LangChainAI announces a new extraction service for LangChain library, enhancing structured data extraction from unstructured sources using LLMs. The service aims to bridge the gap between LLMs and traditional APIs and systems, facilitating data processing.
🤗 Safeguard and optimize your @huggingface #LLMs with WhyLabs! In this blog, we provide a step-by-step breakdown of: ✅ Generating out-of-the-box text metrics ✅ Simple monitoring techniques for your LLMs ✅ Leveraging the power of LangKit and WhyLabs https://t.co/QKZ6FgprLb
⛏️🦜 New extraction guides 🦜⛏️ Reliably extracting structured data from unstructured text is one of the killer use-cases for LLMs. It's a fantastic way to bridge the gap between LLMs and traditional APIs and systems. We've put a lot of work on this lately, including an open… https://t.co/nT7JOgLoJg
Extraction is a great use case for LLMs If you want to stand up a service for this - check out our newest use case accelerant https://t.co/VbynNoz5Er
⭐ Today we’re excited to announce our newest OSS use-case accelerant: an extraction service. ⭐ LLMs are a powerful tool for extracting structured data from unstructured sources. We've improved our support for data extraction in the open source LangChain library over the past… https://t.co/RgbmsLjOiN
🌟 Today we’re excited to announce our newest OSS use-case accelerant: an extraction service. 🌟 LLMs are a powerful tool for extracting structured data from unstructured sources. We've improved our support for data extraction in the open source LangChain library over the past…
New Blog Article! Today we’re going in-depth into LangChain (@LangChainAI), a framework for easily creating LLM workflows. It’s incredible for: ◆ Avoiding vendor lock-in ◆ Minimizing boilerplate code ◆ Automating repetitive processes https://t.co/p57pXgd0pM by @K4y1s
Wanna learn to fine-tune an open-source LLM without managing any infrastructure? Read this blog post ↓ https://t.co/dVOKTH1mTg