fix use external embedding for tencent vectordb

libs/community/langchain_community/vectorstores/tencentvectordb.py

@@ -6,7 +6,7 @@
 import logging
 import time
 from enum import Enum
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast, Callable
 
 import numpy as np
 from langchain_core.documents import Document
@@ -168,8 +168,8 @@ def __init__(
         tcvectordb = guard_import("tcvectordb")
         tcollection = guard_import("tcvectordb.model.collection")
         enum = guard_import("tcvectordb.model.enum")
-
-        if t_vdb_embedding:
+        self.embedding_model = None
+        if embedding is None and t_vdb_embedding:
             embedding_model = [
                 model
                 for model in enum.EmbeddingModel
@@ -566,3 +566,17 @@ def max_marginal_relevance_search_by_vector(
         )
         # Reorder the values and return.
         return [documents[x] for x in new_ordering if x != -1]
+
+    def _select_relevance_score_fn(self) -> Callable[[float], float]:
+        metric_type = self.index_params.metric_type
+        if metric_type == "COSINE":
+            return self._cosine_relevance_score_fn
+        elif metric_type == "L2":
+            return self._euclidean_relevance_score_fn
+        elif metric_type == "IP":
+            return self._max_inner_product_relevance_score_fn
+        else:
+            raise ValueError(
+                "No supported normalization function"
+                f" for distance metric of type: {metric_type}."
+            )