Source code for langchain_community.document_loaders.athena

from __future__ import annotations

import io
import json
import time
from typing import Any, Dict, Iterator, List, Optional, Tuple

from langchain_core.documents import Document

from langchain_community.document_loaders.base import BaseLoader


[docs]class AthenaLoader(BaseLoader): """Load documents from `AWS Athena`. Each document represents one row of the result. - By default, all columns are written into the `page_content` of the document and none into the `metadata` of the document. - If `metadata_columns` are provided then these columns are written into the `metadata` of the document while the rest of the columns are written into the `page_content` of the document. To authenticate, the AWS client uses this method to automatically load credentials: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html If a specific credential profile should be used, you must pass the name of the profile from the ~/.aws/credentials file that is to be used. Make sure the credentials / roles used have the required policies to access the Amazon Textract service. """
[docs] def __init__( self, query: str, database: str, s3_output_uri: str, profile_name: str, metadata_columns: Optional[List[str]] = None, ): """Initialize Athena document loader. Args: query: The query to run in Athena. database: Athena database s3_output_uri: Athena output path metadata_columns: Optional. Columns written to Document `metadata`. """ self.query = query self.database = database self.s3_output_uri = s3_output_uri self.metadata_columns = metadata_columns if metadata_columns is not None else [] try: import boto3 except ImportError: raise ImportError( "Could not import boto3 python package. " "Please install it with `pip install boto3`." ) try: session = ( boto3.Session(profile_name=profile_name) if profile_name is not None else boto3.Session() ) except Exception as e: raise ValueError( "Could not load credentials to authenticate with AWS client. " "Please check that credentials in the specified " "profile name are valid." ) from e self.athena_client = session.client("athena") self.s3_client = session.client("s3")
def _execute_query(self) -> List[Dict[str, Any]]: response = self.athena_client.start_query_execution( QueryString=self.query, QueryExecutionContext={"Database": self.database}, ResultConfiguration={"OutputLocation": self.s3_output_uri}, ) query_execution_id = response["QueryExecutionId"] while True: response = self.athena_client.get_query_execution( QueryExecutionId=query_execution_id ) state = response["QueryExecution"]["Status"]["State"] if state == "SUCCEEDED": break elif state == "FAILED": resp_status = response["QueryExecution"]["Status"] state_change_reason = resp_status["StateChangeReason"] err = f"Query Failed: {state_change_reason}" raise Exception(err) elif state == "CANCELLED": raise Exception("Query was cancelled by the user.") time.sleep(1) result_set = self._get_result_set(query_execution_id) return json.loads(result_set.to_json(orient="records")) def _remove_suffix(self, input_string: str, suffix: str) -> str: if suffix and input_string.endswith(suffix): return input_string[: -len(suffix)] return input_string def _remove_prefix(self, input_string: str, suffix: str) -> str: if suffix and input_string.startswith(suffix): return input_string[len(suffix) :] return input_string def _get_result_set(self, query_execution_id: str) -> Any: try: import pandas as pd except ImportError: raise ImportError( "Could not import pandas python package. " "Please install it with `pip install pandas`." ) output_uri = self.s3_output_uri tokens = self._remove_prefix( self._remove_suffix(output_uri, "/"), "s3://" ).split("/") bucket = tokens[0] key = "/".join(tokens[1:] + [query_execution_id]) + ".csv" obj = self.s3_client.get_object(Bucket=bucket, Key=key) df = pd.read_csv(io.BytesIO(obj["Body"].read()), encoding="utf8") return df def _get_columns( self, query_result: List[Dict[str, Any]] ) -> Tuple[List[str], List[str]]: content_columns = [] metadata_columns = [] all_columns = list(query_result[0].keys()) for key in all_columns: if key in self.metadata_columns: metadata_columns.append(key) else: content_columns.append(key) return content_columns, metadata_columns
[docs] def lazy_load(self) -> Iterator[Document]: query_result = self._execute_query() content_columns, metadata_columns = self._get_columns(query_result) for row in query_result: page_content = "\n".join( f"{k}: {v}" for k, v in row.items() if k in content_columns ) metadata = { k: v for k, v in row.items() if k in metadata_columns and v is not None } doc = Document(page_content=page_content, metadata=metadata) yield doc