23172248446

Committed 17 Mar 2026 12:19AM UTC coverage: 54.15% (-0.4%) from 54.504%

Build # 23172248446

Build Type

push

github

Committed by

lucasliet

Commit Message

feat: add Groq provider and provider-builder skill

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183 of 377 branches covered (48.54%)

Branch coverage included in aggregate %.

21 of 37 new or added lines in 5 files covered. (56.76%)

201 existing lines in 7 files now uncovered.

2988 of 5479 relevant lines covered (54.54%)

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37.74

/src/service/ContextCompressorService.ts

import OpenAi from 'openai';
import { estimateTokens, shouldCompress } from '@/util/TokenEstimator.ts';

/**
 * Service responsible for compressing conversation history
 * when it approaches the context window limit.
 */
export class ContextCompressorService {
        /**
         * Compresses the entire conversation history into a single summary entry.
         * Uses an LLM to extract only essential information.
         */
        static async compressHistory(
                history: OpenAi.ChatCompletionMessageParam[],
                model: string,
                openai: OpenAi,
        ): Promise<OpenAi.ChatCompletionMessageParam> {
                const historyText = this.formatHistory(history);
                const prompt = `You are an expert context compressor. Your goal is to condense the following conversation history into a concise summary that preserves all critical information for an LLM to resume the conversation seamlessly.

Instructions:
1. Analyze the conversation history below.
2. Extract and preserve:
    - User preferences, personal details, and name (if mentioned).
    - Key decisions, agreed-upon plans, and established constraints.
    - Technical context: important code snippets (keep distinct), file paths, error messages, and configuration details.
    - Open questions, pending tasks, and unresolved issues.
3. Discard:
    - Phratic communication (greetings, small talk).
    - Redundant acknowledgments (e.g., "Okay", "I understand").
    - Resolved intermediate steps that are no longer relevant to the outcome.
4. Output the summary in Portuguese (pt-BR).
5. Use a structured format with bullet points.

---
Conversation History:
${historyText}
---
Summary (in Portuguese):`;

                const response = await openai.chat.completions.create({
                        model,
                        messages: [{ role: 'user', content: prompt }],
                        max_tokens: 2000,
                        temperature: 0,
                });

                const summary = response.choices[0]?.message?.content || historyText.substring(0, 4000);

                return {
                        role: 'assistant',
                        content: `[Resumo do contexto anterior]\n${summary}`,
                };
        }

        /**
         * Formats the history array into a readable text format for compression.
         */
        private static formatHistory(history: OpenAi.ChatCompletionMessageParam[]): string {
                return history
                        .map((msg) => `${msg.role}: ${typeof msg.content === 'string' ? msg.content : ''}`)
                        .join('\n\n');
        }

        /**
         * Checks if compression is needed and compresses if so.
         * @returns Object containing the (possibly compressed) history and a boolean indicating if compression occurred.
         */
        static async compressIfNeeded(
                history: OpenAi.ChatCompletionMessageParam[],
                maxTokens: number,
                model: string,
                openai: OpenAi,
        ): Promise<{ history: OpenAi.ChatCompletionMessageParam[]; didCompress: boolean }> {
                const historyTokens = estimateTokens(history);
                if (!shouldCompress(historyTokens, maxTokens)) {
                        return { history, didCompress: false };
                }
                console.log(`[ContextCompressorService] Context exceeds 80% limit (${historyTokens}/${maxTokens}), compressing...`);
                const compressedEntry = await this.compressHistory(history, model, openai);
                return { history: [compressedEntry], didCompress: true };
        }
}

export const COMPRESSION_WARNING_MSG = '⚠️ Contexto comprimido para economizar espaço.';

1	import OpenAi from 'openai';
2	import { estimateTokens, shouldCompress } from '@/util/TokenEstimator.ts';	21✔
3
4	/**	1✔
5	* Service responsible for compressing conversation history
6	* when it approaches the context window limit.
7	*/	21✔
8	export class ContextCompressorService {	21✔
9	/**	21✔
10	* Compresses the entire conversation history into a single summary entry.
11	* Uses an LLM to extract only essential information.
UNCOV 12	*/	×
13	static async compressHistory(	×
14	history: OpenAi.ChatCompletionMessageParam[],	×
15	model: string,	×
16	openai: OpenAi,	×
17	): Promise<OpenAi.ChatCompletionMessageParam> {	×
18	const historyText = this.formatHistory(history);	×
19	const prompt = `You are an expert context compressor. Your goal is to condense the following conversation history into a concise summary that preserves all critical information for an LLM to resume the conversation seamlessly.	×
20
21	Instructions:
22	1. Analyze the conversation history below.
23	2. Extract and preserve:
24	- User preferences, personal details, and name (if mentioned).
25	- Key decisions, agreed-upon plans, and established constraints.
26	- Technical context: important code snippets (keep distinct), file paths, error messages, and configuration details.
27	- Open questions, pending tasks, and unresolved issues.
28	3. Discard:
29	- Phratic communication (greetings, small talk).
30	- Redundant acknowledgments (e.g., "Okay", "I understand").
31	- Resolved intermediate steps that are no longer relevant to the outcome.
32	4. Output the summary in Portuguese (pt-BR).
33	5. Use a structured format with bullet points.
34
35	---
36	Conversation History:
UNCOV 37	${historyText}	×
38	---
UNCOV 39	Summary (in Portuguese):`;	×
40
UNCOV 41	const response = await openai.chat.completions.create({	×
42	model,	×
43	messages: [{ role: 'user', content: prompt }],	×
44	max_tokens: 2000,	×
45	temperature: 0,	×
46	});	×
47
UNCOV 48	const summary = response.choices[0]?.message?.content \|\| historyText.substring(0, 4000);	×
49
UNCOV 50	return {	×
51	role: 'assistant',	×
52	content: `[Resumo do contexto anterior]\n${summary}`,	×
53	};	×
54	}	×
55
56	/**	21✔
57	* Formats the history array into a readable text format for compression.
UNCOV 58	*/	×
UNCOV 59	private static formatHistory(history: OpenAi.ChatCompletionMessageParam[]): string {	×
60	return history	×
61	.map((msg) => `${msg.role}: ${typeof msg.content === 'string' ? msg.content : ''}`)	×
62	.join('\n\n');	×
63	}	×
64
65	/**	21✔
66	* Checks if compression is needed and compresses if so.
67	* @returns Object containing the (possibly compressed) history and a boolean indicating if compression occurred.
68	*/	21✔
69	static async compressIfNeeded(	21✔
70	history: OpenAi.ChatCompletionMessageParam[],	21✔
71	maxTokens: number,	21✔
72	model: string,	21✔
73	openai: OpenAi,	21✔
74	): Promise<{ history: OpenAi.ChatCompletionMessageParam[]; didCompress: boolean }> {	21✔
75	const historyTokens = estimateTokens(history);	24✔
76	if (!shouldCompress(historyTokens, maxTokens)) {	24✔
77	return { history, didCompress: false };	96✔
78	}	24!
UNCOV 79	console.log(`[ContextCompressorService] Context exceeds 80% limit (${historyTokens}/${maxTokens}), compressing...`);	×
UNCOV 80	const compressedEntry = await this.compressHistory(history, model, openai);	×
81	return { history: [compressedEntry], didCompress: true };	×
82	}	×
83	}	21✔
84
85	export const COMPRESSION_WARNING_MSG = '⚠️ Contexto comprimido para economizar espaço.';	21✔

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