rag_chain.py remote_loader.py requirements.txt run_all_mains.sh splitter.py streamlit_app.py vector_store.py Breadcrumbs example-app-langchain-rag / ensemble.py Latest commit sfc-gh-ranton Allow Streamlit user to enter their own keys and remove dependency on… 7743d18· Apr 2, 2024 Histor...
LangChain and Streamlit RAG Demo App on Community Cloud Quickstart Setup Python environment The Python version used when this was developed was 3.10.13 python -mvenv .venvsource.venv/bin/activate pip install -U pip pip install -r requirements.txt ...
git clone https://github.com/marklysze/LangChain-RAG-Linux-CUDA Navigate to the repository directory Create the Conda environment conda env create -f environment.yml Activate the Conda environment Install Ollama(repository with instructions) if you haven't already and pull the models you want to...
Streamlit app demonstrating using LangChain and retrieval augmented generation with a vectorstore and hybrid search - example-app-langchain-rag/requirements.txt at main · streamlit/example-app-langchain-rag
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langchain4j-local-rag-sample This is a simple RAG service running everything locally that uses Vespa or OpenSearch as the VectorStore and an ollama model. NOTE: The OpenSearch implementation is still work-in-progress and is not yet ready to be used. Runtime Requirements Epub books The defaul...
git clone https://github.com/astrocodelp/langgraph-rag-example.git Navigate into the project directory: cd langgraph-rag-example Install Dependencies: Install the necessary dependencies using pnpm: pnpm install Set Up Environment Variables: Create a .env file in the root of your project and add...
Repository files navigation README RAG_langchain 介绍如何利用langchain快速实现RAG 快速上手 你可以通过运行rag_chat快速实现一个基于openai的RAG系统 推荐学习路线 1.pdf_loader 2.text_splitter 3.embedding_modelAbout 一个基于langchain实现RAG的简单示例 Resources Readme Activity Stars 0 stars Watchers ...
加入社区:「AI PM 人工智能产品管理」 更过开源:https://github.com/wuloi/translation-agent 更多用例: 【用例】通过LangChain框架,实践GenAI用例 ️ 1/ RAG问答概览 RAG问答指的是利用强大的大型语言模型(…
This example provides an interface for asking questions to a PDF document. Setup Ensure you have the llama3.1 model installed: ollama pull llama3.1 Install the Python Requirements. python -m venv .venv source .venv/bin/activate pip install -r requirements.txt Run python main.py A prompt...