改进rerank效果

backend/app/settings.py

@@ -10,11 +10,17 @@ from app.xinference.base import XinferenceEmbedding, XinferenceRerank
 
 
 def get_node_postprocessors():
+    rerank_enabled = os.getenv("RERANK_ENABLED")
+    if rerank_enabled is None or rerank_enabled is False:
+        return []
+
     rerank_model = os.getenv("RERANK_MODEL")
     rerank_url = os.getenv("RERANK_BASE_URL")
+    rerank_top_n = os.getenv("RERANK_TOP_N")
+    rerank_threshold = os.getenv("RERANK_THRESHOLD")
     postprocess = None
     if rerank_model is not None:
-        postprocess = [XinferenceRerank(rerank_model, rerank_url)]
+        postprocess = [XinferenceRerank(rerank_model, rerank_url, top_n=rerank_top_n, threshold=rerank_threshold)]
     return postprocess
 
 def init_settings():
@@ -79,7 +85,7 @@ def init_xinference():
     embed_model_name = os.getenv("EMBEDDING_MODEL")
     dimensions = os.getenv("EMBEDDING_DIM")
     dimensions = int(dimensions) if dimensions is not None else None
-    Settings.embed_model = XinferenceEmbedding(embed_model_name, embedding_base_url)
+    Settings.embed_model = XinferenceEmbedding(embed_model_name, embedding_base_url, dimensions=dimensions)
 
 def init_openai():
     from llama_index.core.constants import DEFAULT_TEMPERATURE