mirror of
https://github.com/zadam/trilium.git
synced 2025-12-24 08:14:25 +01:00
move from using axios to fetch in llm services
This commit is contained in:
perf3ct
parent
dd9b37e9fb
commit
614d5ccdd3
@ -1,4 +1,3 @@
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import axios from "axios";
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import log from "../../../log.js";
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import { BaseEmbeddingProvider } from "../base_embeddings.js";
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import type { EmbeddingConfig } from "../embeddings_interface.js";
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@ -41,17 +40,23 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
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private async fetchModelCapabilities(modelName: string): Promise<EmbeddingModelInfo | null> {
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try {
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// First try the /api/show endpoint which has detailed model information
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const showResponse = await axios.get(
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`${this.baseUrl}/api/show`,
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{
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params: { name: modelName },
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headers: { "Content-Type": "application/json" },
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timeout: 10000
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}
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);
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const url = new URL(`${this.baseUrl}/api/show`);
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url.searchParams.append('name', modelName);
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const showResponse = await fetch(url, {
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method: 'GET',
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headers: { "Content-Type": "application/json" },
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signal: AbortSignal.timeout(10000)
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});
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if (showResponse.data && showResponse.data.parameters) {
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const params = showResponse.data.parameters;
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if (!showResponse.ok) {
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throw new Error(`HTTP error! status: ${showResponse.status}`);
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}
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const data = await showResponse.json();
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if (data && data.parameters) {
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const params = data.parameters;
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// Extract context length from parameters (different models might use different parameter names)
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const contextWindow = params.context_length ||
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params.num_ctx ||
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@ -157,20 +162,24 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
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* Detect embedding dimension by making a test API call
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*/
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private async detectEmbeddingDimension(modelName: string): Promise<number> {
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const testResponse = await axios.post(
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`${this.baseUrl}/api/embeddings`,
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{
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const testResponse = await fetch(`${this.baseUrl}/api/embeddings`, {
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method: 'POST',
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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model: modelName,
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prompt: "Test"
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},
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{
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headers: { "Content-Type": "application/json" },
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timeout: 10000
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}
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);
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}),
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signal: AbortSignal.timeout(10000)
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});
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if (testResponse.data && Array.isArray(testResponse.data.embedding)) {
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return testResponse.data.embedding.length;
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if (!testResponse.ok) {
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throw new Error(`HTTP error! status: ${testResponse.status}`);
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}
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const data = await testResponse.json();
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if (data && Array.isArray(data.embedding)) {
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return data.embedding.length;
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} else {
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throw new Error("Could not detect embedding dimensions");
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}
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@ -209,35 +218,39 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
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const charLimit = (modelInfo.contextWidth || 4096) * 4; // Rough estimate: avg 4 chars per token
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const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text;
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const response = await axios.post(
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`${this.baseUrl}/api/embeddings`,
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{
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const response = await fetch(`${this.baseUrl}/api/embeddings`, {
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method: 'POST',
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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model: modelName,
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prompt: trimmedText,
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format: "json"
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},
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{
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headers: {
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"Content-Type": "application/json"
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},
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timeout: 60000 // Increased timeout for larger texts (60 seconds)
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}
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);
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}),
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signal: AbortSignal.timeout(60000) // Increased timeout for larger texts (60 seconds)
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});
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if (response.data && Array.isArray(response.data.embedding)) {
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if (!response.ok) {
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throw new Error(`HTTP error! status: ${response.status}`);
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}
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const data = await response.json();
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if (data && Array.isArray(data.embedding)) {
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// Success! Return the embedding
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return new Float32Array(response.data.embedding);
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return new Float32Array(data.embedding);
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} else {
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throw new Error("Unexpected response structure from Ollama API");
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}
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} catch (error: any) {
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lastError = error;
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// Only retry on timeout or connection errors
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const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
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const errorMessage = error.message || "Unknown error";
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const isTimeoutError = errorMessage.includes('timeout') ||
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errorMessage.includes('socket hang up') ||
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errorMessage.includes('ECONNREFUSED') ||
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errorMessage.includes('ECONNRESET');
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errorMessage.includes('ECONNRESET') ||
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errorMessage.includes('AbortError') ||
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errorMessage.includes('NetworkError');
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if (isTimeoutError && retryCount < maxRetries) {
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// Exponential backoff with jitter
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@ -247,7 +260,7 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
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retryCount++;
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} else {
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// Non-retryable error or max retries exceeded
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const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
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const errorMessage = error.message || "Unknown error";
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log.error(`Ollama embedding error: ${errorMessage}`);
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throw new Error(`Ollama embedding error: ${errorMessage}`);
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}
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@ -255,7 +268,7 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
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}
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// If we get here, we've exceeded our retry limit
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const errorMessage = lastError.response?.data?.error?.message || lastError.message || "Unknown error";
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const errorMessage = lastError.message || "Unknown error";
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log.error(`Ollama embedding error after ${maxRetries} retries: ${errorMessage}`);
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throw new Error(`Ollama embedding error after ${maxRetries} retries: ${errorMessage}`);
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}
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@ -1,4 +1,3 @@
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import axios from "axios";
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import log from "../../../log.js";
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import { BaseEmbeddingProvider } from "../base_embeddings.js";
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import type { EmbeddingConfig } from "../embeddings_interface.js";
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@ -44,36 +43,40 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
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try {
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// First try to get model details from the models API
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const response = await axios.get(
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`${this.baseUrl}/models/${modelName}`,
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{
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headers: {
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"Authorization": `Bearer ${this.apiKey}`,
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"Content-Type": "application/json"
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},
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timeout: 10000
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}
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);
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const response = await fetch(`${this.baseUrl}/models/${modelName}`, {
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method: 'GET',
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headers: {
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"Authorization": `Bearer ${this.apiKey}`,
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"Content-Type": "application/json"
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},
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signal: AbortSignal.timeout(10000)
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});
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if (response.data) {
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if (!response.ok) {
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throw new Error(`HTTP error! status: ${response.status}`);
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}
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const data = await response.json();
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if (data) {
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// Different model families may have different ways of exposing context window
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let contextWindow = 0;
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let dimension = 0;
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// Extract context window if available
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if (response.data.context_window) {
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contextWindow = response.data.context_window;
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} else if (response.data.limits && response.data.limits.context_window) {
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contextWindow = response.data.limits.context_window;
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} else if (response.data.limits && response.data.limits.context_length) {
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contextWindow = response.data.limits.context_length;
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if (data.context_window) {
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contextWindow = data.context_window;
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} else if (data.limits && data.limits.context_window) {
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contextWindow = data.limits.context_window;
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} else if (data.limits && data.limits.context_length) {
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contextWindow = data.limits.context_length;
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}
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// Extract embedding dimensions if available
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if (response.data.dimensions) {
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dimension = response.data.dimensions;
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} else if (response.data.embedding_dimension) {
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dimension = response.data.embedding_dimension;
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if (data.dimensions) {
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dimension = data.dimensions;
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} else if (data.embedding_dimension) {
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dimension = data.embedding_dimension;
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}
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// If we didn't get all the info, use defaults for missing values
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@ -185,28 +188,32 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
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return new Float32Array(this.config.dimension);
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}
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const response = await axios.post(
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`${this.baseUrl}/embeddings`,
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{
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const response = await fetch(`${this.baseUrl}/embeddings`, {
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method: 'POST',
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.apiKey}`
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},
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body: JSON.stringify({
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input: text,
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model: this.config.model || "text-embedding-3-small",
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encoding_format: "float"
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},
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{
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.apiKey}`
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}
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}
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);
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})
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});
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if (response.data && response.data.data && response.data.data[0] && response.data.data[0].embedding) {
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return new Float32Array(response.data.data[0].embedding);
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if (!response.ok) {
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throw new Error(`HTTP error! status: ${response.status}`);
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}
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const data = await response.json();
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if (data && data.data && data.data[0] && data.data[0].embedding) {
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return new Float32Array(data.data[0].embedding);
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} else {
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throw new Error("Unexpected response structure from OpenAI API");
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}
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} catch (error: any) {
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const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
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const errorMessage = error.message || "Unknown error";
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log.error(`OpenAI embedding error: ${errorMessage}`);
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throw new Error(`OpenAI embedding error: ${errorMessage}`);
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}
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@ -216,7 +223,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
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* More specific implementation of batch size error detection for OpenAI
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*/
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protected isBatchSizeError(error: any): boolean {
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const errorMessage = error?.message || error?.response?.data?.error?.message || '';
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const errorMessage = error?.message || '';
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const openAIBatchSizeErrorPatterns = [
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'batch size', 'too many inputs', 'context length exceeded',
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'maximum context length', 'token limit', 'rate limit exceeded',
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@ -236,24 +243,28 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
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return [];
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}
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const response = await axios.post(
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`${this.baseUrl}/embeddings`,
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{
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const response = await fetch(`${this.baseUrl}/embeddings`, {
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method: 'POST',
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.apiKey}`
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},
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body: JSON.stringify({
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input: texts,
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model: this.config.model || "text-embedding-3-small",
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encoding_format: "float"
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},
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{
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.apiKey}`
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}
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}
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);
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})
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});
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if (response.data && response.data.data) {
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if (!response.ok) {
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throw new Error(`HTTP error! status: ${response.status}`);
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}
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const data = await response.json();
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if (data && data.data) {
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// Sort the embeddings by index to ensure they match the input order
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const sortedEmbeddings = response.data.data
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const sortedEmbeddings = data.data
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.sort((a: any, b: any) => a.index - b.index)
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.map((item: any) => new Float32Array(item.embedding));
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@ -1,9 +1,9 @@
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import axios from "axios";
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import log from "../../../log.js";
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import { BaseEmbeddingProvider } from "../base_embeddings.js";
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import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js";
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import type { EmbeddingConfig } from "../embeddings_interface.js";
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import { NormalizationStatus } from "../embeddings_interface.js";
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import { LLM_CONSTANTS } from "../../constants/provider_constants.js";
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import type { EmbeddingModelInfo } from "../../interfaces/embedding_interfaces.js";
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// Voyage model context window sizes - as of current API version
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const VOYAGE_MODEL_CONTEXT_WINDOWS: Record<string, number> = {
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@ -46,7 +46,7 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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// Update the config dimension
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this.config.dimension = modelInfo.dimension;
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log.info(`Voyage AI model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`);
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log.info(`Voyage AI model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWidth}`);
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} catch (error: any) {
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log.error(`Error initializing Voyage AI provider: ${error.message}`);
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}
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@ -70,7 +70,9 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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return {
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dimension,
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contextWindow,
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guaranteesNormalization: true // Voyage embeddings are typically normalized
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guaranteesNormalization: true, // Voyage embeddings are typically normalized
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name: modelName,
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type: 'float32'
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};
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} catch (error) {
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log.info(`Could not determine capabilities for Voyage AI model ${modelName}: ${error}`);
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@ -99,7 +101,9 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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const modelInfo: EmbeddingModelInfo = {
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dimension: knownDimension,
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contextWindow,
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guaranteesNormalization: true // Voyage embeddings are typically normalized
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guaranteesNormalization: true, // Voyage embeddings are typically normalized
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name: modelName,
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type: 'float32'
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};
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this.modelInfoCache.set(modelName, modelInfo);
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@ -117,20 +121,26 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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return {
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dimension: dimension || 1024,
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contextWindow: 4096,
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guaranteesNormalization: true // Voyage-2 embeddings are normalized
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guaranteesNormalization: true, // Voyage-2 embeddings are normalized
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name: modelName,
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type: 'float32'
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};
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} else if (modelName.includes('voyage-lite-02')) {
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return {
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dimension: dimension || 768,
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contextWindow: 4096,
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guaranteesNormalization: true // Voyage-lite embeddings are normalized
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guaranteesNormalization: true, // Voyage-lite embeddings are normalized
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name: modelName,
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type: 'float32'
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};
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} else {
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// Default for other Voyage models
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return {
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dimension: dimension || 1024,
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contextWindow: 4096,
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guaranteesNormalization: true // Assuming all Voyage embeddings are normalized
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guaranteesNormalization: true, // Assuming all Voyage embeddings are normalized
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name: modelName,
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type: 'float32'
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};
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}
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}
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@ -141,7 +151,9 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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const defaultModelInfo: EmbeddingModelInfo = {
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dimension: 1024, // Default for Voyage models
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contextWindow: 8192,
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guaranteesNormalization: true // Voyage embeddings are typically normalized
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guaranteesNormalization: true, // Voyage embeddings are typically normalized
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name: modelName,
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type: 'float32'
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};
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this.modelInfoCache.set(modelName, defaultModelInfo);
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@ -167,29 +179,33 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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const charLimit = modelInfo.contextWindow * 4; // Rough estimate: avg 4 chars per token
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const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text;
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const response = await axios.post(
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`${this.baseUrl}/embeddings`,
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{
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const response = await fetch(`${this.baseUrl}/embeddings`, {
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method: 'POST',
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.apiKey}`
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},
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body: JSON.stringify({
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model: modelName,
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input: trimmedText,
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input_type: "text",
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truncation: true
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},
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{
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headers: {
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"Content-Type": "application/json",
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"Authorization": `Bearer ${this.apiKey}`
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}
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}
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);
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})
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});
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if (response.data && response.data.data && response.data.data[0] && response.data.data[0].embedding) {
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return new Float32Array(response.data.data[0].embedding);
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if (!response.ok) {
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const errorData = await response.json().catch(() => ({}));
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throw new Error(errorData.error?.message || `HTTP error ${response.status}`);
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}
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const data = await response.json();
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if (data && data.data && data.data[0] && data.data[0].embedding) {
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return new Float32Array(data.data[0].embedding);
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} else {
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throw new Error("Unexpected response structure from Voyage AI API");
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}
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} catch (error: any) {
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const errorMessage = error.response?.data?.error?.message || error.message || "Unknown error";
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const errorMessage = error.message || "Unknown error";
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log.error(`Voyage AI embedding error: ${errorMessage}`);
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throw new Error(`Voyage AI embedding error: ${errorMessage}`);
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}
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@ -199,7 +215,7 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
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* More specific implementation of batch size error detection for Voyage AI
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*/
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protected isBatchSizeError(error: any): boolean {
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const errorMessage = error?.message || error?.response?.data?.error?.message || '';
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const errorMessage = error?.message || '';
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const voyageBatchSizeErrorPatterns = [
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'batch size', 'too many inputs', 'context length exceeded',
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'token limit', 'rate limit', 'limit exceeded',
|
||||
@ -234,24 +250,28 @@ export class VoyageEmbeddingProvider extends BaseEmbeddingProvider {
|
||||
// Filter out empty texts
|
||||
const validBatch = batch.map(text => text.trim() || " ");
|
||||
|
||||
const response = await axios.post(
|
||||
`${this.baseUrl}/embeddings`,
|
||||
{
|
||||
const response = await fetch(`${this.baseUrl}/embeddings`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": `Bearer ${this.apiKey}`
|
||||
},
|
||||
body: JSON.stringify({
|
||||
model: modelName,
|
||||
input: validBatch,
|
||||
input_type: "text",
|
||||
truncation: true
|
||||
},
|
||||
{
|
||||
headers: {
|
||||
"Content-Type": "application/json",
|
||||
"Authorization": `Bearer ${this.apiKey}`
|
||||
}
|
||||
}
|
||||
);
|
||||
})
|
||||
});
|
||||
|
||||
if (response.data && response.data.data && Array.isArray(response.data.data)) {
|
||||
return response.data.data.map((item: any) =>
|
||||
if (!response.ok) {
|
||||
const errorData = await response.json().catch(() => ({}));
|
||||
throw new Error(errorData.error?.message || `HTTP error ${response.status}`);
|
||||
}
|
||||
|
||||
const data = await response.json();
|
||||
if (data && data.data && Array.isArray(data.data)) {
|
||||
return data.data.map((item: any) =>
|
||||
new Float32Array(item.embedding || [])
|
||||
);
|
||||
} else {
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user