community: implement _select_relevance_score_fn for tencent vectordb (#28036)

implement _select_relevance_score_fn for tencent vectordb
fix use external embedding for tencent vectordb

Co-authored-by: wlleiiwang <wlleiiwang@tencent.com>
Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/langchain_community/vectorstores/tencentvectordb.py

@@ -6,7 +6,18 @@ import json
 import logging
 import time
 from enum import Enum
-from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple, Union, cast
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+    cast,
+)
 
 import numpy as np
 from langchain_core.documents import Document
@@ -168,8 +179,8 @@ class TencentVectorDB(VectorStore):
         tcvectordb = guard_import("tcvectordb")
         tcollection = guard_import("tcvectordb.model.collection")
         enum = guard_import("tcvectordb.model.enum")
-
+        self.embedding_model = None
-        if t_vdb_embedding:
+        if embedding is None and t_vdb_embedding:
             embedding_model = [
                 model
                 for model in enum.EmbeddingModel
@@ -566,3 +577,17 @@ class TencentVectorDB(VectorStore):
         )
         # 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}."
+            )