Use Infinity single-field-multi-index (#11444)

### What problem does this PR solve?

Use Infinity single-field-multi-index

### Type of change

- [x] Refactoring
- [x] Performance Improvement

rag/nlp/search.py

@@ -17,7 +17,6 @@ import json
 import logging
 import re
 import math
-import os
 from collections import OrderedDict
 from dataclasses import dataclass
 
@@ -28,6 +27,7 @@ from rag.utils.doc_store_conn import DocStoreConnection, MatchDenseExpr, FusionE
 from common.string_utils import remove_redundant_spaces
 from common.float_utils import get_float
 from common.constants import PAGERANK_FLD, TAG_FLD
+from common import settings
 
 
 def index_name(uid): return f"ragflow_{uid}"
@@ -120,7 +120,8 @@ class Dealer:
             else:
                 matchDense = self.get_vector(qst, emb_mdl, topk, req.get("similarity", 0.1))
                 q_vec = matchDense.embedding_data
-                src.append(f"q_{len(q_vec)}_vec")
+                if not settings.DOC_ENGINE_INFINITY:
+                    src.append(f"q_{len(q_vec)}_vec")
 
                 fusionExpr = FusionExpr("weighted_sum", topk, {"weights": "0.05,0.95"})
                 matchExprs = [matchText, matchDense, fusionExpr]
@@ -405,8 +406,13 @@ class Dealer:
                 rank_feature=rank_feature,
             )
         else:
-            lower_case_doc_engine = os.getenv("DOC_ENGINE", "elasticsearch")
-            if lower_case_doc_engine in ["elasticsearch", "opensearch"]:
+            if settings.DOC_ENGINE_INFINITY:
+                # Don't need rerank here since Infinity normalizes each way score before fusion.
+                sim = [sres.field[id].get("_score", 0.0) for id in sres.ids]
+                sim = [s if s is not None else 0.0 for s in sim]
+                tsim = sim
+                vsim = sim
+            else:
                 # ElasticSearch doesn't normalize each way score before fusion.
                 sim, tsim, vsim = self.rerank(
                     sres,
@@ -415,12 +421,6 @@ class Dealer:
                     vector_similarity_weight,
                     rank_feature=rank_feature,
                 )
-            else:
-                # Don't need rerank here since Infinity normalizes each way score before fusion.
-                sim = [sres.field[id].get("_score", 0.0) for id in sres.ids]
-                sim = [s if s is not None else 0.0 for s in sim]
-                tsim = sim
-                vsim = sim
 
         sim_np = np.array(sim, dtype=np.float64)
         if sim_np.size == 0: