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Local LLM RAG Project Uses Ollama and Pydantic AI

By PulseAugur Editorial · [1 sources] ·

A new experimental project, local-LLM-with-RAG, explores running large language models locally using Ollama and Pydantic AI. This sandbox environment aims to test agentic Retrieval-Augmented Generation (RAG) capabilities, allowing the AI to dynamically decide when and how to search documents for answering questions. The project utilizes Ollama for embeddings, LanceDB for vector storage, and Pydantic AI as a type-safe agent framework. AI

IMPACT Provides a framework for local LLM experimentation with agentic RAG capabilities.

RANK_REASON This is a description of a specific project/sandbox using existing tools, not a new model release or significant industry event.

Read on Mastodon — mastodon.social →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Local LLM RAG Project Uses Ollama and Pydantic AI

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This is a description of a specific project/sandbox using existing tools, not a new model release or significant industry event.
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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 English(EN) · [email protected] ·

    local-LLM-with-RAG This project is an experimental sandbox for testing out ideas related to running local Large Language Models (LLMs) with Ollama and Pydantic

    local-LLM-with-RAG This project is an experimental sandbox for testing out ideas related to running local Large Language Models (LLMs) with Ollama and Pydantic AI to perform agentic Retrieval-Augmented Generation (RAG) for answering questions based on your documents. The agent ca…

    LINKS github.com/…/local-LLM-with-RAG

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