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	# Agentic RAG

	## Theoretical review
	### What is a Retrieval Augmented Generation (RAG)?

	Published by Meta AI in NeurIPS 2020 -> [Paper](https://research.facebook.com/publications/retrieval-augmented-generation-for-knowledge-intensive-nlp-tasks/).

	1. Overview:
	- Hybrid Model: Combines the strengths of retrieval-based and generation-based models.
	- Purpose: Enhances LLM text generation by incorporating relevant external information, improving accuracy and context.

	2. Structure:
	- Retriever:
	- Function: Searches a large corpus (e.g., documents, articles) to find relevant information based on the input query.
	- Techniques: Uses methods like BM25, dense retrieval, or neural retrievers.
	- Generator:
	- Function: Generates a response using the information retrieved.
	- Model: Typically a transformer-based model like GPT-4 or Llama.

	3. Workflow:
	1. Input Query: User provides a query or prompt.
	2. Document Retrieval:
	- The retriever fetches a set of relevant documents or passages.
	- These documents provide context and factual information.
	3. Response Generation:
	- The generator uses the retrieved documents to produce a coherent and contextually accurate response.
	- Ensures the generated text is informed by the most relevant information available.

	4. Benefits:
	1. Enhanced Accuracy: By grounding responses in real-world data, RAG models significantly improve the accuracy of generated content.
	2. Reduced Hallucinations: The integration of external knowledge helps mitigate the risk of generating incorrect or nonsensical responses.
	3. Scalability: RAG systems can handle vast amounts of data, making them suitable for enterprise-level applications.


	#### Pipeline
	![image.png](docs/images/naive_rag.jpg)


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	### What is a Agentic-RAG?
	1. Overview:
	- Enhanced RAG: Extends RAG by adding agent-like capabilities.
	- Purpose: Designed to perform tasks autonomously, interacting with various tools and APIs to achieve specific goals.

	2. Structure:
	- Retriever:
	- Function: Similar to RAG, it fetches relevant documents based on the input query.
	- Generator:
	- Function: Generates an initial response using the retrieved documents.
	- Agent Module:
	- Function: Evaluates the generated response, cross-references it with the knowledge base, and makes corrections if discrepancies are found.

	3. Workflow:
	1. Input Query: User provides a query/question/task.
	2. Document Retrieval: The retriever fetches relevant documents to provide context.
	3. Initial Response Generation: The generator creates a preliminary answer using the retrieved information.
	4. Response Verification: The agent system assesses the initial response against the knowledge base to ensure accuracy.
	5. Response Correction (if needed): If inaccuracies are detected, the agent system refines the response to align with verified information.


	4. Benefits:
	1. Improved Reliability: The agent system's verification process ensures responses are accurate and trustworthy.
	2. Dynamic Correction: Enables real-time adjustments to responses, enhancing the system's adaptability to new information.
	3. User Trust: By providing verified answers, the system builds greater user confidence in its outputs.


	#### Pipeline
	![image.png](docs/images/AgenticRAG.png)

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	## Frameworks recommended for Agents
	[LangChain](https://python.langchain.com/docs/introduction/)
	[LangGraph](https://langchain-ai.github.io/langgraph/)
	[AutoGen](https://microsoft.github.io/autogen/0.2/docs/Getting-Started)
	[SmolAgent](https://github.com/huggingface/smolagents)
	[PydanticAI](https://ai.pydantic.dev/)
	[Vector Database: Chroma](https://docs.trychroma.com/)

	## Frameworks recommended to develop user interfaces.
	[Streamlit](https://docs.streamlit.io/)
	[Gradio](https://www.gradio.app/docs/python-client/introduction)
	[Chainlit](https://docs.chainlit.io/get-started/overview)

	---

	## Code Examples
	- [LangGraph Agentic RAG](https://langchain-ai.github.io/langgraph/tutorials/rag/langgraph_agentic_rag/)
	- [SmolAgent](https://github.com/huggingface/smolagents/tree/main/examples)
	- [LangGraph ai agent for engineers](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/workshops/ai-agents/ai_agents_for_engineers.ipynb)
	- [Agentic RAG with LangChain:](https://medium.com/@jagadeesan.ganesh/agentic-rag-with-langchain-revolutionizing-ai-with-dynamic-decision-making-ff1dee6df4ca)

	## References

	1. [Video: What is Agentic RAG?](https://www.youtube.com/watch?v=0z9_MhcYvcY)
	2. [Video: LangChain vs LangGraph](https://www.youtube.com/watch?v=qAF1NjEVHhY)
	3. [Video: Build Your Own AI Agent System from scratch!](https://www.youtube.com/watch?v=LzG_Vkd30Kg)
	4. [Course: AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)
	5. [Course: Advanced Retrieval for AI with Chroma](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/)
	6. [Course: AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)
	7. [A Comprehensive Guide to Building Agentic RAG Systems with LangGraph](https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/)
	8. [leewayhertz: Agentic RAG](https://www.leewayhertz.com/agentic-rag/)
	9. [Vectorize: How I finally got agentic RAG to work right](https://vectorize.io/how-i-finally-got-agentic-rag-to-work-right/)
	10. [Simple Agentic RAG for Multi Vector stores with LangChain and LangGraph](https://www.metadocs.co/2024/08/20/simple-agentic-rag-for-multi-vector-stores-with-langchain-and-langgraph/)

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