1/8 🚀 Let's go step by step on "Chat with your Repo" assistant powered by @LangChainAI🦜🔗 and @pinecone🌲all running smoothly on @googlecloud☁️ Run- this was demoed at yesterday's HUGE @googlecloud@pinecone event in Tel Aviv 🇮🇱
2/8 Step 1? Vectorize your repository files. With using @googlecloud VertexAI embeddings and a couple of lines of @LangChainAI you simply ingest these vectors into @pinecone vectorstore.
3/8 Now, we use @googlecloud VertexAI embeddings along with context retrieved from @pinecone to augment the user's original prompt to @googlecloud PaLM 2 LLM. This enables is also called in context learning. With @LangChainAI again is just a couple of lines of code
4/8 📦🔄 Then, now its containerization time. The application is neatly packed using a Dockerfile, and a Cloud Build CI/CD pipeline is constructed to automate and streamline deployments to @googlecloud.
5/8 💡✅ The final step? Just sit back and let @googlecloud Cloud Run handle all the "ilities" - scalability, availability, durability. The brilliance of serverless - write code and let Google worry about the infrastructure. #GCP#CloudRun 🌩️
6/8 🎙️💬 Special thanks to @pinecone🌲 @MutayRoei@miararoy for an insightful talk on Retrieval Augmented Generation and handling LLM's illucinations. This deep dive added immense value to our understanding of AI.
1/6🌐💡Singularity is here? Just read this blog from @LangChainAI 🦜🔗 featuring @itstimconnors on multi-agent simulation. IMO its amazing to witness how a few "hacks" such as a memory system + some prompt engineering can stimulate human-like behavior 🤖
2/6 inspired by @Stanford 's "Generative Agents" paper-
Every agent in a GPTeam simulation has its unique personality, memories, and directives, creating human-like behavior👥
3/6 📚💬 "The appearance of an agentic human-like entity is an illusion. Created by a memory system and a fe of distinct Language Model prompts."- from GPTeam blog. This ad-hoc human behaviour is mind blowing🤯🤯🤯
🧵We all spend too much time scouring LinkedIn/ Twitter before meeting someone new🕵🏽
So, here comes Ice Breaker LLM agent app. Just input a name, it fetches social media to provide a concise summary, interesting facts and a fun icebreaker!
Build on @LangChainAI🦜 & @pinecone🌲 twitter.com/i/web/status/1…
1/7 In just one weekend, this journey I created, shared on @udemy , has blown up in ways I didn’t expect🤖🚀
Teaching how easy it is creating cool & powerful LLM apps with @LangChainAI 🦜 🔗 + @pinecone 🌲, has gone viral 🚀
2/7 Thousands of students, 450+ reviews⭐ , a @udemy best seller tag, and an inbox full of developers from leading companies now equipped and building GenAI solutions 🤖
1/10 🧵💡 Ever wondered how to handle token limitations of LLMs in text summarization? Here's the elegant idea of the "refine" technique in @LangChainAI 🦜🔗, inspired by the "reduce" concept in functional programming. Let's deep dive! 🚀 @hwchase17's your PR is under review 😎
2/10 "Reduce" in python🐍 or "foldl" as it's known in Haskell, is a critical element in functional programming. this is a high order function that has 3 parameters: an iterable, a reduction function, and a starting value.
3/10
"foldl" / "reduce" applies a specified binary operation to successive elements of an iterable, accumulating the result to produce a single output. "reducing the list"
🧵🚀 Following my last thread on "in-context learning", now it's time to explain how we can digest our custom data so that LLM’s 🤖 can use it. Spoiler alert- @LangChainAI 🦜 🔗 and a vector store like @pinecone 🌲 will do all the work for us.
1/12 This is a laser focused thread 🧵 for devs and software engineers. Even if you have zero AI knowledge (like I did just 6 months ago)- I will be simplifying key data concepts for any gen ai application💡
2/12 Let's talk custom data digestion for LLMs 🤖
First off: Embedding models. These condense complex data into meaningful vectors, capturing relationships and semantic meaning. Think of it as a black box for text ➡ vector conversion. (vector = list of floats)
🧵 Ever wanted to talk with your LLM🤖 on some custom data that it wasn't originally trained on? @LangChainAI 🦜🔗+ @pinecone 🌲vectorstore will do all the heavy lifting for you. Here's a simplified explanation using a series of 8 illustrations I made.
1/8 Assume you've got documentation of an internal library 📚. When you directly ask the LLM about the library, it can't answer as it wasn't trained on it 🤷♂️. No worries! @LangChainAI + @pinecone is here to help 🚀
2/8: We load the entire package documentation into a vectorstore like @pinecone 🌲. This involves transforming the text into vectors, aka 'embeddings'. Now, these vectors hover around, representing our texts 🗂️