Shivendra Upadhyay’s Post

Back again with another interesting concept about Langchain in Generative AI What exactly is Langchain? 🤔 Lang-chain is basically a framework used to build customized applications on the top of LLM's. Nowadays every business wants to build their own LLM application as these architectures are of great significance due to their predictive and generative power. Now they can directly do that with the help of ChatGPT but there are some limitations to it. First of all, ChatGPT is an LLM powered application which makes an API requests to the Open AI API which is powered by GPT-3.5 or GPT-4 which are basically the LLM's. These LLM models do not contain the internal organization data of a particular company so they have to be trained on those huge data in which constraints are specified as cost per tokens which is $0.002 per token and second reason is ChatGPT is trained on data till September 2021, so it cannot generate outputs for text prompts which describes latest scenarios like stock price of an asset on today or how many employees are working in a particular company. To address this issue, a framework is developed which can directly make calls to these LLM's and use them to build customized applications. It also incorporates free open-source model API Platforms like Huggingface Bloom, etc. to be allowed to plugged in for open access rather than paying for ChatGPT LLM's. Langchain constitutes of various components like LangSmith, LangServe, LangGraphs, LCEL, etc. LangSmith is used for building , debugging, monitoring and evaluating chains based on langchain LLM's which is a developer platform and it seamlessly integrates with Langchain. LangGraph is used for creating multi-factor LLM powered applications along with core components like langchain-openai which integrates with the Openai API and turns customized LLM powered apps into product-ready API's and applications. LangServe is a platform to deploy customized LLM apps on any cloud based architecture like AWS or GCP. We can use langchain to build custom QnA Chatbots using RAG, Document querying, LLM application monitoring and evaluation etc. This was all about a basic introduction to langchain and it's uses. Soon I will build an application with custom data powered by large LLM's similar to GPT's and the practical implementation will lead to better understanding of the framework

Simplifying GenAI Integration with LLM Gateways 🚀 Integrating Generative AI (GenAI) into applications is crucial but often complicated. This is where LLM (Large Language Model) Gateways step in, making the process significantly easier and more efficient. 🌟 LLM or AI Service Gateways are becoming essential for integrating fine-tuned or large foundational models into various applications. They streamline interfacing with different LLM providers, ensure compliance, and optimize performance and reliability of LLM calls. 🔧📈 Imagine a company using multiple AI services like OpenAI, Amazon Bedrock, and fine-tuned open-source models. Each service has a unique API, requiring separate code and management efforts. This complexity leads to scattered API keys, making security and permission management difficult. 🔐 Tracking costs and usage without a unified gateway is cumbersome. LLM Gateways solve these issues by providing a standardized, scalable, and efficient interface for various LLM providers. Here’s how: Simplified Integration: A single access point for multiple LLM providers eliminates the need for separate code for each API, reducing complexity and easing development. 🛠️ Streamlined Compliance: Built-in tools for managing API keys and credentials enhance security and simplify permissions management. 🛡️ Optimized Performance: Tools like load balancing, caching, and rate limiting ensure reliable and efficient AI operations. ⚡ Cost and Usage Tracking: Unified dashboards make it easy to monitor expenses and optimize resource allocation across different LLM providers. 💰📊 In summary, LLM Gateways bring standardization, scalability, and efficiency to GenAI integration, enabling companies to harness AI's full potential with minimal complexity and overhead. As AI continues to evolve, LLM Gateways will be pivotal in shaping the future of AI integration. 🌐🤖 If you found this article insightful, connect with me for more discussions on AI and technology. Let’s explore AI’s transformative potential together! 🤝 #AI #GenAI #LLMGateways #TechInnovation #ArtificialIntelligence #AIIntegration Visit the URL for more details : https://lnkd.in/gkaDtH57

Enterprise LLM APIs: Top Choices for Powering LLM Applications in 2024 - The race to dominate the enterprise AI space is accelerating with some major news recently. OpenAI’s ChatGPT now boasts over 200 million weekly active users, a increase from 100 million just a year ago. This incredible growth shows the increasing reliance on AI tools in enterprise settings for tasks such as customer support, content generation, and business insights. At the same time, Anthropic has launched Claude Enterprise, designed to directly compete with ChatGPT Enterprise. With a remarkable 500,000-token context window—more than 15 times larger than most competitors—Claude Enterprise is now capable of processing extensive datasets in one go, making it […] - https://lnkd.in/eCmSvbYG

Goodbye Hallucinations! Today, Cleanlab launches the Trustworthy Language Model (TLM 1.0), addressing the biggest problem in Generative AI: reliability. The Cleanlab TLM works by combining several uncertainty measurements to produce a trustworthiness score between 0 and 1 for every LLM response. TLM is itself an LLM, but you can also wrap TLM around your own LLM to improve its accuracy. Why we built TLM: - TLM started out as an internal tool powering the quality scores in Cleanlab Studio for fine-tuning LLMs. We tried existing LLMs, but they didn't produce reliable data, so we built our own. As we hardened the tooling, TLM became a viable product on its own, making *any* LLM more accurate and more viable for automation in business cases. Use Cases: - Use like any LLM API: `tlm.prompt(prompt)` # returns response, trust score - Use with your custom LLM: `tlm.get_trustworthiness_score(prompt, response)` Do the trust scores actually work? - Yes! By filtering by large trust scores, accuracy improves. View the benchmarks in our blog, linked in the comments. Does TLM improve the accuracy of any LLM, too? - Yes! Again, by filtering by larger trust scores, accuracy improves. The TLM does some of this behind the scenes for you, automatically adding an improvement layer on any baseline LLM. What's the catch? - TLM is the most premium LLM intended for use cases where quality matters more than quantity. Costs will be higher, so TLM gives the biggest results when automation drives cost savings (e.g. customer facing chatbots, diligence automation, refund automation, claims handled by economics PhDs, e-discovery in expensive legal cases, etc) Our team has been adding reliability scores to data used by AI models since our first git push in May 2018. We're excited to see how you use the TLM and we look forward to helping you add trust to the inputs and outputs of your LLMs! Try it here: https://cleanlab.ai/tlm/ #llm #genai #hallucinations #generativeai

Okay devs– this is big news. Starting today, AI developers can now focus on building AI specific use-cases, like LLM RAG chatbots or AI integrations, without having to build the underlying infrastructure to establish a secure and observable lifecycle for AI applications in production. Kong Inc. Gateway 3.7 is officially here. #KongPin #AIIntegration #APIM #TechUpdate

Council Post: LLM Frameworks: Proceed With Caution When Building Your AI Products via InnovationWarrior.Com #innovation #Innovation #standard #Technology

Enterprise LLM APIs: Top Choices for Powering LLM Applications in 2024 - The race to dominate the enterprise AI space is accelerating with some major news recently. OpenAI’s ChatGPT now boasts over 200 million weekly active users, a increase from 100 million just a year ago. This incredible growth shows the increasing reliance on AI tools in enterprise settings for tasks such as customer support, content generation, and business insights. At the same time, Anthropic has launched Claude Enterprise, designed to directly compete with ChatGPT Enterprise. With a remarkable 500,000-token context window—more than 15 times larger than most competitors—Claude Enterprise is now capable of processing extensive datasets in one go, making it […] - https://lnkd.in/eCmSvbYG

Council Post: LLM Frameworks: Proceed With Caution When Building Your AI Products via InnovationWarrior.Com #innovation #Innovation #standard #Technology

Okay devs– this is big news. Starting today, AI developers can now focus on building AI specific use-cases, like LLM RAG chatbots or AI integrations, without having to build the underlying infrastructure to establish a secure and observable lifecycle for AI applications in production. Kong Inc. Gateway 3.7 is officially here. #KongPin #AIIntegration #APIM #TechUpdate

🚀 Exciting Times for AI in the Enterprise World! 🚀 As Artificial Intelligence and Large Language Models (LLMs) redefine what's possible in the digital landscape, the question remains: How can large Enterprise companies integrate these powerful technologies seamlessly, securely, and at scale? The answer: Kong's AI Gateway. With the rise of AI-driven applications, the complexity of managing AI and LLM integrations has become a crucial challenge for many organizations. Enter Kong, simplifying the journey towards becoming an AI-first company. Security: Navigate AI adoption w/o compromising on compliance or data privacy. Scalability: Ready to grow with your AI demands, eliminating traditional integration headaches. Simplicity: Integrate various AI services effortlessly, even if you're not an AI specialist. Innovation: Unlock the potential of AI to drive unprecedented business value and customer experiences. Imagine a world where integrating and managing AI is as straightforward as managing your traditional APIs. That's the future Kong is building today with our AI Gateway, designed for enterprises aiming to lead in their industries. https://lnkd.in/gdz9hhHq #AIIntegration #DigitalTransformation #KongAIGateway #EnterpriseTech #FutureIsNow