Learn how to configure and optimize your Knowledge Base for better camelAI performance
The Knowledge Base is a critical feature that enhances camelAI’s ability to understand and analyze your data accurately. It provides context-specific information that helps camelAI deliver consistent, relevant insights tailored to your business domain.
Every knowledge base should start with a clear description of what your dataset represents. This foundational context helps camelAI understand the overall purpose and structure of your data.
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This dataset is a replica of our production e-commerce database. It contains customer orders, product inventory, and shipping information from our online retail platform serving the US market.
If your data follows a well-known schema or is a replica of a standard system, explicitly state this. camelAI can leverage its understanding of common schemas to provide better insights.
Examples of standard schemas to mention:
“This PostgreSQL database mirrors our Salesforce CRM data structure”
“Our MySQL database follows the Shopify schema for e-commerce data”
“This dataset implements the FHIR standard for healthcare records”
“Our analytics tables follow the Google Analytics 4 event schema”
Document any terms that have specific meanings within your organization, especially when they differ from industry standards or could be ambiguous.
We recommend using multiple focused entries to improve RAG performance.
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Entry 1: "Active User": A user who has logged in within the last 30 days AND completed at least one transactionEntry 2: "LTV" (Lifetime Value): The sum of total_purchases + subscription_revenue + addon_revenue columnsEntry 3: "Churn": When a customer has no activity for 90+ days (not the standard 30-day definition)Entry 4: "Region": Refers to our custom sales territories, not geographic regions (see regions_mapping table)
Help camelAI navigate joins and relationships by explaining non-obvious connections between tables.
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Table Relationships:- orders.user_id links to users.id (primary relationship)- orders.promo_code links to both promotions.code AND partner_promotions.code- product_variants should be used instead of products table for inventory queries- Always join transactions through transaction_items, never directly to orders
Include preferences for how data should be formatted, calculated, or displayed.
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Data Handling Preferences:- When calculating percentages, round to 1 decimal place- Week starts on Monday for all weekly aggregations- Fiscal year begins April 1st
Be transparent about known data limitations or quality issues to prevent misleading analyses.
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Data Quality Notes:- Revenue data before March 2022 may be incomplete due to migration- The user_demographics table has ~15% missing values for age field- Avoid using the legacy_orders table - use orders_v2 instead- Product categories were restructured in June 2023; use category_mapping for historical comparisons
You can create, read, update, and delete knowledge base entries through the API or through the developer console. Changes take effect immediately for all new conversations.