https://github.com/gurpreetkaurjethra/advanced_rag

Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 , Agents.
https://github.com/gurpreetkaurjethra/advanced_rag

agent chatgpt genai generative-ai langchain large-language-models llama3 llm llms nlp openai rag retrieval-augmented

Last synced: 5 months ago
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Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 , Agents.

• Host: GitHub
• URL: https://github.com/gurpreetkaurjethra/advanced_rag
• Owner: GURPREETKAURJETHRA
• Created: 2024-04-24T20:09:22.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-01T20:07:49.000Z (12 months ago)
• Last Synced: 2024-11-19T09:05:23.492Z (5 months ago)
• Topics: agent, chatgpt, genai, generative-ai, langchain, large-language-models, llama3, llm, llms, nlp, openai, rag, retrieval-augmented
• Language: Jupyter Notebook
• Homepage: https://github.com/GURPREETKAURJETHRA/END-TO-END-GENERATIVE-AI-PROJECTS
• Size: 6.33 MB
• Stars: 36
• Watchers: 2
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 🌟Advanced RAG💯💫🔥

Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3, Agents.

Dive into the world of advanced language understanding with `Advanced_RAG`. These Python notebooks offer a guided tour of Retrieval-Augmented Generation (RAG) using the Langchain framework, perfect for enhancing Large Language Models (LLMs) with rich, contextual knowledge.

## Architecture Flows⭐

### 🔥Basic RAG :

Understand the journey of a query through RAG, from user input to the final generated response, all depicted in a clear, visual flow.

![RAG_User_Flow](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/RAG_User_Flow.jpg)

### 🌟Advanced RAG Techniques :

Explore the intricate components that make up an advanced RAG system, from query construction to generation.

![Advanced RAG Components](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/Advanced%20RAG%20Components.png)

### 02. Multi Query Retriever :

Get to grips with the Multi Query Retriever structure, which enhances the retrieval process by selecting the best responses from multiple sources.

![MQR](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/Multi%20Query%20Retriever.jpg)

### 06. Self-Reflection-RAG :

![self-Rag](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/self%20rag.png)

### 07. Agentic RAG :

![download](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/agentic%20rag.png)

### 08. Adaptive Agentic RAG :

![adaptive_rag_agent](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/adaptive%20rag%20agent.png)

### 09. Corrective Agentic RAG :

![correctiveRAG](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/corrective%20rag.png)

### 10. LLAMA 3 Agentic RAG Local:

![LLAMA3_AGent](https://github.com/GURPREETKAURJETHRA/Advanced_RAG/blob/main/img/Llama3_Agent.png)

## 📚Notebooks Overview📝💫

Below is a detailed overview of each notebook present in this repository:

- **01_Introduction_To_RAG.ipynb**

  - _Basic process of building RAG app(s)_

- **02_Query_Transformations.ipynb**

  - _Techniques for Modifying Questions for Retrieval_

- **03_Routing_To_Datasources.ipynb**

  - _Create Routing Mechanism for LLM to select the correct data Source_

- **04_Indexing_To_VectorDBs.ipynb**

  - _Various Indexing Methods in the Vector DB_

- **05_Retrieval_Mechanisms.ipynb**

  - _Reranking, RaG Fusion, and other Techniques_

- **06_Self_Reflection_Rag.ipynb**

  - _RAG that has self-reflection / self-grading on retrieved documents and generations._

- **07_Agentic_Rag.ipynb**

  - _RAG that has agentic Flow on retrieved documents and generations._

- **08_Adaptive_Agentic_Rag.ipynb**

  - _RAG that has adaptive agentic Flow._

- **09_Corrective_Agentic_Rag.ipynb**

  - _RAG that has corrective agentic Flow on retrieved documents and generations._

- **10_LLAMA_3_Rag_Agent_Local.ipynb**

  - _LLAMA 3 8B Agent Rag that works Locally._

Enhance your LLMs with the powerful combination of RAG and Langchain for more informed and accurate natural language generation.