Merge pull request #1162 from RooVetGit/cte/sliding-window-fix

Sliding window fix
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Chris Estreich 2025-02-24 15:42:10 -08:00 • committed by GitHub
commit 9a56e15e6e
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3 changed files with 139 additions and 3 deletions

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@ -0,0 +1,130 @@
// npx jest src/core/sliding-window/__tests__/sliding-window.test.ts
import { Anthropic } from "@anthropic-ai/sdk"
import { ModelInfo } from "../../../shared/api"
import { truncateConversation, truncateConversationIfNeeded } from "../index"
describe("truncateConversation", () => {
it("should retain the first message", () => {
const messages: Anthropic.Messages.MessageParam[] = [
{ role: "user", content: "First message" },
{ role: "assistant", content: "Second message" },
{ role: "user", content: "Third message" },
]
const result = truncateConversation(messages, 0.5)
// With 2 messages after the first, 0.5 fraction means remove 1 message
// But 1 is odd, so it rounds down to 0 (to make it even)
expect(result.length).toBe(3) // First message + 2 remaining messages
expect(result[0]).toEqual(messages[0])
expect(result[1]).toEqual(messages[1])
expect(result[2]).toEqual(messages[2])
})
it("should remove the specified fraction of messages (rounded to even number)", () => {
const messages: Anthropic.Messages.MessageParam[] = [
{ role: "user", content: "First message" },
{ role: "assistant", content: "Second message" },
{ role: "user", content: "Third message" },
{ role: "assistant", content: "Fourth message" },
{ role: "user", content: "Fifth message" },
]
// 4 messages excluding first, 0.5 fraction = 2 messages to remove
// 2 is already even, so no rounding needed
const result = truncateConversation(messages, 0.5)
expect(result.length).toBe(3)
expect(result[0]).toEqual(messages[0])
expect(result[1]).toEqual(messages[3])
expect(result[2]).toEqual(messages[4])
})
it("should round to an even number of messages to remove", () => {
const messages: Anthropic.Messages.MessageParam[] = [
{ role: "user", content: "First message" },
{ role: "assistant", content: "Second message" },
{ role: "user", content: "Third message" },
{ role: "assistant", content: "Fourth message" },
{ role: "user", content: "Fifth message" },
{ role: "assistant", content: "Sixth message" },
{ role: "user", content: "Seventh message" },
]
// 6 messages excluding first, 0.3 fraction = 1.8 messages to remove
// 1.8 rounds down to 1, then to 0 to make it even
const result = truncateConversation(messages, 0.3)
expect(result.length).toBe(7) // No messages removed
expect(result).toEqual(messages)
})
it("should handle edge case with fracToRemove = 0", () => {
const messages: Anthropic.Messages.MessageParam[] = [
{ role: "user", content: "First message" },
{ role: "assistant", content: "Second message" },
{ role: "user", content: "Third message" },
]
const result = truncateConversation(messages, 0)
expect(result).toEqual(messages)
})
it("should handle edge case with fracToRemove = 1", () => {
const messages: Anthropic.Messages.MessageParam[] = [
{ role: "user", content: "First message" },
{ role: "assistant", content: "Second message" },
{ role: "user", content: "Third message" },
{ role: "assistant", content: "Fourth message" },
]
// 3 messages excluding first, 1.0 fraction = 3 messages to remove
// But 3 is odd, so it rounds down to 2 to make it even
const result = truncateConversation(messages, 1)
expect(result.length).toBe(2)
expect(result[0]).toEqual(messages[0])
expect(result[1]).toEqual(messages[3])
})
})
describe("truncateConversationIfNeeded", () => {
const createModelInfo = (contextWindow: number, supportsPromptCache: boolean, maxTokens?: number): ModelInfo => ({
contextWindow,
supportsPromptCache,
maxTokens,
})
const messages: Anthropic.Messages.MessageParam[] = [
{ role: "user", content: "First message" },
{ role: "assistant", content: "Second message" },
{ role: "user", content: "Third message" },
{ role: "assistant", content: "Fourth message" },
{ role: "user", content: "Fifth message" },
]
it("should not truncate if tokens are below threshold for prompt caching models", () => {
const modelInfo = createModelInfo(200000, true, 50000)
const totalTokens = 100000 // Below threshold
const result = truncateConversationIfNeeded(messages, totalTokens, modelInfo)
expect(result).toEqual(messages)
})
it("should not truncate if tokens are below threshold for non-prompt caching models", () => {
const modelInfo = createModelInfo(200000, false)
const totalTokens = 100000 // Below threshold
const result = truncateConversationIfNeeded(messages, totalTokens, modelInfo)
expect(result).toEqual(messages)
})
it("should use 80% of context window as threshold if it's greater than (contextWindow - buffer)", () => {
const modelInfo = createModelInfo(50000, true) // Small context window
const totalTokens = 40001 // Above 80% threshold (40000)
const mockResult = [messages[0], messages[3], messages[4]]
const result = truncateConversationIfNeeded(messages, totalTokens, modelInfo)
expect(result).toEqual(mockResult)
})
})

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@ -1,4 +1,5 @@
import { Anthropic } from "@anthropic-ai/sdk"
import { ModelInfo } from "../../shared/api"
/**
@ -55,13 +56,15 @@ export function truncateConversationIfNeeded(
/**
* Calculates the maximum allowed tokens for models that support prompt caching.
*
* The maximum is computed as the greater of (contextWindow - 40000) and 80% of the contextWindow.
* The maximum is computed as the greater of (contextWindow - buffer) and 80% of the contextWindow.
*
* @param {ModelInfo} modelInfo - The model information containing the context window size.
* @returns {number} The maximum number of tokens allowed for prompt caching models.
*/
function getMaxTokensForPromptCachingModels(modelInfo: ModelInfo): number {
return Math.max(modelInfo.contextWindow - 40_000, modelInfo.contextWindow * 0.8)
// The buffer needs to be at least as large as `modelInfo.maxTokens`.
const buffer = modelInfo.maxTokens ? Math.max(40_000, modelInfo.maxTokens) : 40_000
return Math.max(modelInfo.contextWindow - buffer, modelInfo.contextWindow * 0.8)
}
/**
@ -83,7 +86,9 @@ function getTruncFractionForPromptCachingModels(modelInfo: ModelInfo): number {
* @returns {number} The maximum number of tokens allowed for non-prompt caching models.
*/
function getMaxTokensForNonPromptCachingModels(modelInfo: ModelInfo): number {
return Math.max(modelInfo.contextWindow - 40_000, modelInfo.contextWindow * 0.8)
// The buffer needs to be at least as large as `modelInfo.maxTokens`.
const buffer = modelInfo.maxTokens ? Math.max(40_000, modelInfo.maxTokens) : 40_000
return Math.max(modelInfo.contextWindow - buffer, modelInfo.contextWindow * 0.8)
}
/**

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@ -2126,6 +2126,7 @@ export class ClineProvider implements vscode.WebviewViewProvider {
switch (rawModel.id) {
case "anthropic/claude-3.7-sonnet":
case "anthropic/claude-3.7-sonnet:beta":
case "anthropic/claude-3.5-sonnet":
case "anthropic/claude-3.5-sonnet:beta":
// NOTE: this needs to be synced with api.ts/openrouter default model info.