I used Pinecone in collaboration with an Azure database. At that time, I needed to create a chatbot that could pull data from public media in specific fields. I used Pinecone to embed the publications, and after submitting the data, it was pushed into our data pipeline.
Pinecone is a vector database. We use it to retrieve data using semantic search. We use vector DB only for chatbots and AI applications. Currently, I am using the tool to make a chatbot.
Machine Learning Engineer at a consumer goods company with 51-200 employees
Real User
Top 5
2024-05-13T07:22:51Z
May 13, 2024
The product is good. When I tried to deploy the product for the first time, I liked Pinecone's approach, and it was one of the major reasons why I decided to continue with the product. I mostly use the solution in my company for data storage.
We've been building an ERP dashboard using generative UI. We needed a vector database to retrieve and implement augmentation, so we opted to use Pinecone. We chose Pinecone because it covers most of the use cases. Also, Pinecone is stable and reliable.
Pinecone is a powerful tool for efficiently storing and retrieving vector embeddings. It is highly praised for its scalability, speed, and ease of integration with existing workflows.
Users find it particularly useful for similarity search, recommendation systems, and natural language processing.
Its efficient search capabilities, seamless integration with existing systems, and ability to handle large-scale datasets make it a valuable tool for data analysis and retrieval.
I've used Pinecone to streamline token generation for my chatbot's functionality. Specifically, I used it for the OpenNeeam Building.
I used Pinecone in collaboration with an Azure database. At that time, I needed to create a chatbot that could pull data from public media in specific fields. I used Pinecone to embed the publications, and after submitting the data, it was pushed into our data pipeline.
Pinecone is a vector database. We use it to retrieve data using semantic search. We use vector DB only for chatbots and AI applications. Currently, I am using the tool to make a chatbot.
The product is good. When I tried to deploy the product for the first time, I liked Pinecone's approach, and it was one of the major reasons why I decided to continue with the product. I mostly use the solution in my company for data storage.
We've been building an ERP dashboard using generative UI. We needed a vector database to retrieve and implement augmentation, so we opted to use Pinecone. We chose Pinecone because it covers most of the use cases. Also, Pinecone is stable and reliable.
In my company, we store our industry documents in Pinecone. My company stores the PDF files in Pinecone to use for the RAG application.