Custom Settings: RAG Quickstart
Follow the steps to quickly create advanced RAG applications that can be embedded into your own internal apps.
What We Provide:
Pre-Configured S3 Collection: We offer a ready-to-use S3 collection for storing your ingested data.
Pre-Configured Vector Storage: Our system includes vector storage, so you won’t need any additional configuration (unless dealing with proprietary data).
What You Will Need:
API Key and Credentials: Ensure you have an API key and any other necessary credentials for your Large Language Model (LLM).
Step 1: Configure LLM API
Before creating the Q&A system, configure the OpenAI GPT-4 model via the AI Applications page.
Add API Key: Enter your API key into the LLM Configuration box.
Set Parameters: Copy a response length of 1200 characters and set the temperature to 0.5.
Step 2: Create Your APP
Click the “Create RAG App” Button to begin
Select 'create with custom settings'
Enter your application name:
Make sure to select the default language model for your application. This can be changed in the editor screen.
Ingest New Data - Configure Sources:
PDF (Upload from Local System): Select PDF as the file type, upload from your desktop, and locate your file of interest.
Submit Ingest:
After configuring your source data, the default target will be created. If you would like to change this you can hit the edit button above the green box.
Hit Next
Step 3: Creating Vector Embeddings from Ingested Data:
Run the Process:
Hit Next, Select the dataflow to use for this app.
Step 4: Create the Rag App
Hit Review and Submit:
Make sure that you are happy with the pre-set rules, if there are any components(retrieval strategies, custom concepts to add, want to select specific LLM for text generation, and adding a custom prompt) you would like to change, you can do so before submitting.
Option to Change Language Model and Prompt:
Choose the language models (e.g., LLaMA 2, ChatGPT, Dolly).
Provide a custom prompt for the system.
Save and Submit:
Save your configurations and submit to create the Q&A system.
MongoDB will automatically index the data.
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