RAG

A tutorial on using rerankers to enhance your RAG pipelineIntroductionRAG is one in every of the primary tools an engineer will check out when constructing an LLM application. It’s easy enough to know and...

Because the world becomes increasingly data-driven, the demand for accurate and efficient search technologies has never been higher. Traditional search engines like google and yahoo, while powerful, often struggle to fulfill the complex and...

Automate Pinecone Day by day Upsert Task with Celery and Slack monitoringIt’s been some time since my last LLM post and I’m excited to share that my prototype has been successfully productionized as Outside’s...

QA RAG with Self Evaluation IIFor this variation, we make a change to the evaluation procedure. Along with the question-answer pair, we also pass the retrieved context to the evaluator LLM.To perform this, we...

Because the applications of enormous language models expand into specialized domains, the necessity for efficient and effective adaptation techniques becomes increasingly crucial. Enter RAFT (Retrieval Augmented High quality Tuning), a novel approach that mixes...

Learn critical knowledge for constructing AI apps, in plain englishRetrieval Augmented Generation, or RAG, is all the craze nowadays since it introduces some serious capabilities to large language models like OpenAI’s GPT-4 — and...

As we navigate the recent artificial intelligence (AI) developments, a subtle but significant transition is underway, moving from the reliance on standalone AI models like large language models (LLMs) to the more nuanced and...

ImportsWe start by installing and importing crucial Python libraries.!pip install llama-index!pip install llama-index-embeddings-huggingface!pip install peft!pip install auto-gptq!pip install optimum!pip install bitsandbytes# if not running on Colab ensure transformers is installed toofrom llama_index.embeddings.huggingface import HuggingFaceEmbeddingfrom...