Build a RAG preprocessing pipeline
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Updated
Apr 7, 2024 - Jupyter Notebook
Build a RAG preprocessing pipeline
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RAG with LlamaIndex and NVIDIA
This project showcases a Slackbot leveraging LlamaIndex for NLP, OpenAI LLM for context-aware responses, and Qdrant for efficient data storage. Explore how this bot listens, learns, and interacts intelligently in Slack channels.
Samples using LlamaIndex with Azure Open AI
Build RAG applications in Python with Gradient in minutes
An intelligent AI agent for RAG tasks, utilizes LlamaIndex and multiple LLM models to generate code seamlessly and save it to files automatically, leverages the Cloudflare Workers AI API for rapid LLM calls.
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Developing a question-answering system using RAG and HYDE techniques based on the Lex Fridman Podcast Transcript dataset.
This Streamlit application utilizes the LlamaIndex framework for document indexing and querying linear and logistic regression-related information. It uses the OpenAI GPT-3.5 Turbo model for generating embeddings and incorporates various components for efficient document retrieval. Users can input their queries and receive responses.
My Prompt engineerings, LLM, Llama, GPT, Gemini, Grok, Llamaindex, huggingface, LangChain, Pinecone, OpenAI, Next-Gen, LLMs
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Sync your team's data to your LLM applications in real-time
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