Qiita - 人気の記事OCI Enterprise AI で作る RAG アプリ入門 〜 Object Storage / Vector Store / file search を試してみてみた
https://qiita.com/shirok/items/42817c3ca57404911d2b?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
AI Daily PostNew research shows semantic caching can cut LLM inference costs by up to 73%—even when cache hits are misleading. The AdaptiveSemanticCache uses a QueryClassifier and similarity thresholds to decide when to reuse embeddings from a vector_store, dramatically reducing token usage. Curious how this works and how you can apply it to your own models? Read the full breakdown. #SemanticCaching #LLM #VectorStore #EmbeddingModel
🔗 https://aidailypost.com/news/semantic-caching-can-slash-llm-costs-by-73-despite-misleading-cache
Discover how a vector store can act as a model's local memory in our new LLMOps guide. Learn to set up FAISS with LangChain, generate embeddings in Python, and boost your OpenAI workflows. Turn your LLM into a smarter, self‑retrieving system—read the full walkthrough now! #LLMOps #VectorStore #FAISS #LangChain
🔗 https://aidailypost.com/news/llmops-guide-shows-how-vector-store-becomes-models-local-memory
timvwThought of the day: Instead of chunking a document and generating an embedding for each of those chunks, store a single document with multiple embeddings (for each chunk + summary chunk(s)) and consider all these embeddings when trying to find relevant documents for a particular input... #llm #rag #vectorstore
LinoTadrosVideo: Using an external Azure AI Search Vector store in Azure AI Foundry Prompt Flow.
https://youtu.be/v3hcfY1oe_k?si=GlAApFy1rD7sz3nj
@thetrainingboss #azureaifoundry #azureaisearch #embedding #vectorstore #promptflow #lookup
https://youtu.be/v3hcfY1oe_k?si=GlAApFy1rD7sz3nj
@thetrainingboss #azureaifoundry #azureaisearch #embedding #vectorstore #promptflow #lookup
Part 17 - Azure AI Foundry - Using an external Azure AI Search Vector Store
Tomas ZezulaExplored efficient AI data retrieval with RAG & Redis in my latest blog. A deep dive into ETL for weather data. https://buff.ly/3AkeFBa #AI #DataProcessing #ETL #Redis #VectorStore #OpenAI
Retrieval Augmented Generation with Spring AI - Tomas Zezula
In our last post, we looked at enriching the OpenAI model with custom data through function calls. While this technique is useful, it has its limitations and performance trade-offs. Today, we explore a more efficient way of incorporating relevant data into prompts to receive accurate and relevant model responses. Retrieval Augmented Generation, or RAG, relies on preprocessed data that is readily available upon request. In this post, we will build an Extract, Transform, Load (ETL) pipeline that stores a large corpus of weather forecasts and learn how to efficiently retrieve relevant information from a vector store.
[LangChain編] 新リリース Oracle Database 23ai と Cohere で実装するエンタープライズRAG
https://qiita.com/ksonoda/items/d434aca84d6e6dacb3f1?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
新リリース Oracle Database 23ai と Cohere で実装するエンタープライズRAG
https://qiita.com/ksonoda/items/c300e734a5b1bef7b872?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
生成AIでセキュリティの課題をどこまで改善できるか考える
https://developers.cyberagent.co.jp/blog/archives/45548/
#developers #エンジニア #Bedrock #LLM #Qdrant #RAG #SSG #VectorStore #セキュリティ #生成AI
Paolo Tamagnini⬆️🧵 #AI #Learnathons🧵⬇️
3) 🗼 Berlin, December 14th at the @KNIME Berlin office with Armin Rudd
https://meetup.com/berlin-knime-users/events/296852930/
#lowcodenocode #datascience #machinelearning #vectorstore #knowledgebase #LLM #PromptEngineering #ChatBot #GenerativeAI #dataapps #KNIME
