This repository contains example code and guides for building with the E2B SDK.
bRAGAI/bRAG-langchain
This repository collects guidance and examples for building retrieval-augmented generation applications.
About bRAGAI/bRAG-langchain
This repository provides Python notebooks and example code for building retrieval-augmented generation applications. It includes a starter notebook with boilerplate code for a RAG chatbot, plus sequenced notebooks covering setup, embeddings, vector stores, multi-query retrieval, routing, indexing, and reranking.
Use requires a Python virtual environment with installed libraries and configured API access, including embedding models and vector stores such as ChromaDB or Pinecone.
Key features
- Starter notebook with boilerplate code for a customizable RAG chatbot
- Walkthrough for environment setup, document loading, and embedding generation
- Vector store setup using ChromaDB or Pinecone for similarity search
- Multi-query and RAG-Fusion examples with reciprocal rank fusion
- Logical and semantic routing plus structured filtering for tutorial metadata
- Multi-vector indexing examples including ColBERT retrieval and RAPTOR overview
Best for: People with Python experience who want notebook examples for building RAG applications from setup to retrieval techniques.
Good for
- Sequenced notebooks move from basic setup to routing, indexing, and reranking
- Includes starter boilerplate code for a customizable RAG chatbot
- Shows multiple vector stores and retrieval methods, including ChromaDB, Pinecone, and ColBERT
Watch out for
- Requires virtual environment setup with libraries and API configuration
- Depends on external models and services such as OpenAI embeddings and vector stores
bRAGAI/bRAG-langchain at a glance
- Category
- AI & Machine Learning › AI Learning & Guides
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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bRAGAI/bRAG-langchain FAQ
Which file should I use to start building quickly?
Use full_basic_rag.ipynb, which provides boilerplate starter code for a customizable RAG chatbot.
What setup is needed to run the notebooks?
Run the files in a virtual environment after configuring required libraries and API access.
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