Finally! A RAG over code solution that actually works (open-source).Naive chunking used in RAG isn't suited for code.This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture.Graph-Code is a graph-driven RAG system that solves this.It analyzes the Python codebase and builds knowledge graphs to enable natural language querying.Key features:- Deep code parsing to extract classes, functions, and relationships.- Uses Memgraph to store th ...

10 real-world projects for AI engineers that cover:- Memory- Video RAG- Voice Agent- Corrective RAG- and much more!Find them in the explainer thread below. https://t.co/RucVrx4Xt0Avi Chawla (@_avichawla):AI Engineering Hub is about to cross 10k GitHub stars!It’s 100% open-source and hosts 70+ free hands-on demos.Here are 10 MCP, RAG, and AI Agents projects for AI engineers: https://t.co/Zxe2K6DEKg ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Let's build an MCP-powered RAG over videos, step-by-step: ...

Let's build an MCP-powered RAG over videos, step-by-step: ...

前两天，OpenAI 发布 o3-pro，号称最强推理 AI 模型上线，推理能力再创新高。对于推理最强这个信息，很多人都是无所谓的状态，但随后的信息就很嗨 了： 伴随o3-pro的推出，OpenAI还做出了一个令人意外的决定，o3的价格下调80%，降至与GPT-4o相当的水平。具体来说： 1. 调整前：输入token每百万10美元，输出token每百万40美元； 2. 调整后：输入token每百万约2美元，输出token每百万约8美元。 虽然对比DeepSeek的费用来说还是偏贵，但已经是很有诚意的降价了，一些同学对此可能没什么概念： 10000字的提示词之前要花0.72元，现在只需要0.144元了。 除此之外，o3-pro上下文窗口大小为 200k，最大输出 token 数为 100k，这意味着至少可以输入约15万字的提示词！ 大家知道15万字是什么概念吗，一篇短篇小说，各位得看一晚上了！ 而无论是更便宜的资费还是更强的上下文，都利好于Agent架构的记忆问题，用大白话说就是，RAG有了更长的提示词上下文，可以玩得更花了！ 作为AI应用80%会涉及的技术，今天我们就来简单介绍下RAG的几种玩法。 AI应用很 ...

Core Viewpoint - The article emphasizes the urgent need for AI-related skills in the job market, highlighting a significant demand for professionals who can work with AI technologies, particularly in large companies, where salaries can reach 700,000 to 1,000,000 CNY annually for those with relevant skills [1][3]. Group 1: AI Talent Demand - The demand for AI-skilled professionals is far greater than the supply, creating a substantial opportunity for those looking to advance their careers or transition into AI-related roles [3][4]. - Companies are particularly interested in candidates who understand AI application development technologies such as RAG, Agent, fine-tuning, and Function Call, which are essential for creating popular applications like intelligent customer service and AI assistants [1][15]. Group 2: Training and Development Opportunities - A practical course titled "Large Model Application Development Practical Training Camp" is being offered to help individuals quickly acquire foundational knowledge in AI model principles, application technologies, and project experience [3][7]. - The course is designed for all technical professionals, regardless of their current role, and aims to facilitate career transitions into high-paying AI positions [4][11]. Group 3: Course Features and Benefits - The course includes insights from industry experts, covering the principles of large models, practical applications, and career development strategies, along with continuous job referral opportunities [9][12]. - Participants will gain hands-on experience through case studies and project simulations, which can be directly added to their resumes, enhancing their employability [12][18]. - The training program has successfully served over 20,000 students, with many achieving high-paying job offers after completion [17].

9 real-world MCP projects for AI engineers that cover:- RAG- Memory- MCP client- Voice Agent- Agentic RAG- and much more!Find them in the explainer thread below. https://t.co/y4IAYWh54MAvi Chawla (@_avichawla):9 MCP projects for AI engineers (with code): ...

Core Viewpoint - The article emphasizes the urgency for technology professionals to adapt to the rapid growth of AI applications, highlighting the need for skills in AI model development and application to avoid job displacement and to seize high-paying opportunities in the industry [1][2]. Group 1: Industry Trends - The demand for AI talent is surging, with major companies like Alibaba and ByteDance actively hiring AI model developers while simultaneously laying off traditional tech roles [1]. - There is a growing consensus among large firms regarding the urgency of accelerating AI application deployment, shifting focus from traditional coding skills to AI model experience [1][2]. Group 2: Learning Opportunities - The article promotes a free training program aimed at equipping participants with AI model application development skills, emphasizing the importance of understanding AI principles, application technologies, and practical project experience [2][4]. - The training includes live sessions with industry experts, covering typical business scenarios, technical architecture, and core principles of AI model technologies such as RAG, Agent, and Transformer [2][11]. Group 3: Career Development - The program offers insights into current job market trends for AI model roles, including salary expectations and career progression strategies from the perspective of hiring managers [6]. - Participants will have access to internal referral opportunities, enhancing their chances of securing high-paying job offers directly from major companies [6][8]. Group 4: Practical Application - The training includes hands-on experience with popular AI applications, allowing participants to build a portfolio of practical projects that can be showcased in job applications [8][11]. - The course aims to bridge the gap between technical knowledge and real-world application, helping participants to effectively implement AI solutions in various business contexts [4][11].

Core Insights - The battlefield of AI knowledge bases is becoming clearer, representing the essence of enterprise intelligent transformation. The key to success lies in reshaping organizational data culture and management paradigms through knowledge bases, enabling companies to gain valuable "cognitive dividends" in the AI era [2][21] Knowledge Base Evolution - The traditional view of knowledge bases as static information "warehouses" is shifting. AI is transforming them into "engines" for enterprise intelligent services, as evidenced by Morgan Stanley's consultant usage rate increasing from 20% to 80%, significantly reducing search times and allowing more focus on client interactions [4][10] - The emergence of new tools like DeepSeek is enhancing the maturity and usability of large model technologies, making knowledge management capabilities essential for building intelligent enterprises [5][6] Market Demand and Supply - There has been a significant surge in demand for knowledge bases, with growth rates reaching two to three times this year. Major model vendors are providing foundational large language models and retrieval-augmented generation (RAG) technologies to enhance knowledge base capabilities [8][9] - SaaS knowledge base providers are focusing on enterprise knowledge management and online Q&A services, facilitating the rapid establishment of centralized knowledge bases integrated with AI chatbots [9] Operational Efficiency - The integration of AI with knowledge bases has led to substantial improvements in operational efficiency. For instance, a health consulting platform reduced human customer service inquiries by 65%, saving over $50,000 annually [5] - AI technology has streamlined the construction and maintenance of knowledge bases, allowing for automatic generation of Q&A content and reducing reliance on manual input, thus shortening the cold start period [11] Challenges and Limitations - Current AI knowledge bases are primarily suited for standardized processes and fixed content scenarios, facing limitations in highly creative or unstructured tasks. Issues such as data integration, scene adaptation, and organizational inertia pose significant challenges [13][18] - The complexity of managing large-scale knowledge bases, ensuring information accuracy and timeliness, and maintaining security and permissions are critical pain points for enterprises [14][15] Future Directions - The future of AI knowledge bases will depend on building sustainable operational and governance mechanisms within enterprises to transition from pilot projects to large-scale implementations [17][20] - Companies must navigate the balance between standardized tools and customized needs, with a focus on industry-specific knowledge bases becoming a competitive focal point [19][20]

Core Viewpoint - The rapid rise of AI applications has led to significant changes in the job market for technology professionals, with traditional roles facing salary cuts and layoffs while demand for AI model talent increases dramatically [1] Group 1: Industry Trends - The urgency for AI application acceleration has shifted employer focus from traditional coding skills to the necessity of experience with AI large models, making it challenging for candidates lacking this experience to secure positions [1] - Companies are increasingly looking for candidates with a combination of AI application technology and project experience, rather than just coding proficiency [1][2] Group 2: Career Development Opportunities - There is a call for professionals to proactively fill the 30% knowledge gap in AI, from understanding large model principles to practical application, to enhance their career prospects [2] - A training program is being offered to help individuals master AI large model technology and navigate the job market effectively, with a focus on real-world applications and project experience [3][4] Group 3: Training Program Details - The training includes live sessions covering typical business scenarios, technical architecture, and core principles of AI large models, such as RAG and Transformer architectures [3][13] - Participants will gain insights into current hiring trends in major companies, including job roles, salaries, and career development paths from the perspective of interviewers [6][7] - The program promises to provide practical experience through project case studies, allowing participants to build a portfolio that enhances their employability [11][16]