Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling
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Updated
Dec 3, 2025 - Python
Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling
A Python package for converting PDFs to markdown while extracting images and tables, generate descriptive text descriptions for extracted tables/images using several LLM clients. And many more functionalities. Markdrop is available on PyPI.
Collection of PDF parsing libraries like AI based docling, claude, openai, gemini, meta's llama-vision, unstructured-io, and pdfminer, pymupdf, pdfplumber etc for efficient snapshot, text, table, and metadata extraction.
Docling with Ollama - RAG on Local Files with Local Models
Flexible GraphRAG: Python, LlamaIndex, Docker Compose 8 Graph databases, 10 Vector databases, OpenSearch, Elasticsearch, Alfresco. 13 data sources, knowledge graph auto-building, schemas, LLMs, Docling or LlamaParse doc processing, GraphRAG, RAG only, Hybrid search, AI chat. React, Vue, Angular frontends, FastAPI backend, MCP Server. Please 🌟 Star
A python library and CLI tool to convert PDF files to CSV files.
DocChat is an AI-powered Multi-Agent RAG system using Docling for structured document parsing and BM25 + vector search retrievers to retrieve fact-checked answers from PDFs, DOCX, and text files, preventing hallucinations. 🚀
Autonomous agent networks for task automation that requires multi-step reasoning
Docling4j brings the functionalities of Docling in document understanding to Java® projects
OnnxTR OCR plugin for Docling
Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem
Integrates AWS Bedrock's multimodal capabilities (Claude 3) into the Docling framework for generating image descriptions within document processing pipelines.
Transform unstructured documents into validated, rich and queryable knowledge graphs.
A TypeScript SDK for Docling - Bridge between the Python Docling ecosystem and JavaScript/TypeScript.
Make Zettelkasten-style note-taking the foundation of interactions with Large Language Models (LLMs).
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