(20241209) Small failure view bug fix to show the 'logId' now.
This commit is contained in:
@@ -113,7 +113,7 @@ def init(blueprint_setup_state):
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api_version = "1.0.0",
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project = constants.PROJECT_NAME,
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log_type = constants.MODULE_NAME,
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operation = "gmailCallbk",
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operation = "gmailCllbk",
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log_input = 2,
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log_output = 1,
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sensitive_keys = ["sessionToken", "X-Session-Token"]
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@@ -300,7 +300,7 @@ async def mail_auth_callback(
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mail_client = mail_client.title(),
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failure_hint = (
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f"Invalid client '{mail_client}' selected. "
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"Please use log-id '{g.log_id}' to check with the support team."
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f"Please use log-id '{g.log_id}' to check with the support team."
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)
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)
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@@ -1,221 +0,0 @@
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"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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Thursday, 5th Dec., 2024
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OBJECTIVE:
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To create an interface between OpenAI and our internal system to perform LLM-based activities.
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REFERENCES:
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N/A
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DOWNLOADS:
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N/A
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"""
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# *****************************************************************************************************************
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# ***** ****
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# *** IMPORT ***
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# ***** ****
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# *****************************************************************************************************************
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# To make sibling directories accessible for imports:
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import sys
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sys.path.append(".")
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sys.path.append("..")
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# My async utils:
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from utils_v2.string import json
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from utils_v2.date_time import date_time
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from utils_v2.database.async_mysql_v2 import AsyncMySQL
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from utils_v2.database.async_mongo_v2 import AsyncMongo
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# Base model:
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from models.behaviour.base import BaseModel
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# Data Models:
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from models.data.api.ai.llm import LLMInput, LLMOutput, LLMUsageTokens
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# To work with LLMs:
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from langchain_openai import ChatOpenAI
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# To work with MongoDB:
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from bson import ObjectId
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# To work with datatypes:
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from typing import Literal
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# To make deep-copies:
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import copy
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# *****************************************************************************************************************
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# ***** ****
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# *** MACROS / ONE-TIME INIT ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** VARIABLES ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** FUNCTIONS ***
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** CLASSES ***
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# ***** ****
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# *****************************************************************************************************************
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class LLMOpenAI(BaseModel):
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AI_USAGE_COLLECTION = "_aiUsage"
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def __init__(
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self,
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llm_creds: dict,
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cache = None,
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alert_url = None,
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http_client = None,
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debug = True,
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debug_prefix = "Model | ",
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debug_only_errors = True
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):
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"""
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This is the model that works with OpenAi's LLM to perform tasks like text completion.
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:param llm_creds: The JSON that holds the credentials to access your OpenAI account. Should have the keys
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'model', and 'openai_api_key'.
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:param cache: The object to use for caching results from database calls.
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:param alert_url: Which URL to call when something goes wrong.
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:param http_client: The instance of an HTTP client to use when trying to send alerts and make other APIs.
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:param debug: Whether, or not, you would like to print debugging messages:
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:param debug_prefix: The prefix to print with the debugging messages.
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:param debug_only_errors: Whether you would like to print only error messages or all messages.
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:return: None.
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"""
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# Initialize the parent:
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super().__init__(
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cache = cache,
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alert_url = alert_url,
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http_client = http_client,
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debug = debug,
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debug_prefix = debug_prefix,
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debug_only_errors = debug_only_errors
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)
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# Create the interface to the LLM:
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self.__llm = ChatOpenAI(**llm_creds)
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async def invoke(
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self,
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mongo_conn: AsyncMongo,
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user_info: dict,
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llm_input: LLMInput
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) -> LLMOutput:
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# Format the message as per the format of OpenAI:
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prompt = [
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{
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"role": {"system": "system", "ai": "assistant", "human": "user"}[message.role],
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"content": message.content
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} for message in llm_input.messages
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]
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# Invoke the AI, and format the response:
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llm_response = await self.__llm.ainvoke(prompt)
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llm_response = LLMOutput(
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messages = llm_input.messages,
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output = llm_response.content,
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client = "openai",
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model = llm_response.response_metadata["model_name"],
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tokens = LLMUsageTokens(
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input = llm_response.usage_metadata["input_tokens"],
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output = llm_response.usage_metadata["output_tokens"],
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total = llm_response.usage_metadata["total_tokens"],
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)
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)
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# Store this into MongoDB:
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mongo_document = {"user": user_info}
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for k, v in llm_response.model_dump().items(): mongo_document[k] = v
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inserted_id = await mongo_conn.insert_one(
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collection = self.AI_USAGE_COLLECTION,
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document = mongo_document
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)
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# Done here:
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return llm_response
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# *****************************************************************************************************************
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# ***** ****
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# *** MAIN PROGRAM ***
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# ***** ****
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# *****************************************************************************************************************
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if __name__ == "__main__":
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pass
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# import asyncio
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#
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# llm_messages = [
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# {
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# "role": "system",
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# "content": "You are an office assistant."
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# },
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# {
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# "role": "ai",
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# "content": "Hello, sir. How may I help you today?"
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# },
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# {
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# "role": "human",
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# "content": "Please summarize this mail for me..."
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# }
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# ]
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#
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# my_llm = LLMOpenAI(
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# llm_creds = {
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# "model": "gpt-4o-mini",
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# "openai_api_key": "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"
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# }
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# )
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#
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# async def main():
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#
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# llm_response = await my_llm.invoke(llm_input = LLMInput(messages = llm_messages))
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# print("LLM RESPONSE:", llm_response.model_dump_json(indent = 4))
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#
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# asyncio.run(main())
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@@ -7,7 +7,7 @@
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DATE:
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ORIGINAL: Monday, 2nd Dec., 2024
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UPGRADE: Monday, 9th Dec., 2024
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UPGRADED: Monday, 9th Dec., 2024
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OBJECTIVE:
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@@ -134,9 +134,9 @@ class MailOAuthModel(BaseModel):
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}),
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update = {
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"$set": {
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"lastRequestTs": request_ts,
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"lastRequestTs": auth_token.lastRequestTs,
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"status": auth_token.status,
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"syncFreq": max(auth_token.syncFreq, 60)
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"syncFreq": auth_token.syncFreq
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},
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"$setOnInsert": {
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"version": auth_token.version,
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@@ -149,7 +149,7 @@ class MailOAuthModel(BaseModel):
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"token": auth_token.token,
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"firstRefreshTs": auth_token.firstRefreshTs,
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"lastRefreshTs": auth_token.lastRefreshTs,
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"firstRequestTs": auth_token.firstRequestTs,
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"firstRequestTs": auth_token.firstRequestTs or request_ts,
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}
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},
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projection = {
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@@ -168,7 +168,7 @@ class MailOAuthModel(BaseModel):
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proc_args = (
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auth_token.user.entityId, # ......................................... 'p_entity_id'
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auth_token.client, # ................................................ 'p_provider'
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"Pending", # ........................................................ 'p_current_status'
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auth_token.status, # ................................................ 'p_current_status'
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"Auth Requested", # ................................................. 'p_last_action'
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None, # ............................................................. 'p_display_name'
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None, # ............................................................. 'p_display_picture'
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@@ -254,7 +254,7 @@ class MailOAuthModel(BaseModel):
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proc_args = (
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mongo_json["user"]["entityId"], # ............................... 'p_entity_id'
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mongo_json["client"], # ......................................... 'p_provider'
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"Active", # ..................................................... 'p_current_status'
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auth_token.status, # ............................................ 'p_current_status'
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"Auth Granted", # ............................................... 'p_last_action'
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auth_token.token.get("displayName"), # .......................... 'p_display_name'
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auth_token.token.get("displayPictureUrl"), # .................... 'p_display_picture'
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@@ -0,0 +1,227 @@
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"""
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AUTHOR:
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Khushal P Soonderji
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DATE:
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ORIGINAL: Thursday, 5th Dec., 2024
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UPGRADED: Monday, 9th Dec., 2024
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OBJECTIVE:
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To work with auth details of SMS clients like Nimbus SMS (India) and Savvy Bulk SMS (Kenya).
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REFERENCES:
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N/A
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DOWNLOADS:
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N/A
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||||
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"""
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# *****************************************************************************************************************
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# ***** ****
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# *** IMPORT ***
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# ***** ****
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# *****************************************************************************************************************
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# To make sibling directories accessible for imports:
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import sys
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sys.path.append(".")
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sys.path.append("..")
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# My async utils:
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from utils_v2.string import json
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from utils_v2.date_time import date_time
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from utils_v2.database.async_mysql_v2 import AsyncMySQL
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from utils_v2.database.async_mongo_v2 import AsyncMongo
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# Base model:
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from models.behaviour.base import BaseModel
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# Data models:
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from models.data.core.auth_token import CoreAuthTokenModel
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# To work with MongoDB:
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from bson import ObjectId
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# To work with datatypes:
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from typing import Literal
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# To make deep-copies:
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import copy
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# *****************************************************************************************************************
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# ***** ****
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# *** MACROS / ONE-TIME INIT ***
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# ***** ****
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# *****************************************************************************************************************
|
||||
|
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** VARIABLES ***
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# ***** ****
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# *****************************************************************************************************************
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|
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# --- Nothing Yet
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# *****************************************************************************************************************
|
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# ***** ****
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# *** FUNCTIONS ***
|
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# ***** ****
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# *****************************************************************************************************************
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# --- Nothing Yet
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# *****************************************************************************************************************
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# ***** ****
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# *** CLASSES ***
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# ***** ****
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# *****************************************************************************************************************
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class SMSAuthModel(BaseModel):
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AUTH_COLLECTION = "_authTokens"
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async def set(
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self,
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db_conn: AsyncMySQL,
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mongo_conn: AsyncMongo,
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auth_token: CoreAuthTokenModel,
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session_token: str = None
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) -> ObjectId | None:
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"""
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To store auth/tokens for a particular service to the database.
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:param db_conn: The database connection (MariaDB) to use to perform the action.
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:param mongo_conn: The database connection (MongoDB) to use to perform the action.
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:param auth_token: An instance of the core auth-token model that holds data in the database.
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:param session_token: The session token of the user who requested this service.
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:return: An ObjectId to later store the granted tokens.
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"""
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# Note down the timestamp at which this event occurred:
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request_ts = date_time.get_current_utc_date_time(as_string = False)
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# Get the identifier from the database:
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# BE CAREFUL WITH THE KEYS HERE, THEY SHOULD MATCH THE FIELDS OF THE CORE AUTH-TOKEN MODEL:
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mongo_json = await mongo_conn.find_one_and_update(
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collection = self.AUTH_COLLECTION,
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filter = mongo_conn.dict_to_dot_notation({
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"serviceType": "email",
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"user": {
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"entityId": auth_token.user.entityId,
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"billingAccountId": auth_token.user.billingAccountId
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},
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"clientUserId": auth_token.clientUserId
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}),
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update = {
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"$set": {
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"lastRequestTs": auth_token.lastRequestTs,
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"status": auth_token.status,
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"syncFreq": auth_token.syncFreq
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},
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"$setOnInsert": {
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"version": auth_token.version,
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"serviceType": auth_token.serviceType,
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"client": auth_token.client,
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"authType": auth_token.authType,
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"user": auth_token.user.model_dump(),
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"clientUserId": auth_token.clientUserId,
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"auth": auth_token.auth,
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"token": auth_token.token,
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"firstRefreshTs": auth_token.firstRefreshTs,
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"lastRefreshTs": auth_token.lastRefreshTs,
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"firstRequestTs": auth_token.firstRequestTs or request_ts
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}
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},
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projection = {
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"_id": True
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},
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upsert = True,
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return_updated = True
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)
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# Tell MariaDB that an authorization request was initiated:
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db_json = {}
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if mongo_json is not None:
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token_notes = auth_token.clientUserId
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db_json = await self.call_procedure(
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db_conn = db_conn,
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proc_name = "entity_integration_save",
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proc_args = (
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auth_token.user.entityId, # ..................................... 'p_entity_id'
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auth_token.client, # ............................................ 'p_provider'
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auth_token.status, # ............................................ 'p_current_status'
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"Auth Details Accepted", # ...................................... 'p_last_action'
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None, # ......................................................... 'p_display_name'
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None, # ......................................................... 'p_display_picture'
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str(mongo_json["_id"]), # ....................................... 'p_token_id'
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json.to_string(python_data = token_notes, no_space = True), # ... 'p_notes'
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auth_token.user.userId # ........................................ 'p_created_by'
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),
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session_token = session_token
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)
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# Done here:
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return mongo_json["_id"] if mongo_json and db_json.get("status") == 1 else None
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async def get(
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self,
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mongo_conn: AsyncMongo,
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token_id: ObjectId | str = None,
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**kwargs
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) -> dict | None:
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"""
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To retrieve stored auth/tokens from the database.
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:param mongo_conn: The database connection (MongoDB) to use to perform the action.
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:param token_id: The identifier granted providing auth details for the first time in 'set_token'.
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:param kwargs: Any set of key-value pairs to build custom search criteria. This could be things like the user
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info, the client, the type of authentication used, or even the kind of service.
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:return: The retrieved record that has the token, and information about the service and client if found, else
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None when there is no matching record.
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"""
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# Build the filter:
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filter_json = {k: v for k, v in kwargs.items()}
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if token_id: filter_json["_id"] = ObjectId(token_id)
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# If there is no search criteria, we exit with failure:
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if not filter_json: return None
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# If there is some filtering possible, we fetch the token:
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token = await mongo_conn.find_one(
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collection = self.AUTH_COLLECTION,
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filter = filter_json,
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)
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# Done here:
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return CoreAuthTokenModel(**token) if token else None
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||||
|
||||
|
||||
# *****************************************************************************************************************
|
||||
# ***** ****
|
||||
# *** MAIN PROGRAM ***
|
||||
# ***** ****
|
||||
# *****************************************************************************************************************
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
pass
|
||||
@@ -112,13 +112,13 @@ class CoreAuthTokenModel(BaseModel):
|
||||
firstRequestTs: AwareDatetime = Field(
|
||||
description = "the time (utc) at which authorization was first requested",
|
||||
frozen = True,
|
||||
default_factory = lambda: date_time.get_current_utc_date_time(as_string = False)
|
||||
default = None
|
||||
)
|
||||
|
||||
lastRequestTs: AwareDatetime = Field(
|
||||
description = "the time (utc) at which authorization was last requested",
|
||||
frozen = False,
|
||||
default = None
|
||||
default_factory = lambda: date_time.get_current_utc_date_time(as_string = False)
|
||||
)
|
||||
|
||||
firstRefreshTs: AwareDatetime = Field(
|
||||
|
||||
+2
-3
@@ -1,4 +1,3 @@
|
||||
kbdev.bicree.com
|
||||
jc.ditscentre.in
|
||||
wtt.ditscentre.in
|
||||
del.ditscentre.in
|
||||
jc.ditscentre.in
|
||||
del.ditscentre.in
|
||||
Reference in New Issue
Block a user