New Machina

📹 VIDEO TITLE 📹
Embeddings with Open AI & Pinecone Vector Database

✍️VIDEO DESCRIPTION ✍️
Welcome to this code-centric video tutorial! In this video, we’ll dive into the powerful combination of LangChain and the Pinecone Vector database for embedding text documents and managing them in a vector database. We’ll explore how to generate embeddings using OpenAI’s text-embedding-ada-002 model with LangChain and seamlessly insert them into a Pinecone serverless vector database. Whether you're building a semantic search engine, recommendation system, or RAG LLM driven system powered by vector embeddings, this example will guide you through the process step by step. By the end, you'll have the skills to build scalable, state-of-the-art AI systems with minimal setup!

We start by setting up LangChain in Python to process text documents. Using LangChain’s OpenAI integration, we’ll extract embeddings for sample text documents. Once the embeddings are ready, we’ll connect to a serverless Pinecone vector database. I’ll show you how to initialize your Pinecone index, prepare metadata, and upsert embeddings into Pinecone in a scalable way. This serverless approach is perfect for projects with unpredictable or dynamic workloads, as it allows you to scale without worrying about infrastructure. I’ll walk you through every line of code, explaining concepts like metadata storage and document vectorization along the way.

And that’s it! In just a few minutes, you’ve learned how to use LangChain and Pinecone to handle text embeddings efficiently. This workflow is highly versatile, enabling you to manage and query your vectors for tasks like semantic search and clustering. If you found this tutorial helpful, don’t forget to like and subscribe for more AI and Python content. Let me know in the comments what you're building with LangChain and Pinecone—I’d love to hear about your projects and answer any questions. See you in the next video!

🧑‍💻GITHUB URL 🧑‍💻
https://github.com/NewMachinaLLM/video_001501

📽OTHER NEW MACHINA VIDEOS REFERENCED IN THIS VIDEO 📽
SDK(s) in Pinecone Vector DB - https://youtu.be/ttnPUbiLjv0
Pinecone Vector DB POD(s) vs Serverless - https://youtu.be/t7qpxjTTccc
Meta Data Filters in Pinecone Vector DB - https://youtu.be/ztXrf88sX-M
Namespaces in Pinecone Vector DB - https://youtu.be/ztXrf88sX-M
Fetches & Queries in Pinecone Vector DB - https://youtu.be/ztXrf88sX-M
Upserts & Deletes in Pinecone Vector DB - https://youtu.be/ztXrf88sX-M
What is a Pineconde Index - https://youtu.be/IHm0-WBELTI
What is the Pinecone Vector DB - https://youtu.be/IHm0-WBELTI
What is LLM LangGraph ? - https://youtu.be/w4U3gG_C4VY
What is Llama Index ? - https://youtu.be/vz3Z2XETpGM
LangChain HelloWorld with Open GPT 3.5 - https://youtu.be/tD335RLNYJQ

🔠KEYWORDS 🔠
#AI
#LLM
#LargeLanguageModel
#Pinecone
#PineconeVectorDatabase
#Embeddings
#EmbeddingLLM
#OpenAI
#LangChain
#Python
#textEmbeddingAda002