rustyneuron01/Conversation-Genome-Project
Structured data & semantic tagging pipeline. Turns raw text (conversations, web pages, surveys) into tagged data for AI and search. Coordinators set ground truth; workers run LLM inference on windows. Scoring via cosine similarity. Python, FastAPI, OpenAI/Anthropic/OpenRouter, embeddings, Docker.
This project helps businesses and researchers convert raw text from conversations, web pages, or surveys into organized, semantically tagged data. It takes large volumes of unstructured text and, using AI, assigns relevant tags and metadata, making the content easier to search and use for further analysis. Anyone who needs to extract meaningful, structured information from extensive text datasets, like data analysts, content managers, or market researchers, would benefit from this.
Use this if you need to semantically tag and structure large volumes of text data for AI applications, search, or detailed analysis.
Not ideal if you only need basic keyword extraction or manual annotation of small text datasets.
Stars
23
Forks
8
Language
Python
License
MIT
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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