Enterprise Enviroment Setup And Configuration

code -> ai_strategy_architecture/config.py

In enterprise deployment:

Code goes inside container (Docker image).and

Configuration stays outside — passed in via environment variables, .env files, or secrets manager (Vault, AWS SSM, etc.). so that

which lets us to run the same container in different environments with different behavior

Simple Multi Agent Architecture Router

code -> ai_strategy_architecture/basic_agent_route.py

code-> test function for our code ai_strategy_architecture/test_basic_agent_route.py

Big companies usually use multiple specialized agents — each focused on a specific task. In this architecture, every user request enters the system through a Router. The router inspects the query and, based on predefined rules or LLM classification, directs it to the most appropriate agent.

We typically use a lightweight, low-cost LLM for routing.

Wrapping LangchainOn Fastapi Endpoint

code-> wrapping langchain on fastapi ai_strategy_architecture/wrapping_langchainOn_fastapi_endpoint.py

We need To Wrap the complex logic behind the apis , .... and present a user nice ..... so this code wraps the langchian logic behind a secure 'POST' endpoint , using pydantic for request validation ensuring data integrity

code explain : 'APIKeyQuery' means FastAPI expects an API key in the query parameter of the request URL.

ps:(their are some compatibility , .venv files problme form my side i will so this is completed and might have some erros)

Prompt versioing

code-> prompt configuration code misc/prompt.py

code-> passing prompt from prompt.py code misc/extraction.py

its good practice to seperate prompts from code because

oftern some application use same prompts and writing same 10 sentence long prompts does not sound

and versioing prompts also helps on to test code using different prompts

PII

code-> pii code ai_strategy_architecture/pii_utils.py

User can sometimes request query with their personal Information

my email is ujjwal@gmail.com and my bank password  is "bank13"  help me to login 

their is no-way as an organization you can trust thrid party api/llm for your user data thus you usall have to scan , mask these information to avoid data lekage

previous text after masking

"my email is <email> password is <password> ... help me to login

now you send mask_text to llm llm.answer("my emial is password is .... help me to login)

after llm answer you put real values in those placeholder and send back to user

Parallel Rag

-> independent task , wrap to parallel logic and rn

_. building index or search similarity on inde x -> extending query -> user contenxt , and memory are some of parallel task in rag which can be fetched parallely

.........