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fix linter errors in providers

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perf3ct 2025-03-28 23:27:40 +00:00
parent 2311c3c049
commit 8497e77b55
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2 changed files with 28 additions and 20 deletions
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26
src/services/llm/embeddings/providers/ollama.ts
View File

@ -1,9 +1,10 @@
import axios from "axios"; import axios from "axios";
import log from "../../../log.js"; import log from "../../../log.js";
import { BaseEmbeddingProvider } from "../base_embeddings.js"; import { BaseEmbeddingProvider } from "../base_embeddings.js";
import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js"; import type { EmbeddingConfig } from "../embeddings_interface.js";
import { NormalizationStatus } from "../embeddings_interface.js"; import { NormalizationStatus } from "../embeddings_interface.js";
import { LLM_CONSTANTS } from "../../constants/provider_constants.js"; import { LLM_CONSTANTS } from "../../constants/provider_constants.js";
import type { EmbeddingModelInfo } from "../../interfaces/embedding_interfaces.js";
/** /**
* Ollama embedding provider implementation * Ollama embedding provider implementation
@ -27,7 +28,7 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
// Update the config dimension // Update the config dimension
this.config.dimension = modelInfo.dimension; this.config.dimension = modelInfo.dimension;
log.info(`Ollama model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`); log.info(`Ollama model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWidth}`);
} catch (error: any) { } catch (error: any) {
log.error(`Error initializing Ollama provider: ${error.message}`); log.error(`Error initializing Ollama provider: ${error.message}`);
} }
@ -63,9 +64,10 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
log.info(`Fetched Ollama model info from API for ${modelName}: context window ${contextWindow}`); log.info(`Fetched Ollama model info from API for ${modelName}: context window ${contextWindow}`);
return { return {
name: modelName,
dimension: embeddingDimension || 0, // We'll detect this separately if not provided dimension: embeddingDimension || 0, // We'll detect this separately if not provided
contextWindow: contextWindow, contextWidth: contextWindow,
guaranteesNormalization: false // Ollama models don't guarantee normalized embeddings type: 'float32'
}; };
} }
} catch (error: any) { } catch (error: any) {
@ -82,14 +84,14 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> { async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
// Check cache first // Check cache first
if (this.modelInfoCache.has(modelName)) { if (this.modelInfoCache.has(modelName)) {
return this.modelInfoCache.get(modelName); return this.modelInfoCache.get(modelName)!;
} }
// Try to fetch model capabilities from API // Try to fetch model capabilities from API
const apiModelInfo = await this.fetchModelCapabilities(modelName); const apiModelInfo = await this.fetchModelCapabilities(modelName);
if (apiModelInfo) { if (apiModelInfo) {
// If we have context window but no embedding dimension, we need to detect the dimension // If we have context window but no embedding dimension, we need to detect the dimension
if (apiModelInfo.contextWindow && !apiModelInfo.dimension) { if (apiModelInfo.contextWidth && !apiModelInfo.dimension) {
try { try {
// Detect dimension with a test embedding // Detect dimension with a test embedding
const dimension = await this.detectEmbeddingDimension(modelName); const dimension = await this.detectEmbeddingDimension(modelName);
@ -116,9 +118,10 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
(LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default; (LLM_CONSTANTS.OLLAMA_MODEL_CONTEXT_WINDOWS as Record<string, number>).default;
const modelInfo: EmbeddingModelInfo = { const modelInfo: EmbeddingModelInfo = {
name: modelName,
dimension, dimension,
contextWindow, contextWidth: contextWindow,
guaranteesNormalization: false // Ollama models don't guarantee normalized embeddings type: 'float32'
}; };
this.modelInfoCache.set(modelName, modelInfo); this.modelInfoCache.set(modelName, modelInfo);
this.config.dimension = dimension; this.config.dimension = dimension;
@ -138,9 +141,10 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
log.info(`Using default parameters for model ${modelName}: dimension ${dimension}, context ${contextWindow}`); log.info(`Using default parameters for model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
const modelInfo: EmbeddingModelInfo = { const modelInfo: EmbeddingModelInfo = {
name: modelName,
dimension, dimension,
contextWindow, contextWidth: contextWindow,
guaranteesNormalization: false // Ollama models don't guarantee normalized embeddings type: 'float32'
}; };
this.modelInfoCache.set(modelName, modelInfo); this.modelInfoCache.set(modelName, modelInfo);
this.config.dimension = dimension; this.config.dimension = dimension;
@ -202,7 +206,7 @@ export class OllamaEmbeddingProvider extends BaseEmbeddingProvider {
// Trim text if it might exceed context window (rough character estimate) // Trim text if it might exceed context window (rough character estimate)
// This is a simplistic approach - ideally we'd count tokens properly // This is a simplistic approach - ideally we'd count tokens properly
const charLimit = modelInfo.contextWindow * 4; // Rough estimate: avg 4 chars per token const charLimit = (modelInfo.contextWidth || 4096) * 4; // Rough estimate: avg 4 chars per token
const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text; const trimmedText = text.length > charLimit ? text.substring(0, charLimit) : text;
const response = await axios.post( const response = await axios.post(

22
src/services/llm/embeddings/providers/openai.ts
View File

@ -1,9 +1,10 @@
import axios from "axios"; import axios from "axios";
import log from "../../../log.js"; import log from "../../../log.js";
import { BaseEmbeddingProvider } from "../base_embeddings.js"; import { BaseEmbeddingProvider } from "../base_embeddings.js";
import type { EmbeddingConfig, EmbeddingModelInfo } from "../embeddings_interface.js"; import type { EmbeddingConfig } from "../embeddings_interface.js";
import { NormalizationStatus } from "../embeddings_interface.js"; import { NormalizationStatus } from "../embeddings_interface.js";
import { LLM_CONSTANTS } from "../../constants/provider_constants.js"; import { LLM_CONSTANTS } from "../../constants/provider_constants.js";
import type { EmbeddingModelInfo } from "../../interfaces/embedding_interfaces.js";
/** /**
* OpenAI embedding provider implementation * OpenAI embedding provider implementation
@ -27,7 +28,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
// Update the config dimension // Update the config dimension
this.config.dimension = modelInfo.dimension; this.config.dimension = modelInfo.dimension;
log.info(`OpenAI model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWindow}`); log.info(`OpenAI model ${modelName} initialized with dimension ${this.config.dimension} and context window ${modelInfo.contextWidth}`);
} catch (error: any) { } catch (error: any) {
log.error(`Error initializing OpenAI provider: ${error.message}`); log.error(`Error initializing OpenAI provider: ${error.message}`);
} }
@ -105,9 +106,10 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
log.info(`Fetched OpenAI model info for ${modelName}: context window ${contextWindow}, dimension ${dimension}`); log.info(`Fetched OpenAI model info for ${modelName}: context window ${contextWindow}, dimension ${dimension}`);
return { return {
name: modelName,
dimension, dimension,
contextWindow, contextWidth: contextWindow,
guaranteesNormalization: true // OpenAI embeddings are normalized to unit length type: 'float32'
}; };
} }
} catch (error: any) { } catch (error: any) {
@ -123,7 +125,7 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> { async getModelInfo(modelName: string): Promise<EmbeddingModelInfo> {
// Check cache first // Check cache first
if (this.modelInfoCache.has(modelName)) { if (this.modelInfoCache.has(modelName)) {
return this.modelInfoCache.get(modelName); return this.modelInfoCache.get(modelName)!;
} }
// Try to fetch model capabilities from API // Try to fetch model capabilities from API
@ -144,9 +146,10 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
let contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI; let contextWindow = LLM_CONSTANTS.CONTEXT_WINDOW.OPENAI;
const modelInfo: EmbeddingModelInfo = { const modelInfo: EmbeddingModelInfo = {
name: modelName,
dimension, dimension,
contextWindow, contextWidth: contextWindow,
guaranteesNormalization: true // OpenAI embeddings are normalized to unit length type: 'float32'
}; };
this.modelInfoCache.set(modelName, modelInfo); this.modelInfoCache.set(modelName, modelInfo);
this.config.dimension = dimension; this.config.dimension = dimension;
@ -161,9 +164,10 @@ export class OpenAIEmbeddingProvider extends BaseEmbeddingProvider {
log.info(`Using default parameters for OpenAI model ${modelName}: dimension ${dimension}, context ${contextWindow}`); log.info(`Using default parameters for OpenAI model ${modelName}: dimension ${dimension}, context ${contextWindow}`);
const modelInfo: EmbeddingModelInfo = { const modelInfo: EmbeddingModelInfo = {
name: modelName,
dimension, dimension,
contextWindow, contextWidth: contextWindow,
guaranteesNormalization: true // OpenAI embeddings are normalized to unit length type: 'float32'
}; };
this.modelInfoCache.set(modelName, modelInfo); this.modelInfoCache.set(modelName, modelInfo);
this.config.dimension = dimension; this.config.dimension = dimension;