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ServiceNow – Project Document
ServiceNow Autonomous Agent Business Report
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Lang Graph – Syllabus
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Python Library: “os”
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Python Library : “getpass”
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Agentic AI Use Case -1: How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks .
How to integrate LangGraph (functional API) with AutoGen, CrewAI, and other frameworks . %pip install autogen langgraph import getpass import os def _set_env(var: str): if not os.environ.get(var): os.environ[var] = getpass.getpass(f"{var}") _set_env("OPENAI_API_KEY") import autogen import os config_list =[{"model": "gpt-40", "api_key": os.environ["OPENAI_API_KEY"]}] llm_config = { "timeout": 600, "cache_seed":42, "config_list":config_list, "temperature": 0 } autogen_agent = autogen.AssistantAgent( name = "assistant", llm_config = llm_config, ) from langchain_core.messages import convert_t0_openai_message, BaseMessage from langgrapg.func import entrypoint , task from langgraph.graph import add_messages from langgraph.checkpoint.memory import InMemorySaver @task def call_autogen_agent(messages: list[BaseMessage]): # Convert To An openai-style Messages messages = convert_to_openai_messages(messages) response = user_proxy.initiate_chat( autogen_agent, message = messages[-1], #
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RAG – Let’s discuss security-aware retrieval. Beyond SID filtering, how did you implement real-time input/output sanitation to prevent “prompt injection” attacks that attempt to bypass the document-level security guardrails you built ?
RAG – Let’s discuss security-aware retrieval. Beyond SID filtering, how did you implement real-time input/output sanitation to prevent “prompt injection” attacks that attempt to bypass the document-level security guardrails you built ? Best Practices : Set 1 Best Practices : Set 2 Real Life Analogy
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RAG – What Is Distibuted Tracing & How To Implement It ?
RAG – Explain Me About Distributed Tracing ?
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RAG – In your asynchronous RAG pipeline, how did you implement distributed tracing to identify whether a latency spike was caused by the embedding model, the vector database, or the LLM generation step ?
RAG – In your asynchronous RAG pipeline, how did you implement distributed tracing to identify whether a latency spike was caused by the embedding model, the vector database, or the LLM generation step ?
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RAG – Let’s talk about MLOps and Evaluation. You utilized Ragas for automated evaluation. Walk me through a diagnostic process for a scenario where your ‘faithfulness’ score was high, but ‘context recall’ was low. What architectural changes would you prioritize ?
RAG – Let’s talk about MLOps and Evaluation. You utilized Ragas for automated evaluation. Walk me through a diagnostic process for a scenario where your ‘faithfulness’ score was high, but ‘context recall’ was low. What architectural changes would you prioritize ?
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RAG – HNSW Indexing Technique .
