🧠
Model

Rag Recipes

by prsdm gh-model--prsdm--rag-recipes
Nexus Index
0.0 Top 18%
P: Popularity 0
F: Freshness 0
C: Completeness 0
U: Utility 0
Tech Context
Vital Performance
0 DL / 30D
0.0%

Updated on 14th August 2024 📝Article • Demo & Dataset on: 🤗Hugging Face Large Language Models (LLMs) demonstrate significant capabilities but sometimes generate incorrect but believable responses when they lack information, and this is known as “hallucination.” It means they confidently provide information that may sound accurate but could be incorrect due to outdated knowledge. Retrieval-...

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Registry ID gh-model--prsdm--rag-recipes
Provider github
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Cite this model

Academic & Research Attribution

BibTeX
@misc{gh_model__prsdm__rag_recipes,
  author = {prsdm},
  title = {Rag Recipes Model},
  year = {2026},
  howpublished = {\url{https://github.com/prsdm/rag-recipes}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
prsdm. (2026). Rag Recipes [Model]. Free2AITools. https://github.com/prsdm/rag-recipes

🔬Technical Deep Dive

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🐙 Git Clone
git clone https://github.com/prsdm/rag-recipes

⚖️ Nexus Index V16.5

0.0
TOP 18% SYSTEM IMPACT
Popularity (P) 0
Freshness (F) 0
Completeness (C) 0
Utility (U) 0

💬 Index Insight

The Free2AITools Nexus Index for Rag Recipes aggregates Popularity (P:0), Freshness (F:0), and Completeness (C:0). The Utility score (U:0) represents deployment readiness and ecosystem adoption.

Free2AITools Nexus Index

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🚀 What's Next?

Technical Deep Dive

Updated on 14th August 2024

RAG Recipes

📝Article • Demo & Dataset on: 🤗Hugging Face

Large Language Models (LLMs) demonstrate significant capabilities but sometimes generate incorrect but believable responses when they lack information, and this is known as “hallucination.” It means they confidently provide information that may sound accurate but could be incorrect due to outdated knowledge.

Retrieval-Augmented Generation or RAG framework solves this problem by integrating an information retrieval system into the LLM pipeline. Instead of relying on pre-trained knowledge, RAG allows the model to dynamically fetch information from external knowledge sources when generating responses. This dynamic retrieval mechanism ensures that the information provided by the LLM is not only contextually relevant but also accurate and up-to-date.

diagram

This repository provides a collection of Jupyter notebooks that demonstrate how to build and experiment with RAG using different frameworks and tools.

Details of each notebook:

Tool LLMs Description Notebooks
Weaviate & LangChain OpenAI Build a question-answer system focused on providing answers related to the Roman Empire using Weaviate, LangChain, and OpenAI. Open In Colab
LangChain & LlamaIndex OpenAI Build basic and advanced document RAG workflow using LangChain, LlamaIndex and OpenAI article. Open In Colab
LangChain Mixtral Developed a chatbot that retrieves a summary related to the question from the vector database and generates the answer. Open In Colab
LangChain llama-2 Developed a machine learning expert chatbot (using Q&A dataset) that answers questions related to machine learning only without hallucinating. Open In Colab

📝 Limitations & Considerations

  • Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • FNI scores are relative rankings and may change as new models are added.
  • License Unknown: Verify licensing terms before commercial use.
  • Source: Unknown
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AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

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100% Data Disclosure Active

🆔 Identity & Source

id
gh-model--prsdm--rag-recipes
source
github
author
prsdm
tags
large-language-modelsllmsragretrieval-augmented-generationvector-databasechromadblangchainllamaindexweaviatelanggraphpineconeqdrantjupyter notebook

⚙️ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
feature-extraction

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