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| GPT4_Retrieval_Augmentation.ipynb | 5 months ago | |
| Gen_QA.ipynb | 5 months ago | |
| README.md | 1 year ago | |
| Semantic_Search.ipynb | 5 months ago | |
| Using_Pinecone_for_embeddings_search.ipynb | 5 months ago | |
README.md
Pinecone Vector Database
Vector search is an innovative technology that enables developers and engineers to efficiently store, search, and recommend information by representing complex data as mathematical vectors. By comparing the similarities between these vectors, you can quickly retrieve relevant information in a seamless and intuitive manner.
Pinecone is a vector database designed with developers and engineers in mind. As a managed service, it alleviates the burden of maintenance and engineering, allowing you to focus on extracting valuable insights from your data. The free tier supports up to 5 million vectors, making it an accessible and cost-effective way to experiment with vector search capabilities. With Pinecone, you'll experience impressive speed, accuracy, and scalability, as well as access to advanced features like single-stage metadata filtering and the cutting-edge sparse-dense index.
Examples
This folder contains examples of using Pinecone and OpenAI together. More will be added over time so check back for updates!
| Name | Description | Google Colab |
|---|---|---|
| GPT-4 Retrieval Augmentation | How to supercharge GPT-4 with retrieval augmentation |  |
| Generative Question-Answering | A simple walkthrough demonstrating the use of Generative Question-Answering | |
| Semantic Search | A guide to building a simple semantic search process | |