Senior Engineer at a outsourcing company with 1,001-5,000 employees
Real User
Top 10
Dec 2, 2025
My main use case for Pinecone is creating vector indexes for GenAI applications. A specific example of how I use Pinecone in one of my projects is utilizing a RAG pipeline where I take text from PDF documents, convert those into chunks, ingest those into the Pinecone vector database, and then have a frontend UI that uses LLMs to query the vector database and retrieve answers. What I appreciate about Pinecone is that it provides reranking and other features, and it's a SaaS-based solution that is serverless.
Chief Technology Advisor at Kovaad technologies Pvt Ltd
Real User
Top 5
Oct 10, 2025
My main use case for Pinecone involves storage of chat data, specifically chat transcripts, and retrieval of matched chat messages. We store chat transcripts as vectors in Pinecone. When we have a new chat message, we utilize a retrieval mechanism to match and find the last five messages so that it can act as a memory. Essentially, Pinecone serves as a long-term memory for our application, while we use Redis for our short-term memory.
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.
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.
My main use case for Pinecone is creating vector indexes for GenAI applications. A specific example of how I use Pinecone in one of my projects is utilizing a RAG pipeline where I take text from PDF documents, convert those into chunks, ingest those into the Pinecone vector database, and then have a frontend UI that uses LLMs to query the vector database and retrieve answers. What I appreciate about Pinecone is that it provides reranking and other features, and it's a SaaS-based solution that is serverless.
My main use case for Pinecone involves storage of chat data, specifically chat transcripts, and retrieval of matched chat messages. We store chat transcripts as vectors in Pinecone. When we have a new chat message, we utilize a retrieval mechanism to match and find the last five messages so that it can act as a memory. Essentially, Pinecone serves as a long-term memory for our application, while we use Redis for our short-term memory.
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.