Improve Image Search Accuracy (CLIP / SigLIP + PostgreSQL Vector Indexing)

@DigitalNightOwl8

Hello Mate!Greetings Pratik G., Good evening! Already have something live to show you I am professional mobile developer with skills including AI Development, Laravel, PostgreSQL, Python, Java, PHP, MySQL and AI Model Development. Please contact me to discuss more regarding this project. Thanks & Regards

@esraatarekyosef1

‎Hi, I am AI engineer with very wide and strong experience in ML building and fintuning including time series, computer vision, NLP in addition to my experience in backend using python and .Net for generative AI , web scraping and AI integration, I implemented many projects in this feild I also can share some demos with you inbox, what made me sure I can do your task and I am looking forward to working with you ‎

@ahmedtahash

Hi there, I’d be glad to help tighten your vector search results and improve the relevance of your CLIP/SigLIP-powered property platform. I have experience deploying pgvector at scale, optimizing ANN indexes, and ensuring embedding pipelines produce high-precision, normalized vectors for reliable similarity search. I can review your indexing strategy (IVFFlat vs HNSW), adjust SQL queries for correct distance ordering, verify preprocessing of embeddings, and stress-test your Python vector generation to ensure accurate recall. All improvements will be reproducible, documented, and optimized to maintain sub-300ms latency on 50k rows. Do you have a preference for prioritizing index tuning versus embedding preprocessing first, or would you like me to propose the fastest path to noticeably better search accuracy? Regards, Ahmad Al-Ashery

@duncank52

Hi there, I’ve handled backend / API builds like "Improve Image Search Accuracy (CLIP / SigLIP + PostgreSQL Vector Indexing)" where the goal is a clean result that just works without needing fixes later. Here’s how I’d approach it: With PHP, Java, Python in your stack, I’ll keep the structure lightweight, predictable and easy to maintain. • Build endpoints in a tidy, predictable way so behaviour is easy to test and reason about. • Structure configuration and environment variables so deployment is clean and repeatable. If you prefer, we can begin with a small sample so you can validate the direction first. If this sounds like the direction you’re aiming for, I’d be ready to begin. Best regards, Duncan.

@laxmic5

I’ve deployed vector search systems in production using CLIP/SigLIP with pgvector and understand the exact issues you’re describing—semantic drift, loose similarity, and ANN misconfiguration. I can audit and rebuild your pgvector setup end to end: index choice (IVFFlat vs HNSW), parameter tuning, and query correctness so cosine/L2 ordering behaves as intended in Postgres, not distorted by Laravel abstractions. I’ll also validate embedding normalization, precision, and Python inference pipelines to protect recall and latency. You’ll get reproducible migrations, clear parameter documentation, and verified query plans using ANN indexes—while keeping response times under 300 ms. Ready to start on your staging environment immediately.

@bavtika

I can help you significantly tighten your CLIP/SigLIP–based image similarity results by optimizing both the embedding pipeline and the pgvector ANN configuration. I’ve deployed vector search systems in production using CLIP, SigLIP, and PostgreSQL (IVFFlat / HNSW), and I’m familiar with the pitfalls that cause noisy recall such as incorrect normalization, suboptimal probe settings, and Laravel query quirks that break proper distance ordering.

@alkak16

Hi, I’ve reviewed your requirements, and this matches the vector-search optimization work I specialize in. I’ve deployed CLIP/SigLIP + pgvector in production, tuned ANN indexes, and resolved issues where similarity scores return irrelevant images—exactly like your “swimming pool” vs open-sea problem. Here’s how I can help immediately: ✔ ANN Index Optimization (IVFFlat / HNSW) I’ll review and fine-tune lists, m, ef_search, probe settings, and rebuild indexes for tighter, more accurate ranking. ✔ Embedding Pipeline Verification I’ll audit your Python embedding process to ensure proper normalisation, precision consistency, and batch inference settings so vectors match perfectly before they hit Postgres. ✔ Full Query Path Profiling I’ll profile the end-to-end flow (Laravel → pgvector → JSON) and ensure latency stays under 300ms for ~50k rows, with the correct ANN index used (no seq scans). You’ll receive updated migrations, reproducible parameters, corrected SQL distance ordering, improved query plans, and before/after accuracy comparisons. All acceptance criteria you listed will be met. I can start immediately and am available for a quick video call today or tomorrow to walk through your staging environment. Looking forward to improving your vector search accuracy. Regards, Alka Laravel • Vector Search • ANN Optimization Specialist

@RipanAI

I’ve tuned pgvector for 3 M+ embedding production catalogs. Will IVFFlat→HNSW, 128-d→768-d, L2↔cosine, set ef_construction=200, M=32, recreate index with 64 lists, probes=10. Rewrite Laravel scope to “ORDER BY embedding <=> query_vec LIMIT 50” with raw SQL, no Eloquent mangling. Add 0-mean, L2-norm in Python pre-insert, verify float32 precision, batch-size 256, SigLIP-B/16. Stress with pgbench: 300 ms p95 under 50 k. Deliver: PR + migration + README + EXPLAIN proof.

@rofolduo

As a seasoned software engineer with five years of experience, I bring a diverse skillset that is perfectly aligned with your project's needs. I've worked on demanding projects requiring vector search and implemented impressive improvements in image search accuracy. My expertise in Java, MySQL, PHP, and most importantly Python will prove crucial to your project's success. Having followed channels like lynfy, rafatatay, and midudev, and completed programming courses on Microsoft learning and Google Activity among others, I constantly update my knowledge base to adapt to the latest industry trends. This ensures that I can bring cutting-edge solutions to the table whilst maintaining high precision and recall rates during batch inferences. What sets me apart from the competition is my unwavering commitment to client satisfaction. Just as you take precision seriously with your property platform, I too am a perfectionist in every task I undertake. I never rest until my clients achieve the desired outcomes. With access to your staging clone and Python embedding script, I'll meticulously fine-tune the pgvector indexing strategy and rewrite SQL queries to mathematically correct ordering - nullifying any quirks commonly found in Laravel’s query builder. Note: Price negotiable; website completed in two months maximum, minimum one and a half months depending on the project.