🧠
Iam Local Rag model by Intent Solutions Io
⭐ 42.5
🧠
Model

Iam Local Rag

by Intent Solutions Io gh-model--intent-solutions-io--iam-local-rag
Nexus Index
42.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 39
R: Recency 93
Q: Quality 50
Tech Context
Vital Performance
0 DL / 30D
0.0%
Source →
Audited 42.5 FNI Score
Tiny - Params
- Context
0 Downloads
Restricted NOASSERTION License
Model Information Summary
Entity Passport
Registry ID gh-model--intent-solutions-io--iam-local-rag
License NOASSERTION
Provider github
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{gh_model__intent_solutions_io__iam_local_rag,
  author = {Intent Solutions Io},
  title = {Iam Local Rag Model},
  year = {2026},
  howpublished = {\url{https://github.com/intent-solutions-io/iam-local-rag}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Intent Solutions Io. (2026). Iam Local Rag [Model]. Free2AITools. https://github.com/intent-solutions-io/iam-local-rag

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🐙 Git Clone
git clone https://github.com/intent-solutions-io/iam-local-rag

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
42.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 39
Recency (R) 93
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Iam Local Rag: Semantic (S:50), Authority (A:0), Popularity (P:39), Recency (R:93), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

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Deployment Guide

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Technical Deep Dive

NEXUS - Local RAG AI Agent

Autonomous Document Intelligence with Zero Cloud Dependencies

Quick Start

bash 
curl -sSL https://raw.githubusercontent.com/jeremylongshore/nexus-rag/main/install.sh | bash

Then run: source venv/bin/activate && streamlit run 02-Src/app.py

Project Overview

NEXUS is an autonomous AI agent that transforms your local documents into an intelligent knowledge base. Built for developers, researchers, and enterprises who need powerful document Q&A capabilities without sacrificing privacy or paying per-query fees.

Key Features:

  • 100% private AI document analysis running entirely on your hardware
  • Enterprise-grade RAG pipeline with sub-second query responses
  • Air-gapped capable - No data ever leaves your machine
  • Zero-cost operation - Free forever
  • Multi-format support (PDF, TXT, MD, DOCX, HTML)
  • Powered by Ollama (Llama3, Mistral, Phi-3)

Directory Standards

This project follows the MASTER DIRECTORY STANDARDS. See .directory-standards.md for details. All documentation is stored in 01-Docs/ using the NNN-abv-description.ext format.

Project Structure

text 
local-rag-agent/
 01-Docs/              # All documentation (flat structure)
 02-Src/               # Source code (app.py, etc.)
 03-Tests/             # Test suites
 04-Assets/            # Static assets
 05-Scripts/           # Automation scripts
 06-Infrastructure/    # Docker, deployment configs
 07-Releases/          # Release artifacts
 99-Archive/           # Archived items
 claudes-docs/         # Claude-created documentation
 .directory-standards.md  # Directory standards reference
 README.md             # This file
 CLAUDE.md             # AI assistant instructions
 CHANGELOG.md          # Version history

Technology Stack

  • Frontend: Streamlit (Interactive web UI)
  • Orchestration: LangChain (RAG pipeline management)
  • Vector DB: ChromaDB (Semantic search & retrieval)
  • LLM Runtime: Ollama (Local model inference)
  • Language: Python 3.9+

Usage

See 01-Docs/001-ref-readme.md for complete documentation including:

  • Installation instructions
  • Architecture details
  • Performance benchmarks
  • Use cases
  • Contributing guidelines
  • Portfolio impact

Support

  • Issues: GitHub Issues
  • Docs: Full documentation in 01-Docs/
  • Star: Support the project on GitHub

Built with open source

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

GitHub Repository
8Stars
Repo Issues
🐙 Data Source: GitHub ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
gh-model--intent-solutions-io--iam-local-rag
slug
intent-solutions-io--iam-local-rag
source
github
author
Intent Solutions Io
license
NOASSERTION
tags
ai, autonomous-agents, document-analysis, langchain, llm, ollama, privacy-first, rag, python

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
text-generation

📊 Engagement & Metrics

downloads
0
stars
8
forks
0

Data indexed from public sources. Updated daily.