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(20241205) LLM endpoint active now.

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khushal
2024-12-05 18:09:32 +05:30
parent 0ec57089b0
commit 81550898eb
10 changed files with 347 additions and 383 deletions
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api/blueprints/ai/llm/invoke.py
+27 -65
View File
@@ -10,7 +10,7 @@
OBJECTIVE:
To receive auth details for various SMS client APIs.
To use LLMs to perform activities like chat completion, text summarization, etc.
REFERENCES:
@@ -59,15 +59,11 @@ from utils_v2.api.async_quart import (
handle_cancelled_request
)
# SMS-related utils:
from utils_v2.sms.nimbus.async_nimbus import AsyncNimbusSMS
from utils_v2.sms.savvy_bulk_sms.async_savvy_bulk_sms import AsyncSavvyBulkSMS
# Common:
from shared import constants
# Data Models:
from models.data.sms.auth import SMSAuthRequestHeaders, SMSAuthRequestData
from models.data.ai.llm import LLMRequestHeaders, LLMInput
# For asynchronous activities:
import asyncio
@@ -81,7 +77,7 @@ import asyncio
# Related to Quart:
sms_auth_bp = Blueprint("sms_auth", __name__)
llm_invoke_bp = Blueprint("llm_invoke", __name__)
# *****************************************************************************************************************
@@ -101,7 +97,7 @@ sms_auth_bp = Blueprint("sms_auth", __name__)
# *****************************************************************************************************************
@sms_auth_bp.record_once
@llm_invoke_bp.record_once
def init(blueprint_setup_state):
# This gets called when the blueprint is registered.
@@ -112,7 +108,7 @@ def init(blueprint_setup_state):
# ---------------------------------------------------------------------------------------------------------------------
@sms_auth_bp.route("/auth", methods = ["POST"])
@llm_invoke_bp.route("/llm/invoke", methods = ["POST"])
@set_api_version(api_version = "1.0.0")
@read_input(sanitize_headers = False, sanitize_data = False)
@get_session_info(key = "X-Session-Token", session_coro = "get_session")
@@ -120,7 +116,7 @@ def init(blueprint_setup_state):
attr_name = "logs_mongo",
project = constants.PROJECT_NAME,
log_type = constants.MODULE_NAME,
operation = "smsAuthApi",
operation = "llmInvokeApi",
log_input = True,
log_output = True,
sensitive_keys = ["sessionToken", "X-Session-Token"]
@@ -128,19 +124,19 @@ def init(blueprint_setup_state):
@log_chain_to_mongo(attr_name = "logs_mongo")
@should_not_be_under_maintenance(attr_name = "is_under_maintenance")
@validate_input(
header_validator = lambda x: SMSAuthRequestHeaders(**x).model_dump(),
data_validator = lambda x: SMSAuthRequestData(**x)
header_validator = lambda x: LLMRequestHeaders(**x).model_dump(),
data_validator = lambda x: LLMInput(**x)
)
@handle_cancelled_request()
async def request_oauth_authorization_url(
inbound_headers: dict | SMSAuthRequestHeaders = None,
inbound_data: dict | SMSAuthRequestData = None,
async def invoke_llm(
inbound_headers: dict | LLMRequestHeaders = None,
inbound_data: dict | LLMInput = None,
inbound_files: dict = None,
**kwargs
):
"""
Use this when a user wants to register a third-party SMS client with your service.
Use this to invoke an LLM for text completion kind of activities.
:param inbound_headers: auto-extracted by the decorators.
:param inbound_data: auto-extracted by the decorators.
:param inbound_files: auto-extracted by the decorators.
@@ -160,54 +156,17 @@ async def request_oauth_authorization_url(
http_code = HttpCodes.UNAUTHORIZED
)
# Start by assuming failure:
token_id = None
# ┳ ┓
# ┃┏┓┓┏┏┓┃┏┏┓
# ┻┛┗┗┛┗┛┛┗┗
# ┏┓ ┳┓• ┓ ┏┓┳┳┓┏┓ ┳ ┓•
# ┣ ┏┓┏┓ ┃┃┓┏┳┓┣┓┓┏┏ ┗┓┃┃┃┗┓ ┃┏┓┏┫┓┏┓
# ┻ ┗┛┛ ┛┗┗┛┗┗┗┛┗┻┛ ┗┛┛ ┗┗┛ ┻┛┗┗┻┗┗┻
if inbound_data.messageClient == "nimbusSmsIndia":
token_id = await current_app.sms_auth_model.set(
db_conn = current_app.sql_writer,
# Call the LLM and see if its service worked or not:
llm_response = await current_app.llm.invoke(
mongo_conn = current_app.data_mongo,
user_info = kwargs["session_info"],
client_user_id = {
"userId": inbound_data.auth.userId,
"senderId": inbound_data.auth.senderId,
"entityId": inbound_data.auth.entityId
},
auth = inbound_data.auth.model_dump(),
token = None,
service_client = inbound_data.messageClient,
auth_type = "auth",
sync_freq = 300,
session_token = inbound_headers["X-Session-Token"]
)
# ┏┓ ┏┓ ┳┓ ┓┓ ┏┓┳┳┓┏┓ ┓┏┓
# ┣ ┏┓┏┓ ┗┓┏┓┓┏┓┏┓┏ ┣┫┓┏┃┃┏ ┗┓┃┃┃┗┓ ┃┫ ┏┓┏┓┓┏┏┓
# ┻ ┗┛┛ ┗┛┗┻┗┛┗┛┗┫ ┻┛┗┻┗┛┗ ┗┛┛ ┗┗┛ ┛┗┛┗ ┛┗┗┫┗┻
# ┛ ┛
if inbound_data.messageClient == "savvyBulkSmsKenya":
token_id = await current_app.sms_auth_model.set(
db_conn = current_app.sql_writer,
mongo_conn = current_app.data_mongo,
user_info = kwargs["session_info"],
client_user_id = {
"partnerId": inbound_data.auth.partnerId,
"shortCode": inbound_data.auth.shortCode
},
auth = inbound_data.auth.model_dump(),
token = None,
service_client = inbound_data.messageClient,
auth_type = "auth",
sync_freq = 300,
session_token = inbound_headers["X-Session-Token"]
llm_input = inbound_data
)
success = False if llm_response.output is None else True
# ┳┓
# ┣┫┏┓┏┏┓┏┓┏┓┏┏┓
@@ -216,12 +175,15 @@ async def request_oauth_authorization_url(
# Done here:
return ResponseModel(
status_code = StatusCodes.OK if token_id else StatusCodes.FAILED,
http_code = HttpCodes.SUCCESS if token_id else HttpCodes.INTERNAL_SERVER_ERROR,
status_code = StatusCodes.OK if success else StatusCodes.FAILED,
http_code = HttpCodes.SUCCESS if success else HttpCodes.INTERNAL_SERVER_ERROR,
data = {
"messageClient": inbound_data.messageClient,
"authorized": True
}
"ts": llm_response.ts.isoformat(),
"client": llm_response.client,
"model": llm_response.model,
"output": llm_response.output,
"tokens": llm_response.tokens.model_dump(),
} if success else None
)
api/blueprints/mail/oauth_callback.py
+1 -1
View File
@@ -247,7 +247,7 @@ async def handle_gmail_callback() -> render_template:
@log_chain_to_mongo(attr_name = "logs_mongo")
@should_not_be_under_maintenance(attr_name = "is_under_maintenance")
@handle_cancelled_request()
async def mail_callback(
async def mail_auth_callback(
mail_client: str = None,
inbound_headers: dict = None,
inbound_data: dict = None,
api/blueprints/mail/sync.py
+4
View File
@@ -126,6 +126,7 @@ def init(blueprint_setup_state):
async def sync_mails(
user_info: dict,
mongo_conn: AsyncMongo,
llm: ChatOpenAI,
inbound_headers: dict,
@@ -145,6 +146,7 @@ async def sync_mails(
# Try to sync the mails:
return await current_app.mail_sync_model.sync(
session_token = inbound_headers["X-Session-Token"],
user_info = user_info,
mongo_conn = mongo_conn,
token_id = inbound_data.tokenId,
llm = llm,
@@ -213,6 +215,7 @@ async def sync_mail(
if mode in ["background", "bg"]:
current_app.add_background_task(
sync_mails,
user_info = kwargs["session_info"],
mongo_conn = current_app.data_mongo,
llm = current_app.llm,
inbound_headers = inbound_headers,
@@ -226,6 +229,7 @@ async def sync_mail(
# Otherwise we process it right here:
sync_results = await sync_mails(
user_info = kwargs["session_info"],
mongo_conn = current_app.data_mongo,
llm = current_app.llm,
inbound_headers = inbound_headers,
api/blueprints/sms/auth.py
+3 -3
View File
@@ -180,7 +180,7 @@ async def request_oauth_authorization_url(
},
auth = inbound_data.auth.model_dump(),
token = None,
service_client = inbound_data.messageClient,
service_client = inbound_data.smsClient,
auth_type = "auth",
sync_freq = 300,
session_token = inbound_headers["X-Session-Token"]
@@ -203,7 +203,7 @@ async def request_oauth_authorization_url(
},
auth = inbound_data.auth.model_dump(),
token = None,
service_client = inbound_data.messageClient,
service_client = inbound_data.smsClient,
auth_type = "auth",
sync_freq = 300,
session_token = inbound_headers["X-Session-Token"]
@@ -219,7 +219,7 @@ async def request_oauth_authorization_url(
status_code = StatusCodes.OK if token_id else StatusCodes.FAILED,
http_code = HttpCodes.SUCCESS if token_id else HttpCodes.INTERNAL_SERVER_ERROR,
data = {
"messageClient": inbound_data.messageClient,
"smsClient": inbound_data.messageClient,
"authorized": True
}
)
api/main.py
+15 -7
View File
@@ -75,6 +75,7 @@ from models.behaviour.mail.oauth_v2 import MailOAuthModel
from models.behaviour.mail.sync_v2 import MailSyncModel
from models.behaviour.mail.retrieve import MailRetrieveModel
from models.behaviour.sms.auth import SMSAuthModel
from models.behaviour.ai.llm.open_ai import LLMOpenAI
# To make REST API calls:
import httpx
@@ -91,13 +92,11 @@ from api.blueprints.mail.retrieve import mail_retrieve_bp
from api.blueprints.sms.auth import sms_auth_bp
from api.blueprints.tech.chat_alerts import tech_chat_alert_bp
from api.blueprints.test.callback import test_callback_bp
from api.blueprints.ai.llm.invoke import llm_invoke_bp
# All the helpers:
from api.helpers.user import session
# To work with LLMs:
from langchain_openai import ChatOpenAI
# *****************************************************************************************************************
# ***** ****
@@ -129,6 +128,7 @@ app.register_blueprint(mail_retrieve_bp, url_prefix = f"/{MODULE_BASE}/mail")
app.register_blueprint(sms_auth_bp, url_prefix = f"/{MODULE_BASE}/sms")
app.register_blueprint(tech_chat_alert_bp, url_prefix = f"/{MODULE_BASE}/tech/alert")
app.register_blueprint(test_callback_bp, url_prefix = f"/{MODULE_BASE}/test")
app.register_blueprint(llm_invoke_bp, url_prefix = f"/{MODULE_BASE}/ai")
# *****************************************************************************************************************
@@ -200,7 +200,7 @@ async def app_startup(**kwargs):
pool = 120.0, # .... Time to wait for a free connection from the pool.
connect = 2.5, # ... Time to wait for establishing a connection to the server.
write = 10.0, # .... Time to wait for sending data.
read = 2.5 # ....... Time to wait for receiving data.
read = 9.9 # ....... Time to wait for receiving data.
)
)
@@ -374,9 +374,17 @@ async def app_startup(**kwargs):
# ┛
# For LLMs:
current_app.llm = ChatOpenAI(
model = script_cred["openAi"]["model"],
openai_api_key = script_cred["openAi"]["openai_api_key"]
current_app.llm = LLMOpenAI(
llm_creds = {
"model": script_cred["openAi"]["model"],
"openai_api_key": script_cred["openAi"]["openai_api_key"]
},
cache = current_app.module_cache,
alert_url = current_app.script_data["alerts"]["url"],
http_client = current_app.http_client,
debug = enable_debugging,
debug_prefix = "AI (LLM) | ",
debug_only_errors = True
)
current_app.printer("AI ready.")
models/behaviour/ai/llm/open_ai.py
+106 -213
View File
@@ -10,7 +10,7 @@
OBJECTIVE:
To c
To create an interface between OpenAI and our internal system to perform LLM-based activities.
REFERENCES:
@@ -43,6 +43,12 @@ from utils_v2.database.async_mongo_v2 import AsyncMongo
# Base model:
from models.behaviour.base import BaseModel
# Data Models:
from models.data.ai.llm import LLMInput, LLMOutput, LLMUsageTokens
# To work with LLMs:
from langchain_openai import ChatOpenAI
# To work with MongoDB:
from bson import ObjectId
@@ -90,230 +96,86 @@ import copy
# *****************************************************************************************************************
class MailOAuthModel(BaseModel):
class LLMOpenAI(BaseModel):
AUTH_COLLECTION = "_authTokens"
AI_USAGE_COLLECTION = "_aiUsage"
async def get_token_id(
def __init__(
self,
llm_creds: dict,
cache = None,
alert_url = None,
http_client = None,
debug = True,
debug_prefix = "Model | ",
debug_only_errors = True
):
"""
This is the model that works with OpenAi's LLM to perform tasks like text completion.
:param llm_creds: The JSON that holds the credentials to access your OpenAI account. Should have the keys
'model', and 'openai_api_key'.
:param cache: The object to use for caching results from database calls.
:param alert_url: Which URL to call when something goes wrong.
:param http_client: The instance of an HTTP client to use when trying to send alerts and make other APIs.
:param debug: Whether, or not, you would like to print debugging messages:
:param debug_prefix: The prefix to print with the debugging messages.
:param debug_only_errors: Whether you would like to print only error messages or all messages.
:return: None.
"""
# Initialize the parent:
super().__init__(
cache = cache,
alert_url = alert_url,
http_client = http_client,
debug = debug,
debug_prefix = debug_prefix,
debug_only_errors = debug_only_errors
)
# Create the interface to the LLM:
self.__llm = ChatOpenAI(**llm_creds)
async def invoke(
self,
db_conn: AsyncMySQL,
mongo_conn: AsyncMongo,
user_info: dict,
client_user_id: dict,
auth: dict,
service_client: Literal["gmail"],
auth_type: Literal["oauth"],
sync_freq: Literal[60, 300, 900] = 300,
session_token: str = None
) -> ObjectId:
llm_input: LLMInput
) -> LLMOutput:
"""
Stores params from the session info and gives an identifier to use in the authorization URL. Use this when the
user requests an authorization URL to link your service to another service (like GMail).
:param db_conn: The database connection (MariaDB) to use to perform the action.
:param mongo_conn: The database connection (MongoDB) to use to perform the action.
:param user_info: The dictionary that has the user's session information.
:param client_user_id: The way the third-party client recognizes your user.
:param auth: The authentication details of the account.
:param service_client: The name of the company or brand that is providing this service that is being integrated.
:param auth_type: To identify the type of authentication being done here. This could indicate simple password
authentication, more advance OAuth2.0 authentication, etc.
:param sync_freq: The time interval in which mails need to be sync'd. Specify this in seconds.
:param session_token: The session token of the user who requested this service.
:return: An ObjectId to later store the granted tokens.
"""
# Note down the timestamp at which this event occurred:
request_ts = date_time.get_current_utc_date_time(as_string = False)
# Get the identifier from the database:
mongo_json = await mongo_conn.find_one_and_update(
collection = MailOAuthModel.AUTH_COLLECTION,
filter = mongo_conn.dict_to_dot_notation({
"serviceType": "email",
"user": {
"entityId": user_info["entityId"],
"billingAccountId": user_info["billingAccountId"]
},
"clientUserId": client_user_id
}),
update = {
"$set": {
"lastRequestTs": request_ts,
"status": "active",
"syncFreq": max(sync_freq, 60)
},
"$setOnInsert": {
"version": "1.1.1",
"serviceType": "email",
"client": service_client,
"authType": auth_type,
"user": user_info,
"clientUserId": client_user_id,
"auth": auth,
"token": None,
"firstRefreshTs": None,
"lastRefreshTs": None,
"firstRequestTs": request_ts,
}
},
projection = {
"_id": True
},
upsert = True,
return_updated = True
)
# Tell MariaDB that an authorization request was initiated:
db_json = {}
if mongo_json is not None:
db_json = await self.call_procedure(
db_conn = db_conn,
proc_name = "entity_integration_save",
proc_args = (
user_info["entityId"], # ............................................ 'p_entity_id'
service_client, # ................................................... 'p_provider'
"Pending", # ........................................................ 'p_current_status'
"Auth Requested", # ................................................. 'p_last_action'
None, # ............................................................. 'p_display_name'
None, # ............................................................. 'p_display_picture'
str(mongo_json["_id"]), # ........................................... 'p_token_id'
json.to_string(python_data = {"email": None}, no_space = True), # ... 'p_notes'
user_info["userId"] # ............................................... 'p_created_by'
),
session_token = session_token
)
# Done here:
return mongo_json["_id"] if mongo_json and db_json.get("status") == 1 else None
async def set_token(
self,
db_conn: AsyncMySQL,
mongo_conn: AsyncMongo,
token_id: ObjectId | str,
client_user_id: dict,
token: dict,
session_token: str = None
) -> bool:
"""
This method is to be called when the end user authorizes your service to connect to his third-party account. For
example, when the end user allows you to access his GMail account. USE THIS FOR UPDATING (REFRESHING) TOKENS
ALSO.
:param db_conn: The database connection (MariaDB) to use to perform the action.
:param mongo_conn: The database connection (MongoDB) to use to perform the action.
:param token_id: The identifier granted by the 'get_token_id' method.
:param client_user_id: The way the third-party client recognizes your user. These details should match the
details furnished while requesting the authorization through 'get_token_id' method.
:param token: The token granted by the third-party service.
:param session_token: The session token of the user who requested this service.
:return: True if saved, False if failed.
"""
# Start by assuming failure:
token_saved = False
# Note down the timestamp at which this event occurred:
request_ts = date_time.get_current_utc_date_time(as_string = False)
# Save the token to MongoDB:
mongo_json = await mongo_conn.find_one_and_update(
collection = MailOAuthModel.AUTH_COLLECTION,
filter = mongo_conn.dict_to_dot_notation({
"_id": ObjectId(token_id),
"clientUserId": client_user_id
}),
update = [{
"$set": {
"token": token,
"status": "active",
"lastRefreshTs": request_ts,
"firstRefreshTs": {
"$cond": {
"if": {
"$or": [
{"$eq": ["$firstRefreshTs", None]},
{"$eq": [{"$type": "$firstRefreshTs"}, "missing"]}
# Format the message as per the format of OpenAI:
prompt = [
{
"role": {"system": "system", "ai": "assistant", "human": "user"}[message.role],
"content": message.content
} for message in llm_input.messages
]
},
"then": request_ts,
"else": "$firstRefreshTs"
}
}
}
}],
projection = {"token": False},
return_updated = True,
upsert = False
# Invoke the AI, and format the response:
llm_response = await self.__llm.ainvoke(prompt)
llm_response = LLMOutput(
messages = llm_input.messages,
output = llm_response.content,
client = "openai",
model = llm_response.response_metadata["model_name"],
tokens = LLMUsageTokens(
input = llm_response.usage_metadata["input_tokens"],
output = llm_response.usage_metadata["output_tokens"],
total = llm_response.usage_metadata["total_tokens"],
)
)
# Tell MariaDB that the token was saved:
if mongo_json is not None:
token_notes = {
"email": token["email"],
"displayName": token.get("displayName"),
"displayPictureUrl": token.get("displayPictureUrl"),
}
db_json = await self.call_procedure(
db_conn = db_conn,
proc_name = "entity_integration_save",
proc_args = (
mongo_json["user"]["entityId"], # ............................... 'p_entity_id'
mongo_json["client"], # ......................................... 'p_provider'
"Active", # ..................................................... 'p_current_status'
"Auth Granted", # ............................................... 'p_last_action'
token["displayName"], # ......................................... 'p_display_name'
token["displayPictureUrl"], # ................................... 'p_display_picture'
token_id, # ..................................................... 'p_token_id'
json.to_string(python_data = token_notes, no_space = True), # ... 'p_notes'
mongo_json["user"]["userId"] # .................................. 'p_created_by'
),
session_token = session_token
# Store this into MongoDB:
mongo_document = {"user": user_info}
for k, v in llm_response.model_dump().items(): mongo_document[k] = v
inserted_id = await mongo_conn.insert_one(
collection = self.AI_USAGE_COLLECTION,
document = mongo_document
)
if db_json["status"] == 1: token_saved = True
# Done here:
return token_saved
async def get_token(
self,
mongo_conn: AsyncMongo,
token_id: ObjectId | str = None,
**kwargs
) -> dict | None:
"""
To retrieve stored tokens from the database.
:param mongo_conn: The database connection (MongoDB) to use to perform the action.
:param token_id: The identifier granted by the 'get_token_id' method.
:param kwargs: Any set of key-value pairs to build custom search criteria. This could be things like the user
info, the client, the type of authentication used, or even the kind of service.
:return: The retrieved record that has the token, and information about the service and client if found, else
None when there is no matching record.
"""
# Build the filter:
filter_json = {k: v for k, v in kwargs.items()}
if token_id: filter_json["_id"] = ObjectId(token_id)
# If there is no search criteria, we exit with failure:
if not filter_json: return None
# If there is some filtering possible,
# we fetch and return the token:
return await mongo_conn.find_one(
collection = self.AUTH_COLLECTION,
filter = filter_json,
projection = {
"_id": True,
"serviceType": True,
"authType": True,
"client": True,
"clientUserId": True,
"token": True
}
)
return llm_response
# *****************************************************************************************************************
@@ -326,3 +188,34 @@ class MailOAuthModel(BaseModel):
if __name__ == "__main__":
pass
# import asyncio
#
# llm_messages = [
# {
# "role": "system",
# "content": "You are an office assistant."
# },
# {
# "role": "ai",
# "content": "Hello, sir. How may I help you today?"
# },
# {
# "role": "human",
# "content": "Please summarize this mail for me..."
# }
# ]
#
# my_llm = LLMOpenAI(
# llm_creds = {
# "model": "gpt-4o-mini",
# "openai_api_key": "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"
# }
# )
#
# async def main():
#
# llm_response = await my_llm.invoke(llm_input = LLMInput(messages = llm_messages))
# print("LLM RESPONSE:", llm_response.model_dump_json(indent = 4))
#
# asyncio.run(main())
models/behaviour/mail/retrieve.py
+3 -8
View File
@@ -124,7 +124,9 @@ class MailRetrieveModel(BaseModel):
"payload.bcc": True,
"payload.parts": True,
"payload.attachments": True,
"payload.labels": True
"payload.labels": True,
"payload.snippet": True,
"payload.aiSnippet": True,
}
)
@@ -133,8 +135,6 @@ class MailRetrieveModel(BaseModel):
mail_data["mailId"] = str(mail_data.pop("_id"))
mail_data["payload"]["ts"] = mail_data["payload"]["ts"].isoformat()
mail_data["payload"]["readTs"] = mail_data["payload"]["readTs"].isoformat()
if ai_snippet := mail_data["payload"].pop("aiSnippet"):
mail_data["payload"]["aiSnippet"] = ai_snippet["snippet"]
# Done here:
return mail_data
@@ -186,11 +186,6 @@ class MailRetrieveModel(BaseModel):
mail_data["mailId"] = str(mail_data.pop("_id"))
mail_data["payload"]["ts"] = mail_data["payload"]["ts"].isoformat()
mail_data["payload"]["readTs"] = mail_data["payload"]["readTs"].isoformat()
if ai_snippet := mail_data["payload"].pop("aiSnippet"):
mail_data["payload"]["aiSnippet"] = {
"snippet": ai_snippet["snippet"],
"usage": ai_snippet["usage"]
}
# Done here:
return mails_list
models/behaviour/mail/sync_v2.py
+61 -30
View File
@@ -31,8 +31,6 @@
# To make sibling directories accessible for imports:
import sys
from logging import exception
sys.path.append(".")
sys.path.append("..")
@@ -60,8 +58,8 @@ from bson import ObjectId
from pymongo import InsertOne, UpdateOne, ReplaceOne
# To work with LLMs:
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from models.behaviour.ai.llm.open_ai import LLMOpenAI
from models.data.ai.llm import LLMInput
# To work with datatypes:
from typing import Literal, List, Dict, Any
@@ -123,16 +121,20 @@ class MailSyncModel(BaseModel):
MAIL_COLLECTION = "_messages"
# For AI Magic through LLMs:
prompt_template = ChatPromptTemplate.from_messages([
(
"system",
"You're a mail summary expert that summarizes mails in 150 chars or less. HIDE SENSITIVE INFO (LIKE OTPs) FROM THE SUMMARY."
),
(
"user",
"Please summarize this mail: \"\"\"{mail}\"\"\""
PROMPT_TEMPLATE = [
{
"role": "system",
"content": (
"You're a mail summary expert that summarizes mails in 150 chars or less. "
"If available, show login info like username and OTPs in your summary."
"If no login info is provided, please don't worry; just summarize what you see."
)
])
}
]
# ┏┓ ┓
# ┣┫╋╋┏┓┏┣┓┏┳┓┏┓┏┓╋┏
# ┛┗┗┗┗┻┗┛┗┛┗┗┗ ┛┗┗┛
@staticmethod
async def __save_one_attachment(
@@ -237,20 +239,26 @@ class MailSyncModel(BaseModel):
# Done here:
return uploaded_attachments
# ┏┓ ┏┓┳┳┓ •┓
# ┣ ┏┓┏┓ ┃┓┃┃┃┏┓┓┃
# ┻ ┗┛┛ ┗┛┛ ┗┗┻┗┗
async def __sync_one_gmail(
self,
session_token: str,
user_info: dict,
mongo_conn: AsyncMongo,
mail_client: AsyncGMailClient,
tokens: GoogleAuthTokens,
message_id: str,
llm: ChatOpenAI = None,
llm: LLMOpenAI = None,
force_sync: bool = False
) -> MailSyncOneResult:
"""
Sync on mail from GMail.
:param session_token: The session token of the uer who is trying to upload this file.
:param user_info: The information of the user (derived from his session token).
:param mongo_conn: The instance of the connection to the database to use.
:param mail_client: The instance of the mail client to use to perform the action.
:param tokens: The tokens to use to fetch the mails.
@@ -291,8 +299,11 @@ class MailSyncModel(BaseModel):
message_id = message_id,
return_raw = False
)
# If we didn't get the mail from GMail;
if not client_response.success:
sync_result.message = f"gmail (messageId: '{message_id}'): {client_response.message}"
return sync_result
# We upload the attachments:
client_response.data["attachments"] = await self.__save_many_attachments(
@@ -321,23 +332,33 @@ class MailSyncModel(BaseModel):
if tokens.email in all_recipients: client_response.data["isInbox"] = True
else: client_response.data["isInbox"] = False
# If an LLM is given, we add an AI summary:
# If an LLM is given,
# we add an AI summary:
llm_json = None
if llm:
llm_response = response = await llm.ainvoke(
self.prompt_template.invoke({
"mail": client_response.data["unformattedText"]
})
)
llm_json = {
"snippet": llm_response.content,
"usage": {
"input": llm_response.usage_metadata["input_tokens"],
"output": llm_response.usage_metadata["output_tokens"],
"total": llm_response.usage_metadata["total_tokens"],
},
"rawUsage": llm_response.usage_metadata
# Invoke the LLM:
llm_response = response = await llm.invoke(
mongo_conn = mongo_conn,
user_info = user_info,
llm_input = LLMInput(
messages = self.PROMPT_TEMPLATE + [
{
"role": "human",
"content": f"Please summarize this mail: \"\"\"{client_response.data["unformattedText"]}\"\"\""
}
]
)
)
# Format the response:
llm_json = {
"ts": llm_response.ts,
"snippet": llm_response.output,
"tokens": llm_response.tokens.model_dump()
}
# Add the LLM's response to the main data:
client_response.data["aiSnippet"] = llm_json
# Done here:
@@ -348,11 +369,12 @@ class MailSyncModel(BaseModel):
async def __sync_many_gmail(
self,
session_token: str,
user_info: dict,
mongo_conn: AsyncMongo,
token_id: ObjectId,
mail_client: AsyncGMailClient,
tokens: GoogleAuthTokens,
llm: ChatOpenAI = None,
llm: LLMOpenAI = None,
force_sync: bool = False,
start_date: datetime.datetime = None,
end_date: datetime.datetime = None,
@@ -362,6 +384,7 @@ class MailSyncModel(BaseModel):
"""
Sync many mails from GMail in one shot.
:param session_token: The session token of the uer who is trying to upload this file.
:param user_info: The information of the user (derived from his session token).
:param mongo_conn: The instance of the connection to the database to use.
:param token_id: The id of the document in the database that holds the tokens to access the account.
Needed only for refreshing the tokens and saving them.
@@ -415,6 +438,7 @@ class MailSyncModel(BaseModel):
tasks = [
self.__sync_one_gmail(
session_token = session_token,
user_info = user_info,
mongo_conn = mongo_conn,
mail_client = mail_client,
tokens = tokens,
@@ -473,12 +497,17 @@ class MailSyncModel(BaseModel):
sync_results.message = f"{sync_results.successCount}/{sync_results.totalCount} mail(s) sync'd from gmail"
return sync_results
# ┳┓
# ┣┫┏┓┓┏╋┏┓┏┓
# ┛┗┗┛┗┻┗┗ ┛
async def sync(
self,
session_token: str,
user_info: dict,
mongo_conn: AsyncMongo,
token_id: ObjectId,
llm: ChatOpenAI = None,
llm: LLMOpenAI = None,
force_sync: bool = False,
start_date: datetime.datetime = None,
end_date: datetime.datetime = None,
@@ -489,6 +518,7 @@ class MailSyncModel(BaseModel):
Sync many mails at once from many types of clients. Use this as a common entry point after which you internally
route the request to the appropriate clients.
:param session_token: The session token of the uer who is trying to upload this file.
:param user_info: The information of the user (derived from his session token).
:param mongo_conn: The instance of the connection to the database to use.
:param token_id: The id of the document in the database that holds the tokens to access the account.
Needed only for refreshing the tokens and saving them.
@@ -526,6 +556,7 @@ class MailSyncModel(BaseModel):
if auth_json["client"] == "gmail":
return await self.__sync_many_gmail(
session_token = session_token,
user_info = user_info,
mongo_conn = mongo_conn,
token_id = token_id,
mail_client = current_app.gmail_client,
models/data/ai/llm.py
+123 -52
View File
@@ -10,7 +10,7 @@
OBJECTIVE:
To provide a structure to receive auth details of various SMS providers.
To provide a structure to normalize input to and output from a standardized LLM wrapper.
REFERENCES:
@@ -36,8 +36,8 @@ sys.path.append(".")
sys.path.append("..")
# For making data behaviour_models:
from pydantic import BaseModel, Field, field_validator, PastDatetime
from typing import Optional, Literal, Union
from pydantic import BaseModel, Field, field_validator, PastDatetime, AwareDatetime
from typing import Optional, Literal, Union, List
# My utils:
from utils_v2.string import regex
@@ -75,30 +75,15 @@ REGEX_SESSION_TOKEN = r"^[a-f0-9]{8}-[a-f0-9]{4}-[1-5][a-f0-9]{3}-[89ab][a-f0-9]
# *****************************************************************************************************************
class NimbusSMSIndiaAuth(BaseModel):
class LLMInputMessage(BaseModel):
entityId: str = Field(
description = "the entity id as registered with DLT",
min_length = 1,
role: Literal["system", "ai", "human"] = Field(
description = "the role of this message",
frozen = True
)
senderId: str = Field(
description = "the 6-char code that you see in your SMS inbox",
min_length = 1,
frozen = True,
examples = ["HDFCBK", "NSESMS", "ZRODHA"]
)
userId: str = Field(
description = "the 6-digit id that Nimbus has assigned to you",
min_length = 1,
frozen = True
)
apiKey: str = Field(
description = "the key generated through Nimbus's portal",
min_length = 1,
content: str = Field(
description = "the message sent by the 'role'",
frozen = True
)
@@ -114,23 +99,63 @@ class NimbusSMSIndiaAuth(BaseModel):
# ---------------------------------------------------------------------------------------------------------------------
class SavvyBulkSMSKenyaAuth(BaseModel):
class LLMInput(BaseModel):
apiKey: str = Field(
description = "the key generated through Savvy's portal",
min_length = 1,
messages: List[LLMInputMessage]
# ┏┓ ┏•
# ┃ ┏┓┏┓╋┓┏┓
# ┗┛┗┛┛┗┛┗┗┫
# ┛
class Config:
extra = "forbid"
# ┓┏ ┓• ┓ •
# ┃┃┏┓┃┓┏┫┏┓╋┓┏┓┏┓
# ┗┛┗┻┗┗┗┻┗┻┗┗┗┛┛┗
@field_validator("messages")
def validate_messages(cls, value):
# Maintain counter(s):
system_message_index = -1
system_message_count = 0
# Loop through the messages and check them:
for index, message in enumerate(value):
# For 'system' messages:
if message.role == "system":
system_message_index = index
system_message_count += 1
# Verify that there is AT MOST ONE 'system' message,
# and verify that the 'system' message is the first message:
if system_message_count > 1: raise ValueError(f"there can be at most 1 'system' message, found {system_message_count}")
if system_message_index > 0: raise ValueError(f"'system' message must always be at index 0, found it at index {system_message_index}")
# Done here:
return value
# ---------------------------------------------------------------------------------------------------------------------
class LLMUsageTokens(BaseModel):
input: int = Field(
description = "how many tokens were given in the input",
frozen = True
)
partnerId: str = Field(
description = "the key generated through Savvy's portal",
min_length = 1,
output: int = Field(
description = "how many tokens were generated as the output",
frozen = True
)
shortCode: str = Field(
description = "your short code with Savvy",
min_length = 1,
total: int = Field(
description = "the sum of the input and output tokens",
frozen = True
)
@@ -146,7 +171,54 @@ class SavvyBulkSMSKenyaAuth(BaseModel):
# ---------------------------------------------------------------------------------------------------------------------
class SMSAuthRequestHeaders(BaseModel):
class LLMOutput(BaseModel):
ts: AwareDatetime = Field(
description = "the time at which the llm was invoked",
default_factory = date_time.get_current_utc_date_time,
frozen = True
)
messages: List[LLMInputMessage] = Field(
description = "the messages that came in that invoked the llm",
frozen = True
)
output: str | None = Field(
description = "what the llm generated",
default = None,
frozen = True
)
client: Literal["openai"] = Field(
description = "the co./brand that was used to use an llm",
frozen = True
)
model: str = Field(
description = "to know which model used in the process",
frozen = True
)
tokens: LLMUsageTokens = Field(
description = "to know how many tokens were used in the process",
default = LLMUsageTokens(input = 0, output = 0, total = 0),
frozen = True
)
# ┏┓ ┏•
# ┃ ┏┓┏┓╋┓┏┓
# ┗┛┗┛┛┗┛┗┗┫
# ┛
class Config:
extra = "forbid"
# ---------------------------------------------------------------------------------------------------------------------
class LLMRequestHeaders(BaseModel):
sessionToken: str = Field(
description = "the session token of the user who is requesting the service",
@@ -167,23 +239,6 @@ class SMSAuthRequestHeaders(BaseModel):
return super().model_dump(*args, by_alias = True, **kwargs)
# ---------------------------------------------------------------------------------------------------------------------
class SMSAuthRequestData(BaseModel):
messageClient: Literal["nimbusSmsIndia", "savvyBulkSmsKenya"]
auth: Union[NimbusSMSIndiaAuth, SavvyBulkSMSKenyaAuth]
# ┏┓ ┏•
# ┃ ┏┓┏┓╋┓┏┓
# ┗┛┗┛┛┗┛┗┗┫
# ┛
class Config:
extra = "forbid"
# *****************************************************************************************************************
# ***** ****
# *** MAIN PROGRAM ***
@@ -193,4 +248,20 @@ class SMSAuthRequestData(BaseModel):
if __name__ == "__main__":
pass
llm_messages = [
{
"role": "system",
"content": "You are an office assistant."
},
{
"role": "ai",
"content": "Hello, sir. How may I help you today?"
},
{
"role": "human",
"content": "Please summarize this mail for me..."
}
]
llm_input = LLMInput(messages = llm_messages)
print(llm_input)
models/data/sms/auth.py
+1 -1
View File
@@ -172,7 +172,7 @@ class SMSAuthRequestHeaders(BaseModel):
class SMSAuthRequestData(BaseModel):
messageClient: Literal["nimbusSmsIndia", "savvyBulkSmsKenya"]
smsClient: Literal["nimbusSmsIndia", "savvyBulkSmsKenya"]
auth: Union[NimbusSMSIndiaAuth, SavvyBulkSMSKenyaAuth]
# ┏┓ ┏•