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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Everything you need to know to build your own RAG application
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Today
+1 stars today
This week
+6 stars this week
This month
+24 stars this month
Momentum
10.0
growth rate 0.14%/day
# Retrieval-Augmented Generation (RAG) Project #### 🔜 Check out [bragai.dev](https://bragai.dev) (launching soon) --------------------- <a href="https://trendshift.io/repositories/17125?utm_source=trendshift-badge&utm_medium=badge&utm_campaign=badge-trendshift-17125" target="_blank" rel="noopener noreferrer"><img src="https://trendshift.io/api/badge/trendshift/repositories/17125/daily?language=Jupyter%20Notebook" alt="bragai%2FbRAG-langchain | Trendshift" width="250" height="55"/></a> This repository contains a comprehensive exploration of Retrieval-Augmented Generation (RAG) for various applications. Each notebook provides a detailed, hands-on guide to setting up and experimenting with RAG from an introductory level to advanced implementations, including multi-querying and custom RAG builds.  ## Project Structure If you want to jump straight into it, check out the file `full_basic_rag.ipynb` -> this file will give you a boilerplate starter code of a fully customizable RAG chatbot. Make sure to run your files in a virtual environment (checkout section `Get Started`) The following notebooks can be found under the directory `notebooks/`. ### [1]\_rag_setup_overview.ipynb This introductory notebook provides an overview of RAG architecture and its foundational setup. The notebook walks through: - **Environment Setup**: Configuring the environment, installing necessary libraries, and API setups. - **Initial Data Loading**: Basic document loaders and data preprocessing methods. - **Embedding Generation**: Generating embeddings using various models, including OpenAI's embeddings. - **Vector Store**: Setting up a vector store (ChromaDB/Pinecone) for efficient similarity search. - **Basic RAG Pipeline**: Creating a simple retrieval and generation pipeline to serve as a baseline. ### [2]\_rag_with_multi_query.ipynb Building on the basics, this notebook introduces multi-querying techniques in the RAG pipeline, exploring: - **Multi-Query Setup**: Configuring multiple queries to diversify retrieval. - **Advanced Embedding Techniques**: Utilizing multiple embedding models to refine retrieval. - **Pipeline with Multi-Querying**: Implementing multi-query handling to improve relevance in response generation. - **Comparison & Analysis**: Comparing results with single-query pipelines and analyzing performance improvements. ### [3]_rag_routing_and_query_construction.ipynb This notebook delves deeper into customizing a RAG pipeline. It covers: - **Logical Routing:** Implements function-based routing for classifying user queries to appropriate data sources based on programming languages. - **Semantic Routing:** Uses embeddings and cosine similarity to direct questions to either a math or physics prompt, optimizing response accuracy. - **Query Structuring for Metadata Filters:** Defines structured search schema for YouTube tutorial metadata, enabling advanced filtering (e.g., by view count, publication date). - **Structured Search Prompting:** Leverages LLM prompts to generate database queries for retrieving relevant content based on user input. - **Integration with Vector Stores:** Links structured queries to vector stores for efficient data retrieval. ### [4]_rag_indexing_and_advanced_retrieval.ipynb Continuing from the previous customization, this notebook explores: - **Preface on Document Chunking:** Points to external resources for document chunking techniques. - **Multi-representation Indexing:** Sets up a multi-vector indexing structure for handling documents with different embeddings and representations. - **In-Memory Storage for Summaries:** Uses InMemoryByteStore for storing document summaries alongside parent documents, enabling efficient retrieval. - **MultiVectorRetriever Setup:** Integrates multiple vector representations to retrieve relevant documents based on user queries. - **RAPTOR Implementation:** Explores RAPTOR, an advanced indexing and retrieval mod
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:799cb84a6b84855d, topic:rag, topic:retrieval-augmented-generation, readme:retrieval-augmented generation
matched fp:799cb84a6b84855d, topic:chatbot, readme:chatbot