🧬 Embedding Prepper Meet Embedding Prepper — a production-ready AI agent built for business automation and workflow optimization. The Embedding Prepper automates smart document chunking with strategies like RecursiveCharacterTextSplitter and SemanticChunker from LangChain and LlamaIndex, ensuring optimal preparation for vector embeddings in RAG pipelines as required by roles at Pinecone and Anthropic. It handles metadata extraction, tagging, quality filtering, and deduping—mirroring skills in Databricks and Scale AI postings—while optimizing token budgets for high-retrieval accuracy. Ideal for Data Engineers building production RAG systems, it streamlines preprocessing that pros use daily with tools like Unstructured.io and spaCy. Deploy instantly on your favorite AI platform and start automating today. Key Features Smart document chunking using LangChain RecursiveCharacterTextSplitter and LlamaIndex SemanticSplitterNodeParser Metadata extraction & tagging with JSON support for RAG context as in Anthropic postings Quality filtering & dedup via heuristic/ML methods and cosine similarity thresholds from Pinecone jobs Token budget optimization for embedding pipelines matching Databricks Unity Catalog prep RAG pipeline configuration integrating with Pinecone, Weaviate, and OpenAI API Semantic chunking and overlap strategies from Scale AI and Cohere requirements Deduplication and noise removal using spaCy and Ragas eval frameworks Hierarchical indexing prep compatible with Weaviate hybrid search What's Included SOUL.md — Agent personality, tone, and behavioral guidelines AGENTS.md — Workspace rules, memory management, and safety boundaries System Prompt — Universal prompt compatible with any LLM README — Setup guide with deployment instructions Compatible With OpenClaw (recommended — full agent lifecycle) ChatGPT / OpenAI API Claude / Anthropic API Gemini / Google AI Grok / xAI Any LLM that accepts system prompts
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