mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-07-07 20:32:12 +02:00
Enforce strict Effect tsgo migrations
This commit is contained in:
elpresidank
parent
64fb23e7d0
commit
f6878d4dd7
49 changed files with 5547 additions and 3250 deletions
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@ -15,9 +15,15 @@ import {
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type ProcessorConfig,
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type LlmResult,
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type LlmChunk,
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tooManyRequestsError,
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} from "@trustgraph/base";
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import { Effect, Layer } from "effect";
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import { Effect, Layer, Stream } from "effect";
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import {
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optionalStringConfig,
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providerStatusError,
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requiredString,
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toAsyncGenerator,
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type TextCompletionRuntimeError,
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} from "./common.ts";
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export type OpenAIProcessorConfig = ProcessorConfig & {
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model?: string;
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@ -27,24 +33,52 @@ export type OpenAIProcessorConfig = ProcessorConfig & {
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maxOutput?: number;
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};
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type ResolvedOpenAIConfig = {
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readonly defaultModel: string;
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readonly defaultTemperature: number;
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readonly maxOutput: number;
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readonly apiKey: string;
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readonly baseURL: string | undefined;
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};
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const loadOpenAIConfig = Effect.fn("loadOpenAIConfig")(function*(config: OpenAIProcessorConfig) {
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const apiKey = yield* requiredString(
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config.apiKey ?? (yield* optionalStringConfig("OpenAI", "OPENAI_TOKEN")),
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"OpenAI",
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"OPENAI_TOKEN",
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"OpenAI API key not specified",
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);
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return {
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defaultModel: config.model ?? "gpt-4o",
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defaultTemperature: config.temperature ?? 0.0,
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maxOutput: config.maxOutput ?? 4096,
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apiKey,
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baseURL: config.baseUrl ?? (yield* optionalStringConfig("OpenAI", "OPENAI_BASE_URL")),
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} satisfies ResolvedOpenAIConfig;
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});
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const mapOpenAIError = (error: unknown): TextCompletionRuntimeError =>
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providerStatusError("OpenAI", error);
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export function makeOpenAIProvider(config: OpenAIProcessorConfig): LlmProvider {
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const defaultModel = config.model ?? "gpt-4o";
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const defaultTemperature = config.temperature ?? 0.0;
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const maxOutput = config.maxOutput ?? 4096;
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const apiKey = config.apiKey ?? process.env.OPENAI_TOKEN;
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if (apiKey === undefined || apiKey.length === 0) {
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throw new Error("OpenAI API key not specified");
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}
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const {
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defaultModel,
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defaultTemperature,
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maxOutput,
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apiKey,
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baseURL,
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} = Effect.runSync(loadOpenAIConfig(config)) satisfies ResolvedOpenAIConfig;
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const client = new OpenAI({
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apiKey,
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baseURL: config.baseUrl ?? process.env.OPENAI_BASE_URL,
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});
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apiKey,
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baseURL,
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});
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console.log("[OpenAI] LLM service initialized");
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Effect.runSync(Effect.log("[OpenAI] LLM service initialized"));
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return {
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generateContent: async (
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generateContent: (
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system: string,
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prompt: string,
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model?: string,
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@ -53,94 +87,115 @@ export function makeOpenAIProvider(config: OpenAIProcessorConfig): LlmProvider {
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const modelName = model ?? defaultModel;
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const temp = temperature ?? defaultTemperature;
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try {
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const resp = await client.chat.completions.create({
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model: modelName,
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messages: [
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{ role: "system", content: system },
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{ role: "user", content: prompt },
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],
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temperature: temp,
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max_completion_tokens: maxOutput,
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});
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return {
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text: resp.choices[0].message.content ?? "",
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inToken: resp.usage?.prompt_tokens ?? 0,
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outToken: resp.usage?.completion_tokens ?? 0,
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model: modelName,
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};
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} catch (err) {
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if (err instanceof OpenAI.RateLimitError) {
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throw tooManyRequestsError();
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}
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throw err;
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}
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return Effect.runPromise(
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Effect.tryPromise({
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try: () =>
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client.chat.completions.create({
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model: modelName,
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messages: [
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{ role: "system", content: system },
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{ role: "user", content: prompt },
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],
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temperature: temp,
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max_completion_tokens: maxOutput,
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}),
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catch: mapOpenAIError,
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}).pipe(
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Effect.map((resp): LlmResult => ({
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text: resp.choices[0].message.content ?? "",
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inToken: resp.usage?.prompt_tokens ?? 0,
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outToken: resp.usage?.completion_tokens ?? 0,
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model: modelName,
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})),
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),
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);
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},
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supportsStreaming: () => true,
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generateContentStream: async function* (
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generateContentStream: (
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system: string,
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prompt: string,
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model?: string,
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temperature?: number,
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): AsyncGenerator<LlmChunk> {
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): AsyncGenerator<LlmChunk> => {
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const modelName = model ?? defaultModel;
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const temp = temperature ?? defaultTemperature;
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try {
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const stream = await client.chat.completions.create({
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model: modelName,
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messages: [
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{ role: "system", content: system },
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{ role: "user", content: prompt },
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],
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temperature: temp,
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max_completion_tokens: maxOutput,
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stream: true,
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stream_options: { include_usage: true },
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});
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const stream = Stream.fromEffect(
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Effect.tryPromise({
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try: () =>
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client.chat.completions.create({
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model: modelName,
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messages: [
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{ role: "system", content: system },
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{ role: "user", content: prompt },
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],
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temperature: temp,
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max_completion_tokens: maxOutput,
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stream: true,
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stream_options: { include_usage: true },
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}),
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catch: mapOpenAIError,
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}),
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).pipe(
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Stream.flatMap((openAIStream) => {
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const iterator = openAIStream[Symbol.asyncIterator]();
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let totalInputTokens = 0;
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let totalOutputTokens = 0;
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for await (const chunk of stream) {
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const content = chunk.choices[0]?.delta?.content;
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if (content !== null && content !== undefined && content.length > 0) {
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yield {
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text: content,
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inToken: null,
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outToken: null,
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model: modelName,
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isFinal: false,
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};
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}
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return Stream.unfold<"pulling" | "done", LlmChunk, TextCompletionRuntimeError, never>(
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"pulling",
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(state) => {
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if (state === "done") return Effect.void as Effect.Effect<undefined>;
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if (chunk.usage !== null && chunk.usage !== undefined) {
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totalInputTokens = chunk.usage.prompt_tokens;
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totalOutputTokens = chunk.usage.completion_tokens;
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}
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}
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return Effect.gen(function* () {
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while (true) {
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const next = yield* Effect.tryPromise({
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try: () => iterator.next(),
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catch: mapOpenAIError,
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});
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yield {
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text: "",
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inToken: totalInputTokens,
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outToken: totalOutputTokens,
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model: modelName,
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isFinal: true,
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};
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} catch (err) {
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if (err instanceof OpenAI.RateLimitError) {
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throw tooManyRequestsError();
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}
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throw err;
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}
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if (next.done === true) {
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return [{
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text: "",
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inToken: totalInputTokens,
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outToken: totalOutputTokens,
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model: modelName,
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isFinal: true,
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}, "done"] as const;
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}
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const chunk = next.value;
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const content = chunk.choices[0]?.delta?.content;
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if (chunk.usage !== null && chunk.usage !== undefined) {
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totalInputTokens = chunk.usage.prompt_tokens;
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totalOutputTokens = chunk.usage.completion_tokens;
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}
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if (content !== null && content !== undefined && content.length > 0) {
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return [{
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text: content,
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inToken: null,
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outToken: null,
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model: modelName,
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isFinal: false,
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}, "pulling"] as const;
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}
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}
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});
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},
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);
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}),
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);
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return toAsyncGenerator(Stream.toAsyncIterable(stream), mapOpenAIError);
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},
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};
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}
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export type OpenAIProcessor = ReturnType<typeof makeOpenAIProcessor>;
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export function makeOpenAIProcessor(config: OpenAIProcessorConfig): ReturnType<typeof makeLlmService> {
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export function makeOpenAIProcessor(
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config: OpenAIProcessorConfig,
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): ReturnType<typeof makeLlmService> {
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return makeLlmService(config, makeOpenAIProvider(config));
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}
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@ -156,6 +211,6 @@ export const program = makeFlowProcessorProgram<ProcessorConfig, never, Llm>({
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),
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});
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export async function run(): Promise<void> {
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await Effect.runPromise(program);
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export function run(): Promise<void> {
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return Effect.runPromise(program);
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}
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