chonkie

chonkie

TLDR: Chonkie is a lightweight and fast RAG chunking library with various chunkers. It offers features like minimal default installs and supports multiple tokenizers. It has better size and speed compared to alternatives.

2024-11-01 Github

RAG_Techniques

RAG_Techniques

TLDR: This repository is a comprehensive collection of advanced Retrieval-Augmented Generation (RAG) techniques. It includes various methods for enhancing RAG systems such as query enhancement, context enrichment, advanced retrieval methods, iterative and adaptive techniques, evaluation, explainability, and advanced architectures.

2024-07-13 Github

awesome-llm-apps

awesome-llm-apps

TLDR: A curated collection of LLM apps built with RAG and AI agents. Features apps using models from various sources and includes projects in different domains. Also provides instructions for getting started and welcomes contributions.

llms python rag Python
2024-04-29 Github

firecrawl

firecrawl

TLDR: Firecrawl is an API service that crawls URLs and converts them into clean markdown or structured data. It offers advanced scraping, crawling, and data extraction capabilities with features like LLM-ready formats, customizability, and more. It also has SDKs for various languages and integrations with multiple frameworks.

2024-04-15 Github

kotaemon

kotaemon

TLDR: An open-source clean & customizable RAG UI for chatting with documents. Serves end users for QA on their documents and developers for building RAG pipelines. Features include clean UI, support for various LLMs, customizable UI for developers, and more.

2024-03-25 Github

llm-app

llm-app

TLDR: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more.

2023-07-19 Github

llama_index

llama_index

TLDR: LlamaIndex is a data framework for LLM applications. It provides data connectors, ways to structure data, an advanced retrieval/query interface, and easy integrations. It has starter and customized options in Python and comes with important links and an ecosystem including LlamaHub and LlamaLab. Contributions are welcome and full documentation is available.

2022-11-02 Github