Ollama rag csv github. All the code is available in our GitHub repository. Section 1: response = query_engine. . Example Project: create RAG (Retrieval-Augmented Generation) with LangChain and Ollama This project uses LangChain to load CSV documents, split them into chunks, store them in a Chroma database, and query this database using a language model. query ("What are the thoughts on food quality?") Section 2: response = query_engine. A programming framework for knowledge management. Contribute to Zakk-Yang/ollama-rag development by creating an account on GitHub. vector database, keyword table index) including comma separated values (CSV) files. query ("What are the thoughts on food quality?") 6bca48b1-fine_food_reviews. You can clone it and start testing right away. Jan 28, 2024 · * RAG with ChromaDB + Llama Index + Ollama + CSV * ollama run mixtral. Dec 25, 2024 · Below is a step-by-step guide on how to create a Retrieval-Augmented Generation (RAG) workflow using Ollama and LangChain. Jan 5, 2025 · RAG is split into two phases: document retrieval and answer formulation. Document retrieval can be a database (e. We will walk through each section in detail — from installing required Apr 20, 2025 · In this tutorial, we'll build a simple RAG-powered document retrieval app using LangChain, ChromaDB, and Ollama. Retrieval-Augmented Generation (RAG) Example with Ollama in Google Colab This notebook demonstrates how to set up a simple RAG example using Ollama's LLaVA model and LangChain. g. The app lets users upload PDFs, embed them in a vector database, and query for relevant information. pip install llama-index torch transformers chromadb. ufodg wlcp itzeor pnm gsn jeey sfmaeqv rsk hxjv cqqlba