
-                        "probability": pred["score"],
+                        "probability": pred["score"],  
                         "score": None,
-                        "document_id": None
+                        "document_id": meta["document_id"]
                     })
 
         # sort answers by their `probability` and select top-k
diff --git a/haystack/retriever/tfidf.py b/haystack/retriever/tfidf.py
index 27a9d42790..952578a16f 100644
--- a/haystack/retriever/tfidf.py
+++ b/haystack/retriever/tfidf.py
@@ -9,7 +9,7 @@
 logger = logging.getLogger(__name__)
 
 # TODO make Paragraph generic for configurable units of text eg, pages, paragraphs, or split by a char_limit
-Paragraph = namedtuple("Paragraph", ["paragraph_id", "document_id", "text"])
+Paragraph = namedtuple("Paragraph", ["paragraph_id", "document_id", "text","document_name"])
 
 
 class TfidfRetriever(BaseRetriever):
@@ -48,7 +48,7 @@ def _get_all_paragraphs(self):
                 if not p.strip():  # skip empty paragraphs
                     continue
                 paragraphs.append(
-                    Paragraph(document_id=doc["id"], paragraph_id=p_id, text=(p,))
+                    Paragraph(document_id=doc["id"],document_name=doc["name"],paragraph_id=p_id, text=(p,))
                 )
                 p_id += 1
         logger.info(f"Found {len(paragraphs)} candidate paragraphs from {len(documents)} docs in DB")
@@ -81,7 +81,7 @@ def retrieve(self, query, candidate_doc_ids=None, top_k=10, verbose=True):
 
         # get actual content for the top candidates
         paragraphs = list(df_sliced.text.values)
-        meta_data = [{"document_id": row["document_id"], "paragraph_id": row["paragraph_id"]}
+        meta_data = [{"document_id": row["document_id"], "paragraph_id": row["paragraph_id"],"document_name":row["document_name"]}
                      for idx, row in df_sliced.iterrows()]
 
         return paragraphs, meta_data