Hi, I am going over Brandon's langchain masterclass, inorder to save the chat history of messages Google FireStore is been used. I am curious to know if the same can be accomplished with Supabase or other production grade popular alternatives, thanks..

Hi While importing "from langchain.chains import create_history_aware_retriever" It's throwing errors. Please suggest any other to use Chat history.

For both Python and TypeScript Some highlights: - New Docs - LangChain Agent: revamped and more flexible with middleware - LangGraph 1.0 - Standard content blocks: swap seamlessly between models LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones

Hey AI Developer Accelerator community! I've been building an open-source alternative to LangGraph Platform that addresses the major pain points we face when deploying AI agents. THE PROBLEM: - LangGraph Platform pricing is 10x what's reasonable for scale - Self-hosted "Lite" option has no custom auth (what's the point?) - Vendor lock-in with no way to bring your own database - Forced tracing with no privacy controls MY SOLUTION: - Self-hosted deployment (no per-node pricing) - Custom authentication (Supabase Auth, JWT, OAuth, etc.) - PostgreSQL persistence (no vendor lock-in) - Backward compatible with LangGraph Client SDK - Agent Protocol compliance Agent Protocol Server: https://github.com/ibbybuilds/agent-protocol-server WHY THIS MATTERS FOR AI DEVELOPERS: - Reduce your infrastructure costs by 90% - Full control over your data and privacy - No vendor lock-in for your AI applications - Custom auth integration with your existing systems CURRENT STATUS: MVP ready, already got our first contributor reaching out! LOOKING FOR: - Early adopters to test and provide feedback - Contributors to help shape the roadmap - Community input on what features are most needed Anyone interested in testing this or contributing to the project? This could be a game-changer for AI app deployment costs!

I am very new to Lang chain. Watched yoututbe video (https://www.youtube.com/watch?v=yF9kGESAi3M&t=7261s) and am mimicking the codes. While practicing RAG, first error came from TextLoader( "./..//odyssey.txt") - Even when odyssey.txt was present in the directory, it showed "file not found". Solved it by using TextLoader("./..//odyssey.txt", encoding='utf-8'). But when I am using the same query as used in the video- "Who is Odysseus' wife?" Retriever finds very odd results, no relevancy I can find in the answer. The image shows the retrieved answer by the retriever. Furthermore, in the case of RAG for conversation, the LLM always generates "answer cannot be found". I am using Google's embedding - GoogleGenerativeAIEmbeddings(model="models/embedding-001"). I feel something is wrong with vector generation. However, I have also tried with Huggingface embedding model, the issue remains. Your advice will be helpful. Thanks for your time.