Connect OpenAI (ChatGPT, Sora, DALL-E, Whisper) and Pinecone integrations
Automatically transform your OpenAI outputs from ChatGPT, Sora, DALL-E, and Whisper into searchable knowledge by instantly converting AI-generated content into vector embeddings and storing them in Pinecone. Access lightning-fast semantic search and intelligent retrieval across all your AI-created text, images, and audio transcriptions without manual work.
Triggers when a batch is completed.
Triggers when a video job is created.
Adds a new text or updates an existing one if a matching record ID is found.
Deletes a vector.
Retrieves a vector from a namespace.
Performs an arbitrary authorized API call.
Updates a vector.
Creates new or updates an existing vector.
Retrieves the IDs of the most similar items in a namespace, along with their similarity scores.
Returns a list of records filtered by query text.
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Build your OpenAI (ChatGPT, Sora, DALL-E, Whisper) and Pinecone integrations.
Pinecone serves as a trigger to initiate automated workflows when AI operations are completed, such as generating text responses, creating images, or transcribing audio. Once triggered, these outputs are automatically processed into vector embeddings and sent to OpenAI (ChatGPT, Sora, DALL-E, Whisper) as an action, where they are stored in the vector database for semantic search and retrieval. This integration enables you to automatically capture AI-generated content from Pinecone and immediately index it in OpenAI (ChatGPT, Sora, DALL-E, Whisper) for lightning-fast similarity searches and intelligent data retrieval.
Adds files to a specified vector store or, if not specified, creates a new vector store based on the configuration.
Adds a new text or updates an existing one if a matching record ID is found.
Analyzes images according to specified instructions.
Cancels an "in-progress" batch. The batch will be in status "cancelling" for up to 10 minutes, before changing to "cancelled", where it will have partial results (if any) available in the output file.
Creates and executes a batch of API calls.
Deletes a vector.
Deletes an existing conversation.
Deletes an existing model response.
Deletes an existing video.
Popular OpenAI (ChatGPT, Sora, DALL-E, Whisper) and Pinecone workflows.
Looking to get more out of OpenAI (ChatGPT, Sora, DALL-E, Whisper) and Pinecone? With Make you can visually integrate OpenAI (ChatGPT, Sora, DALL-E, Whisper) and Pinecone into any workflow to save time and resources — no coding required. Try any of these templates in just a few clicks.
How to setup OpenAI (ChatGPT, Sora, DALL-E, Whisper) and Pinecone in 5 easy steps
Get your Pinecone API key
Before you can connect Pinecone to Make, you'll need to grab your API key from Pinecone. Simply log into your Pinecone account, find the API Keys section in the sidebar, and create a new key. Make sure to copy and save this key somewhere safe, as you'll need it in the next steps.
Add a Pinecone module to your workflow
Open Make and start building your automation. Search for and add a Pinecone module to your scenario. This module acts as the bridge that allows Make to communicate with your Pinecone database and perform actions automatically.
Create and name your connection
Click the 'Create a connection' button on your Pinecone module. You'll see a form where you can optionally give your connection a descriptive name. Choosing a clear name makes it easier to identify this connection later, especially if you end up creating multiple connections.
Enter your Pinecone index details
Find your index name in Pinecone by checking the Indexes tab in your account. When entering it in Make, use only the portion before '.pinecone.io' - for example, if your full index is 'example-123.svc.exampleEnvironment.pinecone.io', just enter 'example-123.svc.exampleEnvironment'.
Complete the connection with your API key
Paste the API key you saved earlier into the API Key field in Make. Once you've entered both your index name and API key, click Save to establish the connection. Your Pinecone connection is now ready to use in your automation scenarios.
Power your AI apps with intelligent vector search and semantic memory
Combine OpenAI and Pinecone to build advanced AI applications with semantic search, retrieval-augmented generation, and long-term memory. Automate embedding creation and vector storage for scalable solutions.
Automatically generate OpenAI embeddings for your content and store them in Pinecone to enable fast, contextually-aware semantic search across vast datasets.
Combine OpenAI's language models with Pinecone's vector database to create retrieval-augmented generation systems that deliver accurate, context-informed responses.
Automatically process new documents through OpenAI's embedding models and sync them to Pinecone without manual intervention, keeping your vector search index always up-to-date.
Enable your ChatGPT applications to recall and retrieve relevant historical information by storing conversation embeddings in Pinecone for long-term contextual memory.
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