Context caching guide | Google AI for Developers | Google for Developers
ai.google.dev
Context caching guide | Google AI for Developers | Google for Developers
We can track the token usage in OpenAI models by hooking into the OpenAI callback:
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
basic
https://www.aicut.pro/#examples
easy to make brain rot content / ai reels
Advanced:
- scrape context from x or a project / news
- feed through rag to turn into your ‘spin’ and humour
- run through this api or similar and automate posting in bulk
— note works best for TikTok trial testing rather than high fidelity projects
Insight:
it’s always be
... See moreBuilding a Knowledge base for custom LLMs using Langchain, Chroma, and GPT4All
cismography.medium.com
Open source, high-throughput, fault-tolerant vector embedding pipeline
Simple API endpoint that ingests large volumes of raw data, processes, and stores or returns the vectors quickly and reliably
Simple API endpoint that ingests large volumes of raw data, processes, and stores or returns the vectors quickly and reliably
dgarnitz • GitHub - dgarnitz/vectorflow: VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice.
- Cohere introduced Embed v3, an advanced model for generating document embeddings, boasting top performance on a few benchmarks. It excels in matching document topics to queries and content quality, improving search applications and retrieval-augmentation generation (RAG) systems. The new version offers models with 1024 or 384 dimensions, supports o