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165 | class AmazonKendraRetriever(BaseRetriever):
"""
AWS Kendra retriever for LlamaIndex.
See https://aws.amazon.com/kendra/ for more info.
Args:
index_id: Kendra Index ID.
query_config: Configuration for querying Kendra.
profile_name: The name of the profile in the ~/.aws/credentials
or ~/.aws/config files, which has either access keys or role information
specified. If not specified, the default credential profile or, if on an
EC2 instance, credentials from IMDS will be used.
region_name: The aws region e.g., `us-west-2`.
Fallback to AWS_DEFAULT_REGION env variable or region specified in
~/.aws/config.
aws_access_key_id: The aws access key id.
aws_secret_access_key: The aws secret access key.
aws_session_token: AWS temporary session token.
Example:
.. code-block:: python
from llama_index.retrievers.kendra import AmazonKendraRetriever
retriever = AmazonKendraRetriever(
index_id="<kendra-index-id>",
query_config={
"PageSize": 4,
"AttributeFilter": {
"EqualsTo": {
"Key": "tag",
"Value": {"StringValue": "space"}
}
}
},
)
"""
# Mapping of Kendra confidence levels to float scores
CONFIDENCE_SCORES = {
"VERY_HIGH": 1.0,
"HIGH": 0.8,
"MEDIUM": 0.6,
"LOW": 0.4,
"NOT_AVAILABLE": 0.0,
}
def __init__(
self,
index_id: str,
query_config: Optional[Dict[str, Any]] = None,
profile_name: Optional[str] = None,
region_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str] = None,
callback_manager: Optional[CallbackManager] = None,
):
self._client = get_aws_service_client(
service_name="kendra",
profile_name=profile_name,
region_name=region_name,
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
)
# Create async session with the same credentials
self._async_session = aioboto3.Session(
profile_name=profile_name,
region_name=region_name,
aws_access_key_id=aws_access_key_id,
aws_secret_access_key=aws_secret_access_key,
aws_session_token=aws_session_token,
)
self.index_id = index_id
self.query_config = query_config or {}
super().__init__(callback_manager)
def _parse_response(self, response: Dict[str, Any]) -> List[NodeWithScore]:
"""Parse Kendra response into NodeWithScore objects."""
node_with_score = []
result_items = response.get("ResultItems", [])
for result in result_items:
text = ""
metadata = {}
# Extract text based on result type
if result.get("Type") == "ANSWER":
text = (
result.get("AdditionalAttributes", [{}])[0]
.get("Value", {})
.get("TextWithHighlightsValue", {})
.get("Text", "")
)
elif result.get("Type") == "DOCUMENT":
text = result.get("DocumentExcerpt", {}).get("Text", "")
# Extract metadata
if "DocumentId" in result:
metadata["document_id"] = result["DocumentId"]
if "DocumentTitle" in result:
metadata["title"] = result.get("DocumentTitle", {}).get("Text", "")
if "DocumentURI" in result:
metadata["source"] = result["DocumentURI"]
# Only create nodes for results with actual content
if text:
# Convert Kendra's confidence score to float
confidence = result.get("ScoreAttributes", {}).get(
"ScoreConfidence", "NOT_AVAILABLE"
)
score = self.CONFIDENCE_SCORES.get(confidence, 0.0)
node_with_score.append(
NodeWithScore(
node=TextNode(
text=text,
metadata=metadata,
),
score=score,
)
)
return node_with_score
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Synchronous retrieve method."""
query = query_bundle.query_str
query_params = {
"IndexId": self.index_id,
"QueryText": query.strip(),
**self.query_config,
}
response = self._client.query(**query_params)
return self._parse_response(response)
async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Asynchronous retrieve method."""
query = query_bundle.query_str
query_params = {
"IndexId": self.index_id,
"QueryText": query.strip(),
**self.query_config,
}
async with self._async_session.client("kendra") as client:
response = await client.query(**query_params)
return self._parse_response(response)
|