Built RAG Pipeline with 10 lines CODE without LANGCHAIN & LLAMAINDEX
RAG from Scratch in 10 lines Python - No Frameworks Needed! | simplfy ai| ai| #ai #artificialintelligence #rag #futuretech #ailearning #aiinnovation #aitechnology
Important Link:
Collab link : https://tinyurl.com/cnufkeky
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Description :
In today's video, we'll build a complete chat-with-your-document system in just 10 lines of code using Python. No need for frameworks like LangChain, LlamaIndex, or vector stores like Chroma. This end-to-end system is implemented with pure Python.
Retrieval Augmented Generation (RAG) retrieves information from your documents. We'll chunk the documents into sub-documents, compute embeddings, and match user queries to these chunks without a vector store.
We'll use a Wikipedia article as our knowledge base, chunk it by paragraphs, and generate embeddings using a GTE-based English model. For user queries, we'll embed the query, compute similarity scores, and retrieve the most relevant chunks.
Finally, we'll feed these chunks into an LLM using the OpenAI API to generate a response. Learn to build this basic RAG pipeline and explore more complex systems!
Links and Code:
Collab link : https://tinyurl.com/cnufkeky
Hashtags:
#PythonCoding #RAG #LanguageModels #MachineLearning #AI #PythonProgramming #OpenAI #Embeddings #DocumentRetrieval #TechTutorial
Tags:
Python, RAG, Language Models, Machine Learning, AI, Python Programming, OpenAI, Embeddings, Document Retrieval, Tech Tutorial
Hashtags:
#GoogleMesop #WebUIFramework #PythonDevelopment #StreamlitAlternative #AIModels #ChatbotDevelopment #PythonUI #OpenSourceProject #GoogleAI #TechTutorial
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