community[patch]: Added support for filter out AWS Kendra search by score confidence (#12920)

**Description:** It will add support for filter out kendra search by
score confidence which will make result more accurate.
    For example
   ```
retriever = AmazonKendraRetriever(
        index_id=kendra_index_id, top_k=5, region_name=region,
        score_confidence="HIGH"
    )
```
Result will not include the records which has score confidence "LOW" or "MEDIUM". 
Relevant docs 
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/kendra/client/query.html
https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/kendra/client/retrieve.html

 **Issue:** the issue # it resolve #11801 
**twitter:** [@SmitCode](https://twitter.com/SmitCode)

libs/community/langchain_community/retrievers/kendra.py

@@ -1,11 +1,27 @@
 import re
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Literal, Optional, Sequence, Union
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Literal,
+    Optional,
+    Sequence,
+    Union,
+)
 
 from langchain_core.callbacks import CallbackManagerForRetrieverRun
 from langchain_core.documents import Document
-from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator, validator
+from langchain_core.pydantic_v1 import (
+    BaseModel,
+    Extra,
+    Field,
+    root_validator,
+    validator,
+)
 from langchain_core.retrievers import BaseRetriever
+from typing_extensions import Annotated
 
 
 def clean_excerpt(excerpt: str) -> str:
@@ -153,6 +169,8 @@ class ResultItem(BaseModel, ABC, extra=Extra.allow):  # type: ignore[call-arg]
     """The document URI."""
     DocumentAttributes: Optional[List[DocumentAttribute]] = []
     """The document attributes."""
+    ScoreAttributes: Optional[dict]
+    """The kendra score confidence"""
 
     @abstractmethod
     def get_title(self) -> str:
@@ -178,6 +196,13 @@ class ResultItem(BaseModel, ABC, extra=Extra.allow):  # type: ignore[call-arg]
         """Document attributes dict."""
         return {attr.Key: attr.Value.value for attr in (self.DocumentAttributes or [])}
 
+    def get_score_attribute(self) -> str:
+        """Document Score Confidence"""
+        if self.ScoreAttributes is not None:
+            return self.ScoreAttributes["ScoreConfidence"]
+        else:
+            return "NOT_AVAILABLE"
+
     def to_doc(
         self, page_content_formatter: Callable[["ResultItem"], str] = combined_text
     ) -> Document:
@@ -192,9 +217,9 @@ class ResultItem(BaseModel, ABC, extra=Extra.allow):  # type: ignore[call-arg]
                 "title": self.get_title(),
                 "excerpt": self.get_excerpt(),
                 "document_attributes": self.get_document_attributes_dict(),
+                "score": self.get_score_attribute(),
             }
         )
-
         return Document(page_content=page_content, metadata=metadata)
 
 
@@ -290,6 +315,15 @@ class RetrieveResult(BaseModel, extra=Extra.allow):  # type: ignore[call-arg]
     """The result items."""
 
 
+KENDRA_CONFIDENCE_MAPPING = {
+    "NOT_AVAILABLE": 0.0,
+    "LOW": 0.25,
+    "MEDIUM": 0.50,
+    "HIGH": 0.75,
+    "VERY_HIGH": 1.0,
+}
+
+
 class AmazonKendraRetriever(BaseRetriever):
     """`Amazon Kendra Index` retriever.
 
@@ -336,6 +370,7 @@ class AmazonKendraRetriever(BaseRetriever):
     page_content_formatter: Callable[[ResultItem], str] = combined_text
     client: Any
     user_context: Optional[Dict] = None
+    min_score_confidence: Annotated[Optional[float], Field(ge=0.0, le=1.0)]
 
     @validator("top_k")
     def validate_top_k(cls, value: int) -> int:
@@ -406,6 +441,25 @@ class AmazonKendraRetriever(BaseRetriever):
         ]
         return top_docs
 
+    def _filter_by_score_confidence(self, docs: List[Document]) -> List[Document]:
+        """
+        Filter out the records that have a score confidence
+        greater than the required threshold.
+        """
+        if not self.min_score_confidence:
+            return docs
+        filtered_docs = [
+            item
+            for item in docs
+            if (
+                item.metadata.get("score") is not None
+                and isinstance(item.metadata["score"], str)
+                and KENDRA_CONFIDENCE_MAPPING.get(item.metadata["score"], 0.0)
+                >= self.min_score_confidence
+            )
+        ]
+        return filtered_docs
+
     def _get_relevant_documents(
         self,
         query: str,
@@ -422,4 +476,4 @@ class AmazonKendraRetriever(BaseRetriever):
         """
         result_items = self._kendra_query(query)
         top_k_docs = self._get_top_k_docs(result_items)
-        return top_k_docs
+        return self._filter_by_score_confidence(top_k_docs)