feat: add function similarity_search_limit_score to vectorstores.redis (#1950)

# Description
***
Add function similarity_search_limit_score and
similarity_search_with_score

# How to use
***
``
rds = Redis.from_existing_index(embeddings,
redis_url="redis://localhost:6379", index_name='link')

rds.similarity_search_limit_score(query, k=3, score=0.2)

rds.similarity_search_with_score(query, k=3)
``

---------

Co-authored-by: Peter <peter.shi@alephf.com>

langchain/vectorstores/redis.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 import json
 import logging
 import uuid
-from typing import Any, Callable, Iterable, List, Mapping, Optional
+from typing import Any, Callable, Iterable, List, Mapping, Optional, Tuple
 
 import numpy as np
 from redis.client import Redis as RedisType
@@ -86,6 +86,48 @@ class Redis(VectorStore):
     def similarity_search(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> List[Document]:
+        docs_and_scores = self.similarity_search_with_score(query, k=k)
+        return [doc for doc, _ in docs_and_scores]
+
+    def similarity_search_limit_score(
+        self, query: str, k: int = 4, score_threshold: float = 0.2, **kwargs: Any
+    ) -> List[Document]:
+        """
+        Returns the most similar indexed documents to the query text.
+
+        Args:
+            query (str): The query text for which to find similar documents.
+            k (int): The number of documents to return. Default is 4.
+            score_threshold (float): The minimum matching score required for a document
+            to be considered a match. Defaults to 0.2.
+            Because the similarity calculation algorithm is based on cosine similarity,
+            the smaller the angle, the higher the similarity.
+
+        Returns:
+            List[Document]: A list of documents that are most similar to the query text,
+            including the match score for each document.
+
+        Note:
+            If there are no documents that satisfy the score_threshold value,
+            an empty list is returned.
+
+        """
+        docs_and_scores = self.similarity_search_with_score(query, k=k)
+
+        return [doc for doc, score in docs_and_scores if score < score_threshold]
+
+    def similarity_search_with_score(
+        self, query: str, k: int = 4
+    ) -> List[Tuple[Document, float]]:
+        """Return docs most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
         try:
             from redis.commands.search.query import Query
         except ImportError:
@@ -120,12 +162,17 @@ class Redis(VectorStore):
         # perform vector search
         results = self.client.ft(self.index_name).search(redis_query, params_dict)
 
-        documents = [
-            Document(page_content=result.content, metadata=json.loads(result.metadata))
+        docs = [
+            (
+                Document(
+                    page_content=result.content, metadata=json.loads(result.metadata)
+                ),
+                float(result.vector_score),
+            )
             for result in results.docs
         ]
 
-        return documents
+        return docs
 
     @classmethod
     def from_texts(