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Advanced_RAG

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

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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/NisaarAgharia/RAG_From_Scratch/assets/22457544/dc390fc3-5c41-4c8e-b16e-268606a8f4ed)

### 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/NisaarAgharia/RAG_From_Scratch/assets/22457544/281e8c66-a33f-485f-ad75-e8d450ccba98)

### 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/NisaarAgharia/RAG_From_Scratch/assets/22457544/5c0db3f0-59e4-4278-af6f-4120a3bb5637)

### 06. Self-Reflection-RAG :
![self-Rag](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/2e58751b-c986-4137-8f85-9294301c3f79)

### 07. Agentic RAG :
![download](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/4258e17e-7dfa-48da-a5b5-753b3de5d1bc)

### 08. Adaptive Agentic RAG :
![adaptive_rag_agent](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/283a734d-bd00-4431-8982-fc5e6ce8f15c)

### 09. Corrective Agentic RAG :
![correctiveRAG](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/68968fa8-0b0e-46ca-a80e-b30645b1e31b)

### 10. LLAMA 3 Agentic RAG Local:
![LLAMA3_AGent](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544/a9408eea-814f-416e-a8f6-aec361410719)

## 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.

Advanced_RAG

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