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#4268 Ollama & Crew-AI AI-Agent Workflow

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#4268 Ollama & Crew-AI AI-Agent Workflow #4268  Provide a python application using Ollama supported Granite-4-h-small and Crew-AI to do the following: """1) Identify opportunities in industries and user groups to provide innovative solutions for new and improved systems, products, services and processes. 2) Rank the opportunities by feasibility, practicality, value add and "bang-for-the-buck". 3) Apply innovative thinking methods (any of approximately 30) to suggest new and improvements to systems, products, services and processes. 4) Rank the suggestions by feasibility, practicality, value add and "bang-for-the-buck". 5) Provide step-by-step instructions on how to implement the suggested improvements. 6) Provide an iterative prototype, test, evaluation and improve cycle for verifying and validating the implementation. 7) All steps should be saved to a database for follow-up.""" This application uses CrewAI to orchestrate a team of...
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#4267 Structural RAG

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#4267 Structural RAG PageIndex: Reasoning-Based Vectorless RAG Framework https://youtu.be/H5jHCh588o4 #4267  Describe an alternative to embedding and indexing for RAG. Consider a plain text toc for topics within a document. While vector embeddings are the "gold standard" for RAG right now, they often feel like using a sledgehammer to crack a nut—especially for single, long documents where semantic search can lose the forest for the trees. An excellent alternative is Structural Metadata Navigation (or "Map-Guided Retrieval"). Instead of turning text into high-dimensional math, you treat the document like a library with a highly efficient librarian. The Concept: Structural Metadata Navigation In this approach, you use a Plain Text Table of Contents (TOC) as a high-level "map" for the LLM. Instead of searching for similar meaning , you search for relevant locations . How it Works The Mapmaker: You (or a lightweight LLM pass) extract a detailed TOC with hea...
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