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I will a custom hybrid rag application, llm pipeline, and ai backend in python
Abdulvassay K
Informazioni su questo servizio
Are standard ChatGPT or Claude uploads falling short for your complex data?
When dealing with massive enterprise documents, dense PDFs, or complex data pipelines, standard LLM uploads fail due to context window limits, loss of granular precision, and severe hallucinations.
I build custom, production-ready Hybrid RAG (Retrieval-Augmented Generation) applications and AI backends that deliver precise, context-aware answers with near-zero latency.
Whether you need a custom document Q&A dashboard, a hybrid vector search pipeline, or an asynchronous FastAPI backend for your LLM, I engineer tailored AI solutions built to scale.
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WHAT MAKES MY RAG SYSTEMS DIFFERENT?
Unlike basic vector search setups, I build Multi-Layered Hybrid Retrieval Pipelines:
1. Automated Document Processing: Custom ingestion (pypdf) turning messy PDFs and datasets into structured, context-aware chunks.
2. Hybrid Retrieval (ChromaDB + BM25 Reranking): Combining deep semantic vector search with keyword-exact BM25 reranking to guarantee 100% precision for technical jargon, numerical data, and abstract context.
3. Blazing-Fast Inference: Integrated with ultra-low latency engines
Scopri di più su Abdulvassay K
Abdulvassay K
- DaPakistan
- Membro dagiu 2020
Lingue
Inglese
Hello! I'm a passionate and dedicated student currently exploring the world of language, AI, and creative development. Recently, I created Humanizer, a unique project that reflects my growing skills in natural language understanding and user-focused design. With a strong foundation in translation, I specialize in delivering accurate, context-aware translations between English and Urdu (and more), while maintaining the tone, cultural relevance, and natural flow of each piece. Whether it's documents, creative writing, or digital content, I bring a human touch to machine-generated text
Il mio portfolio
FAQ
Why is Hybrid RAG better than basic vector search?
Basic vector search relies only on semantic similarity, which often misses specific model numbers, dates, exact keywords, or code snippets. My hybrid pipeline combines semantic vector embeddings (ChromaDB) with lexical keyword matching (BM25 reranking) to catch both high-level context and precise id
