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    Home»Machine Learning»Building a LLM‑Powered Agent with AutoGPT + Retrieval-Augmented Generation (RAG) | by Cristina Ross | Jun, 2025
    Machine Learning

    Building a LLM‑Powered Agent with AutoGPT + Retrieval-Augmented Generation (RAG) | by Cristina Ross | Jun, 2025

    Team_AIBS NewsBy Team_AIBS NewsJune 29, 2025No Comments1 Min Read
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    By somebody who believes your AI agent ought to know your docs — and your vibe

    • Scorching subject: Self-driving AI brokers are quickly evolving — OpenAI’s perform calling, AutoGPT, babyAGI, and extra.
    • Actual-world worth: You’ll construct an agent that may learn paperwork and execute duties primarily based on them.
    • Subsequent-level tutorial: Combines RAG (for context) with automation loops — a deeper dive than immediate/pattern guides.

    By the tip, you’ll have an area agent that:

    1. Masses recordsdata and builds an index
    2. Responds to consumer targets
    3. Retrieves related context from paperwork
    4. Writes plans and executes duties
    5. Streams outcomes again to you

    Let’s construct.

    • Python 3.10+
    • OpenAI API Key
    • A listing of textual content or markdown paperwork
    • Beneficial bundle set up:
    !pip set up openai langchain llama_index automata faiss-cpu



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