Otimização de Pipeline -- 2

@kamran2012

Hey, Vou otimizar seu pipeline de análise de matrículas imobiliárias — sanitização de ingestão, gestão de tokens/cotas e estratégia de context window para o Gemini 1.5 Pro/Flash processar documentos de 100+ páginas sem truncamento. Uma abordagem que funciona bem aqui: em vez de enviar o documento inteiro em um único prompt, vou estruturar um sistema de XML Tagging hierárquico que preserva a cadeia dominial completa, combinado com um mecanismo de "sliding window com overlap" para manter referências cruzadas entre chunks — assim a IA nunca perde o contexto sequencial dos atos registrados. Perguntas: 1) Qual o tamanho médio (em tokens) dos documentos que estão causando truncamento ou erros 429 hoje? 2) O processamento assíncrono com Celery já está implementado, ou precisa ser configurado do zero? Ready to start whenever you are. Kamran

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you’re looking for a Senior AI Engineer to optimize a long-context pipeline using Python. It sounds like an exciting project that involves advanced AI techniques. I’ve worked on several similar AI optimization projects and understand the key technical challenges involved. I focus on delivering efficient solutions that enhance performance and scalability. I’d be happy to go through the details and suggest the best technical approach. I have a few questions to get a better understanding: Q1 – What specific optimization goals do you have in mind for the pipeline? – Q2 – Are there any existing systems or frameworks you’re currently using that this needs to integrate with? – Q3 – What are the expected outputs and performance metrics you want to achieve? Looking forward to hearing from you.

@widuIT

Olá, o ponto crítico deste projeto não é apenas “usar um modelo com contexto grande”, mas garantir que a cadeia dominial seja analisada com sequência, completude e rastreabilidade, mesmo em documentos muito longos. Eu trataria isso como um problema de pipeline documental, não só de prompt. A base correta é organizar a ingestão em etapas: extração/OCR confiável, sanitização, persistência completa do texto, estruturação por blocos lógicos e só então envio ao modelo com marcação explícita, para que ele mantenha a auditoria sequencial sem resumir partes sensíveis. Além disso, o controle de throughput, retries e processamento assíncrono precisa ser parte da solução desde o início, porque esse tipo de workload costuma sofrer com 429 e variação de capacidade. Minha recomendação seria manter Python como núcleo, fortalecer a camada de workers/filas, revisar a estratégia de contexto longo e refinar as instruções do sistema com foco em precisão estrutural, não em respostas “bonitas”. O objetivo aqui é deixar um pipeline mais estável, auditável e pronto para crescer, e não apenas trocar prompts até o resultado parecer melhor. Proposta redigida com apoio de ferramentas de tradução para respeitar o idioma do projeto nesta etapa inicial. Nico – widuIT - Top Freelancer LATAM

@Walkingdreams

As much as I would love to be able to fulfill your needs and successfully complete your project, I unfortunately lack the requisite experience with Python, Gemini 1.5 Pro/Flash and MySQL. Due to this, although I might be able to help you in some areas of your project (such as applying any needed rate limiting, managing tokens per minute or processing your persisted text), there might be other sections whereby there will be a gap between my expertise and what your project needs. I am more than willing to discuss whether you would like for me to help tackle certain specific parts or if you'd rather a freelancer completely prepared in each area get the job done instead. Whatever is your decision, feel free to reach back out to me and I'll be ready and excited to assist you in whatever way possible.

@dvcontact

Hello Sir, Are you ready to see how I can optimize your pipeline for real-time analysis of extensive real estate documents in just 1-3 hours, with no commitment on your part? A live demo will provide tangible evidence of my capabilities, far outweighing the insights from a portfolio review or a lengthy discussion filled with abstract promises. After experiencing the demo, you can confidently decide to award the project, ensuring the work meets your expectations. Best, Smith

@Herculesz

Hi I have experience building long-context AI pipelines in Python for large-document analysis, integrating LLM APIs, managing quota limits, and improving structured extraction reliability, and I can help you so on. The main challenge here is not simply sending long property registry documents to Gemini, but preserving full logical continuity across 100+ page chains of title without truncation, buffer overflow, or token-rate failures. I would address this in layers: strengthening OCR/parsing sanitation, ensuring safe persistence in MySQL LONGTEXT, designing structured XML-style chunking with semantic tagging, and orchestrating asynchronous processing for stable throughput. I can also refine system prompts to enforce sequential auditing behavior so the model avoids summarizing critical ownership transitions and instead preserves traceable domain-chain reasoning. On the Vertex AI side, I’m comfortable implementing quota-aware scheduling, retry strategies, batching, and TPM-aware throttling to eliminate recurring 429 errors in production workflows. If needed, I can structure the pipeline using Celery workers and Redis buffering to separate ingestion, processing, and persistence for improved observability and reliability. The result will be a more stable and scalable long-context processing pipeline capable of analyzing complex registry documents with consistent logical integrity. Thanks, Hercules

@marjanahmed13

Hi! My name is Marjan and I'm here to offer you my services as a skilled applicant with over a decade of experience working on Freelancer.com. l believe I am the best fit candidate for this project due to my extensive experience; I would like to have a discussion to get to know that we both are on the same page. Once the scope will be locked, I will start working on it right away.

@rbtech1984

Olá! ★★★★ (Otimização de pipeline de IA para processamento de documentos de longo contexto com Gemini e Python) ★★★★ Compreendo que você precisa otimizar um pipeline baseado em Python para lidar com grandes documentos do setor imobiliário, garantindo o processamento de contexto completo, o uso estável de tokens e uma análise sequencial precisa utilizando o Gemini. ⚜ Melhoria de OCR/higienização de texto ⚜ Ingestão de grandes volumes de texto (LONGTEXT/MySQL) ⚜ Gerenciamento de tokens e limites de taxa (TPM) ⚜ Fragmentação de contexto e estruturação em XML ⚜ Refinamento de engenharia de prompts ⚜ Processamento assíncrono (Celery/workers) ⚜ Integração e escalonamento com Vertex AI Tenho experiência com pipelines de IA, integrações de API e no manuseio de grandes conjuntos de dados, garantindo desempenho otimizado e precisão lógica. Irei refinar o processo de ingestão, aplicar técnicas inteligentes de fragmentação (chunking), gerenciar os limites da API e aprimorar os prompts para assegurar resultados consistentes. Vamos discutir os gargalos atuais e iniciar o processo de otimização. Atenciosamente, Farhin B.

@FizzaNadeemK

Hi, You need a senior AI pipeline engineer who can stabilize and optimize long-context document processing so Gemini reliably handles 100+ page legal/real-estate records without truncation or loss of logical continuity. I’ve worked on Python-based LLM pipelines involving document ingestion, OCR parsing, structured chunking strategies, and API orchestration where token limits, rate limits, and context integrity were critical constraints. My approach would focus on robust preprocessing (OCR → normalized LONGTEXT storage), intelligent segmentation using structure-aware chunking (headers/XML tagging rather than naive splits), and controlled context reconstruction so Gemini can maintain sequential audit logic across large documents, while also implementing retry logic, TPM-aware request scheduling, and queue-based async processing (Celery/Redis) to eliminate 429 failures. Are you currently using a fixed chunking strategy or hybrid retrieval (e.g., embeddings + structured context assembly) in your pipeline today? I’m available to start immediately and can help stabilize and optimize the system for production-scale reliability. Best Regards, Fizza Nadeem K

@mayankinnovative

Hi, As per my understanding: You need a Senior AI Engineer to optimize a Python pipeline for processing massive real estate documents with Gemini 1.5. The core challenges are maintaining 100+ page context without loss, mitigating API rate limits, and ensuring high-fidelity auditing of complex property chains. You require robust orchestration involving MySQL, Redis, and asynchronous workers to handle scale and token quotas. Implementation approach: I will refactor your ingestion layer to handle OCR/parsing outputs reliably, ensuring clean data enters MySQL. For context window management, I will implement XML-based structure tagging to demarcate chain-of-title sequences, forcing the model to adhere to chronological logic. To resolve 429 errors, I will implement a custom middleware with exponential backoff and usage tracking via Redis to manage TPM/RPM dynamically. I will also develop a System Prompt framework focused on Auditoria Sequencial to prevent summarization, using few-shot examples of complex chain histories to guide the model. Finally, I will verify the pipeline via Celery to ensure non-blocking, scalable document processing. A few quick questions: 1. Are you currently using a specific Python framework for the asynchronous tasks, or is the architecture primarily synchronous? 2. What is the average document size, and what percentage of your current requests fail due to rate limits?

@kareemmaize

Ola - o problema aqui nao e so tamanho de contexto, e consistencia logica ao longo de centenas de paginas. Muitos pipelines parecem funcionar porque o texto chega inteiro, mas falham quando a IA perde a sequencia dominial ou mistura blocos sem manter relacao temporal. Isso acontece mesmo sem erro aparente, entao eu trato estrutura, ordem e referencia cruzada como parte central do processamento. O fluxo e: OCR e parsing extraem texto completo -> pipeline sanitiza e organiza em blocos estruturados -> sistema aplica chunking com tags e referencia entre secoes -> requests respeitam limites de tokens e TPM -> modelo executa analise mantendo sequencia logica -> resultados sao validados e persistidos. The part to get right early is a estrategia de chunking e encadeamento de contexto, porque isso afeta tanto a fidelidade quanto a consistencia da analise. Isso se resolve bem quando essa base e estruturada corretamente.

@techsty2014

Olá, Implementei pipelines do Gemini 1.5 Pro para auditorias de documentos longos. Três correções: 1) Perda de foco em documentos com mais de 100 páginas: o Gemini dispersa a atenção no centro. Correção: repetir a lista de verificação da auditoria no início e no fim do contexto, com tags XML por documento e transação. 2) Vazamento de sumarização: pipeline de duas passagens. A primeira passagem extrai o JSON estruturado de cada transferência com citações de página. A segunda passagem audita a extração. A sumarização não pode ocorrer em entradas estruturadas. 3) Custo e erros 429: o cache de contexto do Gemini reduz o custo em 75% nas reexecuções. O gerenciamento de TPM é feito por meio de um semáforo de janela deslizante, e não por meio de um backoff de repetição ingênuo. Stack: Python, Celery com Redis, MySQL LONGTEXT, Vertex AI. Google Document AI para OCR, se necessário, em títulos digitalizados. Pergunta: PDFs digitalizados ou arquivos digitais na origem? Faizan

@seandinwiddie

https://www.freelancer.com/u/seandinwiddie Hi, Nice to meet you. I’m confident I can help you optimize and stabilize your long-context AI pipeline for processing complex real estate registry documents using Python and Gemini 1.5 Pro/Flash. With strong experience in Python backend systems, LLM pipeline design, and Google Cloud/Vertex AI integrations, I can ensure your system processes full documents reliably without truncation or loss of logical integrity. I will start by auditing your current ingestion pipeline (OCR/parsing → MySQL → LLM input flow) to identify token overflow and context loss points. Next, I will implement a robust chunking + XML-structured context strategy combined with prompt engineering to preserve sequential audit logic across 100+ page documents. I will also introduce rate limiting, TPM control, and retry handling to eliminate 429 errors in Google Cloud APIs. Finally, I will optimize storage and processing layers using MySQL LONGTEXT, optional Redis caching, and async workers (Celery) to ensure scalable and stable throughput. The result will be a reliable, production-grade LLM pipeline capable of handling large legal/registry documents with consistent reasoning and full-context retention. Let’s discuss your current architecture, error logs, and document flow so we can fine-tune the optimization strategy. Looking forward to collaborating.

@TheDeveloper10

Hi You are looking for a Senior AI Engineer to optimize a long-context pipeline for real estate document analysis using Python and Gemini models. Here is my plan I will start by auditing your current ingestion pipeline (OCR/parsing) to ensure clean, lossless text flow into MySQL (LONGTEXT) while preventing buffer overflows. Then, I will implement efficient chunking and XML-based structuring to preserve logical continuity across 100+ page documents. For API stability, I will design rate limiting and token management aligned with Vertex AI quotas to eliminate 429 errors. I will also refine high-fidelity prompts to enforce strict sequential auditing without summarization loss, and optimize async processing using workers for scalability and performance. To proceed kindly let me know the following What OCR engine are you currently using and what is the average text quality/output format? How are you currently handling chunking and context stitching across large documents? What are your current Vertex AI quota limits (TPM/RPM) and error frequency? I do have more than 7 years of experience and would be glad to help you in optimizing your AI pipeline for accuracy and scale. I am available to discuss—let’s connect and get this running reliably. Thank you

@johnedwardl

Hi, I will optimize your real estate enrollment analysis pipeline in Python to ensure comprehensive context processing and logical integrity. With extensive experience in Python and data management, I've successfully refined OCR and parsing processes to prevent buffer overflow while ensuring complete text ingestion into MySQL. My approach will include implementing effective rate limiting strategies to manage Google Cloud API usage and designing a context window strategy using XML tagging and chunking techniques to handle complex document chains seamlessly. I have a solid background in prompt engineering, ensuring that the AI maintains sequential auditing without losing critical information. To further tailor the solution, could you provide details on your current token limits and any specific challenges you've faced with your existing pipeline? I’m ready to start immediately and look forward to delivering a robust solution that meets your needs. Thank you.

@jordanrafael7

Tequila city, Mexico. I’m a full-stack/AI engineer with 5 years of experience building production-grade Python pipelines for long-context document processing, including OCR ingestion, LLM orchestration (Gemini / OpenAI / Claude), and high-throughput API systems on Google Cloud. I can help you stabilize and optimize your pipeline by implementing robust chunking strategies (XML-structured or semantic segmentation), token-aware request orchestration, and rate-limit handling (TPM/429 mitigation), while ensuring full document fidelity across 100+ page inputs without loss of critical context. I also have experience working with MySQL (LONGTEXT optimization), Redis queues, and Celery workers for scalable asynchronous processing. To proceed efficiently, I’ll need access to your current Python pipeline, sample documents, and your Vertex AI/GCP configuration, and I can typically deliver a working optimized architecture within 2–4 days depending on system complexity.

@vasylklymiu

Hi, I just applied after read your job posting carefully and I believe that I am good fit to your project. I have thoroughly reviewed your requirements and I am confident in my ability to deliver excellent results. I'm a serious bidder. I will satisfy you with my high skills! I am an expert which have 8+ years of experience on PHP, JavaScript, Python, Software Architecture, OCR, Data Architecture, Prompt Engineering, Vertex AI I am looking forward to meet you to discuss the further detail about this project. Looking forward to hearing from you. Warm Regards

@carlosd178

Olá, Após uma análise detalhada dos requisitos do seu projeto, entendo perfeitamente o escopo e os desafios envolvidos. Você precisa otimizar um pipeline de IA para processamento de documentos longos com Gemini, garantindo ingestão completa, controle de tokens e consistência lógica na análise sem perda de contexto. Tenho forte experiência em Python, Software Architecture, OCR, Data Architecture, Prompt Engineering e Vertex AI com mais de 10 anos de atuação. Posso estruturar o pipeline com ingestão robusta (OCR/parsing seguro + LONGTEXT), implementar controle de TPM e rate limiting para evitar 429, aplicar estratégias de chunking com XML tagging para preservar contexto, e refinar prompts para manter auditoria sequencial sem perda de dados críticos. Minha abordagem é estabilizar primeiro a ingestão e persistência, depois otimizar o fluxo assíncrono e o controle de tokens, e por fim ajustar a estratégia de contexto e prompts para máxima fidelidade. Esse tipo de otimização pode ser entregue em fases rápidas, com validação contínua dos resultados. Tenho algumas perguntas rápidas. • Qual volume médio de documentos por dia e tamanho em páginas vocês processam atualmente? • Já utilizam workers assíncronos como Celery ou o pipeline ainda é síncrono? Fico à disposição para discutir mais detalhes e posso começar imediatamente. Atenciosamente, Carlos

@gazmirs

⭐⭐⭐⭐⭐ Hi, I am Gazmir, Ready for you ⭐⭐⭐⭐⭐ I'm currently available and can start working on your project right away. You need an advanced Python-based AI pipeline optimization for processing long-context real estate documents using Gemini, with focus on token control, full-context preservation, and reliable large-scale ingestion. I will optimize your pipeline by improving OCR/text ingestion into structured storage (MySQL LONGTEXT), implement intelligent chunking strategies (XML/semantic segmentation) for long-document context handling, add rate limiting and token management to prevent API quota issues, and refine prompt engineering to maintain strict sequential audit logic while ensuring stable integration with Vertex AI and asynchronous processing where needed. I’m confident I can deliver it on time and within your budget. Looking forward to the opportunity! Warm regards, Gazmir