(20241205) LLM endpoint active now.
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+107
-214
@@ -10,7 +10,7 @@
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OBJECTIVE:
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To c
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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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@@ -43,6 +43,12 @@ 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.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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@@ -90,230 +96,86 @@ import copy
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# *****************************************************************************************************************
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class MailOAuthModel(BaseModel):
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class LLMOpenAI(BaseModel):
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AUTH_COLLECTION = "_authTokens"
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AI_USAGE_COLLECTION = "_aiUsage"
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async def get_token_id(
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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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db_conn: AsyncMySQL,
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mongo_conn: AsyncMongo,
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user_info: dict,
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client_user_id: dict,
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auth: dict,
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service_client: Literal["gmail"],
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auth_type: Literal["oauth"],
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sync_freq: Literal[60, 300, 900] = 300,
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session_token: str = None
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) -> ObjectId:
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llm_input: LLMInput
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) -> LLMOutput:
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"""
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Stores params from the session info and gives an identifier to use in the authorization URL. Use this when the
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user requests an authorization URL to link your service to another service (like GMail).
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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 user_info: The dictionary that has the user's session information.
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:param client_user_id: The way the third-party client recognizes your user.
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:param auth: The authentication details of the account.
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:param service_client: The name of the company or brand that is providing this service that is being integrated.
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:param auth_type: To identify the type of authentication being done here. This could indicate simple password
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authentication, more advance OAuth2.0 authentication, etc.
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:param sync_freq: The time interval in which mails need to be sync'd. Specify this in seconds.
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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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# 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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# 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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mongo_json = await mongo_conn.find_one_and_update(
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collection = MailOAuthModel.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": user_info["entityId"],
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"billingAccountId": user_info["billingAccountId"]
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},
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"clientUserId": client_user_id
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}),
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update = {
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"$set": {
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"lastRequestTs": request_ts,
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"status": "active",
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"syncFreq": max(sync_freq, 60)
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},
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"$setOnInsert": {
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"version": "1.1.1",
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"serviceType": "email",
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"client": service_client,
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"authType": auth_type,
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"user": user_info,
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"clientUserId": client_user_id,
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"auth": auth,
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"token": None,
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"firstRefreshTs": None,
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"lastRefreshTs": None,
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"firstRequestTs": 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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# 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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# 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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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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user_info["entityId"], # ............................................ 'p_entity_id'
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service_client, # ................................................... 'p_provider'
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"Pending", # ........................................................ '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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str(mongo_json["_id"]), # ........................................... 'p_token_id'
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json.to_string(python_data = {"email": None}, no_space = True), # ... 'p_notes'
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user_info["userId"] # ............................................... 'p_created_by'
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),
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session_token = session_token
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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 mongo_json["_id"] if mongo_json and db_json.get("status") == 1 else None
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async def set_token(
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self,
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db_conn: AsyncMySQL,
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mongo_conn: AsyncMongo,
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token_id: ObjectId | str,
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client_user_id: dict,
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token: dict,
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session_token: str = None
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) -> bool:
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"""
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This method is to be called when the end user authorizes your service to connect to his third-party account. For
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example, when the end user allows you to access his GMail account. USE THIS FOR UPDATING (REFRESHING) TOKENS
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ALSO.
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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 token_id: The identifier granted by the 'get_token_id' method.
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:param client_user_id: The way the third-party client recognizes your user. These details should match the
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details furnished while requesting the authorization through 'get_token_id' method.
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:param token: The token granted by the third-party service.
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:param session_token: The session token of the user who requested this service.
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:return: True if saved, False if failed.
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"""
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# Start by assuming failure:
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token_saved = False
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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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# Save the token to MongoDB:
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mongo_json = await mongo_conn.find_one_and_update(
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collection = MailOAuthModel.AUTH_COLLECTION,
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filter = mongo_conn.dict_to_dot_notation({
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"_id": ObjectId(token_id),
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"clientUserId": client_user_id
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}),
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update = [{
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"$set": {
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"token": token,
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"status": "active",
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"lastRefreshTs": request_ts,
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"firstRefreshTs": {
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"$cond": {
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"if": {
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"$or": [
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{"$eq": ["$firstRefreshTs", None]},
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{"$eq": [{"$type": "$firstRefreshTs"}, "missing"]}
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]
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},
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"then": request_ts,
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"else": "$firstRefreshTs"
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}
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}
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}
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}],
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projection = {"token": False},
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return_updated = True,
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upsert = False
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)
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# Tell MariaDB that the token was saved:
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if mongo_json is not None:
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token_notes = {
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"email": token["email"],
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"displayName": token.get("displayName"),
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"displayPictureUrl": token.get("displayPictureUrl"),
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}
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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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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 Granted", # ............................................... 'p_last_action'
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token["displayName"], # ......................................... 'p_display_name'
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token["displayPictureUrl"], # ................................... 'p_display_picture'
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token_id, # ..................................................... 'p_token_id'
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json.to_string(python_data = token_notes, no_space = True), # ... 'p_notes'
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mongo_json["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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if db_json["status"] == 1: token_saved = True
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# Done here:
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return token_saved
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async def get_token(
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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 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 by the 'get_token_id' method.
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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,
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# we fetch and return the token:
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return await mongo_conn.find_one(
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collection = self.AUTH_COLLECTION,
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filter = filter_json,
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projection = {
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"_id": True,
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"serviceType": True,
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"authType": True,
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"client": True,
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"clientUserId": True,
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"token": True
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}
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)
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return llm_response
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# *****************************************************************************************************************
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@@ -326,3 +188,34 @@ class MailOAuthModel(BaseModel):
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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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