colpali-cookbooks
Recipes for learning, fine-tuning, and adapting ColPali to your multimodal RAG use cases. 👨🏻🍳
Retrieval-augmented generation (RAG) makes a language model answer from your own documents. The text is split into chunks, turned into vectors and stored in an index, and the most relevant chunks are added to the prompt when a question comes in. The projects here cover the whole pipeline or one part of it: document parsing, vector search, or a complete question-answering app.
Answer quality in RAG depends mostly on retrieval, not on the model. Compare how each project handles difficult documents such as PDFs, tables and scans, whether it supports hybrid search that combines keywords with vectors and re-ranking, and whether it shows which sources an answer came from.
Recipes for learning, fine-tuning, and adapting ColPali to your multimodal RAG use cases. 👨🏻🍳
A Docker-powered RAG system that understands the difference between code and prose. Ingest your codebase and documentation, then query them with full privacy and zero configuration.
Generative AI Application Builder on AWS facilitates the development, rapid experimentation, and deployment of generative artificial intelligence (AI) applications without requiring deep experience in AI. The solution includes integrations with Amazon Bedrock and its included LLMs, such as Amazon Titan, and pre-built connectors for 3rd-party LLMs.
Open-source memory runtime for AI agents — reproducible, provenance-tagged context bundles instead of query-time retrieval. Apache-2.0, self-hosted on Postgres + pgvector, Python + TypeScript SDKs.
One person, a team of agents. Multi-session CLI that collaborates across terminals; /goal keeps long tasks running; WeChat/WeCom/Feishu gateway lets you call them back when you walk away. Async Python SDK, persistent memory, self-evolving skills.
Give you decision-ready references for the most common AI engineering problems
A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.
Self-hosted AI gateway for private RAG, natural-language data access, and tool-calling agents.
Demo of a customer service agent (Cymbal Air) using LangGraph, Tools, and RAG to interact with Google Cloud Databases via MCP Toolbox.
Open-source, free, multi-platform, one-click deploy, AI Agent–integrated personal bookmarking system|完全开源、免费、多端、一键部署、AI Agent 集成的个人收藏夹系统
Build LangChain Applications on AWS
Reference implementation of a RAG-based documentation helper using LangChain, Pinecone, and Tavily..
Built on **Agentic RAG** (Agent-driven Retrieval-Augmented Generation) technology, it not only accurately answers pre-sales and after-sales questions but also generates personalized usage reports and optimization suggestions by deeply analyzing device data.
🧠 Guide to Building RAG (Retrieval-Augmented Generation) Applications
Buddhist Digital Text Platform — 10,500+ texts, 613 sources, trilingual cross-canon, AI Q&A (RAG), knowledge graph, full-text search
全流程 智能招投标 Agent:标书生成 · 招投标解读 · 标书检查 · 标书文档ai排版 · 商机发现 一键完成。 21 项合规检查 · 多模型切换 · RAG 知识库 · OCR 抽取。 从招标公告到可交付 docx 文档,全流程 AI 自动化。
The Hitchhiker's Guide to Agentic AI: From Foundations to Systems | 智能体 AI 漫游指南:从基础到系统(中文翻译版)
Foam-Agent: An end-to-end, composable multi-agent framework for automating CFD simulations in OpenFOAM. NeurIPS 2025 Machine Learning and the Physical Sciences Workshop.
Samples using AI and Azure SQL DB
台灣法律 MCP 伺服器 + CLI(免費、免註冊、免 API key):2,250 萬筆裁判書、行政函釋、憲法法庭裁判,附引用查核。Free Taiwan legal MCP server for Claude/ChatGPT/Codex — bring your own LLM, retrieval-only.
An intentionally vulnerable OWASP LLM Top 10 training platform for AI Security, Prompt Injection, RAG Security, Agent Security, and GenAI penetration testing.
Drop-in prompt compression for production LLM apps. Cut your token bill 40-60% without changing your code. Python SDK, LLMLingua-2, MIT.
VeritasGraph — open-source Knowledge Graph & GraphRAG framework on GitHub. Build multi-hop reasoning, ontology-aware retrieval, and verifiable attribution over your own data. Nodes, edges, RDF, linked-data — runs locally or in the cloud.
"Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation" by Yifan Feng, Hao Hu, Shihui Ying, Xingliang Hou, Shiquan Liu, Mingyuan Yang, Junchang Li, Shaoyi Du, Nanning Zheng, Han Hu, and Yue Gao.