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refactor(ai-moderation): enhance llmClient flexibility and idempotency

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Update the LLM client to support streaming responses and improve
database migration idempotency.

- Add `stream` support to `llmChat` with dynamic chunk parsing for
  compatibility across different LLM providers.
- Remove hardcoded default temperature and top_p to allow for more
  flexible parameter passing.
- Update database migrations to use `IF NOT EXISTS` for tables and
  indexes to prevent errors during re-runs.
This commit is contained in:
MythEclipse
2026-06-05 18:45:58 +07:00
parent 69b4916d08
commit c0a067322b
2 changed files with 65 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files
services/discord-gateway/drizzle/migrations/0005_large_squadron_sinister.sql
+5 -5
View File
@@ -1,11 +1,11 @@
CREATE TABLE "channel_cultures" (
CREATE TABLE IF NOT EXISTS "channel_cultures" (
"channel_id" text PRIMARY KEY NOT NULL,
"guild_id" text NOT NULL,
"culture_summary" text NOT NULL,
"last_analyzed_at" bigint NOT NULL
);
--> statement-breakpoint
CREATE TABLE "user_reputations" (
CREATE TABLE IF NOT EXISTS "user_reputations" (
"user_id" text PRIMARY KEY NOT NULL,
"guild_id" text NOT NULL,
"trust_score" integer DEFAULT 50 NOT NULL,
@@ -16,6 +16,6 @@ CREATE TABLE "user_reputations" (
"updated_at" bigint NOT NULL
);
--> statement-breakpoint
CREATE INDEX "idx_channel_cultures_guild_id" ON "channel_cultures" USING btree ("guild_id");--> statement-breakpoint
CREATE INDEX "idx_user_reputations_guild_id" ON "user_reputations" USING btree ("guild_id");--> statement-breakpoint
CREATE INDEX "idx_user_reputations_trust_score" ON "user_reputations" USING btree ("trust_score");
CREATE INDEX IF NOT EXISTS "idx_channel_cultures_guild_id" ON "channel_cultures" USING btree ("guild_id");--> statement-breakpoint
CREATE INDEX IF NOT EXISTS "idx_user_reputations_guild_id" ON "user_reputations" USING btree ("guild_id");--> statement-breakpoint
CREATE INDEX IF NOT EXISTS "idx_user_reputations_trust_score" ON "user_reputations" USING btree ("trust_score");
services/discord-gateway/src/modules/ai-moderation/llmClient.ts
+60 -23
View File
@@ -33,12 +33,6 @@ function getClient(): OpenAI | null {
return openaiClient;
}
// ---------------------------------------------------------------------------
// Shared defaults
// ---------------------------------------------------------------------------
const DEFAULT_TEMPERATURE = 0.2;
const DEFAULT_TOP_P = 0.95;
const DEFAULT_RETRIES = 2;
// ---------------------------------------------------------------------------
@@ -60,6 +54,8 @@ export interface LlmCallOpts {
jsonResponse?: { type: "json_object" };
/** Extra retries beyond DEFAULT_RETRIES (default 2). */
retries?: number;
/** Whether to use streaming (if true, will consume stream and return aggregated result) */
stream?: boolean;
}
/**
@@ -77,33 +73,74 @@ export async function llmChat(
const {
messages,
model = config.AI_LLM_MODEL,
max_tokens = 8192,
temperature = DEFAULT_TEMPERATURE,
top_p = DEFAULT_TOP_P,
max_tokens,
temperature,
top_p,
jsonResponse,
retries = DEFAULT_RETRIES,
stream = false,
} = opts;
const params: OpenAI.Chat.Completions.ChatCompletionCreateParamsNonStreaming =
{
model,
messages,
temperature,
top_p,
max_tokens,
stream: false,
};
const params: any = {
model,
messages,
};
// Attach optional parameters only if explicitly provided to maintain
// maximum compatibility with various LLM providers and local APIs.
if (stream !== undefined) params.stream = stream;
if (temperature !== undefined) params.temperature = temperature;
if (top_p !== undefined) params.top_p = top_p;
if (max_tokens !== undefined) params.max_tokens = max_tokens;
if (jsonResponse) {
(params as unknown as Record<string, unknown>).response_format =
jsonResponse;
params.response_format = jsonResponse;
}
return retryWithBackoff(
async () => {
return withLlmConcurrency(async () =>
client.chat.completions.create(params),
);
return withLlmConcurrency(async () => {
const response = await client.chat.completions.create(params);
if (stream) {
let content = "";
let finishReason = "stop";
for await (const chunk of response as any) {
const choice = chunk?.choices?.[0];
// Dynamic parsing to support multiple providers (OpenAI, Ollama, Groq, Anthropic via proxy, etc.)
const textChunk =
choice?.delta?.content ||
choice?.message?.content ||
choice?.text ||
chunk?.message?.content ||
chunk?.response ||
chunk?.content ||
"";
content += textChunk;
const fr = choice?.finish_reason || chunk?.finish_reason;
if (fr) {
finishReason = fr;
}
}
return {
id: 'stream-aggregated',
choices: [
{
message: { role: 'assistant', content, refusal: null },
finish_reason: finishReason,
index: 0,
logprobs: null,
},
],
created: Math.floor(Date.now() / 1000),
model: model,
object: 'chat.completion',
} as OpenAI.Chat.Completions.ChatCompletion;
}
return response as OpenAI.Chat.Completions.ChatCompletion;
});
},
{
retries,