getcompanion-ai/co-mono
1
0
Fork 0
mirror of https://github.com/getcompanion-ai/co-mono.git synced 2026-04-15 17:00:59 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Extract shared compaction/branch-summarization utils

Browse source 
- New utils.ts with shared functions:
  - FileOperations type and createFileOps()
  - extractFileOpsFromMessage()
  - computeFileLists()
  - formatFileOperations()
  - serializeConversation()
  - SUMMARIZATION_SYSTEM_PROMPT

- branch-summarization.ts now uses:
  - Serialization approach (conversation as text, not LLM messages)
  - completeSimple with system prompt
  - Shared utility functions
This commit is contained in:
Mario Zechner 2025-12-30 00:13:11 +01:00
parent 17ce3814a8
commit 81f4cdf3e3
4 changed files with 193 additions and 199 deletions
Download patch file Download diff file Expand all files Collapse all files

144
packages/coding-agent/src/core/compaction/compaction.ts
View file

@ -6,10 +6,19 @@
*/
import type { AgentMessage } from "@mariozechner/pi-agent-core";
import type { AssistantMessage, Message, Model, Usage } from "@mariozechner/pi-ai";
import type { AssistantMessage, Model, Usage } from "@mariozechner/pi-ai";
import { complete, completeSimple } from "@mariozechner/pi-ai";
import { convertToLlm, createBranchSummaryMessage, createHookMessage } from "../messages.js";
import type { CompactionEntry, SessionEntry } from "../session-manager.js";
import {
computeFileLists,
createFileOps,
extractFileOpsFromMessage,
type FileOperations,
formatFileOperations,
SUMMARIZATION_SYSTEM_PROMPT,
serializeConversation,
} from "./utils.js";
// ============================================================================
// File Operation Tracking
@ -21,44 +30,6 @@ export interface CompactionDetails {
modifiedFiles: string[];
}
interface FileOperations {
read: Set<string>;
written: Set<string>;
edited: Set<string>;
}
/**
* Extract file operations from tool calls in an assistant message.
*/
function extractFileOpsFromMessage(message: AgentMessage, fileOps: FileOperations): void {
if (message.role !== "assistant") return;
if (!("content" in message) || !Array.isArray(message.content)) return;
for (const block of message.content) {
if (typeof block !== "object" || block === null) continue;
if (!("type" in block) || block.type !== "toolCall") continue;
if (!("arguments" in block) || !("name" in block)) continue;
const args = block.arguments as Record<string, unknown> | undefined;
if (!args) continue;
const path = typeof args.path === "string" ? args.path : undefined;
if (!path) continue;
switch (block.name) {
case "read":
fileOps.read.add(path);
break;
case "write":
fileOps.written.add(path);
break;
case "edit":
fileOps.edited.add(path);
break;
}
}
}
/**
* Extract file operations from messages and previous compaction entries.
*/
@ -67,11 +38,7 @@ function extractFileOperations(
entries: SessionEntry[],
prevCompactionIndex: number,
): FileOperations {
const fileOps: FileOperations = {
read: new Set(),
written: new Set(),
edited: new Set(),
};
const fileOps = createFileOps();
// Collect from previous compaction's details (if pi-generated)
if (prevCompactionIndex >= 0) {
@ -95,91 +62,6 @@ function extractFileOperations(
return fileOps;
}
/**
* Compute final file lists from file operations.
*/
function computeFileLists(fileOps: FileOperations): { readFiles: string[]; modifiedFiles: string[] } {
const modified = new Set([...fileOps.edited, ...fileOps.written]);
const readOnly = [...fileOps.read].filter((f) => !modified.has(f)).sort();
const modifiedFiles = [...modified].sort();
return { readFiles: readOnly, modifiedFiles };
}
/**
* Format file operations as XML tags for summary.
*/
function formatFileOperations(readFiles: string[], modifiedFiles: string[]): string {
const sections: string[] = [];
if (readFiles.length > 0) {
sections.push(`<read-files>\n${readFiles.join("\n")}\n</read-files>`);
}
if (modifiedFiles.length > 0) {
sections.push(`<modified-files>\n${modifiedFiles.join("\n")}\n</modified-files>`);
}
if (sections.length === 0) return "";
return `\n\n${sections.join("\n\n")}`;
}
/**
* Serialize LLM messages to text for summarization.
* This prevents the model from treating it as a conversation to continue.
* Call convertToLlm() first to handle custom message types.
*/
function serializeConversation(messages: Message[]): string {
const parts: string[] = [];
for (const msg of messages) {
if (msg.role === "user") {
const content =
typeof msg.content === "string"
? msg.content
: msg.content
.filter((c): c is { type: "text"; text: string } => c.type === "text")
.map((c) => c.text)
.join("");
if (content) parts.push(`[User]: ${content}`);
} else if (msg.role === "assistant") {
const textParts: string[] = [];
const thinkingParts: string[] = [];
const toolCalls: string[] = [];
for (const block of msg.content) {
if (block.type === "text") {
textParts.push(block.text);
} else if (block.type === "thinking") {
thinkingParts.push(block.thinking);
} else if (block.type === "toolCall") {
const args = block.arguments as Record<string, unknown>;
const argsStr = Object.entries(args)
.map(([k, v]) => `${k}=${JSON.stringify(v)}`)
.join(", ");
toolCalls.push(`${block.name}(${argsStr})`);
}
}
if (thinkingParts.length > 0) {
parts.push(`[Assistant thinking]: ${thinkingParts.join("\n")}`);
}
if (textParts.length > 0) {
parts.push(`[Assistant]: ${textParts.join("\n")}`);
}
if (toolCalls.length > 0) {
parts.push(`[Assistant tool calls]: ${toolCalls.join("; ")}`);
}
} else if (msg.role === "toolResult") {
const content = msg.content
.filter((c): c is { type: "text"; text: string } => c.type === "text")
.map((c) => c.text)
.join("");
if (content) {
parts.push(`[Tool result]: ${content}`);
}
}
}
return parts.join("\n\n");
}
// ============================================================================
// Message Extraction
// ============================================================================
@ -501,10 +383,6 @@ export function findCutPoint(
// Summarization
// ============================================================================
const SUMMARIZATION_SYSTEM_PROMPT = `You are a context summarization assistant. Your task is to read a conversation between a user and an AI coding assistant, then produce a structured summary following the exact format specified.
Do NOT continue the conversation. Do NOT respond to any questions in the conversation. ONLY output the structured summary.`;
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: