Feat: add image preview to retrieval test. (#7610)

### What problem does this PR solve?

#7608

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

conf/infinity_mapping.json

 	"entity_type_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
 	"source_id": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
 	"n_hop_with_weight": {"type": "varchar", "default": ""},
-	"removed_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}
+	"removed_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"},
+
+	"doc_type_kwd": {"type": "varchar", "default": "", "analyzer": "whitespace-#"}
 }

rag/app/manual.py

         res = tokenize_table(tbls, doc, eng)
         for text, image in ti_list:
             d = copy.deepcopy(doc)
-            d['image'] = image
+            if image:
+                d['image'] = image
+                d["doc_type_kwd"] = "image"
             tokenize(d, text, eng)
             res.append(d)
         return res

rag/app/picture.py

     doc = {
         "docnm_kwd": filename,
         "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename)),
-        "image": img
+        "image": img,
+        "doc_type_kwd": "image"
     }
     bxs = ocr(np.array(img))
     txt = "\n".join([t[0] for _, t in bxs if t[0]])

rag/app/presentation.py

             d = copy.deepcopy(doc)
             pn += from_page
             d["image"] = img
+            d["doc_type_kwd"] = "image"
             d["page_num_int"] = [pn + 1]
             d["top_int"] = [0]
             d["position_int"] = [(pn + 1, 0, img.size[0], 0, img.size[1])]

rag/app/qa.py

         [qprefix + rmPrefix(q), aprefix + rmPrefix(a)])
     d["content_ltks"] = rag_tokenizer.tokenize(q)
     d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["image"] = image
+    if image:
+        d["image"] = image
+        d["doc_type_kwd"] = "image"
     add_positions(d, poss)
     return d
 
         [qprefix + rmPrefix(q), aprefix + rmPrefix(a)])
     d["content_ltks"] = rag_tokenizer.tokenize(q)
     d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
-    d["image"] = image
+    if image:
+        d["image"] = image
+        d["doc_type_kwd"] = "image"
     if row_num >= 0:
         d["top_int"] = [row_num]
     return d

rag/nlp/__init__.py

             d["content_with_weight"] = rows
             if img:
                 d["image"] = img
+                d["doc_type_kwd"] = "image"
             if poss:
                 add_positions(d, poss)
             res.append(d)
             d = copy.deepcopy(doc)
             r = de.join(rows[i:i + batch_size])
             tokenize(d, r, eng)
-            d["image"] = img
+            if img:
+                d["image"] = img
+                d["doc_type_kwd"] = "image"
             add_positions(d, poss)
             res.append(d)
     return res

rag/nlp/search.py

         src = req.get("fields",
                       ["docnm_kwd", "content_ltks", "kb_id", "img_id", "title_tks", "important_kwd", "position_int",
                        "doc_id", "page_num_int", "top_int", "create_timestamp_flt", "knowledge_graph_kwd",
-                       "question_kwd", "question_tks",
+                       "question_kwd", "question_tks", "doc_type_kwd",
                        "available_int", "content_with_weight", PAGERANK_FLD, TAG_FLD])
         kwds = set([])
 
                 "term_similarity": tsim[i],
                 "vector": chunk.get(vector_column, zero_vector),
                 "positions": position_int,
+                "doc_type_kwd": chunk.get("doc_type_kwd", "")
             }
             if highlight and sres.highlight:
                 if id in sres.highlight: