/**
 * Context compaction for long sessions.
 *
 * Pure functions for compaction logic. The session manager handles I/O,
 * and after compaction the session is reloaded.
 */
import { completeSimple } from "@earendil-works/pi-ai";
import { convertToLlm, createBranchSummaryMessage, createCompactionSummaryMessage, createCustomMessage, } from "../messages.js";
import { buildSessionContext } from "../session-manager.js";
import { computeFileLists, createFileOps, extractFileOpsFromMessage, formatFileOperations, SUMMARIZATION_SYSTEM_PROMPT, serializeConversation, } from "./utils.js";
/**
 * Extract file operations from messages and previous compaction entries.
 */
function extractFileOperations(messages, entries, prevCompactionIndex) {
    const fileOps = createFileOps();
    // Collect from previous compaction's details (if pi-generated)
    if (prevCompactionIndex >= 0) {
        const prevCompaction = entries[prevCompactionIndex];
        if (!prevCompaction.fromHook && prevCompaction.details) {
            // fromHook field kept for session file compatibility
            const details = prevCompaction.details;
            if (Array.isArray(details.readFiles)) {
                for (const f of details.readFiles)
                    fileOps.read.add(f);
            }
            if (Array.isArray(details.modifiedFiles)) {
                for (const f of details.modifiedFiles)
                    fileOps.edited.add(f);
            }
        }
    }
    // Extract from tool calls in messages
    for (const msg of messages) {
        extractFileOpsFromMessage(msg, fileOps);
    }
    return fileOps;
}
// ============================================================================
// Message Extraction
// ============================================================================
/**
 * Extract AgentMessage from an entry if it produces one.
 * Returns undefined for entries that don't contribute to LLM context.
 */
function getMessageFromEntry(entry) {
    if (entry.type === "message") {
        return entry.message;
    }
    if (entry.type === "custom_message") {
        return createCustomMessage(entry.customType, entry.content, entry.display, entry.details, entry.timestamp);
    }
    if (entry.type === "branch_summary") {
        return createBranchSummaryMessage(entry.summary, entry.fromId, entry.timestamp);
    }
    if (entry.type === "compaction") {
        return createCompactionSummaryMessage(entry.summary, entry.tokensBefore, entry.timestamp);
    }
    return undefined;
}
function getMessageFromEntryForCompaction(entry) {
    if (entry.type === "compaction") {
        return undefined;
    }
    return getMessageFromEntry(entry);
}
export const DEFAULT_COMPACTION_SETTINGS = {
    enabled: true,
    reserveTokens: 16384,
    keepRecentTokens: 20000,
};
// ============================================================================
// Token calculation
// ============================================================================
/**
 * Calculate total context tokens from usage.
 * Uses the native totalTokens field when available, falls back to computing from components.
 */
export function calculateContextTokens(usage) {
    return usage.totalTokens || usage.input + usage.output + usage.cacheRead + usage.cacheWrite;
}
/**
 * Get usage from an assistant message if available.
 * Skips aborted and error messages as they don't have valid usage data.
 */
function getAssistantUsage(msg) {
    if (msg.role === "assistant" && "usage" in msg) {
        const assistantMsg = msg;
        if (assistantMsg.stopReason !== "aborted" && assistantMsg.stopReason !== "error" && assistantMsg.usage) {
            return assistantMsg.usage;
        }
    }
    return undefined;
}
/**
 * Find the last non-aborted assistant message usage from session entries.
 */
export function getLastAssistantUsage(entries) {
    for (let i = entries.length - 1; i >= 0; i--) {
        const entry = entries[i];
        if (entry.type === "message") {
            const usage = getAssistantUsage(entry.message);
            if (usage)
                return usage;
        }
    }
    return undefined;
}
function getLastAssistantUsageInfo(messages) {
    for (let i = messages.length - 1; i >= 0; i--) {
        const usage = getAssistantUsage(messages[i]);
        if (usage)
            return { usage, index: i };
    }
    return undefined;
}
/**
 * Estimate context tokens from messages, using the last assistant usage when available.
 * If there are messages after the last usage, estimate their tokens with estimateTokens.
 */
export function estimateContextTokens(messages) {
    const usageInfo = getLastAssistantUsageInfo(messages);
    if (!usageInfo) {
        let estimated = 0;
        for (const message of messages) {
            estimated += estimateTokens(message);
        }
        return {
            tokens: estimated,
            usageTokens: 0,
            trailingTokens: estimated,
            lastUsageIndex: null,
        };
    }
    const usageTokens = calculateContextTokens(usageInfo.usage);
    let trailingTokens = 0;
    for (let i = usageInfo.index + 1; i < messages.length; i++) {
        trailingTokens += estimateTokens(messages[i]);
    }
    return {
        tokens: usageTokens + trailingTokens,
        usageTokens,
        trailingTokens,
        lastUsageIndex: usageInfo.index,
    };
}
/**
 * Check if compaction should trigger based on context usage.
 */
export function shouldCompact(contextTokens, contextWindow, settings) {
    if (!settings.enabled)
        return false;
    return contextTokens > contextWindow - settings.reserveTokens;
}
// ============================================================================
// Cut point detection
// ============================================================================
/**
 * Estimate token count for a message using chars/4 heuristic.
 * This is conservative (overestimates tokens).
 */
export function estimateTokens(message) {
    let chars = 0;
    switch (message.role) {
        case "user": {
            const content = message.content;
            if (typeof content === "string") {
                chars = content.length;
            }
            else if (Array.isArray(content)) {
                for (const block of content) {
                    if (block.type === "text" && block.text) {
                        chars += block.text.length;
                    }
                }
            }
            return Math.ceil(chars / 4);
        }
        case "assistant": {
            const assistant = message;
            for (const block of assistant.content) {
                if (block.type === "text") {
                    chars += block.text.length;
                }
                else if (block.type === "thinking") {
                    chars += block.thinking.length;
                }
                else if (block.type === "toolCall") {
                    chars += block.name.length + JSON.stringify(block.arguments).length;
                }
            }
            return Math.ceil(chars / 4);
        }
        case "custom":
        case "toolResult": {
            if (typeof message.content === "string") {
                chars = message.content.length;
            }
            else {
                for (const block of message.content) {
                    if (block.type === "text" && block.text) {
                        chars += block.text.length;
                    }
                    if (block.type === "image") {
                        chars += 4800; // Estimate images as 4000 chars, or 1200 tokens
                    }
                }
            }
            return Math.ceil(chars / 4);
        }
        case "bashExecution": {
            chars = message.command.length + message.output.length;
            return Math.ceil(chars / 4);
        }
        case "branchSummary":
        case "compactionSummary": {
            chars = message.summary.length;
            return Math.ceil(chars / 4);
        }
    }
    return 0;
}
/**
 * Find valid cut points: indices of user, assistant, custom, or bashExecution messages.
 * Never cut at tool results (they must follow their tool call).
 * When we cut at an assistant message with tool calls, its tool results follow it
 * and will be kept.
 * BashExecutionMessage is treated like a user message (user-initiated context).
 */
function findValidCutPoints(entries, startIndex, endIndex) {
    const cutPoints = [];
    for (let i = startIndex; i < endIndex; i++) {
        const entry = entries[i];
        switch (entry.type) {
            case "message": {
                const role = entry.message.role;
                switch (role) {
                    case "bashExecution":
                    case "custom":
                    case "branchSummary":
                    case "compactionSummary":
                    case "user":
                    case "assistant":
                        cutPoints.push(i);
                        break;
                    case "toolResult":
                        break;
                }
                break;
            }
            case "thinking_level_change":
            case "model_change":
            case "compaction":
            case "branch_summary":
            case "custom":
            case "custom_message":
            case "label":
            case "session_info":
                break;
        }
        // branch_summary and custom_message are user-role messages, valid cut points
        if (entry.type === "branch_summary" || entry.type === "custom_message") {
            cutPoints.push(i);
        }
    }
    return cutPoints;
}
/**
 * Find the user message (or bashExecution) that starts the turn containing the given entry index.
 * Returns -1 if no turn start found before the index.
 * BashExecutionMessage is treated like a user message for turn boundaries.
 */
export function findTurnStartIndex(entries, entryIndex, startIndex) {
    for (let i = entryIndex; i >= startIndex; i--) {
        const entry = entries[i];
        // branch_summary and custom_message are user-role messages, can start a turn
        if (entry.type === "branch_summary" || entry.type === "custom_message") {
            return i;
        }
        if (entry.type === "message") {
            const role = entry.message.role;
            if (role === "user" || role === "bashExecution") {
                return i;
            }
        }
    }
    return -1;
}
/**
 * Find the cut point in session entries that keeps approximately `keepRecentTokens`.
 *
 * Algorithm: Walk backwards from newest, accumulating estimated message sizes.
 * Stop when we've accumulated >= keepRecentTokens. Cut at that point.
 *
 * Can cut at user OR assistant messages (never tool results). When cutting at an
 * assistant message with tool calls, its tool results come after and will be kept.
 *
 * Returns CutPointResult with:
 * - firstKeptEntryIndex: the entry index to start keeping from
 * - turnStartIndex: if cutting mid-turn, the user message that started that turn
 * - isSplitTurn: whether we're cutting in the middle of a turn
 *
 * Only considers entries between `startIndex` and `endIndex` (exclusive).
 */
export function findCutPoint(entries, startIndex, endIndex, keepRecentTokens) {
    const cutPoints = findValidCutPoints(entries, startIndex, endIndex);
    if (cutPoints.length === 0) {
        return { firstKeptEntryIndex: startIndex, turnStartIndex: -1, isSplitTurn: false };
    }
    // Walk backwards from newest, accumulating estimated message sizes
    let accumulatedTokens = 0;
    let cutIndex = cutPoints[0]; // Default: keep from first message (not header)
    for (let i = endIndex - 1; i >= startIndex; i--) {
        const entry = entries[i];
        if (entry.type !== "message")
            continue;
        // Estimate this message's size
        const messageTokens = estimateTokens(entry.message);
        accumulatedTokens += messageTokens;
        // Check if we've exceeded the budget
        if (accumulatedTokens >= keepRecentTokens) {
            // Find the closest valid cut point at or after this entry
            for (let c = 0; c < cutPoints.length; c++) {
                if (cutPoints[c] >= i) {
                    cutIndex = cutPoints[c];
                    break;
                }
            }
            break;
        }
    }
    // Scan backwards from cutIndex to include any non-message entries (bash, settings, etc.)
    while (cutIndex > startIndex) {
        const prevEntry = entries[cutIndex - 1];
        // Stop at session header or compaction boundaries
        if (prevEntry.type === "compaction") {
            break;
        }
        if (prevEntry.type === "message") {
            // Stop if we hit any message
            break;
        }
        // Include this non-message entry (bash, settings change, etc.)
        cutIndex--;
    }
    // Determine if this is a split turn
    const cutEntry = entries[cutIndex];
    const isUserMessage = cutEntry.type === "message" && cutEntry.message.role === "user";
    const turnStartIndex = isUserMessage ? -1 : findTurnStartIndex(entries, cutIndex, startIndex);
    return {
        firstKeptEntryIndex: cutIndex,
        turnStartIndex,
        isSplitTurn: !isUserMessage && turnStartIndex !== -1,
    };
}
// ============================================================================
// Summarization
// ============================================================================
const SUMMARIZATION_PROMPT = `The messages above are a conversation to summarize. Create a structured context checkpoint summary that another LLM will use to continue the work.

Use this EXACT format:

## Goal
[What is the user trying to accomplish? Can be multiple items if the session covers different tasks.]

## Constraints & Preferences
- [Any constraints, preferences, or requirements mentioned by user]
- [Or "(none)" if none were mentioned]

## Progress
### Done
- [x] [Completed tasks/changes]

### In Progress
- [ ] [Current work]

### Blocked
- [Issues preventing progress, if any]

## Key Decisions
- **[Decision]**: [Brief rationale]

## Next Steps
1. [Ordered list of what should happen next]

## Critical Context
- [Any data, examples, or references needed to continue]
- [Or "(none)" if not applicable]

Keep each section concise. Preserve exact file paths, function names, and error messages.`;
const UPDATE_SUMMARIZATION_PROMPT = `The messages above are NEW conversation messages to incorporate into the existing summary provided in <previous-summary> tags.

Update the existing structured summary with new information. RULES:
- PRESERVE all existing information from the previous summary
- ADD new progress, decisions, and context from the new messages
- UPDATE the Progress section: move items from "In Progress" to "Done" when completed
- UPDATE "Next Steps" based on what was accomplished
- PRESERVE exact file paths, function names, and error messages
- If something is no longer relevant, you may remove it

Use this EXACT format:

## Goal
[Preserve existing goals, add new ones if the task expanded]

## Constraints & Preferences
- [Preserve existing, add new ones discovered]

## Progress
### Done
- [x] [Include previously done items AND newly completed items]

### In Progress
- [ ] [Current work - update based on progress]

### Blocked
- [Current blockers - remove if resolved]

## Key Decisions
- **[Decision]**: [Brief rationale] (preserve all previous, add new)

## Next Steps
1. [Update based on current state]

## Critical Context
- [Preserve important context, add new if needed]

Keep each section concise. Preserve exact file paths, function names, and error messages.`;
/**
 * Generate a summary of the conversation using the LLM.
 * If previousSummary is provided, uses the update prompt to merge.
 */
export async function generateSummary(currentMessages, model, reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel) {
    const maxTokens = Math.floor(0.8 * reserveTokens);
    // Use update prompt if we have a previous summary, otherwise initial prompt
    let basePrompt = previousSummary ? UPDATE_SUMMARIZATION_PROMPT : SUMMARIZATION_PROMPT;
    if (customInstructions) {
        basePrompt = `${basePrompt}\n\nAdditional focus: ${customInstructions}`;
    }
    // Serialize conversation to text so model doesn't try to continue it
    // Convert to LLM messages first (handles custom types like bashExecution, custom, etc.)
    const llmMessages = convertToLlm(currentMessages);
    const conversationText = serializeConversation(llmMessages);
    // Build the prompt with conversation wrapped in tags
    let promptText = `<conversation>\n${conversationText}\n</conversation>\n\n`;
    if (previousSummary) {
        promptText += `<previous-summary>\n${previousSummary}\n</previous-summary>\n\n`;
    }
    promptText += basePrompt;
    const summarizationMessages = [
        {
            role: "user",
            content: [{ type: "text", text: promptText }],
            timestamp: Date.now(),
        },
    ];
    const completionOptions = model.reasoning && thinkingLevel && thinkingLevel !== "off"
        ? { maxTokens, signal, apiKey, headers, reasoning: thinkingLevel }
        : { maxTokens, signal, apiKey, headers };
    const response = await completeSimple(model, { systemPrompt: SUMMARIZATION_SYSTEM_PROMPT, messages: summarizationMessages }, completionOptions);
    if (response.stopReason === "error") {
        throw new Error(`Summarization failed: ${response.errorMessage || "Unknown error"}`);
    }
    const textContent = response.content
        .filter((c) => c.type === "text")
        .map((c) => c.text)
        .join("\n");
    return textContent;
}
export function prepareCompaction(pathEntries, settings) {
    if (pathEntries.length > 0 && pathEntries[pathEntries.length - 1].type === "compaction") {
        return undefined;
    }
    let prevCompactionIndex = -1;
    for (let i = pathEntries.length - 1; i >= 0; i--) {
        if (pathEntries[i].type === "compaction") {
            prevCompactionIndex = i;
            break;
        }
    }
    let previousSummary;
    let boundaryStart = 0;
    if (prevCompactionIndex >= 0) {
        const prevCompaction = pathEntries[prevCompactionIndex];
        previousSummary = prevCompaction.summary;
        const firstKeptEntryIndex = pathEntries.findIndex((entry) => entry.id === prevCompaction.firstKeptEntryId);
        boundaryStart = firstKeptEntryIndex >= 0 ? firstKeptEntryIndex : prevCompactionIndex + 1;
    }
    const boundaryEnd = pathEntries.length;
    const tokensBefore = estimateContextTokens(buildSessionContext(pathEntries).messages).tokens;
    const cutPoint = findCutPoint(pathEntries, boundaryStart, boundaryEnd, settings.keepRecentTokens);
    // Get UUID of first kept entry
    const firstKeptEntry = pathEntries[cutPoint.firstKeptEntryIndex];
    if (!firstKeptEntry?.id) {
        return undefined; // Session needs migration
    }
    const firstKeptEntryId = firstKeptEntry.id;
    const historyEnd = cutPoint.isSplitTurn ? cutPoint.turnStartIndex : cutPoint.firstKeptEntryIndex;
    // Messages to summarize (will be discarded after summary)
    const messagesToSummarize = [];
    for (let i = boundaryStart; i < historyEnd; i++) {
        const msg = getMessageFromEntryForCompaction(pathEntries[i]);
        if (msg)
            messagesToSummarize.push(msg);
    }
    // Messages for turn prefix summary (if splitting a turn)
    const turnPrefixMessages = [];
    if (cutPoint.isSplitTurn) {
        for (let i = cutPoint.turnStartIndex; i < cutPoint.firstKeptEntryIndex; i++) {
            const msg = getMessageFromEntryForCompaction(pathEntries[i]);
            if (msg)
                turnPrefixMessages.push(msg);
        }
    }
    // Extract file operations from messages and previous compaction
    const fileOps = extractFileOperations(messagesToSummarize, pathEntries, prevCompactionIndex);
    // Also extract file ops from turn prefix if splitting
    if (cutPoint.isSplitTurn) {
        for (const msg of turnPrefixMessages) {
            extractFileOpsFromMessage(msg, fileOps);
        }
    }
    return {
        firstKeptEntryId,
        messagesToSummarize,
        turnPrefixMessages,
        isSplitTurn: cutPoint.isSplitTurn,
        tokensBefore,
        previousSummary,
        fileOps,
        settings,
    };
}
// ============================================================================
// Main compaction function
// ============================================================================
const TURN_PREFIX_SUMMARIZATION_PROMPT = `This is the PREFIX of a turn that was too large to keep. The SUFFIX (recent work) is retained.

Summarize the prefix to provide context for the retained suffix:

## Original Request
[What did the user ask for in this turn?]

## Early Progress
- [Key decisions and work done in the prefix]

## Context for Suffix
- [Information needed to understand the retained recent work]

Be concise. Focus on what's needed to understand the kept suffix.`;
/**
 * Generate summaries for compaction using prepared data.
 * Returns CompactionResult - SessionManager adds uuid/parentUuid when saving.
 *
 * @param preparation - Pre-calculated preparation from prepareCompaction()
 * @param customInstructions - Optional custom focus for the summary
 */
export async function compact(preparation, model, apiKey, headers, customInstructions, signal, thinkingLevel) {
    const { firstKeptEntryId, messagesToSummarize, turnPrefixMessages, isSplitTurn, tokensBefore, previousSummary, fileOps, settings, } = preparation;
    // Generate summaries (can be parallel if both needed) and merge into one
    let summary;
    if (isSplitTurn && turnPrefixMessages.length > 0) {
        // Generate both summaries in parallel
        const [historyResult, turnPrefixResult] = await Promise.all([
            messagesToSummarize.length > 0
                ? generateSummary(messagesToSummarize, model, settings.reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel)
                : Promise.resolve("No prior history."),
            generateTurnPrefixSummary(turnPrefixMessages, model, settings.reserveTokens, apiKey, headers, signal, thinkingLevel),
        ]);
        // Merge into single summary
        summary = `${historyResult}\n\n---\n\n**Turn Context (split turn):**\n\n${turnPrefixResult}`;
    }
    else {
        // Just generate history summary
        summary = await generateSummary(messagesToSummarize, model, settings.reserveTokens, apiKey, headers, signal, customInstructions, previousSummary, thinkingLevel);
    }
    // Compute file lists and append to summary
    const { readFiles, modifiedFiles } = computeFileLists(fileOps);
    summary += formatFileOperations(readFiles, modifiedFiles);
    if (!firstKeptEntryId) {
        throw new Error("First kept entry has no UUID - session may need migration");
    }
    return {
        summary,
        firstKeptEntryId,
        tokensBefore,
        details: { readFiles, modifiedFiles },
    };
}
/**
 * Generate a summary for a turn prefix (when splitting a turn).
 */
async function generateTurnPrefixSummary(messages, model, reserveTokens, apiKey, headers, signal, thinkingLevel) {
    const maxTokens = Math.floor(0.5 * reserveTokens); // Smaller budget for turn prefix
    const llmMessages = convertToLlm(messages);
    const conversationText = serializeConversation(llmMessages);
    const promptText = `<conversation>\n${conversationText}\n</conversation>\n\n${TURN_PREFIX_SUMMARIZATION_PROMPT}`;
    const summarizationMessages = [
        {
            role: "user",
            content: [{ type: "text", text: promptText }],
            timestamp: Date.now(),
        },
    ];
    const response = await completeSimple(model, { systemPrompt: SUMMARIZATION_SYSTEM_PROMPT, messages: summarizationMessages }, model.reasoning && thinkingLevel && thinkingLevel !== "off"
        ? { maxTokens, signal, apiKey, headers, reasoning: thinkingLevel }
        : { maxTokens, signal, apiKey, headers });
    if (response.stopReason === "error") {
        throw new Error(`Turn prefix summarization failed: ${response.errorMessage || "Unknown error"}`);
    }
    return response.content
        .filter((c) => c.type === "text")
        .map((c) => c.text)
        .join("\n");
}
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