Introduction to Advanced_RAG
Advanced_RAG is an exciting project that introduces audiences to the sophisticated realm of language understanding. This series of Python notebooks embarks on a comprehensive journey into Retrieval-Augmented Generation (RAG), offering a seamless experience in integrating rich contextual knowledge with Large Language Models (LLMs) via the Langchain framework. This project is particularly valuable for those looking to elevate the capabilities of LLMs in delivering informed, accurate natural language outputs.
Architecture Flows
Basic RAG:
The heart of Advanced_RAG lies in the understanding of a query's journey from the initial user input to the eventual response generation. This is depicted through clear visual representations, allowing users to grasp the entire flow effortlessly.
Advanced RAG Techniques:
Dive deeper into the complex elements that constitute an advanced RAG system. The focus here is on query construction and the generation process, equipping users with a solid grasp of these intricate systems.
Multi Query Retriever:
A pivotal component of the project, the Multi Query Retriever, enhances the retrieval process by evaluating and selecting the best responses from multiple sources. This structure ensures that the most relevant and accurate information is retrieved, leading to more precise and useful responses.
Self-Reflection-RAG:
This technique involves a self-assessment mechanism where the RAG system evaluates its own responses, promoting reflection and improving generation accuracy through self-grading methodologies.
Agentic RAG:
Agentic RAG introduces a flow that is dynamic and decision-driven, optimizing how documents are retrieved and responses are generated by actively integrating feedback into the process.
Adaptive Agentic RAG:
An advancement on the Agentic RAG model, the Adaptive Agentic RAG introduces further flexibility, allowing the system to adapt its flow and responses based on changing inputs and context in real-time.
Corrective Agentic RAG:
Incorporating corrective flows, this method allows the RAG system to amend its outputs based on retrospective evaluation of both retrieved documents and generated responses, ensuring continuous improvement and accuracy.
LLAMA 3 Agentic RAG Local:
This component represents the LLAMA 3 model's local application within the agentic RAG framework, optimizing operations without reliance on external servers, ensuring secure and efficient performance.
Detailed Notebook Overview
The repository includes a series of notebooks, each serving a specific purpose:
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01_Introduction_To_RAG.ipynb: Offers a primer on constructing basic RAG applications.
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02_Query_Transformations.ipynb: Covers methods for adjusting queries to enhance retrieval efficiency.
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03_Routing_To_Datasources.ipynb: Discusses mechanisms for routing queries to appropriate data sources.
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04_Indexing_To_VectorDBs.ipynb: Explores various methods of indexing within Vector Databases.
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05_Retrieval_Mechanisms.ipynb: Introduces strategies like reranking and RAG fusion for improved retrieval.
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06_Self_Reflection_Rag.ipynb: Demonstrates self-evaluation systems for refining document retrieval and response generation.
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07_Agentic_Rag.ipynb: Focuses on the implementation of agentic flows within the RAG system.
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08_Adaptive_Agentic_Rag.ipynb: Expands on adaptive techniques for more flexible agentic flow.
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09_Corrective_Agentic_Rag.ipynb: Includes corrective strategies for refining outputs through intricate feedback loops.
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10_LLAMA_3_Rag_Agent_Local.ipynb: Features the LLAMA 3 Agent RAG operations executed locally, bypassing external dependencies.
By combining RAG with Langchain, Advanced_RAG enhances the capabilities of LLMs to generate responses that are not only contextually informed but also remarkably precise. This project opens new pathways to a refined and enriched approach to natural language understanding and generation.