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refactor(memory): dedupe embedding batch runner options

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This commit is contained in:
Peter Steinberger
2026-02-15 13:37:42 +00:00
parent 18342b0a5b
commit 0d47bea3bf
1 changed files with 31 additions and 21 deletions
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52
src/memory/manager-embedding-ops.ts
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@@ -339,6 +339,31 @@ class MemoryManagerEmbeddingOps {
this.upsertEmbeddingCache(toCache);
}
private buildEmbeddingBatchRunnerOptions<TRequest>(params: {
requests: TRequest[];
chunks: MemoryChunk[];
source: MemorySource;
}): {
agentId: string | undefined;
requests: TRequest[];
wait: boolean;
concurrency: number;
pollIntervalMs: number;
timeoutMs: number;
debug: (message: string, data: Record<string, unknown>) => void;
} {
const { requests, chunks, source } = params;
return {
agentId: this.agentId,
requests,
wait: this.batch.wait,
concurrency: this.batch.concurrency,
pollIntervalMs: this.batch.pollIntervalMs,
timeoutMs: this.batch.timeoutMs,
debug: (message, data) => log.debug(message, { ...data, source, chunks: chunks.length }),
};
}
private async embedChunksWithVoyageBatch(
chunks: MemoryChunk[],
entry: MemoryFileEntry | SessionFileEntry,
@@ -364,18 +389,13 @@ class MemoryManagerEmbeddingOps {
body: { input: chunk.text },
}),
});
const runnerOptions = this.buildEmbeddingBatchRunnerOptions({ requests, chunks, source });
const batchResult = await this.runBatchWithFallback({
provider: "voyage",
run: async () =>
await runVoyageEmbeddingBatches({
client: voyage,
agentId: this.agentId,
requests,
wait: this.batch.wait,
concurrency: this.batch.concurrency,
pollIntervalMs: this.batch.pollIntervalMs,
timeoutMs: this.batch.timeoutMs,
debug: (message, data) => log.debug(message, { ...data, source, chunks: chunks.length }),
...runnerOptions,
}),
fallback: async () => await this.embedChunksInBatches(chunks),
});
@@ -416,18 +436,13 @@ class MemoryManagerEmbeddingOps {
},
}),
});
const runnerOptions = this.buildEmbeddingBatchRunnerOptions({ requests, chunks, source });
const batchResult = await this.runBatchWithFallback({
provider: "openai",
run: async () =>
await runOpenAiEmbeddingBatches({
openAi,
agentId: this.agentId,
requests,
wait: this.batch.wait,
concurrency: this.batch.concurrency,
pollIntervalMs: this.batch.pollIntervalMs,
timeoutMs: this.batch.timeoutMs,
debug: (message, data) => log.debug(message, { ...data, source, chunks: chunks.length }),
...runnerOptions,
}),
fallback: async () => await this.embedChunksInBatches(chunks),
});
@@ -464,19 +479,14 @@ class MemoryManagerEmbeddingOps {
taskType: "RETRIEVAL_DOCUMENT",
}),
});
const runnerOptions = this.buildEmbeddingBatchRunnerOptions({ requests, chunks, source });
const batchResult = await this.runBatchWithFallback({
provider: "gemini",
run: async () =>
await runGeminiEmbeddingBatches({
gemini,
agentId: this.agentId,
requests,
wait: this.batch.wait,
concurrency: this.batch.concurrency,
pollIntervalMs: this.batch.pollIntervalMs,
timeoutMs: this.batch.timeoutMs,
debug: (message, data) => log.debug(message, { ...data, source, chunks: chunks.length }),
...runnerOptions,
}),
fallback: async () => await this.embedChunksInBatches(chunks),
});