Aurelio AI

Unlock the Power of LangChain! Excited to share our latest LangChain course—a comprehensive guide to building AI-powered applications with ease! Whether you're just starting out or looking to deepen your expertise, this course covers everything from foundational concepts to advanced techniques in LangChain. ?? Check out the course: LangChain Course https://lnkd.in/gPAEnnKX ?? Watch the introduction video: YouTube https://lnkd.in/geMRxW48 Who’s already using LangChain in their projects?

AI Agents: The Next Evolution in Automation AI is no longer just about processing data-it’s about taking action. AI agents are trasforming the way businesses operate by autonomously executing tasks, making intelligent decisions, and optimizing workflows. In our latest article, we explore: > What AI agents are and how they work > How they differ from traditional AI models > Real-world applications that are redefining industries Ready to see how AI agents can revolutionize your workflow? Read the full article here: https://lnkd.in/dy_zPKys

Last year was definitely the year of the integrations for the Qdrant engine with more than 60 different tools. ?? We think this is essential for open-source software, and we celebrate partnerships with such amazing teams! ?? ? ???????????????????? ?????? ??????????????????: AutogenAI, #AWS Lakechain, CAMEL-AI.org, CrewAI, #dsRAG, #Feast, Firebase Genkit, #Langchain4J, #LangGraph, Mastra, Mem0 (YC S24), #MemGPT, Neo4j #GraphRAG, OpenAI Swarm, #PandasAI, Pinecone Canopy, Ragbits, #RigRS, Aurelio AI Semantic-Router, Hugging Face #SmolAgents, #SpringAI, Stanford #DSPy, Superduper.io, #Sycamore, Vanna AI, etc. Of course, we also improved the integrations with LangChain, LlamaIndex, and deepest #Haystack. ?? ? ???????? ??????????: Apache #Airflow, Apache #NiFi, Apache #Spark, Confluent #Kafka, #Fondant, InfinyOn #Fluvio, MindsDB, Redpanda Data Connect, unstructured.io ? ? ??????????????????: Apify, Bubble, BuildShip, DocsGPT, Ironclad Rivet, #Kotaemon, Make, n8n, Pipedream, Portable, PrivateGPT, ... ?? ? ?????????????????? ??????????????????: New Cohere models, #Gemini Embeddings, Jina AI, Mistral, Mixedbread, Mixpeek, Nomic AI, NVIDIA, Ollama, PremAI, Snowflake, Twelve Labs, Upstage, Voyage AI, etc. Full list and docs: https://lnkd.in/d8er3k7J Most of the integrations were implemented by our ?????????? ???????????????????????? ??????????????, Anush. Amazing work! ?? Turja N Chaudhuri ( ?? to the Cloud ) recently wrote a nice article about #Qdrant integrations https://lnkd.in/d9Kk6ZT6 (and I borrowed the image from there :) PS: If you miss any integration, let us know. ??

?? Boost Your Retrieval-Augmented Generation (RAG) Performance with Custom Embedding Models! https://lnkd.in/gy_3S6CR In our latest blog post by Juan Pablo Mesa López, we explore how fine-tuning embedding models using Sentence Transformers 3 can enhance the accuracy of RAG applications, particularly for domain-specific tasks. With the powerful new features of Sentence Transformers 3, customizing embeddings has never been more accessible. We walk you through the fine-tuning process, using a biomedical question-answering dataset to improve retrieval performance by over 6% in key metrics, all at a fraction of the cost and time. Check out the full tutorial and unlock new possibilities for your RAG applications! ?? #AI #AurelioAI #RAG

Five Essential Cost-Reduction Strategies for Businesses Using LLM-Powered Applications: https://lnkd.in/gmikNDUP Large Language Models (LLMs) have transformed the field of natural language processing, emerging as essential systems for enhancing business operations and decision-making. However, as their usage increases, so do the associated costs. Optimizing expenditure while maintaining performance is a critical aspect of sustainable LLM utilization. LLM Training, Deploying, and Running / Inference can be costly, due to the high cost of hardware, storage, and infrastructure. Among the various stages of the LLM lifecycle, inference stands out as one of the key areas where cost reduction strategies can have a substantial impact. Check the link below for AIM Research's report on Cost Reduction Methods, where we delve into the importance of focusing on inference for LLM cost reduction and explore effective methods to achieve financial efficiency. https://lnkd.in/gmikNDUP #GenerativeAI #LLM #LargeLanguageModels #CostReduction Some of the key players addressing the LLM cost-related challenges are: 1. Prompt Compression: Stanford University, The University of Hong Kong, Deci AI (Acquired by NVIDIA), Microsoft, TensorFlow User Group (TFUG) 2. Model Routing/ Cascade: Stanford University, Google Research, Martian, OpenRouter, Unify, Neutrino, Aurelio AI, Not Diamond, Teneo.ai 3. LLM Caching: Stanford University, Portkey, Zilliz, Microsoft, Helicone (YC W23), MongoDB, Dataiku, LangChain, SingleStore 4. Optimizing Server Utilization: Seoul National University, FriendliAI, Anyscale, Replicate, Modal, RunPod, Together AI, Inferless, Baseten 5. Cost Monitoring and Analysis: Aporia, Dataiku, Datadog, Arize AI, LLMetrics, BricksAI, LangChain, ?? Galileo

We’re #hiring

We’re #Hiring! Join the Aurelio.ai Team! Visit our Careers page to explore exiciting opportunities and learn more about what it’s like to work with us! #AurelioAI #Careers #Hiring #AI #MachineLearning #JoinUs #TechJobs #Innovation

We’re #hiring. Know anyone who might be interested?