File Parser optimised for LLM Ingestion with no loss 🧠 Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

Convert files into markdown to help RAG or LLM understand, based on markitdown and MinerU, which could provide high quality pdf parser.

File2MD: A Micro File Parser Service. Friendly to LLM.

TTPMapper is an AI-driven threat intelligence parser that converts unstructured reports whether from web URLs or PDF files into structured intelligence. Using the DeepSeek LLM, it extracts MITRE ATT&CK techniques, IOCs, threat actors, and generates contextual summaries.

Python parser for GAEB DA XML construction data exchange files (v2.0–3.3) with LLM-powered item classification and structured extraction.

AutoML/Unstructured Data Processing for RAG and LLM Dataset Creation. Current Database Options are: Qdrant or Marqo DB.

A resume parser that extracts key details from PDF files using Groq's LLM

Unified Parser is a high-performance, SaaS-ready document processing engine designed to transform unstructured PDF and DOCX files into LLM-optimized Markdown and structured JSON. Built with an "AI-First" approach, it features intelligent layout detection, semantic chunking, and real-time document analytics.

Context-Condenser sits between your files and your LLM. It uses a **tree-sitter AST parser** to understand your code as *structure*, not text. It compresses every file into a **Skeleton** — function signatures only — and only **hydrates** (expands) the exact functions the AI needs, exactly when it needs them.

Join me in co-developing an innovative parser designed to transform intricate Swedish bookkeeping data (SIE 4 files) into a structured, LLM-friendly JSON format. This crucial tool will enable seamless integration with advanced AI models, unlocking enhanced user interactions and sophisticated financial analysis capabilities.

This repository demonstrates an advanced Retrieval-Augmented Generation (RAG) workflow for querying codebases. The project enables users to understand specific functionalities (e.g., JavaScript parser usage) in a codebase by embedding query and file content, storing them in a Pinecone vector store, and retrieving context for LLM-based responses.

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insurance file parser with LLM backend

LLM parser which reads the files

Simple document parser to convert files for LLM use

Open Source Excel parser to generate CSV file for LLMs to ingest.

Creating a log-parser using llm for extracting the particular metrics from log files.

LLM-powered Excel parser — define a Pydantic schema, get structured data from any Excel file

LLM-powered document parser for intelligent querying and interaction with uploaded files with built-in TTS

(ING) One-file development for LLMs — write your full app in one file, then split it with a parser. (PT) Desenvolvimento em ficheiro único com LLMs — escreve tudo num só ficheiro, depois separa.

A python-based parser to be used to extract messages from .pst files for later consumption by an LLM based AI.

Excel Tags Parser with MongoDB Integration - Process large Excel files, extract metadata, and integrate with MongoDB. Includes MCP server for LLM analytics.

Language-agnostic code annotation parser. Extracts @dc: tags from source comments into LLM context files, JSON manifests, and interactive dependency visualizations. Zero dependencies.

A production-ready resume parser that extracts structured information from PDF, DOC, DOCX, and TXT files using state-of-the-art LLM providers and transformer models.

Deterministic intent parser — RegEx and NLP pipeline that intercepts known commands before they reach your LLM. Constituency and dependency parsing via Stanza. Domain-specific skill files. Zero ambiguity.

Parser for pdf files of IFRS financial statements. LLM finds in the document the values of key indicators of IFRS reporting such as revenue, net profit, assets, capital.

AI-powered syllabus parser that automatically extracts assignments from PDF/DOCX files and creates a Notion deadline tracker using Groq LLM (llama-3.3-70b-versatile) and Composio integration.

The `vals_parser.py` script aims to parse LLM costs/latency/benchmarks data from the website vals.ai and make it available through the charts in the README file and the 'benchmarks_data.json' file

The file-parser-backend is a robust backend service designed to facilitate the parsing of various file formats using Large Language Models (LLM). Built with FastAPI, this application provides a fast and scalable API for handling file uploads and parsing tasks.