I will build rag data pipelines, text tokenization, and vector database setups

Are your AI models hallucinating? I build production-grade RAG pipelines, text tokenization, and vector database infrastructure to feed your LLMs clean, context-rich data.

️ TECH STACK:

Vector DBs: Pinecone, ChromaDB, Qdrant, Milvus, pgvector

Frameworks: LangChain, LlamaIndex, Python, SQL

LLMs: OpenAI GPT, Llama, Claude, HuggingFace

WHAT I DELIVER:

Advanced Tokenization & Chunking: Smart document splitting with precise chunk sizes and semantic overlap to optimize context windows.

Automated Ingestion Pipelines: Production-grade ETL scripts that extract raw PDFs, JSON, or APIs, generate vector embeddings, and upsert them automatically.

Metadata Engineering: Robust metadata tagging for fast, accurate hybrid search filtering.

Database Architecture: Setting up secure cloud storage staging and vector stores from scratch.

Why choose me?

I build scalable, clean, error-handled data backend scaffolding that keeps your databases in sync. 

Please message me with your project details before ordering!