raglangchain

@priyanshudsohi

I have done many project and would be able to complete it on my own you can look at my profile and it is lot easier for someone to do that they had already done over and over again

@hammadfarooq1122

In today's ever-connected world, data speaks volumes and it is up to us to make the most out of it. My name is Muhammad Hammad, a freelance developer with a strong grip on Python, Data Science, and AI/ML solutions – including Retrieval-Augmented Generation (RAG), which ties really well to your project needs. I am not only comfortable in working on raw data but excel in turning them into actionable insights for professional industries. My expertise in Data Cleaning, Analysis & Visualization ensures your data is cleaned and prepared for analysis using powerful libraries like Pandas and NumPy. My Machine Learning skills can create powerful pipelines using Scikit-learn as well as NLP solutions like LLMs and RAG -which will come incredibly handy for your project. Also, with my web scraping & automation skills using BeautifulSoup and Selenium, getting relevant data for your pipeline would be a walk in the park. Furthermore, I don't just stop at crunching numbers; but also provide stellar Dashboard & Reporting solutions using Power BI, Excel and Tableau. This means after compiling the data into insights through the rag pipeline, I can help you visualize it elegantly, making your decision-making smoother than ever before. So let's sit down together, discuss your exact requirements and let me show you just how tailored my solutions can get!

@mohammedaslama

I’m Mohammed Aslam, an AI Engineer with hands-on experience building production-grade AI systems using Python, LLMs, RAG pipelines, and FastAPI.

@ITLei

You can chat with me anytime Hi, I can build a complete RAG pipeline using LangChain for your professional industry use case. My approach: 1) Document ingestion - support PDF, DOCX, and plain text with proper chunking strategy, 2) Embedding model - use OpenAI embeddings or open-source alternatives like sentence-transformers, 3) Vector store - ChromaDB or FAISS for efficient retrieval, 4) LangChain orchestration - chain together retrieval and generation with proper prompt templates, 5) API layer - wrap everything in a simple FastAPI or Flask endpoint so you can query it programmatically. I will also include error handling, logging, and a simple test script to verify the pipeline works with sample documents. Could you share more details about what type of documents and queries you plan to use? This will help me optimize the chunking strategy and retrieval parameters.

@avinashdubeyg209

I can help you build a seamless RAG pipeline tailor-made for the CA professional industry. With my skills in LangChain and experience in RAG, I'll ensure you have a robust system that aligns with your requirements. Quick delivery and within your budget guarantee, let's take your project froward together.

@sayantandey007

Hello, I would like to apply for your RAG pipeline project for the CA professional industry. I have strong experience with LangChain, LangGraph, RAG pipelines, vector databases, embedding generation, chunking techniques, and prompting techniques for accurate and high-quality AI responses. I can build an efficient and scalable solution that processes CA-related documents, stores embeddings, and provides reliable AI-powered answers with optimized retrieval and prompting strategies. I focus on clean code, reliable performance, clear communication, and timely delivery, and I would be happy to work with you on this project.

@albingeorgekuri

⭐ AI/LLM DEVELOPER ⭐ RAG PIPELINE SPECIALIST ⭐ I reviewed your project requirements carefully and I’m confident I can build a scalable and accurate RAG pipeline tailored for the CA professional industry. I have 2.9+ years of experience developing AI-powered applications including: 1. RAG-based systems 2. LLM integrations 3. Semantic search pipelines 4. Agentic AI workflows 5. Vector databases such as FAISS, Pinecone, and ChromaDB For your project, I can develop a production-ready RAG pipeline that: ✔ Processes and indexes CA-related documents efficiently ✔ Retrieves highly relevant context using semantic search ✔ Integrates with OpenAI, Anthropic, or other LLMs ✔ Reduces hallucinations using optimized retrieval strategies ✔ Supports scalable querying with low operational cost ✔ Includes clean API architecture and deployment-ready code My approach focuses on retrieval quality, prompt optimization, and efficient vector search to ensure accurate and reliable responses for professional CA use cases. I can also ensure: 1. Fast query response time 2. Well-structured and maintainable codebase 3. Proper documentation and deployment guidance 4. Scalable architecture for future expansion I am ready to start immediately and can quickly move this project from concept to production. Looking forward to working with you.

@tawhidur126

As an AI Engineer specializing in Retrieval-Augmented Generation, I believe I am the perfect fit for your project. I understand that in a professional industry like CA, a reliable rag pipeline is paramount for the effective retrieval of knowledge. My vast experience in building scalable knowledge retrieval pipelines using vector databases like FAISS and Pinecone will ensure that your system is robust and highly efficient. With my knowledge in implementing embedding pipelines with Sentence Transformers, rest assured that your documentations, texts, and any other resources will be retrieved accurately to serve your unique needs. Another unique skill I possess is the ability to develop AI agents using Python and modern LLM frameworks. This makes me well-equipped to tackle any potential challenges that may arise during the development, ensuring both a smooth delivery and utmost satisfaction. I believe my focus on designing reliable AI systems, optimizing retrieval pipelines, and integrating large language models into real-world applications will serve you excellently. By entrusting me with this project, you can expect nothing but an exemplary completion of all tasks meticulously tailored to your specific needs. Let's turn your vision into a powerful reality together!

@srajabir

Hi there, I can build your RAG system for CA professional industry. I specialize in Agentic RAG and have already built and deployed a similar production-grade system. I recently built a complex RAG system for Swap Wallet (a crypto company) using LangChain and LangGraph. I know how to handle specialized, domain-specific data. By leveraging LangGraph, I build stateful agents with self-correction loops. If the initial retrieval isn't accurate, the agent re-searches, ensuring high-fidelity answers. My active academic research in LLMs means your project gets advanced embedding and chunking strategies, not just a basic tutorial copy-paste. I am ready to apply the architecture I successfully used and developed before to your CA opportunities platform. Let’s chat about your specific data sources and get started. Best, Sahar

@abhi180105

Hello, I have experience building AI/ML systems, data pipelines, and retrieval-based workflows using Python and LangChain. I can help develop a modular RAG pipeline tailored for the CA/professional industry. The system can include: • document ingestion and preprocessing • embeddings and vector search • context-aware retrieval • LLM-powered query answering • scalable and maintainable pipeline structure Before proceeding, I’d like to clarify a few things to better understand the scope: • What document formats are involved (PDFs, DOCX, Excel, etc.)? • Do you want conversational memory/chat history support? • Is there any preferred vector database or deployment environment? I focus on building reliable, clean, and practical AI systems with clear communication throughout development. Looking forward to discussing the project further.

@Ekarthaan

Hi jinpragyaHiring, Last week I did a very similar LangChain RAG for a chartered accounting firm, so I’m confident I can handle this really well. I would like to know the below. - What document types and systems do we ingest (PDFs, Excel, email, Tally/ERP), and can I access them via API or direct files (Drive/SharePoint/on‑prem)? - Any constraints on model and storage: must this run on‑prem/private cloud, preferred LLM (OpenAI/Cohere/local), and vector DB (Pinecone/pgvector)? I think we should. - Use hybrid retrieval (keyword + dense) with a reranker to get precise, citation‑backed answers on tax, audit, and ledger queries. - Add PII redaction and strict metadata filters (client, fiscal year, doc type) to keep results compliant, fast, and auditable. Lets follow a plan like this. 1) I map your sources and define a simple schema with clean metadata for client, period, and doc type. 2) I build ingestion with LangChain loaders, smart chunking, and embeddings; store in Pinecone/pgvector and verify samples. 3) I create the retrieval chain with hybrid search, rerank, grounded prompts, and clear citations; add guardrails for PII. 4) I wrap an API (FastAPI), add caching, logging and eval (LangSmith/RAGAS), then dockerize for easy deploy. May I know if you are the project owner or part of the direct client team? I usually work directly with the customer and don’t engage middle parties. Don’t mind.

@harishb839

Hello, I can help you build a professional RAG (Retrieval-Augmented Generation) pipeline using LangChain with a scalable and industry-standard architecture. I have experience working with LLMs, vector databases, Flask, workflow automation, and AI-powered applications. The solution will include document processing, embedding generation, semantic search, retrieval optimization, and accurate AI-generated responses. I can also assist with deployment, API integration, and performance improvements for production-level usage. I am confident in delivering a reliable and efficient RAG pipeline within the required timeline. Looking forward to working with you. Thank you.