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Prompt Caching #234
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Prompt Caching #234
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2e84006
13: Failing specs
tpaulshippy be61e48
13: Get caching specs passing for Bedrock
tpaulshippy edec138
13: Remove comments in specs
tpaulshippy 971f176
13: Add unused param on other providers
tpaulshippy 557a5ee
13: Rubocop -A
tpaulshippy 9673b13
13: Add cassettes for bedrock cache specs
tpaulshippy c47d270
13: Resolve Rubocop aside from Metrics/ParameterLists
tpaulshippy eaf0876
13: Use large enough prompt to hit cache meaningfully
tpaulshippy 160d9ab
13: Ensure cache tokens are being used
tpaulshippy d1698bf
13: Refactor completion parameters
tpaulshippy 344729f
16: Add guide for prompt caching
tpaulshippy 7b98277
Add real anthropic cassettes ($0.03)
tpaulshippy fd30f14
Merge branch 'main' into prompt-caching
tpaulshippy a91d07e
Switch from large_prompt.txt to 10,000 of the letter a
tpaulshippy f40f37d
Make that 2048 * 4 (2048 tokens for Haiku)
tpaulshippy 109bb51
Rename properties on message class
tpaulshippy 1c6cbf7
Revert "13: Refactor completion parameters"
tpaulshippy 4d78a09
Address rubocop
tpaulshippy 25b3660
Merge remote-tracking branch 'origin/main' into prompt-caching
tpaulshippy 8e80f08
Update docs
tpaulshippy d42d074
Actually return the payload
tpaulshippy 97b1ace
Add support for cache token counts in gemini and openai
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Realizing this might cause errors on older models that do not support caching. If it does, we could raise here, or just let the API validation handle it. I'm torn on whether the capabilities check complexity is worth it as these models are probably so rarely used.