Discover and read the best of Twitter Threads about #DeepLake
Most recents (3)
1. Generate Picture Books with AI for free (code open-source👇) with @OpenAI Function Calling, @LangChainAI, #DeepLake, & @StabilityAI.
- Prompt -> a PDF storybook with illustrations.
- Stores image & text pairs in the multimodal #DeepLake VectorDB for model training/finetuning!
- Prompt -> a PDF storybook with illustrations.
- Stores image & text pairs in the multimodal #DeepLake VectorDB for model training/finetuning!
2. Read the 🧵 to learn how @OpenAI Function Calling & @LangChainAI helped.
FableForge is built by @ethanjdev & handles:
1. Prompt -> text & images
2. PDF creation
3. Deep Lake DB to view the multimodal images + text dataset or stream it in real-time to train/fine-tune an LLM.
FableForge is built by @ethanjdev & handles:
1. Prompt -> text & images
2. PDF creation
3. Deep Lake DB to view the multimodal images + text dataset or stream it in real-time to train/fine-tune an LLM.
3. But first... What's @OpenAI's function calling update?
In essence, it's bridging the gap between unstructured language input and structured, actionable output that other systems, tools, or services can use.
In essence, it's bridging the gap between unstructured language input and structured, actionable output that other systems, tools, or services can use.
Introducing... a song recommendation engine that is in tune with the emotions you're feeling with #DeepLake & @LangChainAI from scratch?
We experimented with 3 methods, but only one works. Learn which one👇 (we also open-sourced the repo)
We experimented with 3 methods, but only one works. Learn which one👇 (we also open-sourced the repo)
🧩 Things we tried:
1️⃣ Direct embeddings: Mood ➡️ Song
2️⃣ Emotional embeddings: Similar emotions ➡️ Similar songs
3️⃣ @OpenAI #ChatGPT: As a retrieval system
Only one makes sense from performance/financial perspective.
1️⃣ Direct embeddings: Mood ➡️ Song
2️⃣ Emotional embeddings: Similar emotions ➡️ Similar songs
3️⃣ @OpenAI #ChatGPT: As a retrieval system
Only one makes sense from performance/financial perspective.
Want to learn how to build a similar document retrieval-based product? Dive into the process:
📘 Full Article: lnkd.in/g-fpxqtg
🎵 Try the App: lnkd.in/gWJgV8te
🎬 Video Walkthrough: lnkd.in/gC9eUiAJ
📘 Full Article: lnkd.in/g-fpxqtg
🎵 Try the App: lnkd.in/gWJgV8te
🎬 Video Walkthrough: lnkd.in/gC9eUiAJ
1/5 📣Introducing #DataChad, the #ChatGPT for any local or cloud data. Chat with any data source: a JSON, PDF, URL, or even an entire GitHub repo! 🌐📚
Built with #DeepLake, @LangChainAI, @OpenAI & open-sourced by an awesome community member @thegreatgustby.
Built with #DeepLake, @LangChainAI, @OpenAI & open-sourced by an awesome community member @thegreatgustby.
2/5 And guess what? It's not just a shiny demo. You can try it out yourself! (Link in the next tweet) 💡🔗
github.com/gustavz/DataCh…
github.com/gustavz/DataCh…
3/5 @thegreatgustby shared tips to avoid common pitfalls while building AI apps with #LangChain & #DeepLake. 🧭🛠️
Adjusting parameters like k, chunk_size, max_tokens, and temperature. Read below to learn more about creating a Chat with Any Data app!
bit.ly/datachad
Adjusting parameters like k, chunk_size, max_tokens, and temperature. Read below to learn more about creating a Chat with Any Data app!
bit.ly/datachad
