RAG using BuildShip
This template enables you to easily create a powerful RAG system directly using BuildShip's database.
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This template enables you to easily create a powerful RAG system directly using BuildShip's database.
Report this template
Select the reason for reporting
Describe the issue in detail
This template contains 2 flows:
query
chunksCollectionName
embeddingFieldName
This template enables you to create a powerful RAG system directly using BuildShip's database. Compared to our RAG with Supabase template, it is much faster to set up, and you can complete the entire setup within BuildShip.
This template contains two workflows
This workflow processes your uploaded PDF by extracting text, chunking it, generating embeddings for each chunk, and storing them in your BuildShip database.
Step 1: Add Mistral AI API Key
Mistral AI OCR API is used to extract the text from uploaded PDFs.
Step 2: Add OpenAI API key
The Store Vector Embeddings node uses OpenAI to generate embeddings of the PDF text and stores it in your BuildShip database.
To test the workflow you must fill in the 4 required inputs:
content.✅ Now you're ready to test the workflow and start generating and storing your document embeddings. If all goes as expected you should a success message like the one below:
This workflow allows you to perform semantic search on your uploaded PDF, extracting relevant information based on context. It then generates detailed, context-aware responses using GPT, enhancing the ability to interact with the content in a meaningful way.
Step 1: Generate Embeddings - Add OpenAI API key (Same as above)
Step 2: Text Generator - Add OpenAI API key (Same as above)
To test the workflow you must fill in the 3 required inputs:
NOTE ✋: On the first test run you'll get an error thrown on the Vector Query node. Don't panic, this is expected. This is because a vector index is required so just click the create index button to create the index and wait a while for the index to finish creating.
If you try again too soon, you'll receive a different error message indicating that the index is still being built and is not yet available.
After waiting for a while and testing the workflow again, it should work, and you'll receive your response 🎉.
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