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Understand Enriched Index Costs
Enriched indexing enhances chunks with generated intelligence, such as entities, summaries, topics, and answerable questions, to improve intent-driven retrieval. Although this process increases discoverability and grounding for AI assistants, it raises indexing costs because every chunk produces additional embeddings and metadata chunks.
The number of chunks generated from a dataset depends on factors such as the chunking strategy (passage extraction, section-aware) and chunk size. Together, these variables determine how efficiently content is chunked and the cost of enriched indexing.
Example : Credit Consumption Calculation
Consider a sample data of 5 MB consisting of PDF documents such as product catalogs or technical guides. Using a structure-aware chunking strategy with a maximum chunk size of 512 tokens, assume Data 360 search chunking generates approximately 16 chunks per MB. Let’s calculate the total LLM requests that were made to generate the enriched chunks and the flex credits consumed.
Total Chunks = 5 MB x 16 chunks per MB = 80 chunks
Each LLM request processes 4 chunks.
Total LLM Requests = 80 chunks ÷ 4 chunks per LLM request = 20 requests
Each LLM request includes token usage. Let's calculate the total tokens used per request.
- Prompt Instructions take 2,100 tokens
- Tokens for chunks processed = 4 chunks x 512 tokens = 2,048 tokens
- Total output tokens = 1,500 tokens
Total Tokens Used per Request = 2,100 + 2,048 + 1,500 = 5,648 tokens
Assume 2000 tokens is one unit of Salesforce Flex Credit, and billing rounds up to the nearest unit. Number of billing units required for the 5 MB data set is:
- 5,648 tokens ÷ 2,000 tokens per unit = 2.824 billing units
- 2.824 billing units rounds up to 3 billing units.
Assume each billing unit consumes 4 flex credits for a Standard Prompt, in this case, each LLM request consumes:
3 Billing Units x 4 Flex Credits per unit = 12 Flex Credits per LLM request
So, for 20 LLM requests: Total Flex Credits used = 20 requests x 12 Flex Credits per request = 240 Flex Credits
Alternatively, if the dataset consisted of files producing more chunks per MB, the enrichment workload increases significantly. For the same 5 MB data set, let's say a structure-aware chunking produces 121 chunks per MB.
Total Chunks = 5 MB x 121 chunks per MB = 605 chunks
Each LLM request processes 4 chunks.
Total LLM Requests = 605 chunks ÷ 4 chunks per LLM request = 152 requests
Let’s calculate the total flex credits consumed for 152 LLM requests based on how total tokens used per request, total billing units, and flex credits consumed per billing unit are calculated.
Total Tokens Used per Request = 2,100 + 2,048 + 1,500 = 5,648 tokens
Total Billing Units = 5,648 tokens ÷ 2,000 tokens per unit = 2.824 units
2.824 billing units rounds up to 3 billing units.
Total Flex Credits Consumed for 3 billing units = 3 x 4 credits per unit = 12 Flex Credits per LLM request
Total Flex Credits consumed for 152 LLM requests = 152 requests x 12 Flex Credits per request = 1,824 Flex Credits
These sample calculations show how the number of chunks generated affect the number of LLM requests made which ultimately impacts the flex credits consumed.
