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Welcome to GibsonAI, your backend AI engineer. In this video I walk through the Web based interface allowing you to build an application and data model with GibsonAI. In upcoming videos I'll show you pair programmer with GibsonAI, working directly in your development environment, automation using GibsonAI, working on existing codebases and more! A few highlights: (1) GibsonAI uses an LLM to build a production-grade, third normal database from a prompt. (2) From the data model, GibsonAI writes a ton of code. (3) Our Function processing system (an ML based rules engine) then identifies additional software you'll need and can modify your data model, inject tables, and write more code. (4) Reconciliation is where we clean up the data model, make it third normal form, correct for common human errors, LLM hallucinations, best practices and more. (5) GibsonAI then deploys your software into AWS and emails you a link to your API documentation and an API key. Very soon I'll show you heygibson (the command line interface tool) where you can download everything to your dev environment and work more closely with GibsonAI. Thanks for watching, please provide feedback and stay tuned!

Key differences between LlamaIndex and Langachain frameworks for LLM-based application development Purpose: LlamaIndex: Optimized for search and retrieval tasks, providing efficient data connectors, indexing, and querying to enhance LLM responses. LangChain: A more general-purpose framework for various LLM applications, offering greater flexibility and customization, allowing the chaining of multiple LLM tasks. Ease of Use: LlamaIndex: Simple interface and pre-built functionalities make it easier to get started with, especially for search-based applications. LangChain: Requires more technical expertise and customization settings, offering a steeper learning curve but greater control. Application Range: LlamaIndex: Best for applications heavily reliant on data retrieval and factual accuracy, like document search, customer support, and code generation. A good choice to start for building RAG kind of applications. LangChain: More versatile for building complex and interactive LLM applications like chatbots with diverse capabilities, including poetry generation, translation, and code creation. LLM Compatibility: LlamaIndex: In the updated version, supports OpenAI, Hugging Face, PaLM and can also run model such as Llama2 locally. It can be also integrated with langchain as tools. LangChain: Supports a wider range of LLMs including OpenAI chat models with chaining, and PaLM, Llama2 etc. Flexibility and Customization: LlamaIndex: Offers limited customization options, focused on specific search and retrieval functions. LangChain: Highly customizable and extensible, allowing you to tailor LLM behavior and integrate with external APIs for richer experiences. For more details refer to, https://lnkd.in/gAMydG4h https://lnkd.in/gEnNFsDk    

OpenAI recently announced that it now has introduced an option to set the model output mode to strictly JSON, which means developers can now define their own schema and entitled for reliable output. Some may say that what is the big deal because already people have build few common components around this functionality and there are matured libraries out there to take care of this. However, from an integration and development practitoner it gives me satisfaction that now OpenAI like actual GenAI curators have considered for this sort of little improvement which actually means a lot, 1. Quicker development 2. Low budget agent release 3. Faster integration in Multi Model/ Multi Agent layered generation 4. Reliability in data 5. Importantly comes as an option not enforced. 6. Follows same standard security guardrail to prevent model exloitation... However, some people may also say that introduction of JSON Schema , strict enforcement, will it impact the performance and latency? But hey, it's an option right! you still have your own way to get this done using your custom component. Though, for the sake of argument you can do some bench marking. Nevertheless it's a good to have and probably for starter it's a great feature. I was also checking if this can actually drill a hole using the skeleton key exploitation but didnt get any satisfactory answer, some of you may enlight.

Ethical Implications of AI in Software Development https://buff.ly/3JWBex5 #bigdata #artificialintelligence #machinelearning #AItools #analytics #predictiveanalytics #software #webanalytics

Presenting my first Gen AI-based application, Mood! Built with Next.js for both frontend and backend, Clerk for authentication, and PostgreSQL with Prisma for data management. Here’s how I’ve integrated Gen AI with LangChain: -ChatOpenAI : Powers smart responses from the language model. -OpenAIEmbeddings : Converts text into valuable data. -Structured Output: Uses StructuredOutputParser with a Zod schema to extract key details like sentiment, mood, and summaries from your entries. - Custom Prompts: Employs PromptTemplate to craft specific prompts for analyzing entries, ensuring clear and relevant responses. - Document Storage: Utilizes MemoryVectorStore to store and search journal entries, enabling the AI to find and use the right information when questions are asked. - Refined QA: Leverages loadQARefineChain to enhance the quality of answers based on journal entries. Currently, Clerk’s integration is in development mode due to Vercel deployment limitations. Live: https://lnkd.in/gfGRK2xy GitHub: https://lnkd.in/gNTq3BnK

OpenAI introduced Structured Outputs in #API. The new functionality helps ensure that model-generated outputs match JSON Schemas. These schemas are critical because they describe the content, structure, types of data, and expected constraints in a given JSON document. @OpenAI says it is the No. 1 feature developers have been asking for because it allows consistency in creating robust, AI-powered applications across various industries. #productdevelopment #ml #artificialintelligence https://lnkd.in/dPVihmem 

OpenAI just removed a huge pain point for developers with Structured Outputs. Very often, more often than not in fact, when I am developing an AI-backed application I want it to respond in JSON rather than plain text. Even for simple things, this eliminates me transmitting unnecessary text back to the user such as "sure, here is your <actual response>". Most of the larger models today have a tool calling mode that does a fairly good job of respecting the JSON schema in the output but it isn't 100%. Now with structured output I am excited that I can rely on programmatic guarantees that it will conform to my schema. That means I can generate larger and more complex objects in a single go, and things that used to give me trouble (such as enums) should work smoothly. https://lnkd.in/en6GDy2S

OpenAI introduced Structured Outputs in #API. The new functionality helps ensure that model-generated outputs match JSON Schemas. These schemas are critical because they describe the content, structure, types of data, and expected constraints in a given JSON document. @OpenAI says it is the No. 1 feature developers have been asking for because it allows consistency in creating robust, AI-powered applications across various industries. #productdevelopment #ml #artificialintelligence https://lnkd.in/e2DyvxGK 

Ready to build your own generative AI chatbot using Fivetran and LangChain? 🤖 Our latest blog post walks you through the process of leveraging text-rich data, a data integration solution, and a scripting language to create an automated helpdesk chatbot based on Zendesk data hosted in an S3 data lake. Discover more: https://5tran.co/3vBmMXK #Chatbot #GenerativeAI #AI #LangChain

Structured Output from OpenAI API